Papers

A fundamental challenge in RF sensing is that Doppler signatures observed by a link entangle the target's motion with the sensing geometry, resulting in limited applicability to unconstrained real-world settings. In this paper, we establish a new foundation for physically interpretable RF sensing that disentangles reflector speed from geometry, jointly recovering the speed, geometry factor, relative amplitude, and width of each dominant Doppler ridge. More specifically, we first develop a compact parametric representation of WiFi spectrograms and establish its low-dimensional structure through a systematic computer-vision analysis of a large and diverse human-activity dataset, thereby providing a tractable foundation for learning. Building on this representation, we then design a physics-informed autoencoder whose structured bottleneck and differentiable RF forward model enforce physically meaningful estimates of reflector speed and geometry. We further introduce a synthetic-to-real training framework, eliminating the need for real WiFi training data. We extensively validate the proposed framework under both known and time-varying geometries, using both independently generated synthetic test sets and 31 real WiFi experiments. The results demonstrate the superior performance in speed and geometry extraction, robustly recovering the underlying geometry, speeds, Doppler-ridge amplitudes, and ridge widths across all settings, while substantially outperforming the strongest baselines.

Electromyographic activity can dominate scalp electroencephalography across a broad frequency range and obscure neural structure that is relevant to analysis and brain computer interfaces. This paper evaluates P2P Trans, a conditional adversarial denoiser that combines a U shaped encoder decoder, multi head self attention at the bottleneck, cross attention guided skip fusion, and a PatchGAN discriminator. The reported experiment used EEGdenoiseNet segments represented as 1024 sample sequences, synthesized paired mixtures at ten signal to noise ratios from minus 7 to 2 dB, and evaluated 50000 training examples and 5980 test examples. Against five convolutional encoder decoder baselines, P2P Trans achieved the best mean correlation coefficient of 0.8645 and the lowest mean spectral RRMSE of 0.3596. Its mean temporal RRMSE was 0.5797 and did not lead the comparison, which indicates a tradeoff between spectral suppression and pointwise waveform fidelity. The advantage was strongest under severe contamination. The results support attention guided adversarial restoration as a useful low SNR approach, while also identifying the need for subject independent evaluation, direct one dimensional modeling, and downstream task validation.

Deploying artificial intelligence (AI) in high-stakes wireless applications such as autonomous transportation requires guarantees of reliable operation. Such guarantees can often be obtained by designing systems that act on a set of predictions via conservative policies catering to all possible outcomes within the set. For instance, in location-based beam selection, a base station may identify a set of plausible locations and select a beam that ensures high capacity over the set. The miscoverage probability of the set with respect to the true outcome (e.g., the true user location) then quantifies the outage probability, while the size of the set determines the final performance or resource budget (e.g., the transmission capacity). Conventional conformal prediction (CP) applies when the target miscoverage level is prescribed in advance. However, in practical wireless systems, prediction sets may instead be selected under prescribed operational constraints, requiring the resulting miscoverage probability to be quantified post hoc. This paper develops a formal statistical framework for the data-driven selection of prediction sets that provides a reliable estimate of the resulting miscoverage probability. The methodology builds on backward CP and probably approximately correct CP, yielding distribution-free reliability guarantees on the miscoverage probability. We apply the framework on three distinct wireless applications: narrowband interference detection, near-field localization, and codebook-based beam identification. Numerical results validate the reliability guarantees and show that the proposed post-hoc conformal methods achieve accuracy comparable to the naive probability-based approach across all considered applications.

Intelligent reflecting surface (IRS)-assisted mobile Internet of Things uplinks require joint control of channel aging, costly cascaded channel state information (CSI) acquisition, and coupled IRS/radio resources. This investigation develops a unified prediction-aware deterministic framework for partitioned IRS-assisted massive multiple-input multiple-output (mMIMO) uplinks. Direct channels are recursively tracked, whereas cascaded channels are selectively refreshed by differential semi-blind acquisition reusing the same unknown data block across a minimum-reflection baseline and DFT-coded IRS states. Moreover, finite-block acquisition covariances initialize reduced-order beam-domain prediction, which propagates only unit-beam means online and retrieves age-dependent covariances from precomputed tables. A mixed-integer nonconvex formulation captures net throughput, fairness, outage, uncertainty, switching cost, and sliding-window IRS service. The causal controller integrates covariance-floor actionability, feasible equal-size granularity, residual-feasible ownership, isolated-beam one-sweep refinement, same-block fresh-payload accounting, and service-first allocation using recomputed uncertainty-aware ZF rates and bidirectional receiver-consistent risk. Analysis establishes service-feasibility preservation, effective-channel error bounds, distribution-free reliability, and polynomial online scaling. In unseen environments, the predicted acquisition floor matches the practical semi-blind error, actionable recalibration reduces the mean recalibration rate by 31.5%, and IRS-assisted control achieves a 14.09% mean net-rate gain over direct-only transmission. Receiver-consistent allocation improves realized rate, while ablations support equal-size partitioning and single-sweep refinement; zero post-action service violations occur under nominal, mobility, and load-stress conditions.

The future 6G networks are expected to incorporate a proliferation of wireless services in diverse environments, which presents a significant challenge for information security. Conventionally optimization always requires recalculation and learning strategy often suffers poor generalization, which are thus incapable for the security provisioning with wide scenario coverage. In this paper, we propose an adaptive and robust learning framework that leverages a mixture-of-experts (MoE) architecture to achieve cross-scenario physical layer security guarantee. Specifically, we first select a few representative scenarios and establish the scenario-specific generative diffusion model (GDM)-based experts for secure transmission beamforming with artificial noise. The diffusion nature of experts learns the overall probability distribution of security strategy solution landscape and the Transformer-based denoising process enhances the ability to generalize across varying network configurations. Then, a lightweight gating network is constructed to identify the scenarios by engineering the channel features and select the most relevant experts. Finally, an attention-based combiner is introduced to synthesize the security proposals from the top-rated experts to produce a high-fidelity security strategy to cover the unseen scenarios. Simulation results demonstrate that the proposed GDM-based MoE framework can accurately recognize the scenarios and properly select the experts, maintaining near-optimal secrecy rates across a continuum of wireless scenarios and outperforming traditional single-model paradigms.

The problem of observing particle motion inside an optically opaque, dense bubbling fluidized bed is addressed. Differential-pressure measurements identify the bulk fluidization state but do not reveal where particles move or how their velocities vary with height. A commercial 60 GHz frequency-modulated continuous-wave radar is mounted above the facility to obtain depth-resolved measurements without optical access or an intrusive probe. Each measured depth cell contains an ensemble of 600 micrometre sand grains in the Rayleigh-Mie scattering transition region. Particle displacement, cell exchange, and changes in shadowing alter echo phases and amplitudes. Their coherent sum is therefore a stochastic complex signal, whose covariance and Doppler spectrum describe ensemble motion. A statistical spectral analysis retrieves total power, mean line-of-sight radial velocity, and Doppler spectral width over range and time. Eight-minute measurements reveal an expanding radar-detectable layer and alternating motion towards and away from the radar. Positive- and negative-mean regions coexist during developed fluidization and exchange their contribution to particle power. Most detectable spectra contain one dominant Doppler lobe; a small subset supports two distinguishable lobes, including cases with one negative and one positive mean velocity. The 5-s total-power trend has a correlation of 0.734 with measured pressure drop, whereas the local fitted spectral-width trend has a weaker correlation of 0.152. This work develops a range-resolved statistical Doppler measurement that reveals bidirectional particle motion and determines when multiple local velocity contributions are distinguishable.

We propose an adaptive predictive sampling and communication framework for real-time remote monitoring over packet-erasure channels. Rather than relying on a prescribed dynamical model or continuous sensing, our model-free method uses Taylor-expansion-based prediction to adaptively estimate local signal dynamics and proactively schedule the next sampling times. An analytical compensation factor accounts for packet losses to satisfy a prescribed reconstruction-error level. We develop adaptive sample-wise and episodic policies and evaluate their performance against event-driven and uniform sampling and communication baselines.

Scattered light is common in biomedical images, yet its removal remains challenging. The difficulty arises from three aspects: first, aligned scattered-light-free biomedical ground truth is often unavailable; second, scattering is coupled with weak illumination and sensor-induced noise; and third, many learning-based restoration models are computationally expensive for embedded devices in Internet of Medical Things (IoMT) scenarios. To address these issues, this paper proposes PP-Net, a hybrid physical-prior neural network for biomedical scattered light removal. The proposed method consists of three components: DFN-Net suppresses sensor-induced noise, ASAP estimates the scattering map and recovers a physics-based prior map, and GF-Net refines the prior map by fusing it with the denoised observation. To reduce the dependence on paired biomedical ground truth, a progressive synthetic training and cross-domain transfer strategy is developed. Experiments show that the physical-prior branch improves the peak signal-to-noise ratio (PSNR) by up to 1.26 dB on paired synthetic benchmarks. Under joint noise-and-scattering degradation, PP-Net improves PSNR by more than 10.8 dB and the structural similarity index measure (SSIM) by more than 0.62 compared with representative baseline methods. On real W2S biomedical images, the proposed method reduces the average Natural Image Quality Evaluator (NIQE) score by 43.3%. Edge deployment with RKNN conversion and INT8 quantization achieves an average inference latency of approximately 200 ms per $512\times512$ image over 360 test images. These results demonstrate that PP-Net provides an effective and deployable solution for microscopic imaging, endoscopic inspection, and edge-assisted biomedical analysis in IoMT scenarios.

As next-generation wireless systems migrate to high-frequency bands, extremely large-scale multiple-input multiple-output (XL-MIMO) is indispensable for combating severe path loss. However, the received path energy along the extremely large array aperture (ELAA) may exhibit spatial non-stationarity, rendering the conventional discrete Fourier transform (DFT) codebook inadequate for channel estimation (CE) due to its full-array angular representation. To address this mismatch, we propose a novel joint angle-subarray (JAS) codebook, where each codeword corresponds to an angular direction and a specific subarray. Spatially non-stationary CE is thus transformed into structured sparse support detection in the JAS domain. However, reliable support recovery remains challenging for conventional model-based methods. We therefore develop a signal processing and deep learning co-design framework, where data-driven JAS parallel support detection is followed by least-squares (LS)-based channel reconstruction. The matched filter response of each JAS grid is embedded into a high-dimensional feature vector. We then propose a Gram-attention module that incorporates the Gram matrix as a physical prior to guide feature purification, enabling the network to suppress leakage-related components while preserving support-relevant information of each grid. The purified features are passed to decoupled heads to infer the hierarchical sparse supports in the JAS domain. Finally, LS is performed over the low-dimensional dictionary constructed from the detected supports to retrieve path gains. Simulation results demonstrate that the proposed framework achieves the highest support detection F1-score and the lowest CE normalized mean square error (NMSE) among all baselines, while ablation studies verify the benefits of integrating the Gram-domain physical prior with data-driven representation learning.

This paper investigates the impact of in-phase/quadrature (IQ) imbalance on signal detection in affine frequency-division multiplexing (AFDM) systems over doubly dispersive Rayleigh fading channels. The bit error rate (BER) performance of AFDM under various IQ mismatch conditions is evaluated and compared with that of an ideal transceiver. Simulation results show that, under ideal conditions, BER decreases rapidly with increasing signal-to-noise ratio (SNR). However, IQ imbalance introduces image interference that spreads across the affine domain, causing significant performance degradation. The results further highlight the impact of practical IQ imbalance on AFDM systems across different quadrature amplitude modulation (QAM) orders, channel scenarios, and comparisons with orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency space (OTFS) systems. In particular, higher-order QAM performance degrades significantly, indicating the need for IQ imbalance compensation techniques or impairment-aware detection algorithms to ensure robust operation in practical AFDM systems.

Tensor-based modulation (TBM) schemes are a promising approach for unsourced random access (URA), where user separation relies on decomposing the received signal tensor via the canonical polyadic decomposition (CPD), typically computed with alternating least squares (ALS). Standard ALS, however, treats the factor matrices as unstructured and fails to exploit the discrete structure of the tensor sub-constellations. We propose DVB-ALS, a discrete variational Bayesian CPD framework with specific priors, tailored to a tensor structure with the corresponding encoding strategy, combined with iterative computation of an approximate posterior distribution. A discrete Gaussian mixture prior on one Grassmannian factor softly aligns the estimates toward the constellation points. The remaining factors are jointly modeled with a structured Gaussian prior whose posterior mean is constrained to the Khatri-Rao product manifold and posterior variance upper-bounded to prevent norm divergence during inference. The resulting closed-form coordinate ascent algorithm jointly estimates all latent factors and their uncertainties. We integrate DVB-ALS into DVB-TBM to design a complete URA receiver with single-user demapping, polar decoding with cyclic redundancy check (CRC) verification, and successive interference cancellation (SIC). Simulation results show significant gains over standard ALS-based decomposition and robust detection performance in URA settings, outperforming state-of-the-art schemes under high system loads.

Synthetic multispectral and hyperspectral data are increasingly used to address limited measurements, labels, and acquisition resources in spectral-imaging research. Yet the scope of the evidence produced by these experiments depends not only on the synthesis method, but also on sensing conditions, sample variability, data provenance, evaluation design, and downstream use. We present a literature-informed taxonomy for experiments using synthetic MSI/HSI data through spectral reconstruction and/or data augmentation, with a deliberate focus on agri-food research. The taxonomy is paired with a traversal procedure that connects available evidence, sensing constraints, experimental choices, and evaluation design to the forms of generalization that were actually tested, producing a compact experiment characterization record. Although primarily intended for studies with an existing research question, the same structure can also support earlier-stage planning when only an object of study, dataset, or sensing setup is initially defined. The framework was developed through question-driven reading, practical research experience, and iterative refinement against additional recent studies, and is offered as a basis for further use and refinement.

This work proposes a novel polar code design scheme using puncturing and extending aiming to improve the successive cancellation list decoding (SCLD) performance. The proposed code design is conducted with Reed-Muller (RM) codes called base codes. For a base code, puncturing is first performed to gain a degree of freedom, called transmission holes. To this end, we develop a puncturing scheme minimizing the loss in the minimum distance of the base code. Then, we propose a novel extending scheme to produce additional bits which are transmitted through the transmission holes without rate loss. The extending scheme repeats some coded bits and/or intermediate bits induced during the encoding of base code. The repeated bits are decided in such a way to maximize the SCLD performance by utilizing reinforcement learning (RL). Moreover, we introduce a novel idea to reduce the learning space of RL, which greatly expedites the design of extended bits. While the proposed design scheme assumes RM codes as base codes, we show that it can be applied to various types of polar code. Simulation results show that the polar codes designed with the proposed scheme have notably improved SCLD performance compared to existing polar code designs.

Joint information and energy transfer over inter satellite free space optical links is commonly based on explicit resource splitting, which couples communication reliability and energy delivery. This paper proposes a constant total power Stokes orthogonal architecture that conveys information through polarization while preserving the received optical power for photovoltaic conversion. Complementary polarization branches support balanced information detection through their AC components, while their DC components drive matched multi junction photonic power converters. The developed framework jointly characterizes pointing impaired propagation, bit error rate, outage, and nonlinear harvested power. The analysis establishes that the two information symbols only exchange the branch powers, leaving the unordered input power pair unchanged. Hence, the total harvested energy remains symbol invariant even under nonlinear conversion, without an explicit information energy power split or linear efficiency approximation. Numerical results confirm the analytical model, with the closed form BER approximation remaining within 0.43 percent of the exact evaluation over the examined pointing conditions. Compared with ideal lossless power splitting, the proposed architecture avoids the conventional BER versus harvested power tradeoff and provides an analytical baseline for power neutral joint information and energy transfer in inter satellite optical links.

This article introduces the LUneburg-lens-aided recoNfigurable Array (LUNA), an antenna architecture that unifies multiple-input multiple-output (MIMO) and network-controlled repeater (NCR) functionalities in the Luneburg lens-enabled hardware platform. A Luneburg lens, fabricated from graded-index dielectric materials, passively converts the radiation of a low-gain feed into a highly directional beam without active phase shifting, while a dense passive feed bank and a reconfigurable feed-selection network electronically switch the beam directions with minimal hardware complexity and power consumption. We commence by reviewing the basic principles and application history of Luneburg lenses in radar and wireless communications, which motivates their role in 6G-and-advanced networks. Then, we highlight how a Luneburg lens and a reconfigurable feed array construct both LUNA-MIMO and LUNA-NCR, where the lens and feed bank can be reused across functions and frequency bands. Case studies demonstrate that LUNA achieves the satisfactory spectral and energy efficiency with a few radio-frequency chains, and it also improves positioning performance for sensing tasks. Finally, some open problems and research directions are provided to inspire follow-up research on LUNA.

Fine tracking in inter-satellite optical links must compensate residual line-of-sight (LOS) motion despite sensor noise, vibration, model mismatch, and actuator latency. This paper develops a receiver-side physics-informed residual digital twin (PIR-Twin) that combines a nominal LOS transition, Gaussian optics, a nonlinear quadrant-photodetector observation, and extended Kalman filter synchronization. A normalized autoregressive ridge model learns the transition mismatch from independent calibration estimates, while the known delayed fine-steering-mirror correction remains separate from the physical LOS dynamics. The synchronized twin predicts the LOS at the command-actuation instant and enables proactive fine tracking. A controlled evaluation compares open-loop, reactive, nominal-predictive, and PIR-Twin operation using disjoint tuning, calibration, and test realizations. Under the nominal scenario, PIR-Twin reduces RMS pointing error by 14.1% relative to reactive tracking and also improves actuation-time prediction accuracy. Extended robustness tests show that the proposed method retains the lowest mean pointing error over a broad actuator-delay range and under increased time-varying LOS-motion amplitudes without residual-model retraining. The results demonstrate that correcting systematic short-horizon model mismatch, rather than relying on nominal extrapolation alone, is the key mechanism enabling effective predictive fine tracking.

Inter-satellite optical links demand high-precision fine tracking while preserving sufficient received power for data communication, yet these functions are often treated separately at the receiver. This paper proposes a dual-function optical receiver that integrates data reception and fine tracking on a shared, intentionally defocused receiver plane. The architecture combines a central data lens with an annular four-segment tracking detector, creating a fundamental tradeoff between datapower collection and angular-estimation capability. A scalar Fresnel wave-optical model is developed together with nonlinear two-dimensional calibration and a noise-aware worstcase angular-accuracy framework. The receiver geometry is then jointly optimized to maximize the guaranteed fine-tracking range subject to a minimum data-power constraint. Results demonstrate that appropriate co-design of the central aperture and defocus substantially enlarges the usable fine-tracking region while maintaining the required communication-path power. The proposed framework provides a receiver-level benchmark for analyzing and designing integrated communication-and-tracking architectures in future optical inter-satellite terminals.

Future 5G-Advanced and 6G networks increasingly reuse uplink communication waveforms for positioning and sensing. This raises privacy concerns, as user equipments (UEs) may unintentionally reveal precise timing information even when positioning services are not explicitly requested. While prior works show generic orthogonal frequency division multiplexing (OFDM) pilots can be manipulated to degrade time-of-arrival (ToA) estimation without compromising data links, this paper extends these concepts to a realistic 5G new radio (NR) up-link framework including Sounding Reference Signal (SRS), Demodulation Reference Signal (DMRS), Physical Uplink Shared Channel (PUSCH), and standardized 3GPP channel models. We investigate several UE-side privacy mechanisms: optimized pilot distortion, artificial noise, artificial multipath, and delay spoofing. Through 3GPP-compliant sample-level simulations, we assess their impact via localization, communication, and consistency-based detection metrics. The resulting analysis highlights the trade-offs among privacy, communication reliability, and detectability, providing key insights into waveform-level obfuscation for future integrated sensing and communication (ISAC) systems.

Conventional iterative receiver usually begins with channel estimation using sparse pilot observations and follows with data detection based on the channel estimates, and then updates channel estimation using decision feedback, and so on. In Artificial Intelligence (AI)-based receiver design, it is also crucial to make use of such progressively enriched data observations to enhance the generative channel estimation. However, the statistical characteristics and reliability of the data decisions always evolve with the channel estimation processes, which brings great challenges to the design of the overall learning framework and algorithms. In this paper, we propose Diff-Rx, an incrementally conditioned diffusion-based receiver with co-designed data detection and channel estimation, for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Specifically, we develop a condition-adaptive post-training method which enables the generative channel estimator to adapt to the conditioning inputs that are progressively enriched by soft data decisions, and also to implicitly align the pilot- and data-induced channel feature spaces to mitigate potential estimation errors. The threshold-free soft decisions provide smooth condition updates without condition-specific reliability tuning. We further develop a conditional diffusion transformer that is capable of performing robust channel estimation under noisy observations and various pilot patterns while reducing the conventional multi-step diffusion generation to one single step. Simulations on both statistical and site-specific ray-tracing channels show that, Diff-Rx exhibits consistent gains across different noise levels, pilot densities and modulation schemes, and works well at a pilot density as low as 1/32 while achieving significant improvement in channel estimation and data detection performances.

Cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM) is widely adopted as a reference waveform for integrated sensing and communication (ISAC). However, its high peak-to-average power ratio (PAPR) requires power amplifier back-off, thereby reducing the available sensing link budget. Frequency-modulated OFDM (FM-OFDM) provides a constant-envelope signal with 0 dB PAPR and has demonstrated reliable communication performance in high-mobility scenarios, but its sensing characteristics remain largely unexplored. This paper characterizes FM-OFDM as a sensing waveform based on its bandwidth, ambiguity function, sidelobe behavior, and Doppler estimation capability. The analysis shows that its data dependent sidelobe floor is incoherent and decreases through across frame integration, whereas the corresponding CP-OFDM floor remains unchanged. Consequently, under equal occupied bandwidth and transmit power, frame-level integration reverses the single-symbol performance ordering and provides FM-OFDM with approximately 20 dB of additional dynamic range for weak-target detection. Moreover, the zero-delay cut of the FMOFDM ambiguity function is shown to be deterministic and independent of the transmitted data realization, in contrast to linearly modulated waveforms, while a closed-form expression is derived for the sidelobe floor away from the zero-delay cut. Since the nonlinear mapping between the data symbols and time-domain samples prevents the direct application of the conventional CP-OFDM range-Doppler processing chain, a weighted phase-increment Doppler estimator is developed. The proposed estimator enables closed-form prediction of the sensing floor and its crossover point with a minimum computational complexity.

In this paper, a very compact 2 x 2 wideband beamsteering array with monocone antennas is designed and validated for NATO Band III (1350-2400 MHz) tactical communications with interference suppressing ability. The small 4 x 4 x 4 cm$^3$ size of the array enables placing it for example on vehicles with limited space. The array is manufactured and measured, and the interference suppressing capability thereof is studied with a minimum variance distortionless response beamformer and a phase-only beamformer in the presence of a single interferer. It is shown that the designed antenna array can significantly improve the signal-to-noise ratio over 10 dB for angles separated over 15$^\circ$ from the signal-of-interest, and over 20 dB for separation larger than 43$^\circ$ across the operational band.

AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are insufficient. In this context, we study a sequential decision maker process that jointly decides which actions to take and when to probe given access to an arbitrary state prediction model. We propose online conformal state probing (OCSP), an action and probing policy that certifies worst-case reliability levels without relying on distributional assumptions. OCSP is designed to provably control the missed query error (MQE), i.e., the fraction of instances where probing would have been beneficial, while minimizing the probing rate. OCSP can be applied to existing pre-trained value-based control policies without requiring retraining or fine-tuning. We validate OCSP through numerical simulations to verify theoretical guarantees and to assess performance trade-offs as a function of the calibration of the state predictor.

Classical coherent binary phase-shift keying (BPSK) reception over additive white Gaussian noise (AWGN) channels is analytically well understood, and the optimum hard-decision detector is known. Therefore, the aim of using neural receivers in this study is not to replace the classical AWGN-BPSK detector. Instead, the AWGN-BPSK setting is deliberately selected as a theoretically verifiable benchmark for analyzing how compactly neural receiver architectures can represent a known decision behavior. This paper compares multi-layer perceptron (MLP) and Kolmogorov–Arnold Network (KAN) receivers using a multi-SNR Nyquist-rate BPSK dataset. The models are evaluated using bit error rate (BER), mean squared error (MSE), and trainable parameter count. Beyond reporting accuracy alone, this work emphasizes a common-loss parameter-efficiency perspective: when two receivers reach a comparable practical BER or loss region, the receiver with fewer parameters is more attractive for real-time deployment. The results show that both MLP and KAN receivers reproduce the expected AWGN-BPSK detection trend, while a compact KAN configuration reaches a comparable operating region with substantially fewer trainable parameters. In particular, the KAN receiver with 3281 parameters achieves a test BER of 2.45 x 10^-4, while the MLP baseline with 8513 parameters achieves a test BER of 2.50 x 10^-4. This corresponds to approximately 61.5% fewer trainable parameters at a comparable BER operating point. This reduction is important for real-time neural receivers because it affects memory footprint, parameter access, inference latency, energy consumption, and hardware feasibility on embedded, software-defined radio, FPGA, ASIC, and edge communication platforms.

Indoor positioning benchmarks rarely co-locate multiple technologies with high-accuracy ground truth, limiting cross-method and cross-technology comparison. We evaluate four range-based positioning methods on fused Ultra-wideband, Bluetooth Low Energy, and WiFi ranges recorded at 48 reference points in an industrial facility with available ground truth. The evaluated methods range from least-squares positioning, with and without robust weighting, through a Bayesian grid filter to a ResNet regressor that we report under two protocols, interpolation and spatial generalisation. Per-technology ranging quality spans roughly two orders of magnitude, which caps any geometry-based solver that weighs anchors equivalently. On fused input the grid filter is competitive with deep learning on median error, while the ResNet's advantage concentrates on the upper tail. Holding reference points out of training increases the learned regressor's median error several-fold, exposing a spatial-generalisation penalty invisible under random splits. The dataset and evaluation code are publicly available.

This paper investigates the joint protection of confidential data and legitimate-user directional information in integrated sensing and communication (ISAC) networks. We consider a multiuser downlink in which passive multi-antenna eavesdroppers (Eves) attempt to decode confidential signals while exploiting legitimate-user reflections for unauthorized angular sensing. To address both threats, we develop a robust covariance-design framework that jointly limits Eve decoding, maintains the transmitter's accuracy in estimating the Eves' directions and shifts the dominant passive-sensing response toward prescribed deceptive directions. A Bayesian angular prior and the corresponding Bayesian Cramer-Rao bound (BCRB) characterize the transmitter's eavesdropper-angle estimation accuracy. Angular uncertainty is represented through geometry-consistent samples, such that each candidate Eve direction jointly determines the corresponding transmitter-Eve channel, user-Eve bearing, and deceptive direction. The resulting design balances worst-user secrecy, sensing accuracy, sensing privacy, and deception power. Robust Eve-decoding constraints are handled through finite sufficient conditions with intersample margins, while continuous ghost dominance is enforced using interval sum-of-squares (SOS) constraints. The resulting nonconvex problem is addressed through successive convex approximation (SCA) and semidefinite relaxation. Numerical results show that the proposed design effectively preserves secrecy and sensing privacy under Eve-angle uncertainty, provides controlled angular deception in single- and multiple-Eve scenarios, and outperforms benchmarks.

Integrated sensing and communications (ISAC) spans radiofrequency (RF), visible-light, optical-fiber, power-line, and acoustic systems, yet technology transfer across these domains remains underexplored. This article examines bidirectional knowledge and technology transfers centred on RF-ISAC. It shows how VLC motivates power-domain sensing, fiber enables differential referencing and distributed processing, and PLC inspires topology-aware monitoring and adaptive probing. Conversely, RF-ISAC contributes joint signal design, nuisance suppression, and weak-return recovery. The article highlights transferable principles, required adaptations to improve ISAC, and information potentially lost across physical media.

Distributed integrated sensing and communication (DISAC) networks require multi-target tracking methods whose per-node computation and inter-base-station communication remain bounded as both the network and target populations grow. Existing multisensor fusion methods provide a strong theoretical foundation, but scalability is seldom treated as the primary design objective. We develop a grouped-measurement belief-propagation multi-target tracking (MTT) method and combine it with a proposed event-triggered target-handover protocol. When a tracked target is predicted to become observable at another base station, the owner transfers a predicted track message while retaining its local copy. Under bounded local workload and neighbor degree, we show that all variants of the proposed handover methods have network-size-independent per-node loads. Simulations demonstrate that handover reduces boundary track loss relative to uncoordinated processing and approaches coordinated-processing accuracy with lower communication overhead.

Sixth-generation (6G) applications require radio access networks (RANs) to support reliable communication and seamless sensing across their operating regions. In coupled RAN deployments, shared downlink transmitting and uplink receiving sites constrain the network's ability to accommodate asymmetric links and different sensing geometries. De-Coupled RAN (DC-RAN) separates these functions, allowing independently deployed and coordinated base stations to extend uplink and downlink communication and sensing coverage. How this flexibility translates into balanced communication and sensing services, however, remains insufficiently explored. This article revisits the evolution from coupled to DC-RAN from the perspective of service-balanced integrated sensing and communication (ISAC). It examines how architectural choices affect the availability of both services, with communication-sensing coverage symmetry capturing their spatial alignment under application-specific quality requirements. Practical challenges include preserving communication consistency, maintaining sensing continuity, and coordinating distributed resources. Two case studies illustrate how DC-RAN can support coverage symmetry alongside consistent communication, and how complementary observations can sustain continuous and accurate sensing. These examples inform a discussion of future research toward service-balanced ISAC.

Multi-Object Tracking (MOT) is essential for EdgeAI perception systems, where accurate object localization and reliable identification enable safe decision-making. Singleagent MOT suffers from occlusions, sensor noise, and partial scene understanding in complex real-world scenarios. While multi-agent systems improve robustness by exploiting shared information, they introduce redundant measurements that lead to false data associations, and still struggle to capture nonlinear object dynamics. To address these challenges, we propose CDKFTrack, a Cluster-aware Data-Driven Kalman Filtering framework for Cooperative 3D MOT. The proposed method first fuses multivehicle 3D LiDAR detections through a Graph Laplacian-based formulation. Then, a cluster-aware redundancy reduction scheme groups spatially related detections and selects representative observations to reduce duplicate inputs to the tracker. The resulting detections are processed by a data-driven Kalman filter that learns object motion dynamics from data, reducing dependence on predefined linear motion assumptions. Furthermore, a wavelet-based temporal refinement module leverages the multiresolution decomposition property of wavelets to attenuate shortterm positional fluctuations and improve trajectory continuity. To the best of our knowledge, CDKF-Track is the first framework to jointly address detection-level fusion redundancy and learnable motion modeling in cooperative 3D MOT. Experimental results on the real-world V2V4Real dataset indicate that CDKF-Track achieves up to 27.99% improvements in tracking accuracy over state-of-the-art multi-agent MOT methods.

Periodic basic safety messages (BSMs) exhibit temporal correlation as vehicle motion evolves continuously over time. This temporal structure provides predictive information about the current BSM and motivates prediction-aided recovery after a decoding failure. For low-density parity-check (LDPC)-coded transmission, a straightforward approach maps a predicted vehicle state to a candidate bit sequence and uses it as decoder prior information. However, a point prediction does not capture the uncertainty of the prediction. The resulting hard point prior assigns fixed confidence to each predicted bit and may introduce strong erroneous evidence when the prediction is incorrect. This paper proposes an uncertainty-aware prediction-aided LDPC recovery framework for vehicle-to-everything (V2X) safety messages. Instead of using a deterministic point prediction, the proposed method represents the current vehicle state by a predictive distribution. The distribution is propagated through BSM field quantization and serialization to obtain bit-level probabilities, which are converted into prior log-likelihood ratios (LLRs) whose magnitudes reflect prediction reliability. After an initial cyclic redundancy check (CRC) failure, these probabilistic priors are combined with the original channel observations and used in a second LDPC decoding pass. The proposed method preserves the standardized message representation and channel-coding procedure. Simulation results show that the proposed probabilistic priors outperform the hard point prior across different motion predictors. At $E_b/N_0=0.75$~dB, the proposed method recovers 87.27% of the messages that fail the initial decoding attempt.