Showing 1–50 of 347 results for author: Hanzo, L

Sub-Sampling for Positioning Privacy in ISAC: Deception by Aliasing via Sparse Arrays and Pilots

Authors: L. Yashvanth, Christos Masouros, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

Abstract: Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly expl… ▽ More Integrated sensing and communications (ISAC) enables simultaneous communication and sensing using shared spectrum and hardware resources in wireless systems. However, securing the sensing functionality against unauthorized receivers remains a fundamental challenge. In this paper, we propose a sub-sampling based sensing-privacy framework for communication-centric (CC)-ISAC systems that jointly exploits sparse arrays and sparse pilot allocations to induce controlled aliasing in the spatial and frequency domains, respectively. By interpreting antenna arrays and pilot subcarriers as spatial and frequency sampling mechanisms, respectively, we show that spatial-frequency undersampling naturally distorts the range-angle multiple-input multiple-output (MIMO) ambiguity function (AF) observed by an unauthorized receiver. To this end, we first derive a closed-form expression for the range-angle MIMO-AF, and subsequently characterize the ghost targets that arise due to spatial and frequency-domain aliasing. Next, we establish a sufficient condition under which these ambiguities jointly translate into positioning ambiguity and show that, for sufficiently large spatial and frequency sub-sampling factors, an unauthorized receiver inevitably positions a target at incorrect ghost positions. Finally, we show that the proposed sub-sampling framework preserves the native legitimate ISAC performance without introducing additional trade-offs. Numerical results verify the analysis and show that sparse arrays and sparse pilots naturally enable sensing and positioning privacy through deception by aliasing. △ Less

Submitted 7 August, 2026; originally announced August 2026.

Joint Load Balancing and Transmit Power Control for Energy Efficiency Maximization in the Satellite-Cell-Free Massive MIMO Uplink

Authors: Ngo Tran Anh Thu, Lo Hai Long, Le Duc Anh Vu, Trinh Van Chien, Lajos Hanzo

Abstract: The seamless integration of non-terrestrial and terrestrial infrastructures is a key enabler for ubiquitous connectivity in next-generation (NG) wireless networks. We investigate a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and… ▽ More The seamless integration of non-terrestrial and terrestrial infrastructures is a key enabler for ubiquitous connectivity in next-generation (NG) wireless networks. We investigate a hybrid satellite-cell-free Massive MIMO system, where multiple low-Earth-orbit (LEO) satellites jointly serve users in unison with terrestrial access points (APs) under realistic imperfect channel state information and practical user association constraints. We first derive closed-form expressions of the uplink ergodic throughput by exploiting maximum ratio combining (MRC) for transmission over spatially correlated Rician fading channels. Our analysis reveals the characteristic impact of both user-satellite and user-AP association patterns on both the spectral efficiency and rate-fairness achieved. We then formulate an energy efficiency optimization problem under joint user association and power control. Since the problems are inherently NP-hard due to the binary nature of the user-association variables, we develop an improved Differential Evolution (IDE) framework that efficiently explores the feasible solutions in polynomial time. Numerical results validate our analysis and show that the proposed hybrid scheme substantially improves energy efficiency and network throughput. For large-scale scenarios, the DE framework provides practical user-satellite-AP association guidelines, enabling scalable performance gains. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Comments: 15 pages, 8 figures, and 2 tables. Accepted by IEEE TCOM

Constellation Selection and Power Allocation for Multi-Cell OFDM-ISAC: Managing Inter-Cell Interference and Sensing Sidelobes

Authors: Kaitao Meng, Kawon Han, Christos Masouros, Lajos Hanzo

Abstract: Future integrated sensing and communication (ISAC) networks are expected to operate in dense multi-cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay--Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allo… ▽ More Future integrated sensing and communication (ISAC) networks are expected to operate in dense multi-cell environments, where multiple base stations (BSs) share their time-frequency resources for communication and sensing. In such scenarios, the delay--Doppler (DD) sensing performance is strongly affected by random finite-alphabet orthogonal frequency-division multiplexing (OFDM) symbols, power allocation, receive filtering, and interference. This paper develops a modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems. Starting from a discrete-time OFDM sensing model, we derive closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF). The analysis reveals distinct interference structures: MF depends on fourth-order constellation moments and power-overlap terms, whereas RF is governed by inverse-symbol-power and ratio-type interference terms. Based on these expressions, we obtain sensing-oriented power allocation structures, including a ramped water-filling solution for MF and a square-root allocation rule for RF. Furthermore, we jointly optimize the finite-alphabet constellation selection and power allocation under realistic communication and power constraints, and obtain tractable mixed-integer convex formulations for both MF and RF. Additionally, we study spectrum-overlap coordination in multi-cell scenarios and reveal the distinct MF/RF preferences for shared and orthogonalized tones. Furthermore, we extend the interference model to inter-cell propagation delays exceeding the cyclic prefix (CP), and show how the resultant delay violation redistributes the nominal interference spectrum into a delay-distorted effective spectrum... △ Less

Submitted 23 July, 2026; originally announced July 2026.

Comments: 13 pages, submitted to the IEEE Journal for possible publication

PERA: A Perceive-Reason-Act Interface Bridging Sensing, Cognitive Reasoning, and Trustworthy Agentic Response for 6G

Authors: Mohammad Farzanullah, Melike Erol-Kantarci, Lajos Hanzo

Abstract: The realization of next-generation (NG) networks hinges on a fundamental departure from preprogrammed protocol engineering towards a paradigm of self-consciously evolving, autonomous and trusted intelligence. While conventional machine learning (ML) has introduced localized automation, it remains inherently bounded by single-task processing pipelines incapable of handling complex cross-layer dynam… ▽ More The realization of next-generation (NG) networks hinges on a fundamental departure from preprogrammed protocol engineering towards a paradigm of self-consciously evolving, autonomous and trusted intelligence. While conventional machine learning (ML) has introduced localized automation, it remains inherently bounded by single-task processing pipelines incapable of handling complex cross-layer dynamics. As a partial remedy, large language models (LLMs) excel at generalized cognitive reasoning, but to a degree they remain detached from the rich modalities of wireless telemetry. As a solution, we unveil Generative Network Intelligence conceptualized via the Perceive-Reason-Act (PERA) paradigm. This paradigm treats the wireless channel and the underlying network states as a continuous, multimodal narrative. By synchronizing the perceptual grounding of Large Wireless AI Models (LWAMs) with the cognitive reasoning of LLMs, PERA heralds the era of native NG intelligence. Crucially, this unified intelligence replaces fragmented, task-specific edge models by an efficient multi-task architecture delivering the real-time control needed for supporting dynamic physical applications while reducing both the complexity and energy dissipation. Moreover, we contrast the structural limitations of traditional ML to generative paradigms, conceive agentic reasoning across a NG protocol stack, and detail a practical three-tier design specifically engineered for the resource-constrained wireless edge. This architectural paradigm serves as a foundational framework for realizing fully autonomous, embodied agentic AI in NG networks. To validate this vision, our case study evaluates link-state classification and beam prediction, demonstrating how grounding wireless telemetry within a cognitive engine delivers the transparent, human-readable rationales required for trusted physical-layer diagnostics and beam control. △ Less

Submitted 18 July, 2026; originally announced July 2026.

Comments: 9 pages, 5 figures, 1 table, magazine paper, submitted to IEEE for possible publication

Technical Supplement Report on Full-Duplex FBMC/QAM MIMO Systems: Transceiver Design and Optimization

Authors: Sudhakar Rai, Prem Singh, Ekant Sharma, Aditya K. Jagannatham, Lajos Hanzo

Abstract: This technical report presents the design and analysis of filter bank multicarrier (FBMC)/QAM multi-user MISO systems. We describe the complete uplink and downlink signal processing chains and characterize the end-to-end effective channel, including inter-carrier interference, inter-symbol interference, intrinsic interference, and residual self-interference. We compare FBMC/QAM with CP-OFDM and FB… ▽ More This technical report presents the design and analysis of filter bank multicarrier (FBMC)/QAM multi-user MISO systems. We describe the complete uplink and downlink signal processing chains and characterize the end-to-end effective channel, including inter-carrier interference, inter-symbol interference, intrinsic interference, and residual self-interference. We compare FBMC/QAM with CP-OFDM and FBMC/OQAM through the lens of the Balian-Low theorem, and analyze prototype filter choices (PHYDYAS, Type-I, and Type-II), including the interference power breakdown under MRT and ZF precoding. Furthermore, we present an online stochastic successive convex approximation framework for ergodic sum-rate maximization with closed-form power updates, and contrast it with offline Monte Carlo-based approaches. Simulation results demonstrate the BER and network spectral efficiency advantages of FBMC/QAM over CP-OFDM under residual carrier frequency offset. △ Less

Submitted 11 June, 2026; originally announced June 2026.

OTFS-IM-Assisted Non-Terrestrial Networks Relying on Autoencoder-Aided Soft-Decision Detection

Authors: Xinyu Feng, Chao Zhang, Mohammed EL-Hajjar, Chao Xu, Lajos Hanzo

Abstract: Orthogonal Time Frequency Space ({OTFS}) modulation offers significant advantages over Orthogonal Frequency Division Multiplexing ({OFDM}), particularly in high speed environments. Hence, we consider {OTFS} transmission over high-Doppler Non-Terrestrial Networks ({NTN}). However, OTFS-based systems inherit some deficiencies from {OFDM}, such as its high peak to average power ratio, the bandwidth e… ▽ More Orthogonal Time Frequency Space ({OTFS}) modulation offers significant advantages over Orthogonal Frequency Division Multiplexing ({OFDM}), particularly in high speed environments. Hence, we consider {OTFS} transmission over high-Doppler Non-Terrestrial Networks ({NTN}). However, OTFS-based systems inherit some deficiencies from {OFDM}, such as its high peak to average power ratio, the bandwidth efficiency loss due to the cyclic prefix, and the sensitivity to the carrier frequency offset. Against this background, we harness both Multi-Band Discrete Fourier Transform-based Spreading (MB-DFT-S) and Index Modulation ({IM}) in our {OTFS} system, termed as MB-DFT-S-OTFS-IM. More explicitly, 1) DFT-S has been shown to reduce the {PAPR}; 2) {IM} is capable of improving the throughput by harnessing it in the Delay and Doppler ({DD}) domain; and 3) MB-DFT-S-OTFS-IM provides frequency diversity gain, which benefits the tolerance to carrier frequency offset. Furthermore, we propose a {PAPR} reduction method based on a Deep Learning ({DL}) Autoencoder ({AE}) architecture for both hard- and soft-decision detection, where the encoder is specifically trained for minimizing {PAPR} and the decoder is conceived for accurately reconstructing the transmitted signal. Finally, we extend the proposed {AE}-aided {OTFS-IM} scheme constructed for a practical {NTN} channel model, representing a variety of satellite-to-ground schemes. △ Less

Submitted 9 May, 2026; originally announced May 2026.

Blind OFDM-ISAC Relying on Asymmetric Modem Constellations

Authors: Henglin Pu, Ahmad Musallam, Husheng Li, Lajos Hanzo

Abstract: Integrated sensing and communication (ISAC) is increasingly expected to operate under aggressive spectrum reuse, where co-channel orthogonal frequency division multiplexing (OFDM) interference can be catastrophic for data recovery on the time-frequency (TF) grid. We show that supporting blind ISAC is feasible by exploiting a fundamental asymmetry in the impact of co-channel OFDM interference: whil… ▽ More Integrated sensing and communication (ISAC) is increasingly expected to operate under aggressive spectrum reuse, where co-channel orthogonal frequency division multiplexing (OFDM) interference can be catastrophic for data recovery on the time-frequency (TF) grid. We show that supporting blind ISAC is feasible by exploiting a fundamental asymmetry in the impact of co-channel OFDM interference: while communication is fragile on the TF grid, sensing depends on structured physical parameters whose signatures remain identifiable by relying on higher-order statistics. Based on this observation, we construct a fourth-order measurement tensor from the received OFDM signal whose coherent component preserves the delay-, Doppler-, and angle-dependent phase evolution of each source. We then develop a three-dimensional higher-order-statistics (HOS) based periodogram for iterative peak search and refinement to jointly estimate both range, velocity, and angle in the presence of unknown co-channel interferers. We further exploit constellation asymmetry to resolve the remaining phase ambiguities of blind recovery, enabling blind coherent demodulation via minimum constellation fitting. We also benchmark the performance through matched data-aided and stochastic Cramer-Rao lower bounds. We then quantify the cost of signal blindness. Simulations and experimental validations demonstrate reliable radar parameter estimation together with effective communication demodulation even when the TF-domain link is severely interfered with. △ Less

Submitted 28 April, 2026; originally announced April 2026.

EQE-QAOA: An Equivalence-Preserving Qubit Efficient Framework for Combinatorial Optimization

Authors: Xiaoyu Ma, Fang Fang, Ximing Xie, Xianbin Wang, Lajos Hanzo

Abstract: The limited number of qubits is a major bottleneck in Quantum Approximate Optimization Algorithm (QAOA) for large-scale combinatorial optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. To make progress, existing techniques rely on qubit reduction at the cost of information loss, hence leading to degraded computational performance. As a remedy, we propose the Equivalence-preserving Qu… ▽ More The limited number of qubits is a major bottleneck in Quantum Approximate Optimization Algorithm (QAOA) for large-scale combinatorial optimization in the Noisy Intermediate-Scale Quantum (NISQ) era. To make progress, existing techniques rely on qubit reduction at the cost of information loss, hence leading to degraded computational performance. As a remedy, we propose the Equivalence-preserving Qubit Efficient QAOA (EQE-QAOA), which significantly reduces the required number of qubits without degrading the performance of QAOA. By exploiting intrinsic symmetries and conserved quantities, we first demonstrate that the QAOA dynamics are strictly confined to an invariant subspace of the Hilbert space. We subsequently prove that the evolution within this subspace is exactly equivalent to that of the full-scale system, achieving the same optimal solution as the original QAOA. Moreover, to reduce the number of qubits, we propose an isometric mapping that re-encodes the subspace into a space relying on fewer qubits. Furthermore, we derive the applicability conditions of EQE-QAOA and show that it is broadly applicable to large-scale combinatorial optimization problems, excluding only unconstrained problems with completely independent variables. Numerical simulations based on Max-Cut instances validate that EQE-QAOA significantly reduces qubit requirements and computational resources, while preserving exact optimization performance. △ Less

Submitted 20 April, 2026; originally announced April 2026.

Gaussian Mixture Model Based Bayesian Learning for Sparse Channel Estimation in Orthogonal Time Frequency Space Modulated Systems

Authors: Surbhi Gehlot, Suraj Srivastava, Sandeep Kumar Yadav, Lajos Hanzo

Abstract: A novel Gaussian mixture model (GMM) aided sparse Bayesian learning (SBL) framework is proposed for channel state information (CSI) estimation in orthogonal time-frequency space (OTFS) modulated systems. The key attribute of the proposed algorithm lies in casting CSI recovery as an SBL inference problem, where posterior distributions are iteratively refined under a hierarchical GMM prior. Using th… ▽ More A novel Gaussian mixture model (GMM) aided sparse Bayesian learning (SBL) framework is proposed for channel state information (CSI) estimation in orthogonal time-frequency space (OTFS) modulated systems. The key attribute of the proposed algorithm lies in casting CSI recovery as an SBL inference problem, where posterior distributions are iteratively refined under a hierarchical GMM prior. Using this approach, the sparsity-inducing variances beneficially promote sparsity in the delay Doppler (DD) domain, while additionally augmenting the capability of SBL to exploit channel statistics more effectively. Moreover, to fully exploit the GMMs ability to approximate arbitrary probability density functions and model complex multipath fading scenarios, the channel statistics are represented using a complex Gaussian mixture. Simultaneously, the method leverages time-domain (TD) pilots without requiring wasteful DD domain guard intervals, thereby ensuring low pilot overhead and high spectral efficiency. The CSI recovered is subsequently applied in a linear minimum mean square error (MMSE) detector for reliable data detection. To benchmark performance, the Oracle-MMSE and the Bayesian Cramèr Rao lower bound (BCRLB) are also derived. Our simulation results demonstrate significant performance improvement over the state of the art sparse estimation methods. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: Accepted at IEEE Open Journal of Vehicular Technology

Semi-Blind Channel Estimation and Hybrid Receiver Beamforming in the Tera-Hertz Multi-User Massive MIMO Uplink

Authors: Abhisha Garg, Suraj Srivastava, Varsha Dubey, Aditya Jagannatham, Lajos Hanzo

Abstract: We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the… ▽ More We develop a pragmatic multi-user (MU) massive multiple-input multiple-output (MIMO) channel model tailored to the THz band, encompassing factors such as molecular absorption, reflection losses and multipath diffused ray components. Next, we propose a novel semi-blind based channel state information (CSI) acquisition technique i.e. MU whitening decorrelation semi-blind (MU-WD-SB) that exploits the second order statistics corresponding to the unknown data symbols along with pilot vectors. A constrained Cramer-Rao Lower Bound (C-CRLB) is derived to bound the normalized mean square error (NMSE) performance of the proposed semi-blind learning technique. Our proposed scheme efficiently reduces the training overheads while enhancing the overall accuracy of the channel learning process. Furthermore, a novel hybrid receiver combiner framework is devised for MU THz massive MIMO systems, leveraging multiple measurement vector based sparse Bayesian learning (MMV-SBL) that relies on the estimated CSI acquired through our proposed semi-blind technique relying on low resolution analog-to-digital converters (ADCs). Finally, we propose an optimal hybrid combiner based on MMV-SBL, which directly reduces the MU interference. Extensive simulations are conducted to evaluate the performance gain of the proposed MU-WD-SB scheme over conventional training-based and other semi-blind learning techniques for a practical THz channel obtained from the high-resolution transmission (HITRAN) database. The metrics considered for quantifying the improvements include the NMSE, bit error rate (BER) and spectral-efficiency (SE). △ Less

Submitted 23 March, 2026; originally announced March 2026.

Terahertz Beamforming and Group Sparse Channel Estimation Relying on Low-Resolution ADCs in MU Hybrid MIMO systems

Authors: Abhisha Garg, Suraj Srivastava, Akash Kumar, Nimish Yadav, Aditya Jagannatham, Lajos Hanzo

Abstract: A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter (ADCs) and frequency-dependent propagation effects occurring in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse shaping. To address the non-linear distort… ▽ More A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter (ADCs) and frequency-dependent propagation effects occurring in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse shaping. To address the non-linear distortions introduced by low-resolution ADCs, Bussgang decomposition is employed, leading to a tractable linearized inference process. By leveraging the shared sparsity inherent in a multi-user (MU) scenario of THz systems, we propose a Hierarchical Bayesian Group-sparse Regression (HBG-SR) based channel learning technique that exploits the group-sparse structure of THz band channels. The estimated dominant angle-of-arrival/ angle-of-departure (AoA/AoD) indices are then exploited for appropriately configuring the true-time-delay (TTD) elements in the hybrid transceiver, enabling precise beam alignment across subcarriers and the effective compensation of the beam-squint effect occurring in wideband THz systems. Extensive simulation results validate the efficiency of the proposed channel estimator and the TTD-aided beamforming architecture, highlighting their robustness and performance gains under practical wideband THz system constraints. △ Less

Submitted 21 March, 2026; originally announced March 2026.

Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems

Authors: Marios Aristodemou, Yasaman Omid, Sangarapillai Lambotharan, Mahsa Derakhshan, Lajos Hanzo

Abstract: The integration of satellite communication networks with next-generation (NG) technologies is a promising approach towards global connectivity. However, the quality of services is highly dependant on the availability of accurate channel state information (CSI). Channel estimation in satellite communications is challenging due to the high propagation delay between terrestrial users and satellites,… ▽ More The integration of satellite communication networks with next-generation (NG) technologies is a promising approach towards global connectivity. However, the quality of services is highly dependant on the availability of accurate channel state information (CSI). Channel estimation in satellite communications is challenging due to the high propagation delay between terrestrial users and satellites, which results in outdated CSI observations on the satellite side. In this paper, we study the downlink transmission of multiple satellites acting as distributed base stations (BS) to mobile terrestrial users. We propose a multi-agent reinforcement learning (MARL) algorithm which aims for maximising the sum-rate of the users, while coping with the outdated CSI. We design a novel bi-level optimisation, procedure themes as dual stage proximal policy optimisation (DS-PPO), for tackling the problem of large continuous action spaces as well as of independent and non-identically distributed (non-IID) environments in MARL. Specifically, the first stage of DS-PPO maximises the sum-rate for an individual satellite and the second stage maximises the sum-rate when all the satellites cooperate to form a distributed multi-antenna BS. Our numerical results demonstrate the robustness of DS-PPO to CSI imperfections as well as the sum-rate improvement attached by the use of DS-PPO. In addition, we provide the convergence analysis for the DS-PPO along with the computational complexity. △ Less

Submitted 17 March, 2026; originally announced March 2026.

Comments: 12 pages, 6 Figures, Submit to IEEE Transactions of Vehicular Technology. It has been reviewed once

RIS-Aided RSMA Improves the Latency vs. Energy Trade-off in the Finite Block Length MIMO Downlink

Authors: Mohammad Soleymani, Bruno Clerckx, Robert Schober, Lajos Hanzo

Abstract: We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13\% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each ot… ▽ More We simultaneously minimize the latency and improve energy efficiency (EE) of the multi-user multiple-input multiple-output (MU-MIMO) rate splitting multiple access (RSMA) downlink, aided by a reconfigurable intelligent surface (RIS). Our results show that RSMA improves the EE and may reduce the delay to 13\% of that of spatial division multiple access (SDMA). Moreover, RIS and RSMA support each other synergistically, while an RIS operating without RSMA provides limited benefits in terms of latency and cannot effectively mitigate interference. {Furthermore, increasing the RIS size amplifies the gains of RSMA more significantly than those of SDMA, without altering the fundamental EE-latency trade-offs.} Results also show that latency increases with more stringent reliability requirements, and RSMA yields more significant gains under such conditions, making it eminently suitable for energy-efficient ultra-reliable low-latency communication (URLLC) scenarios. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Comments: Accepted at IEEE Open Journal of Vehicular Technology

Two-Stage Hybrid Transceiver Design Relying on Low-Resolution ADCs in Partially Connected MU Terahertz (THz) MIMO Systems

Authors: Abhisha Garg, Akash Kumar, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

Abstract: A two-stage hybrid transceiver is designed by considering a partially connected architecture at the base station (BS) for a low-resolution multi-user (MU) THz massive multiple input multiple output (MIMO) system. Due to its high bandwidth coupled with a high number of antennas, the THz band suffers from the deleterious spatial-wideband and frequency-wideband effects jointly termed as the dual-wide… ▽ More A two-stage hybrid transceiver is designed by considering a partially connected architecture at the base station (BS) for a low-resolution multi-user (MU) THz massive multiple input multiple output (MIMO) system. Due to its high bandwidth coupled with a high number of antennas, the THz band suffers from the deleterious spatial-wideband and frequency-wideband effects jointly termed as the dual-wideband effect. To address this undesired phenomenon, we rigorously model the THz MIMO channel at each subarray corresponding to each user by incorporating the absorption, reflection, and free-space losses. Subsequently, a novel beamforming technique is proposed that employs only a few true time delay (TTD) lines for eliminating the beam-split effect, which is the manifestation of the spatial-wideband effect in the frequency domain. Our simulation results demonstrate a performance improvement of around 13% in terms of spectral efficiency over the existing state-of-the-art techniques. △ Less

Submitted 10 March, 2026; originally announced March 2026.

In-Wave Computation Aided Stacked Intelligent Metasurfaces in Next-Generation Networks: Challenges and Opportunities

Authors: Mengbing Liu, Chau Yuen, Dusit Niyato, Bruno Clerckx, Lajos Hanzo

Abstract: Stacked intelligent metasurfaces (SIMs) facilitate computation by cascaded programmable layers so that part of the signal processing can be performed in the wave domain during signal propagation, rather than solely after reception. This approach expands the controllable degrees of freedom and supports the joint design of communication, sensing, and computation with the potential for reduced energy… ▽ More Stacked intelligent metasurfaces (SIMs) facilitate computation by cascaded programmable layers so that part of the signal processing can be performed in the wave domain during signal propagation, rather than solely after reception. This approach expands the controllable degrees of freedom and supports the joint design of communication, sensing, and computation with the potential for reduced energy usage, shorter end-to-end latency, and improved task execution. Despite these advances, research on the SIM concept is still at an early stage, with challenges in scalability, controllability, nonlinearity, and robustness. This article reviews the state-of-the-art of SIM research, including applications, functions, and characteristics. We also demonstrate their potential through case studies on neural-like analog inference and communication enhancement. Finally, the paper outlines open challenges and future directions toward establishing SIMs as a new signal processing paradigm for in-wave computation in next-generation (NG) networks. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Comments: Accepted by IEEE Wireless Communications

Quantum-Channel Matrix Optimization for Holevo Bound Enhancement

Authors: Hong Niu, Chau Yuen, Alexei Ashikhmin, Lajos Hanzo

Abstract: Quantum communication holds the potential to revolutionize information transmission by enabling secure data exchange that exceeds the limits of classical systems. One of the key performance metrics in quantum information theory, namely the Holevo bound, quantifies the amount of classical information that can be transmitted reliably over a quantum channel. However, computing and optimizing the Hole… ▽ More Quantum communication holds the potential to revolutionize information transmission by enabling secure data exchange that exceeds the limits of classical systems. One of the key performance metrics in quantum information theory, namely the Holevo bound, quantifies the amount of classical information that can be transmitted reliably over a quantum channel. However, computing and optimizing the Holevo bound remains a challenging task due to its dependence on both the quantum input ensemble and the quantum channel. In order to maximize the Holevo bound, we propose a unified projected gradient ascent algorithm to optimize the quantum channel given a fixed input ensemble. We provide a detailed complexity analysis for the proposed algorithm. Simulation results demonstrate that the proposed quantum channel optimization yields higher Holevo bounds than input ensemble optimization. △ Less

Submitted 18 February, 2026; originally announced February 2026.

Comments: 6 pages, 4 figures, accepted by 2026 IEEE International Conference on Communications (ICC)

Enormous Fluid Antenna Systems (E-FAS) for Multiuser MIMO: Channel Modeling and Analysis

Authors: Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, Wee Kiat New, Chan-Byoung Chae, Lajos Hanzo

Abstract: Enormous fluid antenna systems (E-FAS), the system concept that utilizes position reconfigurability in the large scale, have emerged as a new architectural paradigm where intelligent surfaces are repurposed from passive smart reflectors into multi-functional electromagnetic (EM) interfaces that can route guided surface waves over walls, ceilings, and building facades, as well as emit space waves t… ▽ More Enormous fluid antenna systems (E-FAS), the system concept that utilizes position reconfigurability in the large scale, have emerged as a new architectural paradigm where intelligent surfaces are repurposed from passive smart reflectors into multi-functional electromagnetic (EM) interfaces that can route guided surface waves over walls, ceilings, and building facades, as well as emit space waves to target receivers. This expanded functionality introduces a new mode of signal propagation, enabling new forms of wireless communication. In this paper, we provide an analytical performance characterization of an E-FAS-enabled wireless link. We first develop a physics-consistent end-to-end channel model that couples a surface-impedance wave formulation with small-scale fading on both the base station (BS)-surface and launcher-user segments. We illustrate that the resulting effective BS-user channel remains circularly symmetric complex Gaussian, with an enhanced average power that explicitly captures surface-wave attenuation and junction losses. For single-user cases with linear precoding, we derive the outage probability and ergodic capacity in closed forms, together with high signal-to-noise ratio (SNR) asymptotics that quantify the gain of E-FAS over purely space-wave propagation. For the multiuser case with zero-forcing (ZF) precoding, we derive the distribution of the signal-to-interference-plus-noise ratio (SINR) and obtain tractable approximations for the ergodic sum-rate, explicitly revealing how the E-FAS macro-gain interacts with the BS spatial degrees of freedom (DoF). In summary, our analysis shows that E-FAS preserves the diversity order dictated by small-scale fading while improving the coding gain enabled by cylindrical surface-wave propagation. △ Less

Submitted 11 February, 2026; originally announced February 2026.

Scalable Quantum Message Passing Graph Neural Networks for Next-Generation Wireless Communications: Architectures, Use Cases, and Future Directions

Authors: Le Tung Giang, Nguyen Xuan Tung, Trinh Van Chien, Lajos Hanzo, Won-Joo Hwang

Abstract: Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks in classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency because they reduce the model complexity. This is achieved by leveraging… ▽ More Graph Neural Networks (GNNs) are eminently suitable for wireless resource management, thanks to their scalability, but they still face computational challenges in large-scale, dense networks in classical computers. The integration of quantum computing with GNNs offers a promising pathway for enhancing computational efficiency because they reduce the model complexity. This is achieved by leveraging the quantum advantages of parameterized quantum circuits (PQCs), while retaining the expressive power of GNNs. However, existing pure quantum message passing models remain constrained by the limited number of qubits, hence limiting the scalability of their application to the wireless systems. As a remedy, we conceive a Scalable Quantum Message Passing Graph Neural Network (SQM-GNN) relying on a quantum message passing architecture. To address the aforementioned scalability issue, we decompose the graph into subgraphs and apply a shared PQC to each local subgraph. Importantly, the model incorporates both node and edge features, facilitating the full representation of the underlying wireless graph structure. We demonstrate the efficiency of SQM GNN on a device-to-device (D2D) power control task, where it outperforms both classical GNNs and heuristic baselines. These results highlight SQM-GNN as a promising direction for future wireless network optimization. △ Less

Submitted 26 January, 2026; originally announced January 2026.

Comments: 8 pages, 3 figures, and 3 tables. Submitted for publication

Space-Air-Ground-Integrated Networks: The BER vs. Residual Delay and Doppler Analysis

Authors: Chao Zhang, Kunlun Li, Chao Xu, Lie-Liang Yang, Lajos Hanzo

Abstract: Perfect Doppler compensation and synchronization is nontrivial due to multi-path Doppler effects and Einstein's theory of relativity in the space-air-ground-integrated networks (SAGINs). Hence, by considering the residual Doppler and the synchronization delay, this paper investigates the bit-error-rate (BER) performance attained under time-varying correlated Shadowed-Rician SAGIN channels. First,… ▽ More Perfect Doppler compensation and synchronization is nontrivial due to multi-path Doppler effects and Einstein's theory of relativity in the space-air-ground-integrated networks (SAGINs). Hence, by considering the residual Doppler and the synchronization delay, this paper investigates the bit-error-rate (BER) performance attained under time-varying correlated Shadowed-Rician SAGIN channels. First, a practical SAGIN model is harnessed, encompassing correlated Shadowed-Rician channels, the Snell's law-based path loss, atmospheric absorption, the line-of-sight Doppler compensation, elliptical satellite orbits, and Einstein's theory of relativity. Then, a specific correlation coefficient between the pilot and data symbols is derived in the context of correlated Shadowed-Rician channels. By exploiting this correlation coefficient, the channel distribution is mimicked by a bi-variate Gamma distribution. Then, a closed-form BER formula is derived under employing least-square channel estimation and equalization for 16-QAM. Our analytical results indicate for a 300-km-altitude LEO that 1) the period of realistic elliptical orbits is around 0.8 seconds longer than that of the idealized circular orbits; and 2) the relativistic delay is lower than 1 microsecond over a full LEO pass (from rise to set). Our numerical results for the L bands quantify the effects of: 1) the residual Doppler; 2) atmospheric shadowing; 3) synchronization errors; and 4) pilot overhead. △ Less

Submitted 28 April, 2026; v1 submitted 25 January, 2026; originally announced January 2026.

Stacked Intelligent Metasurface-Aided Wave-Domain Signal Processing: From Communications to Sensing and Computing

Authors: Jiancheng An, Chau Yuen, Marco Di Renzo, Mehdi Bennis, Merouane Debbah, Lajos Hanzo

Abstract: Artificial neural networks possess remarkable capabilities for abstract feature extraction, while electromagnetic computing leverages wave propagation to execute complex mathematical operations. Concurrently, metasurfaces engineered from subwavelength meta-atoms offer unprecedented control over electromagnetic wavefronts. Synthesizing these three cutting-edge fields has sparked significant interes… ▽ More Artificial neural networks possess remarkable capabilities for abstract feature extraction, while electromagnetic computing leverages wave propagation to execute complex mathematical operations. Concurrently, metasurfaces engineered from subwavelength meta-atoms offer unprecedented control over electromagnetic wavefronts. Synthesizing these three cutting-edge fields has sparked significant interest in developing electromagnetic neural networks via stacked intelligent metasurface (SIM) technology, which aims to execute diverse signal processing tasks directly within the wave domain. By enabling direct processing of information-carrying electromagnetic waves, SIMs offer a promising paradigm for high-speed, massively parallel, and low-power signal processing. This article provides a comprehensive overview of SIM technology, beginning with its evolutionary trajectory. We then delve into its theoretical foundations and examine state-of-the-art SIM hardware prototypes. Furthermore, we analyze the optimization and training strategies devised to configure SIM functionalities from two distinct perspectives. Additionally, the diverse applications of SIM technology across the communication, sensing, and computing domains are explored, supported by experimental evidence that highlights its ability to sustain multiple functions within a single device. Finally, we outline critical technical challenges to deploying SIMs in next-generation wireless networks and chart promising research directions to fully unlock their transformative potential. △ Less

Submitted 27 July, 2026; v1 submitted 22 January, 2026; originally announced January 2026.

Comments: 26 pages, 20 figures, 7 tables, Accepted by IEEE COMST

Amalgamated CHIRP and OFDM for ISAC

Authors: Pankaj Kumar, Mohammed El-Hajjar, Ibrahim A. Hemadeh, Yasser Mestrah, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

Abstract: Integrated Sensing and Communication (ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the chirp waveforms to improve both the communication and sensing performance within an ISAC framework. H… ▽ More Integrated Sensing and Communication (ISAC) requires the development of a waveform capable of efficiently supporting both communication and sensing functionalities. This paper proposes a novel waveform that combines the benefits of both the orthogonal frequency division multiplexing (OFDM) and the chirp waveforms to improve both the communication and sensing performance within an ISAC framework. Hence, a new architecture is proposed that utilizes the conventional communication framework while leveraging the parameters sensed at the receiver (Rx) for enhancing the communication performance. We demonstrate that the affine addition of OFDM and chirp signals results in a near constant-envelope OFDM waveform, which effectively reduces the peak-to-average power ratio (PAPR), a key limitation of traditional OFDM systems. Using the OFDM framework for sensing in the conventional fashion requires the allocation of some resources for sensing, which in turn reduces communication performance. As a remedy, the proposed affine amalgam facilitates sensing through the chirp waveform without consuming communication resources, thereby preserving communication efficiency. Furthermore, a novel technique of integrating the chirp signal into the OFDM framework at the slot-level is proposed to enhance the accuracy of range estimation. The results show that the OFDM signal incorporated with chirp has better autocorrelation properties, improved root mean square error (RMSE) of range and velocity, and lower PAPR. Finally, we characterize the trade-off between communications and sensing performance. △ Less

Submitted 21 January, 2026; originally announced January 2026.

Delay-Doppler-Domain Channel Estimation and Reduced-Complexity Detection of Faster-than-Nyquist Signaling Aided OTFS

Authors: Zekun Hong, Shinya Sugiura, Chao Xu, Lajos Hanzo

Abstract: We conceive a novel channel estimation and data detection scheme for OTFS-modulated faster-than-Nyquist (FTN) transmission over doubly selective fading channels, aiming for enhancing the spectral efficiency and Doppler resilience. The delay-Doppler (DD) domain's input-output relationship of OTFS-FTN signaling is derived by employing a root-raised cosine (RRC) shaping filter. More specifically, we… ▽ More We conceive a novel channel estimation and data detection scheme for OTFS-modulated faster-than-Nyquist (FTN) transmission over doubly selective fading channels, aiming for enhancing the spectral efficiency and Doppler resilience. The delay-Doppler (DD) domain's input-output relationship of OTFS-FTN signaling is derived by employing a root-raised cosine (RRC) shaping filter. More specifically, we design our DD-domain channel estimator for FTN-based pilot transmission, where the pilot symbol interval is lower than that defined by the classic Nyquist criterion. Moreover, we propose a reduced-complexity linear minimum mean square error equalizer, supporting noise whitening, where the FTN-induced inter-symbol interference (ISI) matrix is approximated by a sparse one. Our performance results demonstrate that the proposed OTFS-FTN scheme is capable of enhancing the achievable information rate, while attaining a comparable BER performance to both that of its Nyquist-based OTFS counterpart and to other FTN transmission schemes, which employ the same RRC shaping filter. △ Less

Submitted 16 January, 2026; originally announced January 2026.

Comments: 16 pages, 18 figures, 3 tables

Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era

Authors: Trinh Van Chien, Bui Trong Duc, Nguyen Xuan Tung, Van Duc Nguyen, Waqas Khalid, Symeon Chatzinotas, Lajos Hanzo

Abstract: Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intell… ▽ More Next Generation (NG) networks move beyond simply connecting devices to creating an ecosystem of connected intelligence, especially with the support of generative Artificial Intelligence (AI) and quantum computation. These systems are expected to handle large-scale deployments and high-density networks with diverse functionalities. As a result, there is an increasing demand for efficient and intelligent algorithms that can operate under uncertainty from both propagation environments and networking systems. Traditional optimization methods often depend on accurate theoretical models of data transmission, but in real-world NG scenarios, they suffer from high computational complexity in large-scale settings. Stochastic Optimization (SO) algorithms, designed to accommodate extremely high density and extensive network scalability, have emerged as a powerful solution for optimizing wireless networks. This includes various categories that range from model-based approaches to learning-based approaches. These techniques are capable of converging within a feasible time frame while addressing complex, large-scale optimization problems. However, there is currently limited research on SO applied for NG networks, especially the upcoming Sixth-Generation (6G). In this survey, we emphasize the relationship between NG systems and SO by eight open questions involving the background, key features, and lesson learned. Overall, our study starts by providing a detailed overview of both areas, covering fundamental and widely used SO techniques, spanning from single to multi-objective signal processing. Next, we explore how different algorithms can solve NG challenges, such as load balancing, optimizing energy efficiency, improving spectral efficiency, or handling multiple performance trade-offs. Lastly, we highlight the challenges in the current research and propose new directions for future studies. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Comments: 30 pages, 19 figures, and 8 tables. Submitted for publication

MIMO-AFDM Outperforms MIMO-OFDM in the Face of Hardware Impairments

Authors: Zeping Sui, Zilong Liu, Leila Musavian, Yong Liang Guan, Lie-Liang Yang, Lajos Hanzo

Abstract: The impact of both multiplicative and additive hardware impairments (HWIs) on multiple-input multiple-output affine frequency division multiplexing (MIMO-AFDM) systems is investigated. For small-scale MIMO-AFDM systems, a tight bit error rate (BER) upper bound associated with the maximum likelihood (ML) detector is derived. By contrast, for large-scale systems, a closed-form BER approximation asso… ▽ More The impact of both multiplicative and additive hardware impairments (HWIs) on multiple-input multiple-output affine frequency division multiplexing (MIMO-AFDM) systems is investigated. For small-scale MIMO-AFDM systems, a tight bit error rate (BER) upper bound associated with the maximum likelihood (ML) detector is derived. By contrast, for large-scale systems, a closed-form BER approximation associated with the linear minimum mean squared error (LMMSE) detector is presented, including realistic imperfect channel estimation scenarios. Our first key observation is that the full diversity order of a hardware-impaired AFDM system remains unaffected, which is a unique advantage. Furthermore, our analysis shows that 1) the BER results derived accurately predict the simulated ML performance in moderate-to-high signal-to-noise ratios (SNRs), while the theoretical BER curve of the LMMSE detector closely matches that of the Monte-Carlo based one. 2) MIMO-AFDM is more resilient to multiplicative distortions, such as phase noise and carrier frequency offset, compared to its orthogonal frequency division multiplexing (OFDM) counterparts. This is attributed to its inherent chirp signal characteristics; 3) MIMO-AFDM consistently achieves superior BER performance compared to conventional MIMO-OFDM systems under the same additive HWI conditions, as well as different velocity values. The latter is because MIMO-AFDM is also resilient to the additional inter-carrier interference (ICI) imposed by the nonlinear distortions of additive HWIs. In a nutshell, compared to OFDM, AFDM demonstrates stronger ICI resilience and achieves the maximum full diversity attainable gain even under HWIs, thanks to its intrinsic chirp signalling structure as well as to the beneficial spreading effect of the discrete affine Fourier transform. △ Less

Submitted 27 May, 2026; v1 submitted 1 January, 2026; originally announced January 2026.

Comments: 16 pages, 15 figures, accepted by IEEE TCOM

Faster-than-Nyquist Signaling for Next-Generation Wireless: Principles, Applications, and Challenges

Authors: Shuangyang Li, Melda Yuksel, Tongyang Xu, Shinya Sugiura, Jinhong Yuan, Giuseppe Caire, Lajos Hanzo

Abstract: Future wireless networks are expected to deliver ultra-high throughput for supporting emerging applications. In such scenarios, conventional Nyquist signaling may falter. As a remedy, faster-than-Nyquist (FTN) signaling facilitates the transmission of more symbols than Nyquist signaling without expanding the time-frequency resources. We provide an accessible and structured introduction to FTN sign… ▽ More Future wireless networks are expected to deliver ultra-high throughput for supporting emerging applications. In such scenarios, conventional Nyquist signaling may falter. As a remedy, faster-than-Nyquist (FTN) signaling facilitates the transmission of more symbols than Nyquist signaling without expanding the time-frequency resources. We provide an accessible and structured introduction to FTN signaling, covering its core principles, theoretical foundations, unique advantages, open facets, and its road map. Specifically, we present promising coded FTN results and highlight its compelling advantages in integrated sensing and communications (ISAC), an increasingly critical function in future networks. We conclude with a discussion of open research challenges and promising directions. △ Less

Submitted 1 January, 2026; v1 submitted 29 December, 2025; originally announced December 2025.

Simultaneous Classical and Quantum Communications: Recent Progress and Three Challenges

Authors: Phuc V. Trinh, Shinya Sugiura, Carlo Ottaviani, Chao Xu, Lajos Hanzo

Abstract: A critical aspect of next-generation wireless networks is the integration of quantum communications to guard against quantum computing threats to classical networks. Despite successful experimental demonstrations, integrating quantum communications into the classical infrastructure faces substantial challenges, including high costs, compatibility issues, and extra hardware deployment to accommodat… ▽ More A critical aspect of next-generation wireless networks is the integration of quantum communications to guard against quantum computing threats to classical networks. Despite successful experimental demonstrations, integrating quantum communications into the classical infrastructure faces substantial challenges, including high costs, compatibility issues, and extra hardware deployment to accommodate both classical and quantum communication equipment. To mitigate these challenges, we explore novel protocols that enable simultaneous classical and quantum communications, relying on a single set of transceivers to jointly modulate and decode classical and quantum information onto the same signal. Additionally, we emphasize extending quantum communication capabilities beyond traditional optical bands into the terahertz, even possibly to millimeter-wave and microwave frequencies, thereby broadening the potential horizon of quantum-secure applications. Finally, we identify open problems that must be addressed to facilitate practical implementation. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: 8 pages, 5 figures, 1 table

Generalized Pinching-Antenna Systems: A Leaky-Coaxial-Cable Perspective

Authors: Kaidi Wang, Zhiguo Ding, Lajos Hanzo

Abstract: The evolution toward the sixth-generation (6G) wireless networks has flexible reconfigurable antenna architectures capable of adapting their radiation characteristics to the surrounding environment. At the center stage, while waveguide based pinching antennas have been shown to beneficially ameliorate wireless propagation environments, their applications have remained confined to high-frequency sc… ▽ More The evolution toward the sixth-generation (6G) wireless networks has flexible reconfigurable antenna architectures capable of adapting their radiation characteristics to the surrounding environment. At the center stage, while waveguide based pinching antennas have been shown to beneficially ameliorate wireless propagation environments, their applications have remained confined to high-frequency scenarios. As a remedy, we propose a downlink generalized pinching-antenna system that adapts this compelling concept to low-frequency operation through a leaky-coaxial-cable (LCX) implementation. By endowing LCX structures with controllable radiation slots, the system inherits the key capabilities of waveguide based pinching antennas. Explicitly, these include reconfigurable line-of-sight (LoS) links, reduced path loss, and flexible deployment, while supporting a practical implementation of the pinching-antenna concept at low frequencies. A twin-stage propagation model is developed for characterizing both the guided transmission and wireless radiation encountered over LoS and non-line-of-sight (NLoS) paths. Analytical results reveal strong local gain, complemented by rapid distance-dependent decay. Hence, we conceive a matching joint optimization framework, which maximizes throughput by harnessing game theoretic association and convex power allocation. Simulation results demonstrate substantial performance gains over conventional fixed-antenna benchmarks. △ Less

Submitted 26 July, 2026; v1 submitted 4 December, 2025; originally announced December 2025.

Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels

Authors: Guangming Liang, Mingjie Yang, Dongzhu Liu, Paul Henderson, Lajos Hanzo

Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel infer… ▽ More Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems operating in high-Doppler environments. By leveraging the growing availability of environmental sensing data, this treatise investigates pilot-free channel inference that estimates complete CSI directly from multimodal observations, including camera images, LiDAR point clouds, and GPS coordinates. In contrast to prior studies that rely on predefined channel models, we develop a data-driven framework that formulates the sensing-to-channel mapping as a cross-modal flow matching problem. The framework fuses multimodal features into a latent distribution within the channel domain, and learns a velocity field that continuously transforms the latent distribution toward the channel distribution. To make this formulation tractable and efficient, we reformulate the problem as an equivalent conditional flow matching objective and incorporate a modality alignment loss, while adopting low-latency inference mechanisms to enable real-time CSI estimation. In experiments, we build a procedural data generator based on Sionna and Blender to support realistic modeling of sensing scenes and wireless propagation. System-level evaluations demonstrate significant improvements over pilot- and sensing-based benchmarks in both channel estimation accuracy and spectral efficiency for the downstream beamforming task. The source code is available at https://github.com/gm-leung/environment-aware-channel-inference. △ Less

Submitted 22 July, 2026; v1 submitted 4 December, 2025; originally announced December 2025.

Comments: 17 pages, 15 figures, 44 references, to appear in IEEE Transactions on Mobile Computing (TMC)

Bayesian Learning Aided Simultaneous Sparse Estimation of Dual-Wideband THz Channels in Multi-User Hybrid MIMO Systems

Authors: Abhisha Garg, Akash Kumar, Suraj Srivastava, Nimish Yadav, Aditya K. Jagannatham, Lajos Hanzo

Abstract: This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure (AoAs/AoDs) using a Gaussian Mixture Mo… ▽ More This work conceives the Bayesian Group-Sparse Regression (BGSR) for the estimation of a spatial and frequency wideband, i.e., a dual wideband channel in Multi-User (MU) THz hybrid MIMO scenarios. We develop a practical dual wideband THz channel model that incorporates absorption losses, reflection losses, diffused ray modeling and angles of arrival/departure (AoAs/AoDs) using a Gaussian Mixture Model (GMM). Furthermore, a low-resolution analog-to-digital converter (ADC) is employed at each RF chain, which is crucial for wideband THz massive MIMO systems to reduce power consumption and hardware complexity, given the high sampling rates and large number of antennas involved. The quantized MU THz MIMO model is linearized using the popular Bussgang decomposition followed by BGSR based channel learning framework that results in sparsity across different subcarriers, where each subcarrier has its unique dictionary matrix. Next, the Bayesian Cramér Rao Bound (BCRB) is devised for bounding the normalized mean square error (NMSE) performance. Extensive simulations were performed to assess the performance improvements achieved by the proposed BGSR method compared to other sparse estimation techniques. The metrics considered for quantifying the performance improvements include the NMSE and bit error rate (BER). △ Less

Submitted 15 November, 2025; originally announced November 2025.

Towards Global Quantum Key Distribution

Authors: Haoran Zhang, Haotao Zhu, Ruihua He, Yan Zhang, Chao Ding, Lajos Hanzo, Weibo Gao

Abstract: Quantum Key Distribution (QKD) supports the negotiation and sharing of private keys with unconditional security between authorized parties. Over the years, theoretical advances and experimental demonstrations have successfully transitioned QKD from laboratory research to commercial applications. As QKD expands its reach globally, it encounters challenges such as performance limitations, cost, and… ▽ More Quantum Key Distribution (QKD) supports the negotiation and sharing of private keys with unconditional security between authorized parties. Over the years, theoretical advances and experimental demonstrations have successfully transitioned QKD from laboratory research to commercial applications. As QKD expands its reach globally, it encounters challenges such as performance limitations, cost, and practical security concerns. Nonetheless, innovations in satellite-based QKD and the development of new protocols are paving the way towards a worldwide network. In this review, we provide an overview of QKD implementations, with a focus on protocols, devices, and quantum channels. We discuss the challenges of practical QKD and explore long-haul QKD. Additionally, we highlight the future research directions that are expected to significantly advance the realization of a global QKD network. △ Less

Submitted 14 November, 2025; originally announced November 2025.

Pinching Antennas Meet AI in Next-Generation Wireless Networks

Authors: Fang Fang, Zhiguo Ding, Victor C. M. Leung, Lajos Hanzo

Abstract: Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas (PAs), a new flexible low-cost technology, can create line-of-sight links by dynamically activating small dielectric pinches along a waveguide on demand. As a comp… ▽ More Next-generation (NG) wireless networks must embrace innate intelligence in support of demanding emerging applications, such as extended reality and autonomous systems, under ultra-reliable and low-latency requirements. Pinching antennas (PAs), a new flexible low-cost technology, can create line-of-sight links by dynamically activating small dielectric pinches along a waveguide on demand. As a compelling complement, artificial intelligence (AI) offers the intelligence needed to manage the complex control of PA activation positions and resource allocation in these dynamic environments. This article explores the "win-win" cooperation between AI and PAs: AI facilitates the adaptive optimization of PA activation positions along the waveguide, while PAs support edge AI tasks such as federated learning and over-the-air aggregation. We also discuss promising research directions including large language model-driven PA control frameworks, and how PA-AI integration can advance semantic communications, and integrated sensing and communication. This synergy paves the way for adaptive, resilient, and self-optimizing NG networks. △ Less

Submitted 3 November, 2025; originally announced November 2025.

Coverage Analysis and Optimization of FIRES-Assisted NOMA and OMA Systems

Authors: Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, Hanjiang Hong, Chan-Byoung Chae, Lajos Hanzo

Abstract: Fluid integrated reflecting and emitting surfaces (FIRES) are investigated. In these metasurfaces, each subarea hosts an active element capable of simultaneous transmission and reflection, phase, and geometric positioning control within the subarea. We develop a coverage-centric system model for the two-user downlink scenario (one user per half-space) under spatially correlated Rician fading and i… ▽ More Fluid integrated reflecting and emitting surfaces (FIRES) are investigated. In these metasurfaces, each subarea hosts an active element capable of simultaneous transmission and reflection, phase, and geometric positioning control within the subarea. We develop a coverage-centric system model for the two-user downlink scenario (one user per half-space) under spatially correlated Rician fading and imperfect phase control. First, we derive closed-form far-field line-of-sight (LoS) coverage bounds that reveal the effects of aperture size, base station (BS) distance, transmit power, energy-splitting (ES), and phase errors. Protocol-aware corollaries are then presented for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA), including conditions for successful successive interference cancellation (SIC). Second, we formulate coverage maximization as a bi-level optimization problem consisting of (i) an outer search over FIRES element positions, selecting one active preset per subarea under minimum-spacing constraints, and (ii) an inner resource allocation problem tailored to the multiple-access scheme, which is one-dimensional for OMA and a small convex program for NOMA. The proposed framework explicitly accounts for target rate constraints, ES conservation, power budgets, geometric placement limits, and decoding-order feasibility. Extensive simulations demonstrate that FIRES, by jointly exploiting geometric repositioning and passive energy control, substantially enlarges the coverage region compared with a conventional simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) under the same element budget. Furthermore, NOMA yields additional coverage gains when feasible. The analytical coverage bounds closely match the simulation results and quantify the robustness of FIRES to phase-control imperfections. △ Less

Submitted 2 November, 2025; originally announced November 2025.

A New Class of Analog Precoding for Multi-Antenna Multi-User Communications over High-Frequency Bands

Authors: W. Zhu, H. D. Tuan, E. Dutkiewicz, H. V. Poor, L. Hanzo

Abstract: A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose… ▽ More A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users' minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users' minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput. △ Less

Submitted 20 September, 2025; originally announced September 2025.

Holographic Multi-User Multi-Stream Beamforming Maintaining Rate-Fairness

Authors: W. Zhu, H. D. Tuan, E. Dutkiewicz, H. V. Poor, L. Hanzo

Abstract: We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a m… ▽ More We present the first investigation into the transmission of multi-stream information from a base station equipped with reconfigurable holographic surfaces (RHS) to multiple users with the aid of multi-antenna arrays. Building upon this, we propose the joint design of RHS and baseband beamformers that enables multi-stream delivery at fair rates across all users. Specifically, we first introduce a max-min rate optimization approach, which aims for maximizing the minimum rate for all users through iterative solutions of quadratic problems. To reduce complexity, we then propose a surrogate-based optimization approach that offers a low-complexity design alternative relying on closed-form updates. Our simulations show that the surrogate-based approach achieves nearly the same minimum rate as max-min optimization, while delivering sum-rates comparable to those of sum-rate maximization, overcoming the rate-fairness deficiency typical of the latter. △ Less

Submitted 20 September, 2025; originally announced September 2025.

1Q: First-Generation Wireless Systems Integrating Classical and Quantum Communication

Authors: Petar Popovski, Čedomir Stefanović, Beatriz Soret, Israel Leyva-Mayorga, Shashi Raj Pandey, René Bødker Christensen, Jakob Kaltoft Søndergaard, Kristian Skafte Jensen, Thomas Garm Pedersen, Angela Sara Cacciapuoti, Lajos Hanzo

Abstract: We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUEs), and hybrid resource allocation spanning classical time-freque… ▽ More We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUEs), and hybrid resource allocation spanning classical time-frequency and quantum entanglement domains. Several application scenarios are discussed and illustrated through system design requirements for quantum key distribution, blind quantum computing, and distributed quantum sensing. A range of unique quantum constraints are identified, including decoherence timing, fidelity requirements, and the interplay between quantum and classical error probabilities. Protocol adaptations extend cellular connection management to incorporate entanglement generation, distribution, and handover procedures, expanding the Quantum Internet to the cellular wireless. △ Less

Submitted 12 January, 2026; v1 submitted 18 September, 2025; originally announced September 2025.

Comments: 14 pages, 8 figures. Accepted for publication in IEEE Vehicular Technology Magazine. This work is funded the Danish National Research Foundation (DNRF), through the Center CLASSIQUE, details at https://www.classique.aau.dk/. Cacciapuoti's work has been funded by the EU under Horizon Europe ERC-CoG grant QNattyNet, n.101169850, details at https://qnattynet.quantuminternet.it

Journal ref: IEEE Vehicular Technology Magazine, vol. 20, no. 4, pp. 18-33, Dec. 2025

LLM-Enhanced Space-Air-Ground-Sea Integrated Networks

Authors: Halvin Yang, Sangarapillai Lambotharan, Mahsa Derakhshani, Lajos Hanzo

Abstract: The space-air-ground-sea integrated networking (SAGSIN) concept promises seamless global multimedia connectivity, yet two obstacles still limit its practical deployment. Firstly, high-velocity satellites, aerial relays and sea-surface platforms suffer from obsolete channel state information (CSI), undermining feedback-based adaptation. Secondly, data-rate disparity across the protocol stack is ext… ▽ More The space-air-ground-sea integrated networking (SAGSIN) concept promises seamless global multimedia connectivity, yet two obstacles still limit its practical deployment. Firstly, high-velocity satellites, aerial relays and sea-surface platforms suffer from obsolete channel state information (CSI), undermining feedback-based adaptation. Secondly, data-rate disparity across the protocol stack is extreme: terabit optical links in space coexist with kilobit acoustic under-water links. This article shows that a single large language model (LLM) backbone, trained jointly on radio, optical and acoustic traces, can provide a unified, data-driven adaptation layer that addresses both rapid CSI ageing and severe bandwidth disparity across the SAGSIN protocol stack. Explicitly, an LLM-based long-range channel predictor forecasts the strongest delay-Doppler components several coherence intervals ahead, facilitating near-capacity reception despite violent channel fluctuations. Furthermore, our LLM-based semantic encoder turns raw sensor payloads into task-oriented tokens. This substantially reduces the SNR required for high-fidelity image delivery in a coastal underwater link, circumventing the data rate limitation by semantic communications. Inclusion of these tools creates a medium-agnostic adaptation layer that spans radio, optical and acoustic channels. We conclude with promising open research directions in on-device model compression, multimodal fidelity control, cross-layer resource orchestration and trustworthy operation, charting a path from laboratory prototypes to field deployment. △ Less

Submitted 2 September, 2025; originally announced September 2025.

Comments: 6 figures, 7 pages, magazine

Polarization-Aware DoA Detection Relying on a Single Rydberg Atomic Receiver

Authors: Yuanbin Chen, Chau Yuen, Darmindra Arumugam, Chong Meng Samson See, Mérouane Debbah, Lajos Hanzo

Abstract: A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retriev… ▽ More A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retrieved by a single Rydberg atomic receiver via electromagnetically induced transparency (EIT)-based spectroscopy. To be specific, in the presence of a static magnetic bias field that defines a stable quantization axis, a pair of sequential EIT measurements is carried out in the same vapor cell. Firstly, the electric-field polarization angle is extracted from the Zeeman-resolved EIT spectrum associated with an electric-dipole transition driven by the radio frequency (RF) field. Within the same experimental cycle, the RF field is then retuned to a magnetic-dipole resonance, producing Zeeman-resolved EIT peaks for decoding the RF magnetic-field orientation. This scheme exhibits a dual yet independent sensitivity on both angles, allowing for precise DoA reconstruction without the need for spatial diversity or phase referencing. Building on this foundation, we derive the quantum Fisher-information matrix (QFIM) and obtain a closed-form quantum Cramér-Rao bound (QCRB) for the joint estimation of polarization and orientation angles. Finally, simulation results spanning various quantum parameters validate the proposed approach and identify optimal operating regimes. With appropriately chosen polarization and magnetic-field geometries, a single vapor cell is expected to achieve sub-0.1$^\circ$ angle resolution at moderate RF-field driving strengths. △ Less

Submitted 19 April, 2026; v1 submitted 23 August, 2025; originally announced August 2025.

Comments: This paper has been accepted by IEEE JSAC for publication

Multi-Functional Chirp Signalling for Next-Generation Multi-Carrier Wireless Networks: Communications, Sensing and ISAC Perspectives

Authors: Zeping Sui, Qu Luo, Zilong Liu, Murat Temiz, Leila Musavian, Christos Masouros, Yong Liang Guan, Pei Xiao, Lajos Hanzo

Abstract: To meet the increasingly demanding quality-of-service requirements of the next-generation multi-carrier mobile networks, it is essential to design multi-functional signalling schemes facilitating efficient, flexible, and reliable communication and sensing in complex wireless environments. As a compelling candidate, we advocate chirp signalling, beneficially amalgamating sequences (e.g., Zadoff-Chu… ▽ More To meet the increasingly demanding quality-of-service requirements of the next-generation multi-carrier mobile networks, it is essential to design multi-functional signalling schemes facilitating efficient, flexible, and reliable communication and sensing in complex wireless environments. As a compelling candidate, we advocate chirp signalling, beneficially amalgamating sequences (e.g., Zadoff-Chu sequences) with waveforms (e.g., chirp spread spectrum and frequency-modulated continuous wave (FMCW) radar), given their resilience against doubly selective channels. Besides chirp sequences, a wide range of chirp waveforms is considered, ranging from FMCW to affine frequency-division multiplexing (AFDM), to create a promising chirp multicarrier waveform. This study also highlights the advantages of such waveforms in supporting reliable high-mobility communications, plus integrated sensing and communications (ISAC). Finally, we outline several emerging research directions for chirp signalling designs. △ Less

Submitted 4 July, 2026; v1 submitted 8 August, 2025; originally announced August 2025.

Comments: 9 pages, 6 figures, submitted to IEEE Wireless Communications

STAR-RIS-aided RSMA for the URLLC multi-user MIMO Downlink

Authors: Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Robert Schober, Lajos Hanzo

Abstract: Rate splitting multiple access (RSMA) is intrinsically amalgamated with simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) to enhance energy efficiency (EE) of the finite block length (FBL) multiple-input multiple-output (MIMO) downlink. An alternating optimization-based algorithm is proposed to jointly optimize the transmit beamforming matrices, STAR-RIS c… ▽ More Rate splitting multiple access (RSMA) is intrinsically amalgamated with simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) to enhance energy efficiency (EE) of the finite block length (FBL) multiple-input multiple-output (MIMO) downlink. An alternating optimization-based algorithm is proposed to jointly optimize the transmit beamforming matrices, STAR-RIS configurations, and rate-splitting parameters. STAR-RIS attains 360-degree full-plane coverage, while RSMA provides a prominent gain by efficiently managing interference. Numerical results reveal a strong synergy between RSMA and STAR-RIS, demonstreating significant EE gains over reflective RIS and spatial division multiple access (SDMA). △ Less

Submitted 1 August, 2025; originally announced August 2025.

Comments: Accepted at 28th International Workshop on Smart Antennas 2025

Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO

Authors: Trinh Van Chien, Nguyen Hoang Viet, Symeon Chatzinotas, Lajos Hanzo

Abstract: Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the ag… ▽ More Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks. △ Less

Submitted 14 July, 2025; originally announced July 2025.

Comments: 6 pages, 1 figures, and 3 tables. Accepted by IEEE TVT

Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems

Authors: Nguyen Xuan Tung, Le Tung Giang, Trinh Van Chien, Hoang Trong Minh, Lajos Hanzo

Abstract: Cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) systems are investigated where distributed access points jointly serve users and sensing targets. We demonstrate that only a subset of access points (APs) has to be activated for both tasks, while deactivating redundant APs is essential for power savings. This motivates joint active AP selecti… ▽ More Cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) systems are investigated where distributed access points jointly serve users and sensing targets. We demonstrate that only a subset of access points (APs) has to be activated for both tasks, while deactivating redundant APs is essential for power savings. This motivates joint active AP selection and power control for optimizing energy efficiency. The resultant problem is a mixed-integer nonlinear program (MINLP). To address this, we propose a model-based Branch-and-Bound approach as a strong baseline to guide a semi-supervised heterogeneous graph neural network (HetGNN) for selecting the best active APs and the power allocation. Comprehensive numerical results demonstrate that the proposed HetGNN reduces power consumption by 20-25\% and runs nearly 10,000 times faster than model-based benchmarks. △ Less

Submitted 12 July, 2025; originally announced July 2025.

Comments: 6 pages, 4 figures, 2 tables. Accepted by IEEE TVT

Hybrid Quantum Convolutional Neural Network-Aided Pilot Assignment in Cell-Free Massive MIMO Systems

Authors: Doan Hieu Nguyen, Xuan Tung Nguyen, Seon-Geun Jeong, Trinh Van Chien, Lajos Hanzo, Won Joo Hwang

Abstract: A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensi… ▽ More A sophisticated hybrid quantum convolutional neural network (HQCNN) is conceived for handling the pilot assignment task in cell-free massive MIMO systems, while maximizing the total ergodic sum throughput. The existing model-based solutions found in the literature are inefficient and/or computationally demanding. Similarly, conventional deep neural networks may struggle in the face of high-dimensional inputs, require complex architectures, and their convergence is slow due to training numerous hyperparameters. The proposed HQCNN leverages parameterized quantum circuits (PQCs) relying on superposition for enhanced feature extraction. Specifically, we exploit the same PQC across all the convolutional layers for customizing the neural network and for accelerating the convergence. Our numerical results demonstrate that the proposed HQCNN offers a total network throughput close to that of the excessive-complexity exhaustive search and outperforms the state-of-the-art benchmarks. △ Less

Submitted 9 July, 2025; originally announced July 2025.

Comments: 5 pages, 3 figures, and 2 tables. Accepted by IEEE TVT

ISAC Network Planning: Sensing Coverage Analysis and 3-D BS Deployment Optimization

Authors: Kaitao Meng, Kawon Han, Christos Masouros, Lajos Hanzo

Abstract: Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localiza… ▽ More Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localization coverage, while guaranteeing the desired communication performance. In contrast to existing schemes optimized for a single target, an effective network-level approach has to ensure consistent localization accuracy throughout the entire service area. While employing time-of-flight (ToF) based localization, we first analyze the deployment problem from a localization-performance coverage perspective, aiming for minimizing the area Cramer-Rao Lower Bound (A-CRLB) to ensure uniformly high positioning accuracy across the service area. We prove that for a fixed number of BSs, uniformly scaling the service area by a factor κincreases the optimal A-CRLB in proportion to κ^{2β}, where βis the BS-to-target pathloss exponent. Based on this, we derive an approximate scaling law that links the achievable A-CRLB across the area of interest to the dimensionality of the sensing area. We also show that cooperative BSs extend the coverage but yield marginal A-CRLB improvement as the dimensionality of the sensing area grows. △ Less

Submitted 23 December, 2025; v1 submitted 22 June, 2025; originally announced June 2025.

Comments: Accepted by IEEE Transactions on Wireless Communications

Journal ref: IEEE Transactions on Wireless Communications, 2025

Energy Efficiency Optimization of Finite Block Length STAR-RIS-aided MU-MIMO Broadcast Channels

Authors: Mohammad Soleymani, Ignacio Santamaria, Eduard Jorswieck, Robert Schober, Lajos Hanzo

Abstract: Energy-efficient designs are proposed for multi-user (MU) multiple-input multiple-output (MIMO) broadcast channels (BC), assisted by simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) operating at finite block length (FBL). In particular, we maximize the sum energy efficiency (EE), showing that STAR-RIS can substantially enhance it. Our findings demonstrate… ▽ More Energy-efficient designs are proposed for multi-user (MU) multiple-input multiple-output (MIMO) broadcast channels (BC), assisted by simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RIS) operating at finite block length (FBL). In particular, we maximize the sum energy efficiency (EE), showing that STAR-RIS can substantially enhance it. Our findings demonstrate that the gains of employing STAR-RIS increase when the codeword length and the maximum tolerable bit error rate decrease, meaning that a STAR-RIS is more energy efficient in a system with more stringent latency and reliability requirements. △ Less

Submitted 13 June, 2025; originally announced June 2025.

Comments: Accepted at IEEE SPAWC 2025

Joint Angle and Velocity-Estimation for Target Localization in Bistatic mmWave MIMO Radar in the Presence of Clutter

Authors: Priyanka Maity, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

Abstract: Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target-plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D) sparsity arising in the AD domain of the… ▽ More Sparse Bayesian learning (SBL)-aided target localization is conceived for a bistatic mmWave MIMO radar system in the presence of unknown clutter, followed by the development of an angle-Doppler (AD)-domain representation of the target-plus-clutter echo model for accurate target parameter estimation. The proposed algorithm exploits the three-dimensional (3D) sparsity arising in the AD domain of the scattering scene and employs the powerful SBL framework for the estimation of target parameters, such as the angle-of-departure (AoD), angle-of-arrival (AoA) and velocity. To handle a practical scenario where the actual target parameters typically deviate from their finite-resolution grid, a super-resolution-based improved off-grid SBL framework is developed for recursively updating the parameter grid, thereby progressively refining the estimates. We also determine the Cramér-Rao bound (CRB) and Bayesian CRB for target parameter estimation in order to benchmark the estimation performance. Our simulation results corroborate the superior performance of the proposed approach in comparison to the existing algorithms, and also their ability to approach the bounds derived. △ Less

Submitted 13 June, 2025; originally announced June 2025.

Spectral Efficiency Maximization for mmWave MIMO-Aided Integrated Sensing and Communication Under Practical Constraints

Authors: Jitendra Singh, Anand Mehrotra, Suraj Srivastava, Aditya K. Jagannatham, Lajos Hanzo

Abstract: A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal perform… ▽ More A hybrid transmit precoder (TPC) and receive combiner (RC) pair is conceived for millimeter wave (mmWave) multiple input multiple output (MIMO) integrated sensing and communication (ISAC) systems. The proposed design considers a practical mean squared error (MSE) constraint between the desired and the achieved beampatterns constructed for identifying radar targets (RTs). To achieve optimal performance, we formulate an optimization problem relying on sum spectral efficiency (SE) maximization of the communication users (CUs), while satisfying certain radar beampattern similarity (RBPS), total transmit power, and constant modulus constraints, where the latter are attributed to the hybrid mmWave MIMO architecture. Since the aforementioned problem is non-convex and intractable, a sequential approach is proposed wherein the TPCs are designed first, followed by the RCs. To deal with the non-convex MSE and constant modulus constraints in the TPC design problem, we propose a majorization and minimization (MM) based Riemannian conjugate gradient (RCG) method, which restricts the tolerable MSE of the beampattern to within a predefined limit. Moreover, the least squares and the zero-forcing methods are adopted for maximizing the sum-SE and for mitigating the multiuser interference (MUI), respectively. Furthermore, to design the RC at each CU, we propose a linear MM-based blind combiner (LMBC) scheme that does not rely on the knowledge of the TPC at the CUs and has a low complexity. To achieve user fairness, we further extend the proposed sequential approach for maximizing the geometric mean (GM) of the CU's rate. Simulation results are presented, which show the superior performance of the proposed hybrid TPC and RC in comparison to the state-of-the-art designs in the mmWave MIMO ISAC systems under consideration. △ Less

Submitted 5 June, 2025; originally announced June 2025.

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink

Authors: Tierui Gong, Chau Yuen, Chong Meng Samson See, Mérouane Debbah, Lajos Hanzo

Abstract: Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution for evolving wireless receivers from the classical to the quantum domain. To further unleash their great potential in wireless communications, we propose a flexible architecture for Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) receivers in the multi-user uplink. Then the corresponding signal model of… ▽ More Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution for evolving wireless receivers from the classical to the quantum domain. To further unleash their great potential in wireless communications, we propose a flexible architecture for Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) receivers in the multi-user uplink. Then the corresponding signal model of the RAQ-MIMO system is constructed by paving the way from quantum physics to classical wireless communications. Explicitly, we outline the associated operating principles and transmission flow. We also validate the linearity of our model and its feasible region. Based on our model, we derive closed-form asymptotic formulas for the ergodic achievable rate (EAR) of both the maximum-ratio combining (MRC) and zero-forcing (ZF) receivers operating in uncorrelated fading channels (UFC) and the correlated fading channels (CFC), as well as in the standard quantum limit (SQL) and photon shot limit (PSL) regimes, respectively. Furthermore, we unveil that the EAR scales logarithmically without bound with the product of effective number $N_{\text{atom}}$ and coherence time $T_2$ of the atomic ensemble in the SQL regime, but exhibits non-monotonic trade-off between the collective atomic enhancement and optical-depth-dependent attenuation in the PSL regime. More particularly, the transmit power of users can be scaled down quadratically with $N_{\text{atom}} τ$, $τ\in \{ T_2, \frac{ {\cal C} (Ω_{\ell}) }{A_p} \}$, but the EAR per user retains fixed, by increasing $N_{\text{atom}}$ while retaining the sensor number $M \propto N_{\text{atom}} τ$ in the SQL regime or $M \propto \exp \big( \frac{N_{\text{atom}} {\bar χ}}{A_p} \big)$ in the PSL regime.... △ Less

Submitted 14 January, 2026; v1 submitted 2 June, 2025; originally announced June 2025.

Comments: 17 pages, 6 figures (14 subfigures), 3 table

Graph Neural Network Aided Detection for the Multi-User Multi-Dimensional Index Modulated Uplink

Authors: Xinyu Feng, Mohammed EL-Hajjar, Chao Xu, Lajos Hanzo

Abstract: The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarr… ▽ More The concept of Compressed Sensing-aided Space-Frequency Index Modulation (CS-SFIM) is conceived for the Large-Scale Multi-User Multiple-Input Multiple-Output Uplink (LS-MU-MIMO-UL) of Next-Generation (NG) networks. Explicitly, in CS-SFIM, the information bits are mapped to both spatial- and frequency-domain indices, where we treat the activation patterns of the transmit antennas and of the subcarriers separately. Serving a large number of users in an MU-MIMO-UL system leads to substantial Multi-User Interference (MUI). Hence, we design the Space-Frequency (SF) domain matrix as a joint factor graph, where the Approximate Message Passing (AMP) and Expectation Propagation (EP) based MU detectors can be utilized. In the LS-MU-MIMO-UL scenario considered, the proposed system uses optimal Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detectors as benchmarks for comparison with the proposed MP-based detectors. These MP-based detectors significantly reduce the detection complexity compared to ML detection, making the design eminently suitable for LS-MU scenarios. To further reduce the detection complexity and improve the detection performance, we propose a pair of Graph Neural Network (GNN) based detectors, which rely on the orthogonal AMP (OAMP) and on the EP algorithm, which we refer to as the GNN-AMP and GEPNet detectors, respectively. The GEPNet detector maximizes the detection performance, while the GNN-AMP detector strikes a performance versus complexity trade-off. The GNN is trained for a single system configuration and yet it can be used for any number of users in the system. The simulation results show that the GNN-based detector approaches the ML performance in various configurations. △ Less

Submitted 27 May, 2025; originally announced May 2025.

Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning

Authors: Jiaju Qi, Lei Lei, Thorsteinn Jonsson, Lajos Hanzo

Abstract: The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. To overcome the challenge of long-range multi-phase planning with sparse reward, we conceive… ▽ More The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. To overcome the challenge of long-range multi-phase planning with sparse reward, we conceive Hierarchical DRL (HDRL) for decoupling the original MDP into a high-level Semi-MDP (SMDP) and multiple low-level MDPs. The Hierarchical Double Deep Q-Network (HDDQN)-Hindsight Experience Replay (HER) algorithm is proposed for simultaneously solving the decision problems arising at different temporal resolutions. As a result, the high-level agent learns an effective policy for prescribing the charging targets for every charging period, while the low-level agent learns an optimal policy for setting the charging power of every time step within a single charging period, with the aim of minimizing the charging costs while meeting the charging target. It is proved that the flat policy constructed by superimposing the optimal high-level policy and the optimal low-level policy performs as well as the optimal policy of the original MDP. Since jointly learning both levels of policies is challenging due to the non-stationarity of the high-level agent and the sampling inefficiency of the low-level agent, we divide the joint learning process into two phases and exploit our new HER algorithm to manipulate the experience replay buffers for both levels of agents. Numerical experiments are performed with the aid of real-world data to evaluate the performance of the proposed algorithm. △ Less

Submitted 15 May, 2025; originally announced May 2025.

Performance Analysis of Fluid Antenna System Aided OTFS Satellite Communications

Authors: Halvin Yang, Mahsa Derakhshani, Sangarapillai Lambotharan, Lajos Hanzo

Abstract: Internet-of-Things (IoT) networks typically rely on satellite communications to provide coverage in rural areas. However, high-mobility satellite links introduce severe Doppler and delay spreads, which necessitate the use of orthogonal time frequency space (OTFS) modulation for reliable data transmission. Furthermore, the space and energy constraints on IoT devices make the perfect use case for fl… ▽ More Internet-of-Things (IoT) networks typically rely on satellite communications to provide coverage in rural areas. However, high-mobility satellite links introduce severe Doppler and delay spreads, which necessitate the use of orthogonal time frequency space (OTFS) modulation for reliable data transmission. Furthermore, the space and energy constraints on IoT devices make the perfect use case for fluid antenna systems (FAS) due to their mechanical simplicity. Hence, we propose a sophisticated FAS aided OTFS (FAS-OTFS) framework for satellite-based IoT networks. We derive analytical expressions for both the outage probability and ergodic capacity of FAS-OTFS under a general channel model, where the expressions derived are presented in integral form or as analytical bounds for efficient numerical evaluation. Additionally, we investigate a single-path fading scenario, where closed-form expressions are obtained. Our numerical results demonstrate significant performance gains in terms of both the outage probability and capacity compared to conventional OTFS systems, confirming the efficacy of FAS-OTFS in energy-constrained high-mobility environments. Our findings establish FAS-OTFS as a promising candidate for next-generation IoT communications over satellite links. △ Less

Submitted 13 May, 2025; originally announced May 2025.

Comments: 13 pages, 13 Figures, journal paper, JSAC