Papers

Open-source ray tracing (RT) frameworks are increasingly used for site-specific channel modeling due to their reproducibility and extensibility. In non-stationary mobility scenarios, repeated RT evaluations can generate different diffuse propagation paths without preserving their correspondence, limiting a consistent representation of their temporal evolution. To address this limitation, we extend diffuse scattering modeling in Sionna RT using persistent surface-associated scattering points. Their positions remain fixed, while their contributions evolve according to changes in the propagation geometry and visibility. The proposed approach is validated using empirical radio channel impulse response measurements in an urban vehicular scenario at 5.9 GHz. Compared with the standard Sionna RT baseline, the proposed model shows improved agreement with measurements in the delay and Doppler domains.

This paper investigates when and why stacking programmable metasurfaces into Stacked Intelligent Metasurfaces (SIMs) improves linear transformation synthesis at a fixed phase-control budget. The analysis shows that algebraic Jacobian rank alone is insufficient to characterize practical synthesis, which also depends on the strength and balance of the sensitivity directions generated by the available controls. A layer-resolved analysis of the cascade model explains how inter-layer propagation can generate complementary sensitivity directions. The relevance of this mechanism is then assessed using an S-parameter multiport model that includes electromagnetic coupling and multiple scattering. Numerical results in near- and far-field sensing scenarios show that distributing the same control resources across multiple layers can strengthen weak transformation directions, improve Jacobian conditioning, and substantially enhance synthesis accuracy. These findings explain why increasing depth can offer benefits that are not obtained by simply enlarging a single programmable surface, and identify layer complementarity and Jacobian conditioning as useful criteria for SIM architecture design.

This work presents a dual-polarized rate-splitting multiple access (RSMA) design for millimeter-wave (mmWave) or frequency range 2 (FR2) communications. Focusing on a practical implementation, we consider the specific scenario of a single transmitter serving two receivers (users) within the same time, frequency, and code resource block. The transmitter and each receiver are equipped with a pair of dual-polarized antennas. We first present the system formulation and compute the achievable rate expressions for discrete constellations. Based on these expressions, we propose and evaluate two receiver architectures: one based on successive interference cancellation (SIC) and a non-SIC decoder. Both designs exhibit comparable performance, and the combination of polarization diversity and RSMA provides the system with significant flexibility to adapt to different channel conditions when serving both users, while the non-SIC decoder enables a simpler hardware implementation. We then present a prototype implementation of the proposed design, operating at a carrier frequency of 25 GHz, and evaluate it by measuring the symbol error rate (SER) of the different data streams under various signal-to-noise ratio (SNR) and polarization diversity conditions. The obtained measurements show good agreement with software simulations, thereby validating the system implementation. To the best of our knowledge, this is the first mmWave RSMA prototype reported in the literature and the first to exploit polarization diversity. Furthermore, given the selected carrier frequency, the proposed design may also find applicability in the upper portion of the envisioned FR3 band (7-24 GHz).

A single low Earth orbit (LEO) satellite offers almost no spatial degrees of freedom. Its two polarizations, however, can be exploited to carry a second layer to a handset, which 5G New Radio (NR) non-terrestrial networks do not use yet. The gain of this layer in downlink spectral efficiency (SE) is mapped over the footprint of a satellite at 330 km altitude above Munich transmitting at 2 GHz. The channel is modeled as a cascade of Jones factors for the phased-array payload, Faraday rotation, a dual-polarized land mobile satellite (LMS) channel with a line of sight, and a standardized handset antenna element at three attitudes. For a handset with boresight pointing to zenith, ideal precoding selects rank 2 with a probability of at least 0.95 within about 340 km of the nadir point at a reference signal-to-noise ratio (SNR) of 6 dB. With closed-loop precoding, such a handset on the nadir track is in rank 2 during 99% of a pass. At 13 dB, rank selection with closed-loop precoding raises the mean SE over the footprint by 49%, and upright handsets gain about 31%. Their gain is limited to the positions where they face the satellite, since the element gain is low behind the handset. At 3 dB, these gains fall to 15% and 7%.

A generative site-specific beam prediction (Beam-STeP) method is proposed. Spatial priors from site-specific propagation and mobility information are jointly exploited with temporal priors from historical beam observations for beam prediction. To address the beam prediction ambiguity from multimodal spatio-temporal priors, the generative model is adopted, where a history-conditioned diffusion model is proposed to learn the multimodal conditional distribution of future beamforming vectors. Multiple beam candidates are sampled from the distribution using a denoising diffusion implicit model (DDIM). The candidate with the highest received power is then selected for transmission. Simulation results across three urban scenarios demonstrate that: i) Beam-STeP generally achieves higher normalized beamforming gains than the baselines under the same probing budget, particularly as beam prediction progresses; ii) site-specific propagation and mobility priors improve recursive beam prediction performance; and iii) DDIM sampling enables a flexible gain-latency trade-off.

Single-channel radio-frequency (RF) signal separation is important for interference mitigation when a signal of interest overlaps with a co-channel interferer in time and frequency. Classical matched filtering and LMMSE receivers are limited under structured non-Gaussian interference, while diffusion and score-based methods often require iterative sampling or optimization. We propose FlowSep-RF, a rectified flow matching framework that learns a clean-endpoint transport from the received mixture to the signal of interest. The model combines time-domain IQ samples with frequency-domain features and recovers the separated waveform using one endpoint velocity evaluation. We evaluate FlowSep-RF on QPSK mixtures with OFDM-BPSK, OFDM-QPSK, and RFChallenge CommSignal2 interference against classical, discriminative, and generative baselines. FlowSep-RF achieves the lowest average BER and MSE among evaluated baselines across all three settings with one-step inference.

Spatial reception under directed molecule transport requires balancing useful molecule capture against intersymbol interference (ISI). To characterize this balance, we study a fully absorbing spherical receiver that counts absorption events only within a prescribed cap. Using the known joint first-hitting law under aligned drift, we derive exact cap impulse, cumulative, and eventual responses. We perform stable cumulative evaluation. We then assess the derived responses through particle simulations and time-step comparisons, and demonstrate that our conditional-timing analysis explains when opposite drifts of equal magnitude can favor the same wider aperture. We also show that for the studied geometry and a symbol duration of $0.15$ s, the aperture that maximizes the signal-to-interference difference (SID) increases from $29.13^\circ$ at zero drift to $61.86^\circ$ at a drift magnitude of $40$ $\mu$m/s. These results (i) connect joint arrival-time and location statistics to spatial reception design, (ii) characterize the tradeoff between useful molecule capture and ISI, (iii) support SID-based aperture selection for a given drift and symbol duration, and (iv) quantify the SID loss incurred by retaining the zero-drift optimal aperture for the same symbol duration.

Integrated sensing and communication (ISAC) equips cellular networks with the dual capability of data transmission and environmental sensing by sharing spectrum, waveforms, and radio hardware. While network-side sensing via transmission-reception points (TRPs) has been widely investigated, the use of user equipment (UEs) as sensing receivers introduces additional bistatic viewpoints and can improve target observability. UE-assisted operation is nevertheless constrained by small UE apertures and by waveform processing, especially when data-bearing communication signals are reused for sensing. The present paper investigates theoretical limits and operating conditions for UE-assisted passive target positioning, with emphasis on hybrid operation that fuses TRP–TRP and TRP–UE measurements. A unified OFDM-based signal-and-noise model is developed to cover both positioning reference signals (PRS) and communication waveform reuse with modulation-dependent noise enhancement. Based on this model, closed-form Fisher-information expressions and Cramer-Rao lower bounds are derived for delay and angular parameters and are mapped to positioning error bounds (PEBs) for two estimator classes: time-angle positioning based on TDOA + DoA and delay-only 3D ellipsoid positioning based on excess-delay/TDOA measurements. Monte-Carlo simulations across reference SNR, subcarrier spacing and occupied bandwidth, modulation order, radar cross section (RCS), sensing mode, and positioning algorithm identify regimes where UE participation and hybrid sensing provide significant gains and quantify the associated estimator-to-bound trade-offs.

Sensor-based beam prediction is usually evaluated against exhaustive beam training, leaving open when sensing adds useful information beyond a deployed tracker's link feedback and when it should be withheld. We separate system gain from the marginal value of sensing in action-restricted causal replay of recorded 64-beam sweeps from four vehicular DeepSense 6G scenarios. The replay exposes only serving-beam and explicitly probed candidate powers and counts probes and model invocations separately. The proposed controller combines a scheduled post-setup re-sweep with probe-verified sensing admission and blacklist backoff. In three scenarios where feedback-only tracking is within 0.25 dB of the recorded-sweep oracle, the controller stays close to feedback-only in mean loss while rarely invoking the model, whereas always-on sensing performs worse. In the sole high-innovation scenario, the controller's system gain is 0.82 dB (95% confidence interval [0.30, 1.40]) over the plain feedback-only tracker and 0.15 dB over a probe-limited re-sweeping reference, with fewer probes than either. Sensing and the re-sweep both correct setup lock loss. At the native decision interval, enabling sensing in the same controller yields no statistically resolved loss reduction but cuts probing by 21%. In this scenario, accurate sensing reduces loss relative to the sensor-disabled controller under emulated link noise, staleness or longer decision intervals. Weak or delayed sensing can instead increase loss despite probe verification. These exploratory results characterize sensing as a conditional substitute for RF probing or a source of loss reduction, depending on the limitations of link feedback and on the accuracy and timeliness of sensing.

Sensing-aided beam prediction could cut the beam-training overhead of millimeter-wave vehicular links, and two design choices could strengthen a radar-only predictor: a Doppler-resolved input and knowledge distilled from a camera during training. We measure what each is worth in received power rather than in top-1 accuracy, whose differences of a few points amount to only tenths of a decibel. We use real DeepSense 6G measurements, mainly at one roadside site, and one convolutional-recurrent predictor family evaluated open-loop up to 0.67 s ahead. Adding a range-Doppler map to the range-angle map lowers the mean beam-selection loss by 0.28 dB (95% bootstrap interval [0.14, 0.41]). The gain comes from the Doppler content, not from the extra input, and replicates, as pre-specified, at a second base station, although holding the last measured beam beats both predictors there. Cross-modal knowledge distillation from a motion-mask vision-radar teacher, by contrast, changes received power by -0.01 dB ([-0.07, +0.06]). The tested teacher, student, fusion and distillation variants show the same null in the metric each was scored on. Where distillation helps, in an exploratory run with few training passes, a radar-only teacher trained on all training passes reproduces most of the gain. For this predictor family and the full training set, the measurable gain at the link comes from Doppler-resolved radar content; no tested route for transferring training-only visual information adds a resolved gain.

Movable antenna (MA) has emerged as a promising technology for enhancing wireless communication performance by flexibly reconfiguring wireless channels via antenna movement. In this paper, we propose an MA relay-enhanced multiuser communication system that adopts a half-duplex decode-and-forward (DF) relay scheme. The relay node is equipped with an MA array, and a buffer is employed to enhance the end-to-end transmission performance. Specifically, we formulate an optimization problem to maximize the minimum average achievable rate among multiple users over a given transmission period by dynamically optimizing the positions of MAs, transmit power, and beamforming matrices, subject to the constraints on antenna movement, inter-antenna spacing, and total energy budget for antenna movement and transmission. To address this non-convex problem with highly coupled variables, we propose a reinforcement learning (RL)-based algorithm, where a twin delayed deep deterministic policy gradient (TD3) algorithm is utilized to optimize the antenna positions and power allocation, while the transmit and receive beamforming solutions are obtained through closed-form expressions in each time slot. Numerical results validate the efficacy of the proposed RL-based algorithm and demonstrate the superiority of the proposed MA relay-enhanced multiuser communication system over the conventional fixed-position antenna (FPA)-based relay system and other MA-based benchmarks.

Near-field beamfocusing confines radiated power into a localized three-dimensional focal spot, making the analytical characterization of radial and angular resolution critical for sixth-generation (6G) multi-user systems, yet existing models treat the focal resolution as a fixed geometric outcome rather than a tunable design variable. Based on the Angular Representation (ASR) approach, this paper presents a resolution framework to design contiguous focal regions across both the radial and angular domain for steered multi-user systems. By extending the definition of a tunable inter-beam overlap $\rho$, we derive closed-form expressions for the radial and angular focal boundaries and extents, alongside the steering-dependent minimum focal distance $d_{0,\text{min}}$ and maximum steering angle $\theta_{\text{max}}$. Using these boundaries, iterative synthesis algorithms are developed to construct uniform and non-uniform radial and angular beam arrangements. Finally, by analyzing both isolated and joint radial-angular configurations, we quantify the fundamental trade-offs between $\rho$, achievable user capacity, inter-user interference, and system sum-rate across varying steering angles.

Accurate bistatic radar cross-section (RCS) characterization requires near-field measurements over multiple dimensions, which, under typical near-field sampling requirements, can lead to long measurement durations for electrically large radar targets. This paper presents a near-field-to-far-field transformation based on an equivalent surface-current reconstruction using a-priori information about the target to reduce the number of newly acquired near-field samples. It is assumed that the prior target and the target under test (TUT) differ by localized geometrical modifications. Their complex differential near field is reconstructed using Tikhonov-regularized least squares QR factorization (LSQR) method. The reconstructed differential far- field contribution is coherently combined with the prior far-field contribution to estimate the bi-static RCS of the TUT. The method is preliminarily evaluated using simulations of a complex target at 6 GHz. The results indicate that, for the investigated simulation case, prior information can be used to thin out the sampling grid by 90% while maintaining close agreement with the simulation baseline.

Training hardware by gradient descent requires a gradient to be computed using a separate backwards pass. If a circuit is reciprocal, meaning every connection is symmetric, the backward pass is the same network run a second time with the error as its input. The reciprocal tau-cell, a weak-inversion log-domain circuit, makes this practical in standard CMOS. Each cell is a current-mode leaky integrator with a built-in ReLU; each connection is two transistors whose signed strength is a ratio of bias currents. The equilibrium of the network is the solution to a convex program, and latching the cells that are off will hardware linearise the circuit, making its second run exactly linear. Transistor-level SKY130 simulations show that the second run gives the gradient within 1.9%; that hardware linearisation permits large, noise-robust error signals; that a 13-cell network learns the Iris data set; and that learning in situ restores mismatched circuits on which software-trained weights fail, from 53-63% to 88-96% test accuracy.

In hybrid monostatic-bistatic orthogonal frequency-division multiplexing (OFDM) sensing, each sensing node receives a superposition of monostatic and bistatic target returns, whose phase-noise (PN) characteristics differ fundamentally due to oscillator sharing. While monostatic sensing benefits from partial PN cancellation through the common transmit-receive oscillator, bistatic sensing involves independent oscillators and is therefore more strongly affected by PN. In this paper, we characterize the joint residual differential PN statistics of these superimposed components in a centralized two-node sensing system and quantify their impact on target localization via theoretical lower bounds considering three different receivers: (i) PN-free ideal receiver, (ii) PN-aware receiver that exploits the joint PN statistics, and (iii) PN-ignorant receiver that neglects PN.

Motion capture is increasingly important in healthcare, rehabilitation, and sports applications. However, conventional vision-based systems require camera infrastructure and line-of-sight conditions, limiting their use in daily environments. This paper proposes a location-independent platform for motion capture, based on an array of IMU (inertial measurement unit) sensor nodes without individual wiring or onboard batteries, enabled by two-dimensional communication and power delivery over conductive fabric. Pipeline processing in each sensor node, combined with a token-passing protocol, enables collision-free and stable data acquisition at 50 Hz. Real-time three-dimensional reconstruction of flexible objects is further demonstrated by applying Hermite spline interpolation to orientation data from grid-aligned IMUs. Against a cylinder of known radius, the reconstructed surface deviates by 2.60 mm RMS over the 225 mm span of the array. The reconstruction is formulated for the quasi-static regime, and this letter empirically validates it in that regime. The hardware platform and communication protocol implemented in this work enable further investigation toward dynamic motion capture textiles.

Bilinear Compressive Security (BCS) is a lightweight encryption scheme that combines compressed sensing with a fresh random convolution filter to improve resistance against key-recovery attacks under key reuse. Previous work derived information-theoretic guarantees for BCS by relating key identifiability to phase retrieval under the assumption that an adversary has access to the full ciphertext distribution induced by the random filter. In practice, however, an adversary only observes finitely many encryptions, leaving the security of BCS in the finite-sample regime largely unexplored. In this paper, we study key recovery from only finitely many observations. We first develop a covariance-based attack that estimates the linear measurements from repeated encryptions of the same plaintext and subsequently recovers the secret measurement matrix. We analyze the estimation procedure and derive finite-sample guarantees together with corresponding sample-complexity bounds. We further investigate the complete attack experimentally and compare its observed recovery behavior with the previously established information-theoretic identifiability results. Our experiments show two qualitatively different recovery regimes on the structure of the measurement matrix. For dense matrices, including Gaussian matrices, the number of measurements required for successful recovery appears to grow linearly with the signal dimension. This differs from the worst-case behavior suggested by information theory. In contrast, for matrices with sparse rows, we observe a quadratic growth consistent with the theoretical bound. These results suggest that the sparsity structure of the measurement matrix plays an important role in the security of BCS and should be taken into account when selecting parameters and designing practical instances of the scheme.

Background: Estimating subvoxel tissue fractions from magnetic resonance fingerprinting (MRF) is challenging. Conventional dictionary matching and segmentation pipelines can propagate errors from intermediate parametric maps into tissue fraction estimates. Purpose: We introduce DeepPV-MRF, a deep learning framework that directly estimates voxel-wise tissue fractions from raw MRF signals. Methods: A cascaded spatial-temporal network was trained on subject-derived Bloch simulations with radial k-space undersampling. The network combines temporal encoding and spatial U-Net refinement to estimate cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) fractions without intermediate T1/T2 maps. We compared DeepPV-MRF with partial-volume dictionary matching (PV-DM) and pseudoinverse estimation (PV-Pinv) using simulated and in vivo data. Results: DeepPV-MRF outperformed both methods across acquisition settings. In single-shot simulations, CSF/WM/GM mean absolute errors were 0.0418/0.0744/0.0757, versus 0.1281/0.2443/0.2958 for PV-DM and 0.2021/0.2793/0.2630 for PV-Pinv. Corresponding structural similarity values were 0.960/0.916/0.922. In vivo results showed sharper anatomical boundaries and smoother tissue transitions, while cross-shot analysis indicated improved consistency across volunteers. Conclusions: DeepPV-MRF enables accurate and efficient direct tissue fraction estimation from MRF signals, particularly with limited shots. Further validation is needed before clinical deployment.

Power amplifier (PA) nonlinearity is a critical transmitter impairment in the upper mid-band (7 GHz to 24 GHz), designated as Frequency Range 3 (FR3) in emerging 6G standardisation. The 3GPP PA model baseline, TR 38.803 Annex A, was derived from 2.1 GHz and 28 GHz measurements and has not been revisited yet for FR3 standards work. This paper addresses that gap with characterisation measurements of multiple PA devices-under-test (DUTs), driven into gain compression in the 7 GHz and 15 GHz bands with 100 MHz and 200 MHz bandwidth. Memoryless polynomial and Generalized Memory Polynomial (GMP) behavioral models are extracted by least-squares regression and compared with 3GPP models spanning FR1 and FR2. The GaN PA DUT is modelled best at a higher nonlinear order and shows markedly stronger phase distortion than the wideband PA. The memory terms of the GMP model measurably improve the Normalized Mean Square Error (NMSE) at all operating points. An orthogonal frequency-division multiplexing (OFDM) ambiguity function analysis from the Integrated Sensing and Communication (ISAC) literature, using the extracted models, illustrates how PA compression raises the sidelobe floor of the sensing response. These results provide a reference for PA impairment modelling in FR3, directly applicable to ISAC system design. All model coefficients and measurement data are publicly available [1].

This paper proposes a Full Duplex (FD) framework for safeguarding Integrated Sensing and Communications (ISAC) against a passive multi-antenna Eavesdropper (Eve). The adversary is modeled as both a communication and sensing threat, seeking to decode uplink data streams and estimate the surrounding scene. Eve's threat is quantified through two worst-case requirements over a location uncertainty region: a minimum secrecy rate for data confidentiality and a minimum eavesdropping Cramer-Rao Bound (CRB) for sensing privacy. We derive the eavesdropping information measures governing both data interception and scene inference, showcasing that transmitting Artificial Noise (AN) can degrade Eve's capabilities through the inverse interference-plus-noise covariance. Exploiting the underlying structure of the problem, we characterize finite-power local and robust feasibility conditions for target secrecy-privacy pairs. The analysis showcases that AN is fundamentally subspace-limited: increasing power is beneficial only along Eve's communication channel and sensing-derivative directions illuminated by the effective AN subspace. Finally, we formulate an AN power allocation problem that is solved via monotone branch-and-bound, and numerical evaluations quantify the AN power required to guarantee joint secrecy and sensing privacy.

This paper studies resilient angle-constrained leader–follower formation control and localization of multi-agent systems under asynchronous denial-of-service (DoS) attacks, which block individual sensing and communication links. To preserve the well-posedness of angle-based localization, an all-or-nothing triangle integrity protocol (ANTIP) is proposed to convert edge-wise attacks into triangle-wise ones, and DoS attacks are classified into localizability-maintained and localizability-broken attacks. First, with relative position measurements over attack-free links, resilient formation tracking laws are designed for leaders with constant velocities and with time-varying velocities comprising translational, rotational, and scaling motions. These laws guarantee asymptotic tracking and finite-time recovery of tracking, respectively, when the duration of localizability-broken attacks is suitably bounded. Second, with only angle measurements and communications, a resilient simultaneous localization and formation control (RSLAF) scheme is developed. It combines a finite-time distributed observer, which estimates the exosystem matrices and states under asynchronous DoS attacks, with a perturbation-based localization law that prevents localization from getting stuck at degenerate configurations. The theoretical results are rigorously proved and illustrated by numerical simulations.

Recent advances in deep learning have significantly improved underwater acoustic vessel recognition. However, existing publicly available datasets are typically limited in scale, hindering large-scale representation learning and downstream task evaluation. To address this limitation, we introduce ONCship, a large-scale underwater acoustic dataset comprising 4521 hours of curated recordings from Ocean Networks Canada (ONC) hydrophones and Automatic Identification System (AIS) data. We evaluate the proposed dataset across three tasks: self-supervised representation learning, vessel-type classification, and MMSI-Level Vessel Classification. Experiments on DeepShip and ShipsEar demonstrate the effectiveness of ONCship for acoustic representation learning, with joint pre-training on ONCship and AudioSet achieving a classification accuracy of 62.45% on DeepShip through linear probing. Furthermore, incorporating ONCship as auxiliary training data improves downstream vessel-type classification performance. Beyond conventional vessel-type classification, we further investigate MMSI-Level Vessel Classification, treating each vessel as a distinct identity. Experimental results show that models fine-tuned with different loss functions achieve Top-1 accuracies exceeding 53% and Top-5 accuracies approaching 69%, demonstrating the feasibility of distinguishing individual vessels through MMSI-Level Vessel Classification. Overall, ONCship provides a valuable resource for underwater acoustic representation learning and vessel recognition, while its rich vessel-level metadata enables further exploration of MMSI-Level Vessel Classification.

High-frequency and broadband line-of-sight multiple-input multiple-output (LoS-MIMO) is a key solution for massive space-to-Earth data downlinks. However, in low Earth orbit (LEO) scenarios, it faces spatial rank deficiency from time-frequency coupling correlation and clock deviations from distributed oscillators. This paper establishes a dynamic effective channel throughput (ECT) framework incorporating waterfall characteristics of forward error correction. A hardware-friendly OFDM-based transceiver architecture with subband-adaptive truncation and dynamic timing recovery to achieve robust spatial decoupling is proposed. Through rigorous closed-form derivations, the equivalent signal-to-interference-plus-noise ratio (SINR) under joint errors is quantified, and an optimal scheduling algorithm combining truncation with variable coding and modulation is proposed to maximize ECT. Simulations confirm that the design circumvents outages caused by spatial notches with low complexity, stably enhancing the potential average throughput of a single dual-polarized beam to over 100 Gbps, providing a reliable theoretical foundation and baseband solution for next-generation high-throughput satellites.

Integrated sensing and communication (ISAC) with cloud radio access networks (C-RAN) unifies two foundational technologies for the Internet of Things (IoT), enabling both high-data-rate transmission and accurate environmental awareness. This paper investigates an uplink C-RAN system where multiple remote radio units (RRUs) jointly serve user equipment devices (UEs) and sense a target. We demonstrate that only a subset of RRUs is typically needed to meet sensing requirements, while energy is potentially conserved by deactivating redundant sensing RRUs. Motivated by this, we aim to improve the system's energy efficiency (EE) by jointly optimizing RRU activation, user association, and power allocation, while ensuring localization accuracy through the Cramér-Rao lower bound (CRLB). To address this problem, we propose a model-based iterative algorithm that integrates optimal user association, decision tree-based selection of sensing RRUs, and fixed-point power allocation. This algorithm is further used to train a graph neural network (GNN) framework, ISAC-GNN, to overcome scalability and computational complexity limitations. Our theoretical analysis ensures performance guarantees, while a semi-supervised training strategy enhances stability and generalization. Simulation results demonstrate that ISAC-GNN reduces inference time by 67% relative to the model-based approach, enabling real-time implementation for ISAC system resource management. In addition, ISAC-GNN allows scalable deployment across various system scenarios without retraining the model.

As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with little or no direct communication between them. Take autoencoder-based data compression as an example: the ability to independently choose architectures and train encoder and decoder models, while still ensuring they work together, could fundamentally transform how such models are built, deployed, and updated. In this work, we introduce a novel approach that leverages topological regularization to align the latent spaces of two independently trained autoencoder models, entirely eliminating the need for coordination during training. We provide a conditional theoretical analysis showing how the structural agreement encouraged by our regularizer can translate into approximate geometric compatibility between independently learned representations. Reconstruction experiments validate this approach empirically, substantially narrowing the interoperability gap across multiple datasets.

The scarcity of real-world ground penetrating radar (RealGPR) data is a major barrier to the development of deep learning (DL) methods for GPR data processing. Although simulated GPR (SimGPR) datasets can be used to train DL models, differences in hardware configuration and system parameters between SimGPR and RealGPR create a sim-to-real domain gap that leads to poor generalization of DL models to RealGPR measurements. To address this gap, we present a physics-based far-field GPR domain transfer (FFDT) method to transfer far-field SimGPR data to the corresponding RealGPR-domain data in the same scenario. In FFDT, SimGPR and RealGPR are modeled using angle-dependent transfer functions that capture three-dimensional antenna radiation characteristics. These transfer functions are then combined with ray tracing to enable the sim-to-real data transfer. Extensive studies across diverse objects and soil conditions demonstrate that FFDT-transferred data closely match RealGPR ground truth. To further demonstrate its value for RealGPR-domain dataset generation, FFDT is implemented to build a RealGPR-domain dataset for training a DL model for clutter removal. The model achieves robust performance on RealGPR measurements. With one-time SimGPR and RealGPR system calibration, FFDT enables RealGPR-domain dataset generation without requiring task-specific RealGPR field measurements for DL model training or fine-tuning, thereby addressing the RealGPR data scarcity bottleneck and supporting reliable development and deployment of DL methods for real-world GPR applications.

Decarbonizing cellular networks requires coordinating communication-resource and battery operation with time-varying electricity prices and carbon intensity. We jointly optimize base station and battery operation under a carbon-adjusted electricity price and traffic-demand constraints, and solve the mixed-integer nonconvex problem through a two-stage decomposition exact under a minimum-power rule. Using California price and carbon-intensity data, we show that carbon-aware scheduling reduces emissions most when low-price and low-carbon periods are misaligned. Above a carbon price of roughly 100-300 USD/ton, battery operation shifts from cost-driven to carbon-driven, cutting emissions by up to 57.8% relative to a cost-only schedule.

Any AIS transmitter can claim any vessel's identity (MMSI), because nothing in the protocol authenticates it; only the transmitter's analog hardware reveals who is really sending. We treat AIS traffic as streams of bursts keyed by claimed MMSI and accumulate physical-layer evidence with a sequential test. Each burst is scored by its k-nearest-neighbor distance to enrolled fingerprints in the feature space of a classifier whose weights are fixed after training, calibrated into a tail probability, and accumulated by a geometric-entropy-minimization cumulative-sum (GEM-CUSUM) statistic. The same test rejects unknown transmitters when scored against all enrolled vessels, and exposes insider spoofs when scored against the claimed vessel. A threshold refreshed from genuine traffic alone keeps it at a stated false-alarm rate as fingerprints drift. On AIS in-phase/quadrature (I/Q) recordings captured over 13 days at the Istanbul Strait (Bosphorus), the detector rejects unseen vessels at least 10 days after enrollment. Evaluated over 50-burst trials with every vessel weighted equally, it reaches an area under the ROC curve (AUROC) of 0.956 (95% confidence interval 0.913-0.989), where the best score computed from one burst at a time reaches 0.899. A threshold refreshed from the first quarter of the enrolled vessels' traffic holds 5.3% false alarms (3.0-9.2% across five independently trained encoders) at 88.8% detection. Scored against the claimed MMSI, enrolled transmitters spoofing another vessel are rejected at 0.991, a threat that open-set tests cannot see (AUROC 0.500, chance level). The lightweight Transformer GLFormer, reimplemented from its paper (no code is public) and retrained on the same data, reaches 0.652 in the same detector with its published in-phase input and 0.849 when given the same I/Q input as our encoder.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enable programmable wave control on both sides of a surface, extending wireless coverage beyond single-sided operation. However, practical STAR-RIS demonstrations in literature have narrow bandwidths, because it is challenging to achieve wideband reflection and transmission simultaneously from the same aperture. To overcome this limitation, a wideband STAR-RIS architecture is proposed based on shared-aperture polarization conversion and diagonal switching topology. Here, multiple resonances enable wideband magnitude responses and mirror-symmetric current reversal maintains $180^{\circ}$ phase difference in both reflection and transmission modes. Accordingly, a diagonal four-branch unit cell configuration is developed, where the tailored top patch directs the received wave either through a pair of blind vias connected by a buried strip to the shared top aperture for reflection, or to the bottom ring patch using a through via for transmission. Based on the proposed design, a 10 by 10 STAR-RIS prototype is fabricated and experimentally characterized. The measured 3-dB bandwidths reach 19.8% in reflection mode and 21.9% in transmission mode. Near-field measurements show average signal enhancements of 19.8 and 22.6 dB for reflection and transmission, respectively, over the wide frequency range of 6.8-8.4 GHz. Far-field measurements further demonstrate average received-power enhancements of 11.2 dB in reflection and 11.9 dB in transmission. To the best of the authors' knowledge, this work presents the first experimentally demonstrated wideband STAR-RIS covering the 7.125-8.4 GHz band within the emerging 6G FR3 spectrum.

This paper presents a lightweight semantic com- munication framework for diabetic retinopathy (DR) grading from fundus images pertaining specifically to the APTOS2019 dataset. The framework includes semantic encoder designed using ResNet34, U-Net, MobileNetV2, and EfficientNet-B0 architectures. Further, the trained models are used to train the same framework for IDRiD dataset using transfer learning approach. Such a framework will enable the telemedicine sector in the early detection of DR and prevent total blindness. The results reveal that for APTOS, task-oriented semantic systems can achieve satisfactory robust performance (more than 0.8 weighted F1 score for ResNet34 and EfficientNet-B0) across different channels. In particular, considering the trade-off between complexity, latency, throughput, and F1 score, MobileNetV2 is the best model with weighted F1 score of 0.76-0.78 for both task and reconstruction- oriented modules and end-to-end latency of only 8.162 ms.