Showing 1–20 of 20 results for author: Mönich, U J

Real-Time Multi-Target Detection and Tracking with mmWave 5G NR Waveforms on RFSoC

Authors: Xinyang Li, Hian Zing Voon, Vlad C. Andrei, Alexander Sessler, Nunzio Sciammetta, Ullrich J. Mönich, Dominic A. Schupke, Holger Boche

Abstract: We demonstrate a real-time implementation of multi-target detection and tracking using 5G New Radio (NR) physical downlink shared channel (PDSCH) waveform with 400 MHz bandwidth at 28 GHz carrier frequency. The hardware platform is built on a radio frequency system-on-chip (RFSoC) 4x2 board connected with a pair of Sivers EVK02001 mmWave beamformers for transmission and reception. The entire sensi… ▽ More We demonstrate a real-time implementation of multi-target detection and tracking using 5G New Radio (NR) physical downlink shared channel (PDSCH) waveform with 400 MHz bandwidth at 28 GHz carrier frequency. The hardware platform is built on a radio frequency system-on-chip (RFSoC) 4x2 board connected with a pair of Sivers EVK02001 mmWave beamformers for transmission and reception. The entire sensing transceiver processing and fast beam control are realized purely in the programmable logic (PL) part of the RFSoC, enabling low-latency and fully hardware-accelerated operation. The continuously acquired sensing data constitute 3D range-angle (RA) tensors, which are processed on a host PC using adaptive background subtraction, cell-averaging constant false alarm rate (CA-CFAR) detection with density-based spatial clustering of applications with noise (DBSCAN) clustering, and extended Kalman filtering (EKF), to detect and track targets in the environment. Our software-defined radio (SDR) testbed integrates heterogeneous computing resources, including CPUs, GPUs, and FPGAs, thereby providing design flexibility for a wide range of tasks. △ Less

Submitted 27 December, 2025; originally announced December 2025.

Variational Secret Common Randomness Extraction

Authors: Xinyang Li, Vlad C. Andrei, Peter J. Gu, Yiqi Chen, Ullrich J. Mönich, Holger Boche

Abstract: This paper studies the problem of extracting common randomness (CR) or secret keys from correlated random sources observed by two legitimate parties, Alice and Bob, through public discussion in the presence of an eavesdropper, Eve. We propose a practical two-stage CR extraction framework. In the first stage, the variational probabilistic quantization (VPQ) step is introduced, where Alice and Bob e… ▽ More This paper studies the problem of extracting common randomness (CR) or secret keys from correlated random sources observed by two legitimate parties, Alice and Bob, through public discussion in the presence of an eavesdropper, Eve. We propose a practical two-stage CR extraction framework. In the first stage, the variational probabilistic quantization (VPQ) step is introduced, where Alice and Bob employ probabilistic neural network (NN) encoders to map their observations into discrete, nearly uniform random variables (RVs) with high agreement probability while minimizing information leakage to Eve. This is realized through a variational learning objective combined with adversarial training. In the second stage, a secure sketch using code-offset construction reconciles the encoder outputs into identical secret keys, whose secrecy is guaranteed by the VPQ objective. As a representative application, we study physical layer key (PLK) generation. Beyond the traditional methods, which rely on the channel reciprocity principle and require two-way channel probing, thus suffering from large protocol overhead and being unsuitable in high mobility scenarios, we propose a sensing-based PLK generation method for integrated sensing and communications (ISAC) systems, where paired range-angle (RA) maps measured at Alice and Bob serve as correlated sources. The idea is verified through both end-to-end simulations and real-world software-defined radio (SDR) measurements, including scenarios where Eve has partial knowledge about Bob's position. The results demonstrate the feasibility and convincing performance of both the proposed CR extraction framework and sensing-based PLK generation method. △ Less

Submitted 2 October, 2025; originally announced October 2025.

Computation of Capacity-Distortion-Cost Functions for Continuous Memoryless Channels

Authors: Xinyang Li, Ziyou Tang, Vlad C. Andrei, Ullrich J. Mönich, Fan Liu, Holger Boche

Abstract: This paper aims at computing the capacity-distortion-cost (CDC) function for continuous memoryless channels, which is defined as the supremum of the mutual information between channel input and output, constrained by an input cost and an expected distortion of estimating channel state. Solving the optimization problem is challenging because the input distribution does not lie in a finite-dimension… ▽ More This paper aims at computing the capacity-distortion-cost (CDC) function for continuous memoryless channels, which is defined as the supremum of the mutual information between channel input and output, constrained by an input cost and an expected distortion of estimating channel state. Solving the optimization problem is challenging because the input distribution does not lie in a finite-dimensional Euclidean space and the optimal estimation function has no closed form in general. We propose to adopt the Wasserstein proximal point method and parametric models such as neural networks (NNs) to update the input distribution and estimation function alternately. To implement it in practice, the importance sampling (IS) technique is used to calculate integrals numerically, and the Wasserstein gradient descent is approximated by pushing forward particles. The algorithm is then applied to an integrated sensing and communications (ISAC) system, validating theoretical results at minimum and maximum distortion as well as the random-deterministic trade-off. △ Less

Submitted 28 April, 2025; originally announced April 2025.

Comments: Accepted by ISIT 2025

Exploiting Beamforming for Enforcing Semantic Secrecy in 5G NR mmWave Communications

Authors: Luis Torres-Figueroa, Johannes Voichtleitner, Ullrich J. Mönich, Taro Eichler, Moritz Wiese, Holger Boche

Abstract: We experimentally investigate the performance of semantically-secure physical layer security (PLS) in 5G new radio (NR) mmWave communications during the initial cell search procedure in the NR band n257 at 27 GHz. A gNB transmits PLS-encoded messages in the presence of an eavesdropper, who intercepts the communication by non-intrusively collecting channel readings in the form of IQ samples. For th… ▽ More We experimentally investigate the performance of semantically-secure physical layer security (PLS) in 5G new radio (NR) mmWave communications during the initial cell search procedure in the NR band n257 at 27 GHz. A gNB transmits PLS-encoded messages in the presence of an eavesdropper, who intercepts the communication by non-intrusively collecting channel readings in the form of IQ samples. For the message transmission, we use the physical broadcast channel (PBCH) within the synchronization signal block. We analyze different signal-to-noise ratio (SNR) conditions by progressively reducing the transmit power of the subcarriers carrying the PBCH channel, while ensuring optimal conditions for over-the-air frequency and timing synchronization. We measure the secrecy performance of the communication in terms of upper and lower bounds for the distinguishing error rate (DER) metric for different SNR levels and beam angles when performing beamsteering in indoor scenarios, such as office environments and laboratory settings. △ Less

Submitted 10 April, 2025; originally announced April 2025.

Comments: 7 pages, 10 figures, 3 tables, accepted at Proceedings of the IEEE Global Communications Conference (GLOBECOM), Workshop on Enabling Security, Trust, and Privacy in 6G Wireless Systems (WS09), and presented at Globecom 2024 in Cape Town, South Africa

SE(3)-Based Trajectory Optimization and Target Tracking in UAV-Enabled ISAC Systems

Authors: Dongxiao Xu, Xinyang Li, Vlad C. Andrei, Moritz Wiese, Ullrich J. Moenich, Holger Boche

Abstract: This paper presents a novel approach to enhance sensing capabilities in UAV-enabled MIMO-OFDM ISAC systems by leveraging UAV mobility as a mono-static radar. By integrating uniform planar arrays (UPAs) and modeling the UAV dynamics in $SE(3)$, we address key challenges such as 3D space sensing and trajectory design. We propose a target tracking scheme using extended Kalman filtering (EKF) in… ▽ More This paper presents a novel approach to enhance sensing capabilities in UAV-enabled MIMO-OFDM ISAC systems by leveraging UAV mobility as a mono-static radar. By integrating uniform planar arrays (UPAs) and modeling the UAV dynamics in $SE(3)$, we address key challenges such as 3D space sensing and trajectory design. We propose a target tracking scheme using extended Kalman filtering (EKF) in $SE(3)$, along with trajectory optimization based on the conditional Posterior Cramer-Rao bound (CPCRB). Numerical results demonstrate the effectiveness of the proposed trajectory design in enhancing performance of target tracking and physical parameter estimation in UAV-enabled MIMO-OFDM ISAC systems. △ Less

Submitted 29 April, 2025; v1 submitted 20 January, 2025; originally announced January 2025.

Comments: Accepted by IEEE International Symposium on Information Theory 2025

A Simultaneous Decoding Approach to Joint State and Message Communications

Authors: Xinyang Li, Yiqi Chen, Vlad C. Andrei, Aladin Djuhera, Ullrich J. Mönich, Holger Boche

Abstract: The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access to generalized state information and feedback while the receivers jointly decode the messages and estimate the channel state. A coding scheme is proposed based on backward simultaneous decoding of messages and compresse… ▽ More The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access to generalized state information and feedback while the receivers jointly decode the messages and estimate the channel state. A coding scheme is proposed based on backward simultaneous decoding of messages and compressed state descriptions without the need for the Wyner-Ziv random binning technique. For the point-to-point channel, the proposed scheme results in the optimal C-D function. For the state-dependent discrete memoryless degraded broadcast channel (SD-DMDBC), the successive refinement method is adopted for designing multi-stage state descriptions. With the simultaneous decoding approach, the derived achievable region is shown to be larger than the region obtained by the sequential decoding approach that is utilized in existing works. As for the state-dependent discrete memoryless multiple access channel (SD-DMMAC), in addition to the proposed method, Willem's coding strategy is applied to enable partial collaboration between transmitters through the feedback links. Moreover, the state descriptions are shown to enhance both communication and state estimation performance. Examples are provided for the derived results to verify the analysis, either numerically or analytically. With particular focus, simple but representative integrated sensing and communications (ISAC) systems are also considered, and their fundamental performance limits are studied. △ Less

Submitted 13 February, 2025; v1 submitted 19 January, 2025; originally announced January 2025.

Computing Capacity-Cost Functions for Continuous Channels in Wasserstein Space

Authors: Xinyang Li, Vlad C. Andrei, Ullrich J. Mönich, Fan Liu, Holger Boche

Abstract: This paper investigates the problem of computing capacity-cost (C-C) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting in an iterative algorithm related to the Wasserstein gradient descent. Practica… ▽ More This paper investigates the problem of computing capacity-cost (C-C) functions for continuous channels. Motivated by the Kullback-Leibler divergence (KLD) proximal reformulation of the classical Blahut-Arimoto (BA) algorithm, the Wasserstein distance is introduced to the proximal term for the continuous case, resulting in an iterative algorithm related to the Wasserstein gradient descent. Practical implementation involves moving particles along the negative gradient direction of the objective function's first variation in the Wasserstein space and approximating integrals by the importance sampling (IS) technique. Such formulation is also applied to the rate-distortion (R-D) function for continuous source spaces and thus provides a unified computation framework for both problems. △ Less

Submitted 2 March, 2025; v1 submitted 18 January, 2025; originally announced January 2025.

Comments: Accepted to IEEE International Conference on Communications 2025

Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware

Authors: Vlad C. Andrei, Alexandru P. Drăguţoiu, Gabriel Béna, Mahmoud Akl, Yin Li, Matthias Lohrmann, Ullrich J. Mönich, Holger Boche

Abstract: This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval (MHR). By casting the MHR problem as a sparse recovery problem, we devise the currently proposed, deep-unrolling-based Structured Learned Iterative Shrinkage and Thresholding (S-LISTA) algorithm to solve it efficiently using comp… ▽ More This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval (MHR). By casting the MHR problem as a sparse recovery problem, we devise the currently proposed, deep-unrolling-based Structured Learned Iterative Shrinkage and Thresholding (S-LISTA) algorithm to solve it efficiently using complex-valued convolutional neural networks with complex-valued activations, which are trained using a supervised regression objective. Afterward, a novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. At the heart of this method lies the recently proposed Few Spikes (FS) conversion, which is extended by modifying the neuron model's parameters and internal dynamics to account for the inherent coupling between real and imaginary parts in complex-valued computations. Finally, the converted SNNs are mapped onto the SpiNNaker2 neuromorphic board, and a comparison in terms of estimation accuracy and power efficiency between the original CNNs deployed on an NVIDIA Jetson Xavier and the SNNs is being conducted. The measurement results show that the converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs. △ Less

Submitted 5 December, 2024; originally announced December 2024.

Comments: accepted to the 58th Asilomar Conference on Signals, Systems, and Computers, Oct. 27th - Oct. 30th, 2024, Pacific Grove, CA

An Achievable Rate-Distortion Region for Joint State and Message Communication over Multiple Access Channels

Authors: Xinyang Li, Vlad C. Andrei, Ullrich J. Mönich, Holger Boche

Abstract: This paper derives an achievable rate-distortion (R-D) region for the state-dependent discrete memoryless multiple access channel (SD-DMMAC), where the generalized feedback and causal side information are present at encoders, and the decoder performs the joint task of message decoding and state estimation. The Markov coding and backward-forward two-stage decoding schemes are adopted in the proof.… ▽ More This paper derives an achievable rate-distortion (R-D) region for the state-dependent discrete memoryless multiple access channel (SD-DMMAC), where the generalized feedback and causal side information are present at encoders, and the decoder performs the joint task of message decoding and state estimation. The Markov coding and backward-forward two-stage decoding schemes are adopted in the proof. This scenario is shown to be capable of modeling various integrated sensing and communication (ISAC) applications, including the monostatic-uplink system and multi-modal sensor networks, which are then studied as examples. △ Less

Submitted 26 September, 2024; originally announced September 2024.

Comments: Accepted by IEEE Information Theory Workshop 2024

R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models

Authors: Aladin Djuhera, Vlad C. Andrei, Xinyang Li, Ullrich J. Mönich, Holger Boche, Walid Saad

Abstract: Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML), where components of large ML models are outsourced to remote servers. A significant challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming that could jeopardize the learning process. This is particularly pron… ▽ More Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML), where components of large ML models are outsourced to remote servers. A significant challenge in SFL, particularly when deployed over wireless channels, is the susceptibility of transmitted model parameters to adversarial jamming that could jeopardize the learning process. This is particularly pronounced for embedding parameters in large language models (LLMs) and vision language models (VLMs), which are learned feature vectors essential for domain understanding. In this paper, rigorous insights are provided into the influence of jamming embeddings in SFL by deriving an expression for the ML training loss divergence and showing that it is upper-bounded by the mean squared error (MSE). Based on this analysis, a physical layer framework is developed for resilient SFL with LLMs (R-SFLLM) over wireless networks. R-SFLLM leverages wireless sensing data to gather information on the jamming directions-of-arrival (DoAs) for the purpose of devising a novel, sensing-assisted anti-jamming strategy while jointly optimizing beamforming, user scheduling, and resource allocation. Extensive experiments using both LLMs and VLMs demonstrate R-SFLLM's effectiveness, achieving close-to-baseline performance across various natural language processing (NLP) and computer vision (CV) tasks, datasets, and modalities. The proposed methodology further introduces an adversarial training component, where controlled noise exposure significantly enhances the model's resilience to perturbed parameters during training. The results show that more noise-sensitive models, such as RoBERTa, benefit from this feature, especially when resource allocation is unfair. It is also shown that worst-case jamming in particular translates into worst-case model outcomes, thereby necessitating the need for jamming-resilient SFL protocols. △ Less

Submitted 27 October, 2025; v1 submitted 16 July, 2024; originally announced July 2024.

Journal ref: IEEE Transactions on Information Forensics and Security (Volume: 20), 2025

Resilient-By-Design Framework for MIMO-OFDM Communications under Smart Jamming

Authors: Vlad C. Andrei, Aladin Djuhera, Xinyang Li, Ullrich J. Mönich, Holger Boche, Walid Saad

Abstract: Native jamming mitigation is essential for addressing security and resilience in future 6G wireless networks. In this paper a resilient-by-design framework for effective anti-jamming in MIMO-OFDM wireless communications is introduced. A novel approach that integrates information from wireless sensing services to develop anti-jamming strategies, which do not rely on any prior information or assumpt… ▽ More Native jamming mitigation is essential for addressing security and resilience in future 6G wireless networks. In this paper a resilient-by-design framework for effective anti-jamming in MIMO-OFDM wireless communications is introduced. A novel approach that integrates information from wireless sensing services to develop anti-jamming strategies, which do not rely on any prior information or assumptions on the adversary's concrete setup, is explored. To this end, a method that replaces conventional approaches to noise covariance estimation in anti-jamming with a surrogate covariance model is proposed, which instead incorporates sensing information on the jamming signal's directions-of-arrival (DoAs) to provide an effective approximation of the true jamming strategy. The study further focuses on integrating this novel, sensing-assisted approach into the joint optimization of beamforming, user scheduling and power allocation for a multi-user MIMO-OFDM uplink setting. Despite the NP-hard nature of this optimization problem, it can be effectively solved using an iterative water-filling approach. In order to assess the effectiveness of the proposed sensing-assisted jamming mitigation, the corresponding worst-case jamming strategy is investigated, which aims to minimize the total user sum-rate. Experimental simulations eventually affirm the robustness of our approach against both worst-case and barrage jamming, demonstrating its potential to address a wide range of jamming scenarios. Since such an integration of sensing-assisted information is directly implemented on the physical layer, resilience is incorporated preemptively by-design. △ Less

Submitted 16 April, 2024; originally announced April 2024.

Comments: accepted to IEEE International Conference on Communications, 2nd Workshop on Enabling Security, Trust, and Privacy in 6G Wireless Systems

An Analysis of Capacity-Distortion Trade-Offs in Memoryless ISAC Systems

Authors: Xinyang Li, Vlad C. Andrei, Aladin Djuhera, Ullrich J. Mönich, Holger Boche

Abstract: This manuscript investigates the information-theoretic limits of integrated sensing and communications (ISAC), aiming for simultaneous reliable communication and precise channel state estimation. We model such a system with a state-dependent discrete memoryless channel (SD-DMC) with present or absent channel feedback and generalized side information at the transmitter and the receiver, where the j… ▽ More This manuscript investigates the information-theoretic limits of integrated sensing and communications (ISAC), aiming for simultaneous reliable communication and precise channel state estimation. We model such a system with a state-dependent discrete memoryless channel (SD-DMC) with present or absent channel feedback and generalized side information at the transmitter and the receiver, where the joint task of message decoding and state estimation is performed at the receiver. The relationship between the achievable communication rate and estimation error, the capacity-distortion (C-D) trade-off, is characterized across different causality levels of the side information. This framework is shown to be capable of modeling various practical scenarios by assigning the side information with different meanings, including monostatic and bistatic radar systems. The analysis is then extended to the two-user degraded broadcast channel, and we derive an achievable C-D region that is tight under certain conditions. To solve the optimization problem arising in the computation of C-D functions/regions, we propose a proximal block coordinate descent (BCD) method, prove its convergence to a stationary point, and derive a stopping criterion. Finally, several representative examples are studied to demonstrate the versatility of our framework and the effectiveness of the proposed algorithm. △ Less

Submitted 28 March, 2024; v1 submitted 26 February, 2024; originally announced February 2024.

A Digital Twinning Platform for Integrated Sensing, Communications and Robotics

Authors: Vlad C. Andrei, Xinyang Li, Maresa Fees, Andreas Feik, Ullrich J. Mönich, Holger Boche

Abstract: In this paper, a digital twinning framework for indoor integrated sensing, communications, and robotics is proposed, designed, and implemented. Besides leveraging powerful robotics and ray-tracing technologies, the framework also enables integration with real-world sensors and reactive updates triggered by changes in the environment. The framework is designed with commercial, off-the-shelf compone… ▽ More In this paper, a digital twinning framework for indoor integrated sensing, communications, and robotics is proposed, designed, and implemented. Besides leveraging powerful robotics and ray-tracing technologies, the framework also enables integration with real-world sensors and reactive updates triggered by changes in the environment. The framework is designed with commercial, off-the-shelf components in mind, thus facilitating experimentation in the different areas of communication, sensing, and robotics. Experimental results showcase the feasibility and accuracy of indoor localization using digital twins and validate our implementation both qualitatively and quantitatively. △ Less

Submitted 23 February, 2024; originally announced February 2024.

Comments: accepted to the 4th IEEE Joint Communications & Sensing Hybrid Symposium, 19-21 March 2024, Leuven, Belgium

Optimal Linear Precoder Design for MIMO-OFDM Integrated Sensing and Communications Based on Bayesian Cramér-Rao Bound

Authors: Xinyang Li, Vlad Costin Andrei, Ullrich J Mönich, Holger Boche

Abstract: In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cramér-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design… ▽ More In this paper, we investigate the fundamental limits of MIMO-OFDM integrated sensing and communications (ISAC) systems based on a Bayesian Cramér-Rao bound (BCRB) analysis. We derive the BCRB for joint channel parameter estimation and data symbol detection, in which a performance trade-off between both functionalities is observed. We formulate the optimization problem for a linear precoder design and propose the stochastic Riemannian gradient descent (SRGD) approach to solve the non-convex problem. We analyze the optimality conditions and show that SRGD ensures convergence with high probability. The simulation results verify our analyses and also demonstrate a fast convergence speed. Finally, the performance trade-off is illustrated and investigated. △ Less

Submitted 23 August, 2023; originally announced August 2023.

Comments: Accepted to IEEE GLOBECOM 2023

Sensing-Assisted Receivers for Resilient-By-Design 6G MU-MIMO Uplink

Authors: Vlad C. Andrei, Xinyang Li, Ullrich J. Mönich, Holger Boche

Abstract: We address the resilience of future 6G MIMO communications by considering an uplink scenario where multiple legitimate transmitters try to communicate with a base station in the presence of an adversarial jammer. The jammer possesses full knowledge about the system and the physical parameters of the legitimate link, while the base station only knows the UL-channels and the angle-of-arrival (AoA) o… ▽ More We address the resilience of future 6G MIMO communications by considering an uplink scenario where multiple legitimate transmitters try to communicate with a base station in the presence of an adversarial jammer. The jammer possesses full knowledge about the system and the physical parameters of the legitimate link, while the base station only knows the UL-channels and the angle-of-arrival (AoA) of the jamming signals. Furthermore, the legitimate transmitters are oblivious to the fact that jamming takes place, thus the burden of guaranteeing resilience falls on the receiver. For this case we derive one optimal jamming strategy that aims to minimize the rate of the strongest user and multiple receive strategies, one based on a lower bound on the achievable signal-to-interference-to-noise-ratio (SINR), one based on a zero-forcing (ZF) design, and one based on a minimum SINR constraint. Numerical studies show that the proposed anti-jamming approaches ensure that the sum rate of the system is much higher than without protection, even when the jammer has considerably more transmit power and even if the jamming signals come from the same direction as those of the legitimate users. △ Less

Submitted 14 February, 2023; originally announced February 2023.

Comments: Accepted to 3rd IEEE International Symposium on Joint Communications & Sensing

Optimal and Robust Waveform Design for MIMO-OFDM Channel Sensing: A Cramér-Rao Bound Perspective

Authors: Xinyang Li, Vlad C. Andrei, Ullrich J. Mönich, Holger Boche

Abstract: Wireless channel sensing is one of the key enablers for integrated sensing and communication (ISAC) which helps communication networks understand the surrounding environment. In this work, we consider MIMO-OFDM systems and aim to design optimal and robust waveforms for accurate channel parameter estimation given allocated OFDM resources. The Fisher information matrix (FIM) is derived first, and th… ▽ More Wireless channel sensing is one of the key enablers for integrated sensing and communication (ISAC) which helps communication networks understand the surrounding environment. In this work, we consider MIMO-OFDM systems and aim to design optimal and robust waveforms for accurate channel parameter estimation given allocated OFDM resources. The Fisher information matrix (FIM) is derived first, and the waveform design problem is formulated by maximizing the log determinant of the FIM. We then consider the uncertainty in the parameters and state the stochastic optimization problem for a robust design. We propose the Riemannian Exact Penalty Method via Smoothing (REPMS) and its stochastic version SREPMS to solve the constrained non-convex problems. In simulations, we show that the REPMS yields comparable results to the semidefinite relaxation (SDR) but with a much shorter running time. Finally, the designed robust waveforms using SREMPS are investigated, and are shown to have a good performance under channel perturbations. △ Less

Submitted 25 January, 2023; originally announced January 2023.

Comments: 6 pages, 5 figures. Accepted by IEEE International Conference on Communications 2023

On the Need of Analog Signals and Systems for Digital-Twin Representations

Authors: Holger Boche, Ullrich J. Mönich, Yannik N. Böck, Frank H. P. Fitzek

Abstract: We consider the task of converting different digital descriptions of analog bandlimited signals and systems into each other, with a rigorous application of mathematical computability theory. Albeit very fundamental, the problem appears in the scope of digital twinning, an emerging concept in the field of digital processing of analog information that is regularly mentioned as one of the key enabler… ▽ More We consider the task of converting different digital descriptions of analog bandlimited signals and systems into each other, with a rigorous application of mathematical computability theory. Albeit very fundamental, the problem appears in the scope of digital twinning, an emerging concept in the field of digital processing of analog information that is regularly mentioned as one of the key enablers for next-generation cyber-physical systems and their areas of application. In this context, we prove that essential quantities such as the peak-to-average power ratio and the bounded-input/bounded-output norm, which determine the behavior of the real-world analog system, cannot generally be determined from the system's digital twin, depending on which of the above-mentioned descriptions is chosen. As a main result, we characterize the algorithmic strength of Shannon's sampling type representation as digital twin implementation and also introduce a new digital twin implementation of analog signals and systems. We show there exist two digital descriptions, both of which uniquely characterize a certain analog system, such that one description can be algorithmically converted into the other, but not vice versa. △ Less

Submitted 9 October, 2022; originally announced October 2022.

On the Arithmetic Complexity of the Bandwidth of Bandlimited Signals

Authors: Holger Boche, Yannik N. Böck, Ullrich J. Mönich

Abstract: The bandwidth of a signal is an important physical property that is of relevance in many signal- and information-theoretic applications. In this paper we study questions related to the computability of the bandwidth of computable bandlimited signals. To this end we employ the concept of Turing computability, which exactly describes what is theoretically feasible and can be computed on a digital co… ▽ More The bandwidth of a signal is an important physical property that is of relevance in many signal- and information-theoretic applications. In this paper we study questions related to the computability of the bandwidth of computable bandlimited signals. To this end we employ the concept of Turing computability, which exactly describes what is theoretically feasible and can be computed on a digital computer. Recently, it has been shown that there exist computable bandlimited signals with finite energy, the actual bandwidth of which is not a computable number, and hence cannot be computed on a digital computer. In this work, we consider the most general class of band-limited signals, together with different computable representations thereof. Among other things, our analysis includes a characterization of the arithmetic complexity of the bandwidth of such signals and yields a negative answer to the question of whether it is at least possible to compute non-trivial upper or lower bounds for the bandwidth of a bandlimited signal. Furthermore, we relate the problem of bandwidth computation to the theory of oracle machines. In particular, we consider halting and totality oracles, which belong to the most frequently investigated oracle machines in the theory of computation. △ Less

Submitted 3 February, 2022; originally announced February 2022.

Strong Divergence for System Approximations

Authors: Holger Boche, Ullrich J. Mönich

Abstract: In this paper we analyze the approximation of stable linear time-invariant systems, like the Hilbert transform, by sampling series for bandlimited functions in the Paley-Wiener space $\mathcal{PW}_π^{1}$. It is known that there exist systems and functions such that the approximation process is weakly divergent, i.e., divergent for certain subsequences. Here we strengthen this result by proving str… ▽ More In this paper we analyze the approximation of stable linear time-invariant systems, like the Hilbert transform, by sampling series for bandlimited functions in the Paley-Wiener space $\mathcal{PW}_π^{1}$. It is known that there exist systems and functions such that the approximation process is weakly divergent, i.e., divergent for certain subsequences. Here we strengthen this result by proving strong divergence, i.e., divergence for all subsequences. Further, in case of divergence, we give the divergence speed. We consider sampling at Nyquist rate as well as oversampling with adaptive choice of the kernel. Finally, connections between strong divergence and the Banach-Steinhaus theorem, which is not powerful enough to prove strong divergence, are discussed. △ Less

Submitted 12 May, 2015; originally announced May 2015.

Comments: Preprint accepted for publication in Problems of Information Transmission. The material in this paper was presented in part at the 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing

MSC Class: 94A20 (Primary); 30D10; 94A15 (Secondary)

Signal and System Approximation from General Measurements

Authors: Holger Boche, Ullrich J. Mönich

Abstract: In this paper we analyze the behavior of system approximation processes for stable linear time-invariant (LTI) systems and signals in the Paley-Wiener space PW_π^1. We consider approximation processes, where the input signal is not directly used to generate the system output, but instead a sequence of numbers is used that is generated from the input signal by measurement functionals. We consider c… ▽ More In this paper we analyze the behavior of system approximation processes for stable linear time-invariant (LTI) systems and signals in the Paley-Wiener space PW_π^1. We consider approximation processes, where the input signal is not directly used to generate the system output, but instead a sequence of numbers is used that is generated from the input signal by measurement functionals. We consider classical sampling which corresponds to a pointwise evaluation of the signal, as well as several more general measurement functionals. We show that a stable system approximation is not possible for pointwise sampling, because there exist signals and systems such that the approximation process diverges. This remains true even with oversampling. However, if more general measurement functionals are considered, a stable approximation is possible if oversampling is used. Further, we show that without oversampling we have divergence for a large class of practically relevant measurement procedures. △ Less

Submitted 5 February, 2014; originally announced February 2014.

Comments: This paper will be published as part of the book "New Perspectives on Approximation and Sampling Theory - Festschrift in honor of Paul Butzer's 85th birthday" in the Applied and Numerical Harmonic Analysis Series, Birkhauser (Springer-Verlag). Parts of this work have been presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing 2014 (ICASSP 2014)

MSC Class: 30D10; 30E05; 30H05; 94A05