arXiv subjects

Eduard A. Jorswieck

Publications and source records attributed to Eduard A. Jorswieck.

At least 19 recordsLinked to original sources

arXiv2026

Joint Identification, Transmission, and Sensing over State-Dependent Channels with Noiseless Feedback

We consider a discrete memoryless state-dependent channel with noiseless feedback, where the transmitter uses randomized encoding to jointly perform message transmission, identification, and state sensing. In this setting, the transmitter sends a conventional transmission message and an identification message while estimating the unknown channel state sequence under an average distortion constraint. We establish a single-letter characterization of the randomized identification-transmission-sensing capacity-distortion region. The result reveals a single-letter separation structure: the communication rates are determined by the averaged channel, subject to the sensing-induced constraint on admissible input distributions. Specifically, the transmission rate is characterized by mutual information, while the identification rate is characterized by output entropy under the same distortion constraint. The achievability proof combines constant-composition transmission coding, output partitioning, and combinatorial identification coding, while the converse follows from an averaged-channel reduction, distortion lower bounds, and feedback-identification converse arguments.

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arXiv2026

TwinViT-DeepJSCC: Adversarially Robust Semantic Image Communication

Learning-based semantic communication is vulnerable to adversarial perturbations introduced before semantic encoding or over wireless channels. This paper proposes TwinViT-DeepJSCC, a preventive-corrective semantic image transceiver operating under a fixed channel-use budget. Two Vision Transformer (ViT)-based deep joint source-channel coding (DeepJSCC) branches learn complementary latent representations protected by sensitivity-aware masking. At the receiver, confidence-aware fusion, blind corruption-severity estimation, and signal-to-noise ratio (SNR)-severity-conditioned denoising diffusion implicit model (DDIM) purification mitigate residual corruption without requiring attack metadata. Experiments on the Canadian Institute for Advanced Research 100-class (CIFAR-100) dataset consider fast gradient sign method (FGSM), projected gradient descent (PGD), natural evolution strategies (NES), and Carlini-Wagner (CW) source-domain attacks, as well as random jamming and channel-aware adversarial waveforms over additive white Gaussian noise (AWGN) and block-flat Rayleigh fading. Under matched channel-use and attack budgets, TwinViT-DeepJSCC achieves maximum peak signal-to-noise ratio (PSNR) gains of approximately 9.5 dB under 20-step PGD and 10.8 dB under channel-aware waveform attacks over block-flat Rayleigh fading. Under PGD, it also improves Top-1 accuracy by up to approximately 38 percentage points over the undefended baseline and 13 percentage points over the strongest competing defense. Ablation results confirm the complementary contributions of the proposed transmitter- and receiver-side mechanisms.

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arXiv2026

Open-Loop Transmission with Discrete Blocklengths: Characterization of AoI-Optimal Schedules

In this paper, we consider a general status update system, consisting of a source at which a physical process is monitored and a receiver. For transmission of status updates, a discrete set of blocklength error probability tuples is available. A typical example is a communication system with a fixed set of modulation and coding schemes. We assume that no feedback channel for acknowledgments exists and thus investigate the problem of constructing deterministic schedules for the status updating system. The average Age of Information (AoI) and average exponential AoI are appropriate performance metrics to capture the freshness of information. Interestingly, a static blocklength is shown to be suboptimal in general. While for the average AoI the gains that can be achieved by using a non-trivial periodic schedule are relatively small, they are significant for the average exponential AoI. Motivated by this, we prove conditions for single-blocklength optimality when two blocklengths are available. To achieve this, we use a Dinkelbach-like approach to reformulate the problem as an infinite-state transient average-cost Markov Decision Process and apply careful bounding techniques to derive the optimality conditions. As a converse, we analyze stationary randomized policies to certify the parameter regions where a non-trivial deterministic schedule outperforms the best constant schedule. Extensions to the case with more than two transmission lengths are provided. Numerical results show that the derived conditions cover a large portion of the parameter space and suggest that the optimality conditions for a constant schedule with the shorter blocklength, while sufficient, may also be necessary. As an example, we apply the results to the problem of age-optimal transmission with a small LDPC codebook, which illustrates the practical applicability.

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arXiv2026

DRL-Based Spectrum Sharing for RIS-Aided Local High-Quality Wireless Networks

This paper investigates a smart spectrum-sharing framework for reconfigurable intelligent surface (RIS)-aided local high-quality wireless networks (LHQWNs) within a mobile network operator (MNO) ecosystem. Although RISs are often considered harmful due to interference, this work shows that properly controlled RISs can enhance quality of service (QoS). The proposed system enables temporary spectrum access for multiple vertical service providers (VSPs) by dynamically allocating radio resources. The spectrum is divided into dedicated subchannels assigned to individual VSPs and reusable subchannels shared among multiple VSPs, while RIS improves propagation conditions and zero-forcing (ZF) precoding is adopted at the multiantenna base station (BS) to cancel inter-user interference. We formulate a multi-VSP utility maximization problem that jointly optimizes subchannel assignment, transmit power, and RIS phase configuration while accounting for spectrum access costs, RIS leasing costs, and QoS constraints. The resulting mixed-integer non-linear program (MINLP) is modeled as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL). Deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms are developed and compared. Numerical results show that SAC generally outperforms DDPG in convergence, stability, and utility, particularly in larger-scale scenarios. In the reduced-scale ablation study, both joint DRL methods outperform the heuristic benchmark by at least 6.6% in final moving-average reward.

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arXiv2026

Square-Root Law for Covert Communication with Warden-Favorable Side Information

Covert communication enables Alice to transmit to Bob while making the transmission difficult for Willie to detect. We study a scalar Gaussian covert-overlay model in which Alice's low-power covert signal is superimposed on an aggregate public component generated by Alice or other trackable sources. Willie is given all physically obtainable side information, including protocol details, timing, pilots, channel estimates, and calibration information, and subtracts his best estimate of the public component before testing. Covertness is imposed on the resulting residual through a relative-entropy constraint with budget $δ$ conditioned on Willie's side information. In the stationary case, the residual under no covert transmission has variance $σ_0^2=σ_W^2+σ_e^2$, where $σ_W^2$ is Willie's receiver-noise variance and $σ_e^2$ is the irreducible cancellation error. Over $n$ channel uses, the maximal reliably transmissible covert payload is $R_C^\star\sqrt{n}(1+o(1))$ bits, where $R_C^\star=\frac{σ_0^2}{σ_B^2\ln 2}\sqrtδ$, and $σ_B^2$ is Bob's receiver-noise variance. Thus, the square-root-law (SRL) constant is governed by the variance at Willie's actual detector input, not by receiver noise alone. Low-power Gaussian signaling achieves this constant, and a matching converse establishes first-order optimality within the conditioned additive Gaussian innovation model. For known time-varying conditioned residual variances, we also derive the first-order allocation, which assigns more covert power to larger residual variances. The results require a Gaussian post-cancellation null residual with known conditioned variance; non-Gaussian residuals and fixed non-vanishing variance uncertainty are outside the scope of this paper.

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arXiv2026

Enhancing Energy and Spectral Efficiency in IoT-Cellular Networks via Active SIM-Equipped LEO Satellites

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy-spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

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arXiv2026

Scalable Design for RIS-Assisted Multi-User Downlink System Empowered by RSMA under Partial CSI

In large-scale reconfigurable intelligent surface (RIS) communication systems, the precise acquisition of channel state information (CSI) is challenging. Consider a practical RIS configuration where only a few reflective elements serve as anchors to estimate CSI, which are termed partial CSI. To improve the robustness against partial CSI and the scalability of RIS networks, this paper proposes an unsupervised learning-based rate-splitting multiple access (RSMA) scheme for RIS-assisted multi-user systems. Specifically, RISnet, a neural network architecture designed to infer full CSI from partial observations, is employed and integrated with a low-complexity RSMA precoder. Effective channel features are constituted from partial CSI, and the original elements with channel information contribute to new anchors after expansion in RISnet. Numerical results demonstrate that the proposed scheme approximates the performance with a full CSI of RIS under deterministic raytracing channel conditions. When channel uncertainty increases during training, RSMA has been shown to enhance RISnet robustness, significantly mitigating performance loss.

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arXiv2026

Implementation of Oblivious Transfer over Binary-Input AWGN Channels by Polar Codes

We develop a one-out-of-two oblivious transfer protocol over the binary-input additive white Gaussian noise (BI-AWGN) channel using polar codes. The scheme uses two decoder views linked by automorphisms of the polar transform and publicly draws the encoder at random from the corresponding automorphism group. This yields perfect secrecy for Bob at any blocklength. Secrecy for Alice is obtained asymptotically via channel polarization combined with privacy amplification. Because the construction deliberately injects randomness into selected bad bit-channels, we derive a relaxed reliability criterion, which is empirically certified via Monte-Carlo simulations. We also evaluate finite-blocklength performance. Finally, we characterize the polar-transform automorphisms as bit-level permutations of bit-channel indices, and exploit this structure to derive and optimize an achievable finite-blocklength rate.

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arXiv2025

Energy-Efficient Power Control in Single-User M-MIMO-OFDM System with PA Nonlinearity

Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglecting subcarrier intermodulations at the non-linear PA for an Orthogonal Frequency Division Multiplexing (OFDM) system. Therefore, this work investigates the energy-efficient power allocation for a single-user equipment (UE) M-MIMO downlink (DL) system employing OFDM with nonlinear PAs. Unlike prior works, we model wide-band transmission using a soft-limiter PA model and derive a closed-form expression for the signal-to-distortion-and-noise ratio (SNDR) under Rayleigh fading and Maximal Ratio Transmission (MRT) precoding. Next, the Energy Efficiency (EE) function is defined considering two PA architectures and a distorted OFDM signal. We then propose a low complexity root-finding algorithm to maximize EE by transmit power adjustment. Simulation results demonstrate significant EE gains over a fixed PA back-off baseline, with over $100\%$ improvement under both low and high path loss. Our findings reveal how the optimal operating point depends on the antenna count, the PA model, and the propagation conditions.

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arXiv2025

Age of Information in Short Packet Multi-Connectivity Links

In this paper, we investigate multi-connectivity schemes in the context of status update systems with short payloads. As the performance metric, we use the Age of Information (AoI). Due to short payloads, transmission errors must be taken into account. In addition to the well-known schemes of packet duplication, message splitting, and multiplexing, we propose a codeword splitting scheme, where each status update is jointly encoded across multiple channels. We derive closed-form expressions for the average AoI for the different schemes and optimize their corresponding parameters. We show that the AoI of the multiplexing scheme is a convex function in the offset parameters and use this result to prove that for homogeneous channels, the multiplexing scheme outperforms the packet duplication scheme, which is outperformed by the codeword splitting scheme. Analytical comparisons and numerical evaluations show that the codeword splitting scheme achieves the lowest average AoI when joint coding is feasible. In scenarios where joint coding is not feasible, whether message splitting or multiplexing results in a lower average AoI depends on the specific parameters.

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arXiv2025

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge in communication. Compared to state-of-the-art, the proposed approach combines analytical optimal BS precoding and ML-enabled RIS, has a high scalability to control more than 1000 RIS elements, has a low requirement for channel state information (CSI) in input, and addresses the mutual coupling between RIS elements. Beyond the considered problem, this work is an early contribution to domain knowledge enabled ML, which exploit the domain expertise of communication systems to design better approaches than general ML methods.

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arXiv2025

Neural Estimation of Information Leakage for Secure Communication System Design

Underestimating the leakage can compromise secrecy, while overestimating it may lead to inefficient system design. Therefore, a reliable leakage estimator is essential. Neural network-based estimators provide a data-driven way to estimate mutual information without requiring full knowledge of the channel or source distributions. In this work, we aim to scale the blocklength of a wiretap code such that the estimator can still feasibly operate. We propose an improved mutual information estimator based on the variational contrastive log-ration upper bound framework, tailored for both discrete and continuous variables. By using a mixture of Bernoulli experts parameterized by neural networks, the estimator is able to quantify information leakage in communication systems, which employ complex data processing like universal hash family. We further propose a method to utilize the proposed estimator to design the universal hash family for a wiretap code or secret key generation design. Simulation results show thatprior methods significantly underestimate the mutual information, particularly when using universal hash family for higher blocklengths ($n\gg$16). The proposed method can scale the blocklength up to 255, and we conjecture that the design can scale well to even higher blocklengths given adequate training data and model size. Additionally, we contend that our proposed estimator and adaptive hash design framework offer a practical approach for extending physical layer security considerations for wiretap channels into the finite blocklength regime.

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arXiv2025

Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems.

cs.IT↗

arXiv2025

RISnet: A Domain-Knowledge Driven Neural Network Architecture for RIS Optimization with Mutual Coupling and Partial CSI

Space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize SDMA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and high requirement for channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS with a dedicated neural network (NN) architecture RISnet, which has good scalability, desired permutation-invariance, and a low requirement for channel estimation. Moreover, we leverage existing high-performance analytical precoding scheme to propose a hybrid solution of ML-enabled RIS configuration and analytical precoding at BS. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and permutation-invariance.

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arXiv2025

Wrap-Decoding in Asynchronous Unsourced Multiple Access With and Without Delay Information

An asynchronous $\ka$-active-user unsourced multiple access channel (AUMAC) is a key model for uncoordinated massive access in future networks. We focus on a scenario where each transmission is subject to the maximal delay constraint ($\dm$), and the precise delay of each user is unknown at the receiver. The combined effects of asynchronicity and uncertain delays require analysis over all possible delay-codeword combinations, making the complexity of the analysis grow with $\dm$ and $\ka$ exponentially. To overcome the complexity, we employ a wrap-decoder for the AUMAC and derive a uniform upper bound on the per-user probability of error (PUPE). The numerical result shows the trade-off between energy per bit and the number of active users under various delay constraints. Furthermore, in our considered AUMAC, decoding without explicit delay information is shown to achieve nearly the same energy efficiency as decoding with perfect delay knowledge.

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arXiv2024

Power Efficient Cooperative Communication within IIoT Subnetworks: Relay or RIS?

The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this paper, we propose novel time division multiple access (TDMA) and frequency division multiple access (FDMA) communication protocols for cooperative transmission in IIoT subnetworks. These protocols leverage secondary access points (sAPs) as Decode-and-Forward (DF) and Amplify-and-Forward (AF) relays, enabling shorter cycle times while minimizing overall transmit power. A classification mechanism determines whether the highest-gain link for each IoT device is a single-hop or two-hop connection, and selects the corresponding sAP. We then formulate the problem of minimizing transmit power for DF/AF relaying while adhering to the delay and maximum power constraints. In the FDMA case, an additional constraint is introduced for bandwidth allocation to IoT devices during the first and second phases of cooperative transmission. To tackle the nonconvex problem, we employ the sequential parametric convex approximation (SPCA) method. We extend our analysis to a system model with reconfigurable intelligent surfaces (RISs), enabling transmission through direct and RIS-assisted channels, and optimizing for a multi-RIS scenario for comparative analysis. Simulation results show that our cooperative communication approach reduces the emitted power by up to 7.5 dB while maintaining an outage probability and a resource overflow rate below $10^{-6}$. While the RIS-based solution achieves greater power savings, the relay-based protocol outperforms RIS in terms of outage probability.

eess.SP↗

arXiv2024

Converse Techniques for Identification via Channels

The model of identification via channels, introduced by Ahlswede and Dueck, has attracted increasing attention in recent years. One such promising direction is message identification via channels, introduced by Ahlswede and Dueck. Unlike in Shannon's classical model, where the receiver aims to determine which message was sent from a set of $M$ messages, message identification focuses solely on discerning whether a specific message $m$ was transmitted. The encoder can operate deterministically or through randomization, with substantial advantages observed particularly in the latter approach. While Shannon's model allows transmission of $M = 2^{nC}$ messages, Ahlswede and Dueck's model facilitates the identification of $M = 2^{2^{nC}}$ messages, exhibiting a double exponential growth in block length. In their seminal paper, Ahlswede and Dueck established the achievability and introduced a "soft" converse bound. Subsequent works have further refined this, culminating in a strong converse bound, applicable under specific conditions. Watanabe's contributions have notably enhanced the applicability of the converse bound. The aim of this survey is multifaceted: to grasp the formalism and proof techniques outlined in the aforementioned works, analyze Watanabe's converse, trace the evolution from earlier converses to Watanabe's, emphasizing key similarities and differences that underpin the enhancements. Furthermore, we explore the converse proof for message identification with feedback, also pioneered by Ahlswede and Dueck. By elucidating how their approaches were inspired by preceding proofs, we provide a comprehensive overview. This overview paper seeks to offer readers insights into diverse converse techniques for message identification, with a focal point on the seminal works of Hayashi, Watanabe, and, in the context of feedback, Ahlswede and Dueck.

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arXiv2024

An Efficient Frequency Diversity Scheme for Ultra-Reliable Communications in Two-Path Fading Channels

We consider a two-ray ground reflection scenario with unknown distance between transmitter and receiver. By utilizing two frequencies in parallel, we can mitigate possible destructive interference and ensure ultra-reliability with only very limited knowledge at the transmitter. In order to achieve this ultra-reliability, we optimize the frequency spacing such that the worst-case receive power is maximized. Additionally, we provide an algorithm to calculate the optimal frequency spacing. Besides the receive power, we also analyze the achievable rate and outage probability. It is shown that the frequency diversity scheme achieves a significant improvement in terms of reliability over using a single frequency. In particular, we demonstrate the effectiveness of the proposed approach by a numerical simulation of an unmanned aerial vehicle (UAV) flying above flat terrain.

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