arXiv subjects

Rafael F. Schaefer

Publications and source records attributed to Rafael F. Schaefer.

At least 19 recordsLinked to original sources

arXiv2026

Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach

Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off between the two objectives, particularly in the short blocklength regime. This paper proposes an autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings. We derive a modified Cramér-Rao bound for multi-target delay estimation and analyze the maximum-likelihood decoding rule for noncoherent communication under correlated fading. These results reveal structural connections and trade-offs between communication and sensing objectives in waveform design. Based on this analysis, the AE learns waveform representations that jointly optimize both functionalities, with a tunable parameter controlling the trade-off. Simulation results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.

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arXiv2026

Secure Deterministic Identification with Feedback and Sensing over State-Dependent Wiretap Channels

Identification differs from classical transmission in that the receiver determines whether a particular identity was sent rather than recovering a message. We study secure deterministic identification over state-dependent discrete memoryless wiretap channels with private noiseless feedback and a sensing distortion constraint. The encoder has no local randomness and uses only the identity and past feedback. A symbol-wise input restriction guarantees the sensing constraint. We show that the secure deterministic identification capacity equals its non-secure feedback counterpart when the admissible main channel has positive ordinary transmission capacity and H(Y|Z,X=x)>0 for some fixed admissible input x. This equality does not require positive classical wiretap secrecy capacity. For achievability, noiseless feedback generates common randomness for identification and enables secret-key extraction by privacy amplification. The extracted key one-time-pad encrypts a short identification tag, whose reliable transmission requires only a sublinear number of additional channel uses. We also present an alternative construction in which feedback-generated randomness replaces the local randomization of a stochastic wiretap encoder. We further prove that secure identification capacity vanishes whenever non-secure identification capacity is zero. We also identify a class of channels with input-independent wiretapper observations that achieves full non-secure identification capacity even when the private conditional feedback entropy is zero, illustrated by a binary state-dependent wiretap channel. Thus, feedback can support both identification and secrecy without local encoder randomization while satisfying sensing constraints.

cs.IT↗

arXiv2026

Confidence-Guided Adaptive Beam Selection for Vision-Aided Beam Tracking in mmWave Systems

Vision-aided beam tracking reduces mmWave beam-training overhead by predicting beam indices from camera observations. Fixed top-$K$ selection, however, assigns the same candidate beam budget to all predictions, ignoring uncertainty variations across samples and horizons. This letter proposes confidence-guided adaptive candidate selection: predictor logits are temperature-calibrated, and the smallest top-ranked beam set whose cumulative probability reaches a target threshold is retained under a maximum budget. On DeepSense~6G Scenario~8, over five random seeds and at matched overhead, the policy improves candidate set accuracy by up to 3.80 percentage points, increases normalized received power by 0.32 percentage points, and reduces outage probability by 2.06 percentage points without retraining the predictor.

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arXiv2026

Litter-Masked Block z-Channel: Designing Cover Distributions Against Public-Design Observers

Networking defenses against traffic analysis fill idle slots with cover traffic, yet the physical layer carrying it still hands upper layers the erasure-only model of a silent-idle link, leaving the cover content unexploited. We shape that content: the idle-slot waveform is drawn from the codebook complement under a design distribution, the \emph{litter distribution}, chosen to defeat a public-design passive observer while the upper layer keeps its block z-channel model. The maximum a posteriori (MAP) receiver reduces to a single threshold on the log-likelihood ratio between the best codeword and the prior-weighted litter aggregate; its errors are silent corruptions the upper layer cannot detect and erasures that retransmission absorbs. Receiver design is thus one-dimensional, minimizing erasure under a cap on silent corruption, and the joint design a Stackelberg game against a Kullback-Leibler (KL) indistinguishability metric, solved by an alternating convex relaxation and corroborated by an independent reinforcement-learning policy. On a rate-$3/4$ $(16,12)$ parity-check (LDPC) code against an observer who knows her own channel but whose channel the transmitter knows only statistically, shaped litter cuts the observer's Stein detection exponent by $41$ to $48\%$ relative to uniform cover, flat over a $10$~dB span and in the activity rate. Below the point where the legitimate link turns reliable, no cover distribution admits an operating point; at that point shaping costs about one percent of throughput, and nothing measurable $2$~dB above it.

cs.IT↗

arXiv2026

Block Erasure Channel and Block z-Channel with Bounded Decoders and Finite Blocklength

Block error rate is a standard metric in finite blocklength (FBL) communication, yet it conflates two qualitatively different failure modes: block confusions, where the decoder selects a wrong codeword, and block erasures, where it declares a loss. Higher-layer protocols treat physical-layer failures as erasures, but this cross-layer assumption lacks FBL justification. We derive a rigorous upper bound and a companion lower-side estimate on the block confusion probability (BLCP) and block erasure probability (BLEP) for bounded-distance decoders over additive white Gaussian noise (AWGN) channels at finite blocklength, recasting the coding problem as a geometric sphere packing one. We analyze the sensitivity of these bounds to blocklength and signalto-noise ratio, characterize the envelope of the upper bound, and derive closed-form Chernoff approximations. Extending the model to idle transmission blocks, we bound the false alarm probability (FAP) and show that a block z-channel abstraction emerges from the bounded-decoding geometry. Numerical results confirm that confusion and false alarm probabilities lie far below the error rate constraint, providing quantitative physicallayer support for the block erasure channel and block z-channel abstractions assumed in protocol design.

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arXiv2026

Quantization Limitations of Leakage Suppression in Self-Calibrating Monostatic Integrated Sensing and Communication MIMO Systems

Power leaking directly from transmitting into receiving radio-frequency chains is a key challenge in the realization of monostatic sensing applications with multi-antenna communication front-ends, to which a promising solution is digitally precoding transmitted signals for improved leakage suppression. While digital transmit precodings perform well in theory, real-world deployments typically exhibit severely degraded leakage suppression. This work investigates quantization noise as a primary factor limiting the performance of such precoding schemes. A closed-form solution predicting the impact of quantization noise on the performance of arbitrary digital joint leakage estimation and leakage suppression precodings is derived, numerically analyzed, and validated in a hardware testbed.

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arXiv2026

MAP-Based Task-Oriented Precoding for Multiuser Communication

We propose a task-oriented multiuser wireless communication framework for distributed classification based on a MAP-driven system design under wireless channel impairments. \textcolor{black}{By deriving tractable approximations of the MAP error bound}, the proposed approach enables the design of learning-based feature extraction and precoding strategies. Unlike existing approaches that optimize intermediate reconstruction, information-theoretic, or feature-separability objectives, the proposed formulation directly optimizes objectives derived from the MAP decision error, thereby providing a more direct connection to the final classification performance. Simulation results demonstrate that the proposed schemes achieve higher classification accuracy than their counterparts, with lower or comparable computational complexity.

cs.IT↗

arXiv2026

Efficient Feedback Design for Unsourced Random Access with Integrated Sensing and Communication

We consider an unsourced random access (URA) system enhanced with a feedback mechanism that serves both communication and sensing tasks. While traditional URA systems do not incorporate feedback, we propose a novel feedback signal design that announces the decoding status of users and simultaneously enables target sensing. To design this dual-purpose feedback, we introduce a modified projected gradient descent algorithm that minimizes a weighted combination of communication and sensing errors. Simulation results show that the proposed feedback design outperforms the state-of-the-art feedback design in the URA literature. Furthermore, we illustrate the trade-off between communication and sensing capabilities, offering valuable insight into balancing these two tasks.

cs.IT↗

arXiv2026

Secret-key-based physical layer security for feedback-aided unsourced random access

This work introduces security for unsourced random access (URA) via a physical-layer security approach. To achieve confidentiality, the proposed system opportunistically exploits intrinsic features of feedback-aided URA without altering its original structure or operational characteristics. As a result, the system preserves URA's efficiency, including low delay and minimal signaling overhead, while ensuring secure communication. To secure transmission, each user generates a secret key from a feedback signal broadcast by the BS in a previous transmission round, which depends on the BS-user channel and can thus be treated as private. Each user then encrypts its data using the secret key before transmission. Along with the encrypted data, only the parity bits of the LDPC-encoded key are transmitted, enabling secret key recovery at the legitimate receiver via Slepian-Wolf decoding with side information. We propose a receiver algorithm to recover both the encrypted data and the encoded secret key at the legitimate receiver. We further present a theoretical analysis to derive analytical error probabilities for both the legitimate receiver and the passive eavesdropper, as well as to quantify the additional load imposed by the security measures on the URA system. It is shown, based on both theoretical analysis and simulation results, that meaningful secrecy is achieved with only negligible extra overhead compared to the standard URA system.

cs.IT↗

arXiv2026

Condition-Wise Sinkhorn Drifting for One-Shot Learned Channel Simulation

Learned communication systems may evaluate stochastic channel surrogates millions of times inside differentiable training loops, making diffusion-style reverse sampling expensive. This paper proposes condition-wise Sinkhorn drifting, a one-shot channel surrogate that preserves the transmitted symbol and transports only the conditional output laws $p(y\mid x)$. We formulate a conditional Sinkhorn objective over repeated outputs at the same transmitted symbol and train the generator with finite-sample barycentric velocities followed by detached particle regression. Experiments on additive white Gaussian noise (AWGN), Rayleigh fading, solid-state power amplifier (SSPA) nonlinearity, and a compact tapped-delay-line (TDL) channel compare direct drifting, joint Sinkhorn drifting, condition-wise Sinkhorn drifting, conditional denoising diffusion probabilistic modeling (DDPM), denoising diffusion implicit modeling (DDIM), and Wasserstein generative adversarial network (WGAN) references. Within the evaluated one-shot drifting-family variants, condition-wise Sinkhorn is strongest under conditional diagnostics and symbolic-coding checks, while diffusion remains strongest on the hardest downstream symbol-error-rate (SER) curves. The resulting operating point is a condition-preserving one-shot simulator for settings where repeated channel calls make diffusion-style sampling too costly.

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arXiv2026

Integrated Sensing and Communications for Unsourced Random Access: Fundamental Limits

This work considers the problem of integrated sensing and communications (ISAC) with a massive number of unsourced and uncoordinated users. In the proposed model, known as the unsourced ISAC system (UNISAC), all active communication and sensing users simultaneously share a short frame to transmit their signals, without requiring scheduling with the base station (BS). Hence, the signal received from each user is affected by significant interference from numerous interfering users, making it challenging to extract the transmitted signals. UNISAC aims to decode the transmitted message sequences from communication users while simultaneously detecting active sensing users and estimating their angles of arrival, regardless of the identity of the senders. In this paper, we derive an approximate achievable result for UNISAC and demonstrate its superiority over conventional approaches such as ALOHA, time-division multiple access, treating interference as noise, and multiple signal classification. Through numerical simulations, we validate the effectiveness of UNISAC's sensing and communication capabilities for a large number of users.

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arXiv2026

Semantic Communication Through the Lens of Context-Dependent Channel Modeling

Semantic communication has emerged as a promising paradigm for next-generation networks, yet several fundamental challenges remain unresolved. Building on the probabilistic model of semantic communication and leveraging the concept of context, this paper examines a specific subclass of semantic communication problems, where semantic noise originates solely from the semantic channel, assuming an ideal physical channel. To model this system, we introduce a virtual state-dependent channel, where the state-representing context-plays a crucial role in shaping communication. We further analyze the representational capability of the semantic encoder and explore various semantic communication scenarios in the presence of semantic noise, deriving capacity results for some cases and achievable rates for others.

cs.IT↗

arXiv2026

Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications

The application of deep learning to the area of communications systems has been a growing field of interest in recent years. Forward-forward (FF) learning is an efficient alternative to the backpropagation (BP) algorithm, which is the typically used training procedure for neural networks. Among its several advantages, FF learning does not require the communication channel to be differentiable and does not rely on the global availability of partial derivatives, allowing for an energy-efficient implementation. In this work, we design end-to-end learned autoencoders using the FF algorithm and numerically evaluate their performance for the additive white Gaussian noise and Rayleigh block fading channels. We demonstrate their competitiveness with BP-trained systems in the case of joint coding and modulation, and in a scenario where a fixed, non-differentiable modulation stage is applied. Moreover, we provide further insights into the design principles of the FF network, its training convergence behavior, and significant memory and processing time savings compared to BP-based approaches.

cs.IT↗

arXiv2026

Confusions and Erasures of Error-Bounded Block Decoders with Finite Blocklength

This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

cs.IT↗

arXiv2025

Secrecy-Driven Beamforming for Multi-User Integrated Sensing and Communication

This paper proposes a secure integrated sensing and communications (ISAC) framework for multi-user systems with multiple communication users (CUs) and adversarial targets, where the design problem is formulated to maximize secrecy rate under joint sensing and communication constraints. An efficient solution is presented based on an accelerated fractional programming method using a non-homogeneous complex quadratic transform (QT), which decomposes the problem into tractable subproblems for beamforming and artificial noise (AN) optimization. Unlike conventional artificial noise strategies, the proposed approach also exploits AN to enhance sensing while avoiding interference with legitimate users. Simulation results show significant gains in secrecy rate, communication reliability, and sensing accuracy, confirming the effectiveness and scalability of the proposed framework.

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arXiv2025

A Secure ISAC Waveform Design Framework via Random Frequency and PRI Agility

This paper presents a novel framework for enhancing the security, data rate, and sensing performance of integrated sensing and communications (ISAC) systems. We employ a random frequency and pulse repetition interval (PRI) agility (RFPA) method for the waveform design, where the necessary random sequences are governed by shared secrets. These secrets, which can be pre-shared or generated via channel reciprocity, obfuscate critical radar parameters like Doppler frequency and pulse start times, thereby significantly impeding the ability to perform reconnaissance from a passive adversary without the secret key. To further introduce enhanced data throughput, we also introduce a hybrid information embedding scheme that integrates amplitude shift keying (ASK), phase shift keying (PSK), index modulation (IM), and spatial modulation (SM), for which a low-complexity sparse-matched filter receiver is proposed for accurate decoding with practical complexity. Finally, the excellent range-velocity resolution and clutter suppression of the proposed waveform are analyzed via the ambiguity function (AF).

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arXiv2025

Adversarially Robust MIMO Physical Layer Authentication for Non-Stationary Channels

We propose an adversarially robust physical layer authentication (AR-PLA) framework tailored for non-stationary multiple-input multiple-output (MIMO) wireless channels. The framework integrates sequential Bayesian decision-making, deep feature extraction via contrastive learning, and generative adversarial modeling to simulate adaptive spoofers. Unlike conventional methods that assume stationary channels or independent observations, our approach explicitly accounts for temporal and spatial correlations, line-of-sight (LoS) blockages, and dynamic spoofing strategies. A comprehensive analytical characterization of the authentication performance using both 2-state and 3-state hidden Markov models (HMMs) with moving-average online adaptation is also provided, with closed-form recursions for loglikelihood ratios, detection probabilities, and steady-state approximations, which demonstrate significant robustness improvement over classical sequential authentication schemes.

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arXiv2025

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming

We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy rate under constraints balancing both communication and sensing requirements. To enhance fairness among users, an entropy-regularized fairness metric is introduced within the problem framework. We then propose a solution employing an accelerated quadratic transform (QT) with a non-homogeneous bound to iteratively solve two subproblems, thereby effectively optimizing the overall objective. This approach ensures robust security and fairness in resource allocation for ISAC systems. Finally, simulation results verify the performance gains in terms of average secrecy rate, average data rate, and beam gain.

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