Showing 1–50 of 264 results for author: Pan, C

Hybrid Feedback Sampling for Sample-Efficient Model Predictive Control

Authors: Chaoyi Pan, Zeji Yi, John Zhang, Zachary Manchester, Guannan Qu, Guanya Shi

Abstract: Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robotic systems. However, for high-dimensional and open-loop unstable dynamical systems, the required number of samples to improve the control sequence will grow exponentially with the horizon, leading to poor sample efficiency and numerical instabilit… ▽ More Thanks to its parallelizability and flexibility, sampling-based Model Predictive Control (MPC) has become widely popular for controlling real-world robotic systems. However, for high-dimensional and open-loop unstable dynamical systems, the required number of samples to improve the control sequence will grow exponentially with the horizon, leading to poor sample efficiency and numerical instability. This paper investigates the instability of shooting methods in sampling-based MPC and shows that the optimal sampling proposal distribution can be realized by sampling with an optimized feedback policy. We refer to this algorithm as Feedback Sampling MPC (FS-MPC). FS-MPC involves a hybrid sampling design which balances local and global search based on the system stability and the available computation budget. Our theoretical analysis shows that our hybrid sampling approach achieves faster convergence than standard MPPI and better optimality than standard feedback sampling. Empirically, in diverse contact-rich control tasks like humanoid loco-manipulation and dexterous manipulation, we show that FS-MPC successfully tackles dynamically unstable tasks where standard sample-based approaches struggle, and strictly outperforms feedback policies alone. Finally, we validate our method on humanoid robot locomotion and manipulation tasks in the real world. △ Less

Submitted 19 August, 2026; originally announced August 2026.

Comments: 15 pages, 6 figures

SwanTale: Unified Multi-Speaker Speech and Audio Generation for Instruct and Zero-Shot Tasks

Authors: Yu Zhang, Ruiqi Li, Changhao Pan, Ke Lei, Xiang Yin, Cheng Yang

Abstract: Speech and audio generation is often needed in animation dubbing, audio drama, movies, advertising, games, podcasts, and short-video production. In these scenarios, creators may need to design voices without reference recordings, control speaker styles with natural language, support acoustic scenes with environments and audio effects, and later reuse the designed voices. Therefore, it is important… ▽ More Speech and audio generation is often needed in animation dubbing, audio drama, movies, advertising, games, podcasts, and short-video production. In these scenarios, creators may need to design voices without reference recordings, control speaker styles with natural language, support acoustic scenes with environments and audio effects, and later reuse the designed voices. Therefore, it is important to support multi-speaker speech and audio generation for both instruct and zero-shot tasks. The instruct task requires a caption of the environment, speaker styles, and fine-grained content, while the zero-shot task uses reference audio together with the same fine-grained content. We address these tasks from both the data and model sides. First, we propose SwanData-Caption, which cleans raw speech and audio data, adds targeted synthetic coverage, and annotates diverse and accurate multi-level captions. Then, we propose SwanTale, a multi-speaker expressive speech and audio generation model that supports both zero-shot and instruct tasks. We introduce SwanVAE to support high-quality multi-audio-modality generation. Then, we adopt reward-conditioned quality control and Engram conditioning, along with Unified MoE for multi-task and multi-audio-modality modeling. In addition, we use curriculum learning and GRPO post-training to let the model progressively learn and strengthen its capabilities. Experimental results show that SwanTale leads on multiple key zero-shot and instruct metrics, achieves the best expressiveness scores in both tasks, and supports complex instruct generation involving multi-speaker speech and audio. Demos can be found at https://swanaigc.github.io/#swantale. △ Less

Submitted 4 August, 2026; v1 submitted 3 August, 2026; originally announced August 2026.

Comments: Technical Report by ByteDance

Secure Relay Low-Altitude Networks via Hybrid Fixed-Position and Rotatable Antenna Arrays

Authors: Maolin Li, Qi Zhang, Riqing Chen, Wei Gao, Feng Shu, Liang Yang, Cunhua Pan

Abstract: In this paper, a relay network with hybrid fixed-position and rotatable antenna arrays is proposed. The deployment of rotatable arrays in conventional relay networks is considered to provide more secure communications for low-altitude economy applications. Specifically, both the base station and the relay station are equipped with fixed-position antenna arrays and rotatable arrays to serve ground… ▽ More In this paper, a relay network with hybrid fixed-position and rotatable antenna arrays is proposed. The deployment of rotatable arrays in conventional relay networks is considered to provide more secure communications for low-altitude economy applications. Specifically, both the base station and the relay station are equipped with fixed-position antenna arrays and rotatable arrays to serve ground users and aerial users, respectively. To address the challenge of multi-user interference, a low-cost reconfigurable intelligent surface is exploited as a candidate path. Accordingly, under constraints on transmit power, user quality of service, rotatable range, and path selection, the objective is to maximize the worst-case secrecy rate (SR) through joint beamforming, power allocation, and rotatable antenna orientation design. First, the SR performance in the single-user scenario is investigated, and a step-by-step leakage-based scheme is proposed. Then, the general multi-user scenario is studied, and a Distributional Soft Actor-Critic with Three refinements (DSAC-T)-based learning scheme, which supports hybrid discrete and continuous actions, is proposed to maximize the worst-case SR. Simulation results validate the effectiveness of the proposed schemes. The proposed schemes achieve approximately a twofold improvement in SR performance compared to isotropic antennas. The proposed system achieves approximately 71.4\% power saving, 55\% antenna saving, and can serve more users. △ Less

Submitted 2 August, 2026; v1 submitted 29 July, 2026; originally announced July 2026.

Cross-Field Channel Parameter Estimation and Channel Characterization at THz Bands in Indoor Scenarios

Authors: Hengtai Chang, Cheng-Xiang Wang, Cunhua Pan, Jian Sun, Bingchang Hua, Yongchao He, el-Hadi M. Aggoune

Abstract: The terahertz (THz) frequency band offers the potential for ultra-high data rate transmission in future wireless communication systems. To extend the transmission distance and enhance spectral efficiency, the deployment of large-scale antenna arrays emerges as a promising solution in the THz band. This paper targets the critical challenge of cross-field (hybrid near-field/far-field) channel parame… ▽ More The terahertz (THz) frequency band offers the potential for ultra-high data rate transmission in future wireless communication systems. To extend the transmission distance and enhance spectral efficiency, the deployment of large-scale antenna arrays emerges as a promising solution in the THz band. This paper targets the critical challenge of cross-field (hybrid near-field/far-field) channel parameter estimation and channel characterization in such configurations. We first establish a 260-380 GHz virtual uniform linear array (ULA) measurement framework in an indoor scenario, capturing high-resolution channel transfer functions (CTFs) that reveal spatial non-stationarity and cross-field wavefront characteristics. Building upon these empirical observations, we propose a cross-field space-alternating generalized expectation-maximization (SAGE) algorithm that discriminatively estimates near-field and far-field multipath components (MPCs) via Bayesian phase-curvature classification, while explicitly tracking spatial birth-death phenomena through visibility region estimation. Analysis of the measurement data validates the algorithm's effectiveness in resolving cross-field MPCs and quantifies that near-field MPCs account for over 90% of total MPCs at 2 m transmission distance (380 GHz). We observe that spatial non-stationarity intensifies as the carrier frequency increases and the transmission distance decreases. These findings offer quantitative guidelines for channel modeling and system design in wireless THz communication systems. △ Less

Submitted 15 July, 2026; originally announced July 2026.

Comments: 13 pages, 19 figures

Cell-Level Channel Shaping for Rydberg Atomic Quantum Receivers in Satellite Uplinks With Doppler-Enabled Superheterodyne Reception

Authors: Qihao Peng, Qu Luo, Kezhi Wang, Cunhua Pan, Pei Xiao, Trung Q. Duong, Jiangzhou Wang

Abstract: In this paper, we propose a self-superheterodyne Rydberg uniform array receiver for satellite uplink communications, in which the Doppler shift naturally induced by satellite motion is exploited to generate the intermediate-frequency signal. We first develop a near-field local oscillator (LO) synthesis model and characterize the spatially varying LO electric field across the Rydberg vapor cells. B… ▽ More In this paper, we propose a self-superheterodyne Rydberg uniform array receiver for satellite uplink communications, in which the Doppler shift naturally induced by satellite motion is exploited to generate the intermediate-frequency signal. We first develop a near-field local oscillator (LO) synthesis model and characterize the spatially varying LO electric field across the Rydberg vapor cells. Based on a vapor-cell-center approximation, a closed-form radio frequency (RF)-to-optical conversion is derived, establishing an explicit bridge between the incident satellite signal and the LO-induced cell-level response. The derived model reveals that the programmable LO serves as an analog-domain channel-shaping mechanism by controlling the cell-level transduction gain, phase response, and phase-matching behavior. Building upon this equivalent channel model, we formulate an LO design problem that maximizes the Shannon capacity of the effective channel, and develop an efficient optimization algorithm for the LO amplitudes and phases. Simulation results demonstrate that the vapor-cell transduction can reshape the effective channel, adjust the beam-pattern alignment, and moderately reduce the inter-user correlation under suitable LO configurations. Furthermore, the proposed LO design significantly improves the achievable capacity over benchmark schemes, offering a promising self-superheterodyne Rydberg architecture for future satellite communication systems. △ Less

Submitted 7 July, 2026; originally announced July 2026.

Audio Editing in the Era of Foundation Models: A Survey

Authors: Changhao Pan, Yifei Fan, Fan Zhuo, Yifu Chen, Wenxiang Guo, Yu Zhang, Ruiqi Li, Zhiyuan Zhu, Rui Yang, Shengpeng Ji, Chenyuhao Wen, Jiayang Xu, Ke Lei, Xiaoda Yang, Jingyu Lu, Zhou Zhao

Abstract: Audio editing aims to modify a given synthetic or real-world audio signal to satisfy specific user needs. As a promising yet challenging direction in AIGC, it has attracted increasing attention. Recent advances in audio generation have made powerful generative models central to modern audio editing systems. This rapid progress has created a growing need to organize emerging tasks, methods, and res… ▽ More Audio editing aims to modify a given synthetic or real-world audio signal to satisfy specific user needs. As a promising yet challenging direction in AIGC, it has attracted increasing attention. Recent advances in audio generation have made powerful generative models central to modern audio editing systems. This rapid progress has created a growing need to organize emerging tasks, methods, and resources into a coherent view. In this survey, we provide a comprehensive review of audio editing in the era of foundation models. We first present a unified taxonomy of existing editing tasks and then summarize the major foundation-model paradigms that support modern audio editing, covering representative approaches from both training-based and training-free perspectives. We further discuss related resources, including datasets, evaluation protocols, and data construction tools. Finally, we identify open challenges in this field and outline promising directions for future research. The project page is released at https://github.com/DaViD-Pigeon/AudioEditSurvey. △ Less

Submitted 22 June, 2026; originally announced June 2026.

Comments: 23 pages, 3 figures, 2 tables

A Survey of Full-Duplex Spoken Dialogue Systems: Architectural Hierarchy, Interaction Ontology, and Decision State Machine

Authors: Jingyu Lu, Yuhan Wang, Jianming Luo, Yifu Chen, Tianle Liang, Shengpeng Ji, Ziyue Jiang, Xiaoda Yang, Yu Zhang, Xize Cheng, Chenyuhao Wen, Changhao Pan, Haoxiao Wang, Chen Ye, Jian Wu, Xiaoxi Jiang, Guanjun Jiang, Zhou Zhao

Abstract: More than a dozen spoken dialogue systems have recently claimed to be "full-duplex," yet the term has been used to describe substantially different capabilities. Existing surveys collapse them onto a single axis (cascaded/end-to-end, or engineered/learned) and miss the distinctions that matter most for builders. We argue that much of this ambiguity is taxonomical: current terminology does not spec… ▽ More More than a dozen spoken dialogue systems have recently claimed to be "full-duplex," yet the term has been used to describe substantially different capabilities. Existing surveys collapse them onto a single axis (cascaded/end-to-end, or engineered/learned) and miss the distinctions that matter most for builders. We argue that much of this ambiguity is taxonomical: current terminology does not specify where duplex decisions are made, which interaction types are supported, or how a system behaves moment by moment. This paper introduces three complementary frameworks: (i) an L0-L3 Architectural Hierarchy that locates where duplex decisions are made; (ii) a $T\times I\times R$ Interaction Ontology that specifies the temporal relation, user intent, and required system response for each interaction; and (iii) a Decision State Machine (IDLE/LISTEN/SPEAK/WAIT/DUAL) that describes how systems move between states. Across published systems and benchmarks, our audit documents a realization gap: although many architectures can in principle operate in full-duplex states, their observed behavior remains constrained by the interaction patterns represented in training and evaluation. We point to the limited public training-data coverage relative to the (largely undisclosed) industrial corpora, together with the still-unrealized goal of L3 representation-level modeling, as the key frontiers for future research on full-duplex dialogue. The related material is available at https://github.com/DuplexLM/DuplexSurvey. △ Less

Submitted 17 June, 2026; originally announced June 2026.

Comments: 34 pages, 5 figures, 7 tables. Project page and interactive demo: https://github.com/DuplexLM/DuplexSurvey

Explainable Task-Oriented Token Communication for AI-Native 6G Networks

Authors: Feibo Jiang, Lei Mao, Li Dong, Kezhi Wang, Cunhua Pan, Jiangzhou Wang

Abstract: The integration of Foundation Models (FMs) and wireless communications is driving the evolution of image communication from bit-accurate transmission toward task-oriented transmission. However, existing task-oriented image communication methods still face three major challenges: insufficient task-oriented Token representation, inadequate collaboration between Visual Tokens and Task Tokens, and lim… ▽ More The integration of Foundation Models (FMs) and wireless communications is driving the evolution of image communication from bit-accurate transmission toward task-oriented transmission. However, existing task-oriented image communication methods still face three major challenges: insufficient task-oriented Token representation, inadequate collaboration between Visual Tokens and Task Tokens, and limited interpretability of task decisions. To address these challenges, we propose an Explainable Task-Oriented Token Communication (ET-TokenCom) framework. By treating Tokens as unified units for information representation and transmission, the proposed framework constructs an end-to-end communication link that spans visual perception, wireless transmission, and task reasoning. At the transmitter, the ET-TokenCom framework extracts Visual Tokens from images to preserve low-level visual information. Meanwhile, Task Tokens generated by the FM are introduced to represent the target information and decision intent required by the current task. A Cross-Modal Attention (CMA) fusion mechanism is further designed, enabling Task Tokens to explicitly guide the selection, weighting, and transmission of Visual Tokens. At the receiver, the framework integrates Token decoding with an explainable output mechanism, where attention heatmaps are generated to highlight critical perceptual regions under different task objectives and reveal the influence of Task Tokens on the outputs. Finally, simulation results validate the effectiveness and robustness of the proposed ET-TokenCom framework. △ Less

Submitted 12 June, 2026; originally announced June 2026.

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

Authors: Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang, Chengkuan Zeng, Wenbo Cheng, Yunxi Liu, Rui Yang, Steve Yves, Liefeng Bo, Zhou Zhao

Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding. We propose Spatial-Omni, a lightweight method that implements SO-Encoder to inject First-Order Ambisonics (FOA) spatial audio into existing Omni LLMs as an independent mo… ▽ More Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding. We propose Spatial-Omni, a lightweight method that implements SO-Encoder to inject First-Order Ambisonics (FOA) spatial audio into existing Omni LLMs as an independent modality, without modifying their original audio encoders. SO-Encoder provides spatial tokens with limited additional context cost and improves spatial audio understanding through efficient staged training. To support training and evaluation, we construct SO-Dataset, SO-QA, and SO-Bench from open-source data, real recordings, and simulations, containing 400K FOA spatial audio clips and 2.1M spatial question answering pairs. SO-Bench covers 16 spatial audio understanding subtasks, including basic detection and location estimation, spatial relation understanding, and complex spatial reasoning. Experiments show that Spatial-Omni outperforms existing open-source Large Audio-Language Models (LALMs) and Omni LLM models on spatial audio understanding tasks while retaining a reasonable level of general audio understanding. Code and data are available at https://github.com/dieKarotte/Spatial-Omni. △ Less

Submitted 9 June, 2026; originally announced June 2026.

A Hybrid Near-field Indoor Channel Model for THz Bands Based on Surface Scattering Characteristics

Authors: Yongchao He, Taihao Zhang, Cunhua Pan, Hong Ren, Chenzhou Lin, Tian Qiu, Bingchang Hua, Cheng-Xiang Wang, Jiangzhou Wang

Abstract: Terahertz (THz) communication and extremely large-scale MIMO (XL-MIMO) are essential for achieving ultra-high data rates in future 6G systems. However, at sub-millimeter wavelengths, typical indoor materials exhibit significant roughness that invalidates conventional ideal smooth surface assumptions, while massive array apertures introduce pronounced near-field effects and spatial non-stationarity… ▽ More Terahertz (THz) communication and extremely large-scale MIMO (XL-MIMO) are essential for achieving ultra-high data rates in future 6G systems. However, at sub-millimeter wavelengths, typical indoor materials exhibit significant roughness that invalidates conventional ideal smooth surface assumptions, while massive array apertures introduce pronounced near-field effects and spatial non-stationarity. To address these challenges, this paper proposes a hybrid near-field channel model utilizing surface scattering characteristics based on distinct measurement campaigns. First, based on typical indoor materials scattering measurements across the 260-400 GHz band, an improved Beckmann-Kirchhoff (B-K) model is developed to accurately characterize surface roughness and diffuse scattering behavior. The model independently analyzes single-bounce (SB) and multi-bounce (MB) clusters by applying deterministic rough surface scattering theory and geometry-statistical approach, respectively. Then, using near-field spatial non-stationarity measurements from a 630-element virtual array in the 330-360 GHz band, a Dual-Gaussian Mixture Model (DMM) and a Negative Binomial (NB) distribution are adopted to describe the lengths and the number of spatial visibility regions (VRs), respectively. Additionally, a Weibull distribution is employed to model the intra-region power fluctuations. Finally, comprehensive XL-MIMO channel evaluations within the same band demonstrate that the proposed model aligns closely with measured results in terms of the spatial cross-correlation function (SCCF), frequency cross-correlation function (FCF), and channel capacity. By reproducing the spatial sparsity of THz band, the proposed model overcomes the limitation of conventional standard models, such as 3GPP 38.901 and WINNER II, in significantly overestimating channel capacity. △ Less

Submitted 31 May, 2026; originally announced June 2026.

Beyond the RF Paradigm: Rydberg Atomic Receivers for Next-Generation IoT

Authors: Qihao Peng, Qu Luo, Dongnan Xia, Zhehua Zhang, Zeyan Zhang, Jizhou Wu, Kezhi Wang, Cunhua Pan, Maged Elkashlan, Pei Xiao, Derrick Wing Kwan Ng, Trung Q. Duong, George K. Karagiannidis, Jiangzhou Wang

Abstract: Next-generation Internet-of-Things (IoT) is evolving toward a ubiquitous, ultra-low-power, and multi-band heterogeneous networking paradigm that seamlessly integrates terrestrial, non-terrestrial, and ambient devices. This vision places unprecedented demands on conventional radio frequency (RF) receivers, whose fundamental bottlenecks in sensitivity, power consumption, coverage, and multi-band ope… ▽ More Next-generation Internet-of-Things (IoT) is evolving toward a ubiquitous, ultra-low-power, and multi-band heterogeneous networking paradigm that seamlessly integrates terrestrial, non-terrestrial, and ambient devices. This vision places unprecedented demands on conventional radio frequency (RF) receivers, whose fundamental bottlenecks in sensitivity, power consumption, coverage, and multi-band operation are rooted in the RF antenna. To tackle these issues, we show that the quantum properties of Rydberg atomic quantum receivers (RAQRs), including ultra-high sensitivity, broad frequency agility, and diverse reception modalities, provide a physically distinct receiver-side path that replaces the conventional antenna-and-low-noise-amplifier chain. Using LoRa, narrowband IoT, and ambient IoT as case studies, this article shows that RAQRs deliver significant gains in weak-uplink, low-power, and battery-free regimes. A stochastic-geometry analysis in cellular and cell-free architectures then maps these device-level gains onto network coverage, where the RAQR retains roughly a 4 dB half-coverage advantage over the RF receiver in sparse deployments at \(λ\sim 10^{-5}~{\mathrm m}^{-2}\), with the gain eroded as device density grows. The open challenges are presented to stand between current RAQR prototypes and deployable IoT infrastructure. △ Less

Submitted 31 May, 2026; originally announced June 2026.

RFDT-Channel: RGB-LiDAR-Based RF Digital Twin Scene Construction for 28 GHz Indoor Ray-Tracing Channel Simulation

Authors: Chengyang Yao, Cunhua Pan, Jiaming Zeng, Yuquan Sun, Haoyang Weng, Haojian Wang, Hong Ren, Jiangzhou Wang

Abstract: Real-scene indoor millimeter-wave simulation requires efficient modeling of radio frequency (RF)-computable geometry and electromagnetic material properties. To address the low efficiency of manual scene modeling, the limited RF adaptability of visually reconstructed meshes, and the lack of material binding in 28 GHz ray-tracing simulation, RFDT-Channel is developed as an RF digital twin scene con… ▽ More Real-scene indoor millimeter-wave simulation requires efficient modeling of radio frequency (RF)-computable geometry and electromagnetic material properties. To address the low efficiency of manual scene modeling, the limited RF adaptability of visually reconstructed meshes, and the lack of material binding in 28 GHz ray-tracing simulation, RFDT-Channel is developed as an RF digital twin scene construction workflow based on red-green-blue (RGB) images and light detection and ranging (LiDAR) point clouds. Indoor videos and point clouds are collected by a Jetson Orin platform with LiDAR and GMSL cameras. An initial triangular mesh is generated through COLMAP, 3D Gaussian Splatting, and SuGaR. The LiDAR point cloud then provides geometric and scale references for RF-oriented regularization in Blender, including alignment, wall solidification, door/window opening construction, and topology repair. OpenScene semantic segmentation maps major indoor structures to concrete, glass, wood, and metal materials, and Sionna RT performs 28 GHz ray tracing. Under a fixed transmitter-receiver deployment, the generated channel impulse response (CIR), channel frequency response (CFR), and Radio Map results show that material binding mainly changes weak reflection, transmission, and scattering paths, reducing the number of effective paths from about 742 to about 52 while keeping the dominant path amplitude nearly unchanged. △ Less

Submitted 31 May, 2026; originally announced June 2026.

SwanVoice: Expressive Long-Form Zero-Shot Speech Synthesis for Both Monologue and Dialogue

Authors: Ruiqi Li, Yu Zhang, Changhao Pan, Ke Lei, Xiang Yin, Cheng Yang

Abstract: Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent di… ▽ More Zero-shot text-to-speech (TTS) has improved substantially for single-speaker synthesis, yet expressive long-form multi-speaker dialogue remains difficult. A common workaround is to synthesize each turn with a monologue TTS model and stitch the outputs together. This adds inference cost and often breaks acoustic consistency, conversational coherence, and affective continuity across turns. Recent dialogue TTS systems have begun to address this setting, but they still struggle to keep expressive coherence, controllable speaker switching, and monologue quality at the same time. We present SwanData-Speech and SwanVoice. SwanData-Speech builds monologue and dialogue corpora from in-the-wild audio, using Swan Forced Aligner for pause-aware word-level alignment and RobustMegaTTS3 for pronunciation-hard cases. Built on these data, SwanVoice is a zero-shot TTS model for 1--4 speakers, combining a 25 Hz VAE, raw-text conditioning with pause-aware symbols and pinyin substitution, and a flow-matching DiT with speaker-turn conditioning. Training starts from monologue speech, moves through mixed and real dialogue data, and then uses DiffusionNFT post-training with phone-level and speaker-similarity rewards. On SwanBench-Speech, SwanVoice obtains higher richness and hierarchy scores than all evaluated open-source baselines in both monologue and dialogue settings, while content accuracy remains the main limitation. Audio demos are available at https://swanaigc.github.io//#swanvoice. △ Less

Submitted 29 May, 2026; originally announced May 2026.

Comments: Technical Report

Towards Streaming Synchronized Spatial Audio Generation via Autoregressive Diffusion Transformer

Authors: Ke Lei, Yu Zhang, Changhao Pan, Xueyi Pu, Wenxiang Guo, Ruiqi Li, Zhou Zhao

Abstract: Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streami… ▽ More Real-time and accurate spatial audio generation is pivotal for delivering an immersive experience. However, existing spatial audio synthesis technologies are often encumbered by a tradeoff between generation quality and high inference latency, as well as difficulty in capturing precise spatial information from multimodal inputs. To address these challenges, we propose SwanSphere, a unified streaming framework for high-fidelity spatial audio generation from panoramic videos and text prompts. SwanSphere mainly makes the following contributions: 1) We introduce a causal autoregressive diffusion transformer architecture that enables streaming high-quality spatial audio generation. 2) We design a Spatial Video-Audio Contrastive (SVAC) learning strategy to align the video encoder with the acoustic domain, and further employ a multi-objective online direct preference optimization (ODPO) scheme, resulting in strong spatial perception and robust multimodal spatial audio synthesis. 3) To alleviate the current scarcity of spatial audio datasets, we also develop an automated annotation pipeline for generating detailed spatial captions. Experimental results demonstrate that SwanSphere achieves superior performance in both video-to-spatial and text-to-spatial audio generation tasks. Demos can be found at: https://swanaigc.github.io. △ Less

Submitted 29 May, 2026; originally announced May 2026.

Comments: Accepted by ICML 2026

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios

Authors: Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Abstract: Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation benchmark for long-form speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2… ▽ More Recent advances in speech generation have enabled high-fidelity synthesis, yet systematic evaluation of models under long-context conditions remains largely underexplored. A comprehensive evaluation benchmark for long-form speech is indispensable for two reasons: 1) existing test scenarios are often confined to limited domains, creating a significant gap with the diverse downstream applications; 2) existing metrics overlook critical long-text factors such as consistency and coherence, failing to generalize reliably. To this end, we propose Swanbench-Speech, a comprehensive benchmark that decomposes long-form speech quality into specific, disentangled dimensions. SwanBench-Speech has three key properties. 1) Rich speech scenarios: Focusing on long-form speech generation and dialog generation, SwanBench-Speech covers acoustics, semantics, and expressiveness challenges, and consists of 1,101 samples spanning 17 common speech scenarios; 2) Comprehensive evaluation dimensions: Along the acoustics, semantics, and expressiveness axes, SwanBench-Speech defines an automated evaluation protocol with seven metrics to provide a comprehensive, accurate, and standardized assessment; 3) Valuable Insights: Through extensive experiments, we reveal that current models still struggle in highly expressive scenarios and exhibit a notable gap in consistency and hierarchy compared to real recordings. △ Less

Submitted 27 May, 2026; originally announced May 2026.

Comments: Accepted by ACL 2026(Findings). 36pages, 14figures

Channel Measurements and Characterization with Phase Drift Compensation for Outdoor 330-360 GHz MIMO Communications

Authors: Tian Qiu, Taihao Zhang, Cunhua Pan, Hong Re, Yongchao He, Chenzhou Lin, Bingchang Hua, Jiangzhou Wang

Abstract: In this paper, an outdoor channel measurement campaign at 330-360 GHz employing a 128 * 4 virtual antenna array (VAA)-based multiple-input multiple-output (MIMO) configuration is conducted. The transmitter (Tx) and receiver (Rx) location pairs are classified into line-of-sight (LoS) and obstructed-LoS (OLoS) scenarios to enable a detailed investigation of outdoor terahertz (THz) band channel chara… ▽ More In this paper, an outdoor channel measurement campaign at 330-360 GHz employing a 128 * 4 virtual antenna array (VAA)-based multiple-input multiple-output (MIMO) configuration is conducted. The transmitter (Tx) and receiver (Rx) location pairs are classified into line-of-sight (LoS) and obstructed-LoS (OLoS) scenarios to enable a detailed investigation of outdoor terahertz (THz) band channel characteristics. During the measurement process, the stationarity of the outdoor environment is carefully verified, and a linear phase drift (PD) effect is identified. Then, we propose a PD-aware Space-Alternating Generalized Expectation-Maximization (SAGE) algorithm, which significantly improves both delay resolution and channel parameter estimation accuracy. Based on the processed measurement data, we characterize key channel properties, including the power delay profile, path loss, shadow fading, delay spread, angular spread, Rician K-factor, as well as their cumulative distribution functions and correlation characteristics. In addition, near-field effects and MIMO-specific properties, including the spatial non-stationarity and the cluster birth-death property, are analyzed. △ Less

Submitted 27 May, 2026; originally announced May 2026.

A Measurement-Based Parameterization of Physics Reflection Models for Terahertz Communication

Authors: Taihao Zhang, Chenzhou Lin, Cunhua Pan, Hong Ren, Ruyi Liu, Yongchao He, Tian Qiu, Bingchang Hua, Jiangzhou Wang

Abstract: The accurate modeling of reflection coefficients is pivotal for developing reliable channel models in emerging terahertz (THz) communications. This study establishes a 300$\sim$400 GHz channel measurement platform to measure the reflection coefficients of various materials. Based on the analysis of measured data, we propose the single-layer interference with an extended-parameterized Lorentz/Drude… ▽ More The accurate modeling of reflection coefficients is pivotal for developing reliable channel models in emerging terahertz (THz) communications. This study establishes a 300$\sim$400 GHz channel measurement platform to measure the reflection coefficients of various materials. Based on the analysis of measured data, we propose the single-layer interference with an extended-parameterized Lorentz/Drude (SLI-EPLD) reflection coefficient model. In this model, a sub-band modeling strategy is adopted to characterize the variation of reflection coefficients with frequency, while a parameterized mapping approach is employed to ensure the stability of model parameters. Furthermore, the weighted sub-band fitting for trend regression (WF-TREND) algorithm is introduced to achieve precise sub-band parameter fitting. Validation results demonstrate superior performance to existing models across multiple materials. The reflection coefficient model established in this work serves as a critical foundation for channel modeling in 300$\sim$400 GHz for high-THz communication. △ Less

Submitted 22 May, 2026; originally announced May 2026.

CAT-MoEformer: Context-Aware Temporal MoE Transformer for Beam Prediction

Authors: Changkai Zhou, Cunhua Pan, Hong Ren, Jiangzhou Wang

Abstract: This paper proposes CAT-MoEformer, a context-aware transformer with scene-conditioned mixture-of-experts (MoE) feed-forward networks, for proactive mmWave beam prediction from compressed uplink pilot observations. The spatial encoder comprises a three-layer asymmetric convolutional network followed by a squeeze-and-excitation recalibration block, which extracts frequency-beam correlation features… ▽ More This paper proposes CAT-MoEformer, a context-aware transformer with scene-conditioned mixture-of-experts (MoE) feed-forward networks, for proactive mmWave beam prediction from compressed uplink pilot observations. The spatial encoder comprises a three-layer asymmetric convolutional network followed by a squeeze-and-excitation recalibration block, which extracts frequency-beam correlation features from pilot tensors without explicit channel reconstruction. A truncated pretrained GPT-2 backbone models the temporal evolution of beam sequences, with the feed-forward networks in the upper three transformer layers replaced by scene-conditioned MoE-FFN modules. A lightweight gating network maps the scenario label and normalized user equipment speed to expert mixing weights, conditioning the routing decision on physical propagation descriptors rather than on latent hidden states. This design yields interpretable expert assignments and eliminates the load imbalance associated with token-level routing. To prevent expert collapse under soft routing, a three-stage training strategy is introduced: hard expert assignment in the first stage establishes scene-specific specialization, isolated gating network training in the second stage aligns the soft routing distribution with the hard partition, and top-1 hard inference in the third stage fine-tunes the model under deterministic single-expert activation to maximize scene-specific precision. Simulation results on 3GPP TR 38.901 Urban Macro channel simulations with $64{,}000$ user samples demonstrate that CAT-MoEformer achieves a Top-1 beam prediction accuracy of $94.88\%$ and a beam switching instant accuracy of $80.62\%$, representing gains of $2.33\%$ and $9.55\%$ respectively over a CNN+GPT-2 baseline, with an inference latency of $0.52$~ms. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Comments: 13 pages, 5 figures

Channel Estimation for Beyond Diagonal RIS-Aided Multi-User mmWave Systems

Authors: Linyu Peng, Tian Qiu, Cunhua Pan, Jiangzhou Wang, Taihao Zhang, Hong Ren

Abstract: Beyond diagonal reconfigurable intelligent surface (BD-RIS) represents a promising architecture for advancing millimeter-wave (mmWave) communications. However, its intricate inter-element connections invalidate the conventional decoupled mathematical structure, thereby severely complicating cascaded channel estimation. In this paper, we formulate a novel block-Kronecker-structured cascaded channel… ▽ More Beyond diagonal reconfigurable intelligent surface (BD-RIS) represents a promising architecture for advancing millimeter-wave (mmWave) communications. However, its intricate inter-element connections invalidate the conventional decoupled mathematical structure, thereby severely complicating cascaded channel estimation. In this paper, we formulate a novel block-Kronecker-structured cascaded channel model for a \textit{group-connected} BD-RIS-aided multi-user (MU) mmWave system equipped with uniform planar arrays (UPAs). By exploiting the cascaded channel sparsity, an efficient three-stage estimation protocol is proposed. Specifically, Stage I acquires the common angles of arrival (AoAs) at the base station (BS) via a discrete Fourier transform (DFT)-based approach. Stage II leverages the block-Kronecker structure alongside orthogonal matching pursuit (OMP) and correlation-based least squares (LS) to extract the complete cascaded channel for a designated typical user. Finally, Stage III utilizes a Hierarchical Block OMP (HBOMP) algorithm to estimate the other users' channels. This structurally reconstructs the common and user-specific components, which fundamentally reduces the computational complexity and substantially reduces the pilot overhead. Numerical simulations verify that the proposed protocol yields improved channel estimation accuracy while maintaining a relatively low pilot overhead. △ Less

Submitted 19 May, 2026; originally announced May 2026.

Signal-Dependent Shot Noise Modeling of Rydberg Atomic Quantum Receivers: A Design Perspective

Authors: Qihao Peng, Qu Luo, Tierui Gong, Neng Ye, Jizhou Wu, Cunhua Pan, Maged Elkashlan, Pei Xiao, Chau Yuen, George K. Karagiannidis, Jiangzhou Wang

Abstract: In this paper, we develop a communication-oriented complex baseband equivalent model for superheterodyne Rydberg atomic quantum receivers (RAQRs). The model explicitly captures photodetection-induced signal-dependent shot noise and its coupling with the optical operating point. By leveraging an atomic superheterodyne architecture and a strong local oscillator, we construct a complex baseband repre… ▽ More In this paper, we develop a communication-oriented complex baseband equivalent model for superheterodyne Rydberg atomic quantum receivers (RAQRs). The model explicitly captures photodetection-induced signal-dependent shot noise and its coupling with the optical operating point. By leveraging an atomic superheterodyne architecture and a strong local oscillator, we construct a complex baseband representation for both the received signal and the signal-dependent shot noise under both direct incoherent optical detection and balanced coherent optical detection. The derived model reveals that the optical operating point jointly determines the normalized effective receive gain and the equivalent noise background, thereby establishing a traceable gain-noise tradeoff governed by system design. More importantly, the proposed model shows that neglecting signal-dependent shot noise may lead to inaccurate operating-point design. Finally, by extending to the multiple-input-multiple-output (MIMO) case, we derive a lower bound on the achievable rate while considering the signal-dependent shot noise. Our analysis \textcolor{black}{reveals} that the non-zero asymptotic rate of RAQ-MIMO and its superiority over conventional RF-MIMO hinge on the normalized noise floor of the RAQ receive chain falling below that of RF MIMO. Simulation results validate our analysis and yield practical, closed-form design guidelines for RAQR front ends, revealing parameter regimes in which RAQ-MIMO outperforms conventional MIMO systems. △ Less

Submitted 11 May, 2026; originally announced May 2026.

Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers

Authors: Yingzhe Wang, Cunhua Pan, Ruijing Liu, Shaokai Li, Hong Ren, Kezhi Wang, Jiangzhou Wang

Abstract: Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health monitoring in the Internet of Things (IoT) era. However, existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments due to static background overfitting and Non-Line-of-Sight (NLoS) signal attenua… ▽ More Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising, privacy-preserving solution for elderly health monitoring in the Internet of Things (IoT) era. However, existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments due to static background overfitting and Non-Line-of-Sight (NLoS) signal attenuation. To address these critical bottlenecks, we propose a robust, domain-generalizable framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture. First, we design a physics-driven \textbf{Dynamic Variance Gate (DVG)} to dynamically calculate local temporal variance, acting as a soft-attention mask that eliminates static environmental DC components while amplifying dynamic human motion. Second, we introduce a Physics-Aware Data Augmentation strategy to force the network to learn invariant morphological signatures rather than environment-specific noise. Furthermore, a Convolutional Block Attention Module (CBAM) is integrated to refine spatiotemporal features prior to Transformer-based sequence modeling. Extensive cross-domain evaluations across four distinct indoor environments demonstrate that our method achieves 97.6\% accuracy in NLoS scenarios and 98.8\% in completely unseen environments without target-domain fine-tuning. Finally, we deploy the proposed framework on an edge computing system equipped with commercial WiFi NICs. Real-world live inference field tests confirm the system's robustness against unseen environmental layouts and its capability for continuous, low-latency whole-home safety monitoring. △ Less

Submitted 22 April, 2026; originally announced May 2026.

CRS-LLM: Cooperative Beam Prediction with a GPT-Style Backbone and Switch-Gated Fusion

Authors: Fangzhi Li, Cunhua Pan, Hong Ren, Dongming Wang, Jiangzhou Wang

Abstract: Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause error propagation when beam states chan… ▽ More Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause error propagation when beam states change abruptly. To address this issue, this paper proposes Cooperative Radio Sensing with Large Language Models (CRS-LLM), a cooperative beam prediction framework for next-step joint BS-beam prediction. CRS-LLM formulates beam tracking as a single classification problem over the joint BS-beam space, avoiding cascaded decision errors. To adapt channel state information (CSI) to large language models, a dual-view CSI tokenizer extracts frequency-domain and delay-domain channel features through a lightweight CNN front-end and temporal tokenization module. A truncated GPT-style backbone is then used for temporal modeling with parameter-efficient adaptation. In addition, a transition-aware switch-gated predictor combines a stable branch, a residual flip branch, and a low-rank transition prior to capture both smooth evolution and abrupt changes. Simulation results show that CRS-LLM outperforms CSI-Transformer, Hierarchical BS-Beam, and representative CNN- and recurrent-neural-network baselines in Top-1 accuracy and normalized beam gain under different SNR conditions, while also showing strong few-shot performance and promising zero-shot transferability. △ Less

Submitted 30 April, 2026; originally announced April 2026.

Selective Depthwise Separable Convolution for Lightweight Joint Source-Channel Coding in Wireless Image Transmission

Authors: Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li

Abstract: Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the layerwise and ratio-wise replacement of standard convolutional (Conv) layers with DSConv layers in JSCC systems for wireless image transmission remains largely unexplore… ▽ More Depthwise separable convolutional (DSConv) layers have been successfully applied to deep learning (DL)-based joint source-channel coding (JSCC) schemes to reduce computational complexity. However, a systematic investigation of the layerwise and ratio-wise replacement of standard convolutional (Conv) layers with DSConv layers in JSCC systems for wireless image transmission remains largely unexplored. In this letter, we propose a configurable lightweight JSCC framework that incorporates a selective replacement strategy, enabling flexible Conv-to-DSConv replacement at different replacement ratios and positions. By varying the replacement ratio, we obtain models with different computational complexities and analyze their impact on reconstruction performance. Furthermore, we investigate how replacements at different encoder and decoder depths influence reconstruction quality under a fixed replacement ratio. Our results show that Conv-to-DSConv replacement at the intermediate layers of the encoder and decoder achieves a favorable complexity-performance trade-off, revealing layer-wise redundancy in DL-based JSCC systems. Extensive experiments further demonstrate that the proposed framework achieves substantial parameter reduction with only slight performance degradation, enabling flexible complexity-performance trade-offs for resource-constrained edge devices. △ Less

Submitted 11 July, 2026; v1 submitted 24 April, 2026; originally announced April 2026.

Comments: 6 pages, 6 figures, journal

A BEV-Fusion Based Framework for Sequential Multi-Modal Beam Prediction in mmWave Systems

Authors: Jiaming Zeng, Cunhua Pan, Haoyang Weng, Ruijing Liu, Hong Ren, Jiangzhou Wang

Abstract: Beam prediction is critical for reducing beam-training overhead in millimeter-wave (mmWave) systems, especially in high-mobility vehicular scenarios. This paper presents a BEV-Fusion based framework that unifies camera, LiDAR, radar, and GPS modalities in a shared bird's-eye-view (BEV) representation for spatially consistent multi-modal fusion. Unlike priorapproaches that fuse globally pooled one-… ▽ More Beam prediction is critical for reducing beam-training overhead in millimeter-wave (mmWave) systems, especially in high-mobility vehicular scenarios. This paper presents a BEV-Fusion based framework that unifies camera, LiDAR, radar, and GPS modalities in a shared bird's-eye-view (BEV) representation for spatially consistent multi-modal fusion. Unlike priorapproaches that fuse globally pooled one-dimensional features, the proposed method performs fusion in BEV space to preservecross-modal geometric structure and visual semantic density. A learned camera-to-BEV module based on cross-attention is adopted to generate BEV-aligned visual features without relying on precise camera calibration, and a temporal transformer is used to aggregate five-step sequential observations for motion-aware beam prediction. Experiments on the DeepSense 6G benchmark show that BEV-Fusion achieves approximately 87% distance- based accuracy (DBA) on scenarios 32, 33 and 34, outperforming the TransFuser baseline. These results indicate that BEV-space fusion provides an effective spatial abstraction for sensing-assisted beam prediction. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Comments: 13pages,7figures

Ground Reflection-Aided TomoSAR Imaging with 5G NR Signals

Authors: Qiuyuan Yang, Cunhua Pan, Hong Ren, Jiangzhou Wang

Abstract: Tomographic synthetic aperture radar (TomoSAR) enables three-dimensional imaging by resolving targets along the elevation dimension, which is essential for environment reconstruction and infrastructure monitoring. A critical challenge in TomoSAR is the severe multipath propagation that causes ghost targets, range offsets, and elevation ambiguities. To address this, this paper proposes an enhanced… ▽ More Tomographic synthetic aperture radar (TomoSAR) enables three-dimensional imaging by resolving targets along the elevation dimension, which is essential for environment reconstruction and infrastructure monitoring. A critical challenge in TomoSAR is the severe multipath propagation that causes ghost targets, range offsets, and elevation ambiguities. To address this, this paper proposes an enhanced Newtonized orthogonal matching pursuit (NOMP) algorithm to extract the delay, Doppler, and complex amplitude parameters of each propagation path, effectively separating line-of-sight (LoS) and multipath components prior to TomoSAR processing. Additionally, a height fusion strategy combining TomoSAR estimates with LoS-ground reflection delay-based inversion improves elevation accuracy. Simulation results demonstrate that the proposed method achieves improved positioning and elevation accuracy while effectively suppressing multipath-induced artifacts. △ Less

Submitted 12 April, 2026; v1 submitted 3 April, 2026; originally announced April 2026.

Spherical Antenna Arrays for Future Communications: Principles, Applications, and Research Directions

Authors: Cunhua Pan, Xianzhe Chen, Hong Ren, Jiangzhou Wang

Abstract: With the development of 6G technologies, traditional uniform linear arrays (ULAs) and uniform planar arrays (UPAs) can hardly meet the demands of three-dimensional (3D) full-space coverage and high angular resolution. Spherical antenna arrays (SAAs), with elements uniformly distributed on a spherical surface, provide an effective solution. This article analyzes the issues of traditional arrays, su… ▽ More With the development of 6G technologies, traditional uniform linear arrays (ULAs) and uniform planar arrays (UPAs) can hardly meet the demands of three-dimensional (3D) full-space coverage and high angular resolution. Spherical antenna arrays (SAAs), with elements uniformly distributed on a spherical surface, provide an effective solution. This article analyzes the issues of traditional arrays, summarizes the advantages and typical structures of SAAs, discusses their potential application scenarios, and verifies their superiority over UPAs via a case study. Finally, key technical challenges and corresponding research directions of SAAs are identified, providing a reference for their research and application in future wireless communications. △ Less

Submitted 8 April, 2026; v1 submitted 2 April, 2026; originally announced April 2026.

Fairness-Aware Beamforming for Polarimetric ISAC Systems with Polarization-Reconfigurable Antennas

Authors: Weijie Xiong, Jingran Lin, Di Jiang, Cunhua Pan, Hongli Liu, Kai Zhong, Qiang Li

Abstract: Polarization diversity offers significant flexibility for enhancing integrated sensing and communications (ISAC). However, conventional dual-polarized arrays typically require dedicated radio-frequency (RF) chains for each polarization branch, leading to prohibitive hardware costs. To address this, polarization-reconfigurable (PR) antennas have emerged as a cost-effective alternative, enabling pol… ▽ More Polarization diversity offers significant flexibility for enhancing integrated sensing and communications (ISAC). However, conventional dual-polarized arrays typically require dedicated radio-frequency (RF) chains for each polarization branch, leading to prohibitive hardware costs. To address this, polarization-reconfigurable (PR) antennas have emerged as a cost-effective alternative, enabling polarization flexibility with reduced hardware complexity by driving two polarization branches with a single RF chain. In this paper, we investigate fairness-aware beamforming for ISAC systems equipped with PR antennas. Specifically, we jointly optimize the transmit beamforming and PR control coefficients to maximize the minimum signal-to-interference-plus-noise ratio (SINR) for communication users and the minimum signal-to-clutter-plus-noise ratio (SCNR) for sensing targets. The resulting problem is highly nonconvex and nonsmooth due to the strong coupling among optimization variables in the max-min objective, as well as the nonconvex spherical constraints imposed by the PR antennas. To tackle this, we derive an equivalent smooth reformulation by introducing auxiliary variables and transforming the minimum operators into inequality constraints. Subsequently, we develop an exact-penalty product Riemannian manifold gradient descent (EP-PRMGD) algorithm, which integrates an exact penalty method with Riemannian optimization to guarantee convergence to a Karush-Kuhn-Tucker (KKT) point. Numerical results demonstrate that the proposed PR-enabled ISAC scheme achieves performance comparable to dual-polarized architectures while utilizing only half the RF chains, thereby validating its effectiveness in balancing fairness and hardware efficiency. △ Less

Submitted 18 March, 2026; originally announced March 2026.

SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

Authors: Jingyu Lu, Yuhan Wang, Fan Zhuo, Xize Cheng, Changhao Pan, Xueyi Pu, Yifu Chen, Chenyuhao Wen, Tianle Liang, Zhou Zhao

Abstract: The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances and the spontaneous nature of human conversation. However, current methods struggle with two critical gaps: the modality gap, involving prosody and emotion, and the colloquialness gap, distinguishing written scripts from natural speech. To address these challe… ▽ More The rapid evolution of end-to-end spoken dialogue systems demands transcending mere textual semantics to incorporate paralinguistic nuances and the spontaneous nature of human conversation. However, current methods struggle with two critical gaps: the modality gap, involving prosody and emotion, and the colloquialness gap, distinguishing written scripts from natural speech. To address these challenges, we introduce SDiaReward, an end-to-end multi-turn reward model trained on SDiaReward-Dataset, a novel collection of episode-level preference pairs explicitly targeting these gaps. It operates directly on full multi-turn speech episodes and is optimized with pairwise preference supervision, enabling joint assessment of modality and colloquialness in a single evaluator. We further establish ESDR-Bench, a stratified benchmark for robust episode-level evaluation. Experiments demonstrate that SDiaReward achieves state-of-the-art pairwise preference accuracy, significantly outperforming general-purpose audio LLMs. Further analysis suggests that SDiaReward captures relative conversational expressiveness beyond superficial synthesis cues, improving generalization across domains and recording conditions. Code, data, and demos are available at https://github.com/MM-Speech/SDiaReward/. △ Less

Submitted 10 May, 2026; v1 submitted 16 March, 2026; originally announced March 2026.

Comments: Accepted to ACL 2026 Main Conference

Dynamic Stability Assessment of Grid-Connected Data Centers Powered by Small Modular Reactors

Authors: Sobhan Badakhshan, Roshni Anna Jacob, Ali Mahboub Rad, Chao Pan, Yaoyu Li, Jie Zhang

Abstract: The accelerating growth of computational demand in modern data centers has further heightened the need for power infrastructures that are highly reliable, environmentally sustainable, and capable of supporting grid stability. Small Modular Reactors (SMRs) as a clean source of energy are particularly attractive for next-generation hyperscale data centers with significant electrical and cooling dema… ▽ More The accelerating growth of computational demand in modern data centers has further heightened the need for power infrastructures that are highly reliable, environmentally sustainable, and capable of supporting grid stability. Small Modular Reactors (SMRs) as a clean source of energy are particularly attractive for next-generation hyperscale data centers with significant electrical and cooling demands. This paper presents a comprehensive dynamic modeling and stability analysis of a grid-connected Integrated Energy System (IES) designed for data center applications. The proposed IES integrates an SMR and a battery energy storage system to jointly supply electricity for computational and cooling load while providing stability support to the main grid. A coupled computational-thermal load model is developed to capture the real-time power demand of the data center, incorporating CPU utilization, cooling efficiency, and ambient temperature effects. The integrated SMR-powered data center model is implemented in PSSE and tested on the IEEE 118-bus system under various fault scenarios. Simulation results demonstrate that the IES substantially enhances voltage and frequency stability compared to a conventionally grid-connected data center, minimizing disturbance-induced deviations and improving post-fault recovery. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Subspace Fusion Sensing for Cooperative ISAC

Authors: Yining Xu, Cunhua Pan, Jun Tang, Hong Ren, Jiangzhou Wang

Abstract: This paper proposes a subspace fusion sensing algorithm for cooperative integrated sensing and communication. First, we stack the received signals from access points (APs) into a third-order tensor and construct the equivalent virtual antenna (EVA) array via tensor unfolding. Then, a data association-free subspace-based fusion sensing algorithm is developed utilizing the EVA arrays from distribute… ▽ More This paper proposes a subspace fusion sensing algorithm for cooperative integrated sensing and communication. First, we stack the received signals from access points (APs) into a third-order tensor and construct the equivalent virtual antenna (EVA) array via tensor unfolding. Then, a data association-free subspace-based fusion sensing algorithm is developed utilizing the EVA arrays from distributed APs. A derivation of Cramer-Rao lower bound (CRLB) is also presented. Finally, simulation results validate the effectiveness of the proposed algorithm compared to traditional techniques. △ Less

Submitted 25 May, 2026; v1 submitted 9 March, 2026; originally announced March 2026.

Comments: This work has been submitted to the IEEE for possible publication

U-Net-Based Generative Joint Source-Channel Coding for Wireless Image Transmission

Authors: Ming Ye, Kui Cai, Cunhua Pan, Zhen Mei, Wanting Yang, Chunguo Li

Abstract: Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high perceptual quality or incur high computational complexity. In this paper, we propose two DL-based JSCC (DeepJSCC) methods that leverage deep generative architect… ▽ More Deep learning (DL)-based joint source-channel coding (JSCC) methods have achieved remarkable success in wireless image transmission. However, these methods either focus on conventional distortion metrics that do not necessarily yield high perceptual quality or incur high computational complexity. In this paper, we propose two DL-based JSCC (DeepJSCC) methods that leverage deep generative architectures for wireless image transmission. Specifically, we propose G-UNet-JSCC, a scheme comprising an encoder and a U-Net-based generator serving as the decoder. Its skip connections enable multi-scale feature fusion to improve both pixel-level fidelity and perceptual quality of reconstructed images by integrating low- and high-level features. To further enhance pixel-level fidelity, the encoder and the U-Net-based decoder are jointly optimized using a weighted sum of structural similarity and mean-squared error (MSE) losses. Building upon G-UNet-JSCC, we further develop a DeepJSCC method called cGAN-JSCC, where the decoder is enhanced through adversarial training. In this scheme, we retain the encoder of G-UNet-JSCC and adversarially train the decoder's generator against a patch-based discriminator. cGAN-JSCC employs a two-stage training procedure. The outer stage trains the encoder and the decoder end-to-end using an MSE loss, while the inner stage adversarially trains the decoder's generator and the discriminator by minimizing a joint loss combining adversarial and distortion losses. Simulation results demonstrate that the proposed methods achieve superior pixel-level fidelity and perceptual quality on both high- and low-resolution images. For low-resolution images, cGAN-JSCC achieves better reconstruction performance and greater robustness to channel variations than G-UNet-JSCC. △ Less

Submitted 26 February, 2026; originally announced February 2026.

Comments: This work has been submitted to the IEEE for possible publication

Synthetic Singers: A Review of Deep-Learning-based Singing Voice Synthesis Approaches

Authors: Changhao Pan, Dongyu Yao, Yu Zhang, Wenxiang Guo, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Abstract: Recent advances in singing voice synthesis (SVS) have attracted substantial attention from both academia and industry. With the advent of large language models and novel generative paradigms, producing controllable, high-fidelity singing voices has become an attainable goal. Yet the field still lacks a comprehensive survey that systematically analyzes deep-learning-based singing voice synthesis sy… ▽ More Recent advances in singing voice synthesis (SVS) have attracted substantial attention from both academia and industry. With the advent of large language models and novel generative paradigms, producing controllable, high-fidelity singing voices has become an attainable goal. Yet the field still lacks a comprehensive survey that systematically analyzes deep-learning-based singing voice synthesis systems and their enabling technologies. To address the aforementioned issue, this survey first categorizes existing systems by task type and then organizes current architectures into two major paradigms: cascaded and end-to-end approaches. Moreover, we provide an in-depth analysis of core technologies, covering singing modeling and control techniques. Finally, we review relevant datasets, annotation tools, and evaluation benchmarks that support training and assessment. In appendix, we introduce training strategies and further discussion of SVS. This survey provides an up-to-date review of the literature on SVS models, which would be a useful reference for both researchers and engineers. Related materials are available at https://github.com/David-Pigeon/SyntheticSingers. △ Less

Submitted 20 January, 2026; originally announced January 2026.

Comments: Accepetd by IJCNLP-AACL 2025(Oral)

An Energy-Efficient RFET-Based Stochastic Computing Neural Network Accelerator

Authors: Sheng Lu, Qianhou Qu, Sungyong Jung, Qilian Liang, Chenyun Pan

Abstract: Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks (CNNs). However, despite its advantages, stochastic computing neural networks (SCNNs) often suffer from high resource consumption due to components such as stochastic number generators (SNGs) and accumulative parallel counters (APCs), which limit overall performance. This pa… ▽ More Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks (CNNs). However, despite its advantages, stochastic computing neural networks (SCNNs) often suffer from high resource consumption due to components such as stochastic number generators (SNGs) and accumulative parallel counters (APCs), which limit overall performance. This paper proposes a novel SCNN accelerator based on reconfigurable field-effect transistors (RFETs). The inherent reconfigurability at the device level enables the design of highly efficient and compact SNGs, APCs, and other related essential components. To assess their system-level impact, a representative existing SCNN architecture is adopted as an evaluation framework. Based on accessible open-source standard cell libraries, experimental results demonstrate that the proposed RFET-based SCNN accelerator achieves significant reductions in area, latency, and energy consumption compared to its FinFET-based counterpart at the same technology node. △ Less

Submitted 23 July, 2026; v1 submitted 5 December, 2025; originally announced December 2025.

Comments: update the draft for corrected some important contents

WiFi-based Cross-Domain Gesture Recognition Using Attention Mechanism

Authors: Ruijing Liu, Cunhua Pan, Jiaming Zeng, Hong Ren, Kezhi Wang, Lei Kong, Jiangzhou Wang

Abstract: While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of t… ▽ More While fulfilling communication tasks, wireless signals can also be used to sense the environment. Among various types of sensing media, WiFi signals offer advantages such as widespread availability, low hardware cost, and strong robustness to environmental conditions like light, temperature, and humidity. By analyzing Wi-Fi signals in the environment, it is possible to capture dynamic changes of the human body and accomplish sensing applications such as gesture recognition. Although many existing gesture sensing solutions perform well in-domain but lack cross-domain capabilities (i.e., recognition performance in untrained environments). To address this, we extract Doppler spectra from the channel state information (CSI) received by all receivers and concatenate each Doppler spectrum along the same time axis to generate fused images with multi-angle information as input features. Furthermore, inspired by the convolutional block attention module (CBAM), we propose a gesture recognition network that integrates a multi-semantic spatial attention mechanism with a self-attention-based channel mechanism. This network constructs attention maps to quantify the spatiotemporal features of gestures in images, enabling the extraction of key domain-independent features. Additionally, ResNet18 is employed as the backbone network to further capture deep-level features. To validate the network performance, we evaluate the proposed network on the public Widar3 dataset, and the results show that it not only maintains high in-domain accuracy of 99.72%, but also achieves high performance in cross-domain recognition of 97.61%, significantly outperforming existing best solutions. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Rotatable Antenna-array-enhanced Direction-sensing for Low-altitude Communication Network: Method and Performance

Authors: Jinbing Jiang, Feng Shu, Bin Deng, Maolin Li, Jiatong Bai, Yan Wang, Cunhua Pan, Jiangzhou Wang

Abstract: In a practical multi-antenna receiver, each element of the receive antenna array has a directive antenna pattern, which is still not fully explored and investigated in academia and industry until now. When the emitter is deviated greatly from the normal direction of antenna element or is close to the null-point direction, the sensing energy by array will be seriously attenuated such that the direc… ▽ More In a practical multi-antenna receiver, each element of the receive antenna array has a directive antenna pattern, which is still not fully explored and investigated in academia and industry until now. When the emitter is deviated greatly from the normal direction of antenna element or is close to the null-point direction, the sensing energy by array will be seriously attenuated such that the direction-sensing performance is degraded significantly. To address such an issue, a rotatable array system is established with the directive antenna pattern of each element taken into account, where each element has the same antenna pattern. Then, the corresponding the Cramer-Rao lower bound (CRLB) is derived. Finally, a recursive rotation Root-MUSIC (RR-Root-MUSIC) direction-sensing method is proposed and its root-mean-squared-error (RMSE) performance is evaluated by the derived CRLB. Simulation results show that the proposed rotation method converges rapidly with about ten iterations, and make a significant enhancement on the direction-sensing accuracy in terms of RMSE when the target direction departs seriously far away from the normal vector of array. Compared with conventional Root-MUSIC, the sensing performance of the proposed RR-Root-MUSIC method is much closer to the CRLB. △ Less

Submitted 2 April, 2026; v1 submitted 29 November, 2025; originally announced December 2025.

Secure Analog Beamforming for Multi-user MISO Systems with Movable Antennas

Authors: Weijie Xiong, Jingran Lin, Kai Zhong, Liu Yang, Hongli Liu, Qiang Li, Cunhua Pan

Abstract: Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly incre… ▽ More Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly increase hardware costs, making such systems impractical for multi-antenna deployments. To address this, hardware-efficient analog beamforming (AB) offers a cost-effective alternative. This paper investigates the physical layer security (PLS) in an MA-enabled multiple-input single-output (MISO) communication system with an emphasis on AB. In this scenario, an MA-enabled transmitter with AB broadcasts common confidential information to a group of legitimate receivers, while a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our objective is to maximize the multicast secrecy rate (MSR) by jointly optimizing the phase shifts of the AB and the positions of the MAs, subject to constraints on the movement area of the MAs and the constant modulus (CM) property of the analog phase shifters. This MSR maximization problem is highly challenging, as we have formally proven it to be NP-hard. To solve it efficiently, we propose a penalty constrained product manifold (PCPM) framework. Specifically, we first reformulate the position constraints as a penalty function, enabling unconstrained optimization on a product manifold space (PMS), and then propose a parallel conjugate gradient descent algorithm to efficiently update the variables. Simulation results demonstrate that MA-enabled systems with AB can achieve a well-balanced performance in terms of MSR and hardware costs. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Channel Estimation for RIS-Aided MU-MIMO mmWave Systems with Direct Channel Links

Authors: Taihao Zhang, Zhendong Peng, Cunhua Pan, Hong Ren, Jiangzhou Wang

Abstract: In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the developed three-stage… ▽ More In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the developed three-stage strategy stems from the careful design of both the pilot signal sequence of the users and the vectors of RIS. Specifically, in Stage I, the cascaded channel components are eliminated by configuring the RIS phase shift vectors with a π difference to estimate the direct channels for all users. The orthogonal subspace projection is employed in Stage II to obtain equivalent signal matrices, enabling the estimation of angles of departure (AoDs) of the user-RIS channel for all users. In Stage III, we combine the signals of the time slots with the same pilots and project obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and remaining parameters of the cascaded channel for all users by exploiting the sparsity and correlation in the obtained equivalent matrices. Simulation results demonstrate that the proposed method yields better estimation performance than the existing methods. △ Less

Submitted 22 November, 2025; originally announced November 2025.

Comments: 13 pages,11 figures, journal

Theoretical and Empirical Study of Spatial Power Focusing Effect for Sparse Arrays at Terahertz Band

Authors: Yongchao He, Taihao Zhang, Cunhua Pan, Hong Ren, Xianzhe Chen, Tian Qiu, Bingchang Hua, Jiangzhou Wang

Abstract: This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furthermore, the factors influencing the focu… ▽ More This work investigates the spatial power focusing effect for large-scale sparse arrays at terahertz (THz) band, combining theoretical analysis with experimental validation. Specifically, based on a Green's function channel model, we analyze the power distribution along the $z$-axis, deriving a closed-form expression to characterize the focusing effect. Furthermore, the factors influencing the focusing effect, including phase noise and positional deviations, are theoretically analyzed and numerically simulated. Finally, a 300 GHz measurement platform based on a vector network analyzer (VNA) is constructed for experimental validation. The measurement results demonstrate close consistence with theoretical simulation results, confirming the spatial power focusing effect for sparse arrays. △ Less

Submitted 19 November, 2025; originally announced November 2025.

Comments: 5 pages, 7 figures

Enhancing Physical Layer Security in MIMO Systems Assisted by Beyond-Diagonal Reconfigurable Intelligent Surfaces

Authors: Weijie Xiong, Jingran Lin, Cunhua Pan, Yilong Zeng, Qiang Li

Abstract: Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering ma… ▽ More Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Journal ref: IEEE Transactions on Communications, vol. 73, no. 11, pp. 10824-10839, Nov. 2025

A Two-Stage ISAC Framework for Low-Altitude Economy Based on 5G NR Signals

Authors: Haisu Wu, Hong Ren, Cunhua Pan, Boshi Wang, Jun Tang, Haoyang Weng, Feng Shu, Jiangzhou Wang

Abstract: The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Syn… ▽ More The evolution of next-generation wireless networks has spurred the vigorous development of the low-altitude economy (LAE). To support this emerging field while remaining compatible with existing network architectures, integrated sensing and communication (ISAC) based on 5G New Radio (NR) signals is regarded as a promising solution. However, merely leveraging standard 5G NR signals, such as the Synchronization Signal Block (SSB), presents fundamental limitations in sensing resolution. To address the issue, this paper proposes a two-stage coarse-to-fine sensing framework that utilizes standard 5G NR initial access signals augmented by a custom-designed sparse pilot structure (SPS) for high-precision unmanned aerial vehicles (UAV) sensing. In Stage I, we first fuse information from the SSB, Type\#0-PDCCH, and system information block 1 (SIB1) to ensure the initial target detection. In Stage II, a refined estimation algorithm is introduced to overcome the resolution limitations of these signals. Inspired by the sparse array theory, this stage employs a novel SPS, which is inserted into resource blocks (RBs) within the CORSET\#0 bandwidth. To accurately extract the off-grid range and velocity parameters from these sparse pilots, we develop a corresponding high-resolution algorithm based on the weighted unwrapped phase (WUP) technique and the RELAX-based iterative method. Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is adopted to prune the redundant detections arising from beam overlap. Comprehensive simulation results demonstrate the superior estimation accuracy and computational efficiency of the proposed framework in comparison to other techniques. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Sensing-enabled Secure Rotatable Array System Enhanced by Multi-Layer Transmitting RIS

Authors: Maolin Li, Feng Shu, Minghao Chen, Cunhua Pan, Fuhui Zhou, Yongpeng Wu, Liang Yang

Abstract: Programmable metasurfaces and adjustable antennas are promising technologies. The security of a rotatable array system is investigated in this paper. A dual-base-station (BS) architecture is adopted, in which the BSs collaboratively perform integrated sensing of the eavesdropper (the target) and communication tasks. To address the security challenge when the sensing target is located on the main c… ▽ More Programmable metasurfaces and adjustable antennas are promising technologies. The security of a rotatable array system is investigated in this paper. A dual-base-station (BS) architecture is adopted, in which the BSs collaboratively perform integrated sensing of the eavesdropper (the target) and communication tasks. To address the security challenge when the sensing target is located on the main communication link, the problem of maximizing the secrecy rate (SR) under sensing signal-to-interference-plus-noise ratio requirements and discrete constraints is formulated. This problem involves the joint optimization of the array pose, the antenna distribution on the array surface, the multi-layer transmitting RIS phase matrices, and the beamforming matrices, which is non-convex. To solve this challenge, an two-stage online algorithm based on the generalized Rayleigh quotient and an offline algorithm based on the Multi-Agent Deep Deterministic Policy Gradient are proposed. Simulation results validate the effectiveness of the proposed algorithms. Compared to conventional schemes without array pose adjustment, the proposed approach achieves approximately 22\% improvement in SR. Furthermore, array rotation provides higher performance gains than position changes. △ Less

Submitted 17 November, 2025; originally announced November 2025.

Cooperative ISAC for LAE: Joint Trajectory Planning, Power allocation, and Dynamic Time Division

Authors: Fangzhi Li, Zhichu Ren, Cunhua Pan, Hong Ren, Jing Jin, Qixing Wang, Jiangzhou Wang

Abstract: To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum c… ▽ More To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication in each time slot. To maximize the sum communication rate while satisfying a mission-level cumulative radar mutual information (MI) requirement, we jointly optimize the UAV trajectories, communication and sensing power allocation, and the time-division ratio. The resulting highly coupled non-convex optimization problem is efficiently solved using an alternating optimization (AO) and successive convex approximation (SCA) framework, which yields a non-decreasing objective sequence and convergence to a finite objective value under the adopted surrogate-based iterative procedure. Extensive simulation results demonstrate that our proposed joint design significantly outperforms benchmark schemes with static trajectories, partially optimized resources, or non-cooperative single-BS transmission. Furthermore, a comprehensive sensitivity analysis reveals the distinct mechanisms by which sensing thresholds and the number of UAVs influence resource allocation and spatial organization, highlighting the critical importance of dynamic, multi-dimensional resource management for effectively navigating the sensing-communication trade-off in low-altitude economies. △ Less

Submitted 30 April, 2026; v1 submitted 17 November, 2025; originally announced November 2025.

Mutual Coupling Aware Channel Estimation for RIS-Aided Multi-User mmWave Systems

Authors: Tian Qiu, Ruidong Li, Cunhua Pan, Taihaon Zhang, Dongnan Xia, Changhong Wang, Hong Ren

Abstract: This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering… ▽ More This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency. △ Less

Submitted 22 February, 2026; v1 submitted 11 November, 2025; originally announced November 2025.

4D Imaging in ISAC Systems: A Framework Based on 5G NR Downlink Signals

Authors: Haoyang Weng, Haisu Wu, Hong Ren, Cunhua Pan, Jiangzhou Wang

Abstract: Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. This paper proposes a 4D imaging framewor… ▽ More Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. This paper proposes a 4D imaging framework fully compliant with the 5G New Radio (NR) protocol, ensuring compatibility with cellular systems. Specifically, we develop an end-to-end processing chain that covers waveform generation, echo processing, and multi-BS point cloud fusion. Furthermore, we introduce Zoom-OMP, a coarse-to-fine sparse recovery algorithm for high-resolution angle estimation that achieves high accuracy with reduced computational cost. The simulation results demonstrate that the proposed framework achieves robust 4D imaging performance with superior spatial accuracy and reconstruction quality compared to conventional benchmarks, paving the way for practical ISAC-enabled environment reconstruction in 6G networks. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Comments: TVT

UAV SAR Imaging with 5G NR OFDM Signals in NLOS Environments

Authors: Qiuyuan Yang, Cunhua Pan, Ruidong Li, Zhenkun Zhang, Hong Ren, Changhong Wang, Jiangzhou Wang

Abstract: The integration of sensing and communication (ISAC) has significant potential for future wireless systems, enabling efficient spectrum utilization and novel application scenarios. In this paper, we propose a cooperative ISAC framework for synthetic aperture radar (SAR) imaging by leveraging orthogonal frequency division multiplexing (OFDM) communication signals. We address the challenge of severe… ▽ More The integration of sensing and communication (ISAC) has significant potential for future wireless systems, enabling efficient spectrum utilization and novel application scenarios. In this paper, we propose a cooperative ISAC framework for synthetic aperture radar (SAR) imaging by leveraging orthogonal frequency division multiplexing (OFDM) communication signals. We address the challenge of severe imaging degradation in non-line-of-sight (NLOS) environments under the QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa). To detect weak signals and eliminate false points, we develop a two-stage compressed sensing-space alternating generalized expectation maximization (CS-SAGE) scheme for high-precision scatterer localization. In stage I, orthogonal matching pursuit (OMP) is employed for coarse estimation to identify the approximate locations of dominant scatterers. Then, the SAGE algorithm in stage II performs fine estimation to accurately extract scatterer parameters. Simulation results validate the effectiveness of the proposed cooperative ISAC framework, and provide valuable insights for practical system design. △ Less

Submitted 5 November, 2025; originally announced November 2025.

Large-Model AI for Near Field Beam Prediction: A CNN-GPT2 Framework for 6G XL-MIMO

Authors: Wang Liu, Cunhua Pan, Hong Ren, Wei Zhang, Cheng-Xiang Wang, Jiangzhou Wang

Abstract: The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhea… ▽ More The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the far-field case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network-Generative Pre-trained Transformer 2 (CNN-GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. △ Less

Submitted 26 October, 2025; originally announced October 2025.

From Active to Battery-Free: Rydberg Atomic Quantum Receivers for Self-Sustained SWIPT-MIMO Networks

Authors: Qihao Peng, Qu Luo, Zheng Chu, Neng Ye, Hong Ren, Cunhua Pan, Lixia Xiao, Pei Xiao

Abstract: In this paper, we proposed a hybrid simultaneous wireless information and power transfer (SWIPT)-enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional RF transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signal from Internet of Things (IoT) devices. To fully exploit this integration, we join… ▽ More In this paper, we proposed a hybrid simultaneous wireless information and power transfer (SWIPT)-enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional RF transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signal from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free communication. △ Less

Submitted 17 October, 2025; originally announced October 2025.

Comments: Submitted to IEEE journals

Rydberg Atomic Quantum Satellites for Enhanced Ground-to-Space Direct Uplink Access

Authors: Qihao Peng, Tierui Gong, Zihang Song, Qu Luo, Cunhua Pan, Pei Xiao, Chau Yuen

Abstract: This paper investigates the performance advantages of Rydberg atomic quantum (RAQ)-based multiple-input multiple-output (MIMO) satellites for enhancing direct ground-to-space uplink access.We analytically evaluate the impact of Rydberg atoms on channel estimation by deriving closed-form expressions for the mean-square error (MSE) and normalized mean-square error (NMSE). Based on the estimated chan… ▽ More This paper investigates the performance advantages of Rydberg atomic quantum (RAQ)-based multiple-input multiple-output (MIMO) satellites for enhancing direct ground-to-space uplink access.We analytically evaluate the impact of Rydberg atoms on channel estimation by deriving closed-form expressions for the mean-square error (MSE) and normalized mean-square error (NMSE). Based on the estimated channels, we further derive lower bounds on the achievable data rates for maximum ratio combining (MRC) and zero-forcing (ZF) detection schemes. Rigorous analysis demonstrates that RAQ-MIMO outperforms conventional radio-frequency (RF) MIMO under both Rayleigh and satellite channel conditions. Specifically, compared with conventional MIMO, RAQR achieves a ``squaring" gain under Rayleigh fading, especially in long-distance transmission scenarios with stringent power constraints. In contrast, under line-of-sight (LoS)-dominated satellite channels, this gain saturates as channel-estimation benefits diminish, with the remaining improvement primarily arising from the normalized noise background. Monte Carlo simulations validate the analytical results and show that the performance gains of RAQ-MIMO satellites translate into smaller antenna apertures, lower transmit power, and longer communication ranges, thereby paving the way for next-generation satellite networks. △ Less

Submitted 24 October, 2025; v1 submitted 17 October, 2025; originally announced October 2025.

Comments: Submitted to IEEE journals

Fundamentals of Data-Driven Approaches to Acoustic Signal Detection, Filtering, and Transformation

Authors: Chao Pan

Abstract: In recent decades, the field of signal processing has rapidly evolved due to diverse application demands, leading to a rich array of scientific questions and research areas. The forms of signals, their formation mechanisms, and the information extraction methods vary by application, resulting in diverse signal processing techniques. Common techniques can be categorized into three types: transforma… ▽ More In recent decades, the field of signal processing has rapidly evolved due to diverse application demands, leading to a rich array of scientific questions and research areas. The forms of signals, their formation mechanisms, and the information extraction methods vary by application, resulting in diverse signal processing techniques. Common techniques can be categorized into three types: transformation, detection, and filtering. Signal transformation converts signals from their original domain to a more suitable target domain for analysis; signal detection aims to identify the existence of relevant information within a signal and its specific time and location; and signal filtering focuses on extracting or separating source signals of interest from observed signals. In acoustic signal processing, techniques include sound source localization, sound event detection, voiceprint extraction and recognition, noise reduction, and source separation, with applications in speech communication, voice interaction, smart healthcare, and industrial diagnostics. Recently, the advancement of deep learning technologies has shifted methodologies in acoustic signal processing from knowledge-driven to data-driven approaches, leading to significant research outcomes. This paper aims to systematically summarize the principles and methods of data-driven acoustic signal processing, providing a comprehensive understanding framework for academic exploration and practical applications. △ Less

Submitted 29 August, 2025; originally announced August 2025.

Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks

Authors: Feibo Jiang, Li Dong, Xitao Pan, Kezhi Wang, Cunhua Pan

Abstract: This paper proposes a novel Agentic Retrieval-augmented generation with Mamba-Attention Integrated Transformer (ARMAIT) framework for multi-Unmanned Aerial Vehicle (UAV) trajectory optimization. The framework is built upon Large Language Models (LLMs), incorporating Retrieval-Augmented Generation (RAG) empowered by Agentic AI and integrated with a UAV-specific knowledge base. Through the Agentic R… ▽ More This paper proposes a novel Agentic Retrieval-augmented generation with Mamba-Attention Integrated Transformer (ARMAIT) framework for multi-Unmanned Aerial Vehicle (UAV) trajectory optimization. The framework is built upon Large Language Models (LLMs), incorporating Retrieval-Augmented Generation (RAG) empowered by Agentic AI and integrated with a UAV-specific knowledge base. Through the Agentic RAG, the LLM autonomously interprets high-level task requirements and identifies the key components necessary for trajectory optimization, including model inputs and outputs, network architecture, reward functions, and task constraints. To support efficient modeling across different system scales, we introduce the Mamba-Attention Integrated Transformer (MAIT), a hybrid neural architecture that combines the long-range dependency modeling capability of attention mechanisms with the efficient temporal dynamic representation of Mamba. Furthermore, a Trajectory-Group Relative Policy Optimization (T-GRPO) method is proposed to achieve unified policy gradient optimization in both discrete and continuous trajectory spaces for MAIT training. Extensive experimental results validate the feasibility and effectiveness of the proposed ARMAIT framework. △ Less

Submitted 22 August, 2025; originally announced August 2025.