Papers

Channel estimation for extremely large-scale uniform planar arrays (UPAs) requires exploiting near-field channel structure to reduce pilot overhead. With a specifically devised channel representation matrix, the near-field channel exhibits a 2D sparse structure, which a conventional 1D block partition might divide into multiple groups. This work develops a 2D block orthogonal matching pursuit (2D-BOMP) estimator that exploits this structure through rectangular coefficient grouping. We establish sufficient recovery conditions for noiseless and noisy measurements and compare the sufficient pilot-length requirements of 1D and 2D partitions. Numerical simulations demonstrate the estimation benefits of 2D-BOMP in estimating the near-field channels of large-scale UPAs, compared with the conventional 1D-BOMP as well as the polar-domain based OMP method.

This paper delves into the intricate aspects of Reconfigurable Intelligent Surfaces (RIS) in the antenna design. It investigates the properties of the array factor of RIS, highlighting the significance of phase shifts in establishing of the reflect path. Additionally, the paper investigates spatial positions requiring identical phase shifts, nullification of the array factor, the count of total maxima, second maximum determination (amplitude of the second lobe), and the calculation of the 3dB bandwidth. They are very useful for RIS-assisted radio communication systems.

Wireless propagation modeling is foundational to the evaluation and design of modern communication systems. To achieve accurate wireless propagation prediction, recent learning-based approaches leverage scene geometry and materials, demonstrating promising generalization across transceiver locations and unseen scenes. However, these approaches remain largely confined to a fixed configuration of array size, receiver orientation, and transmit beam. In this work, we introduce RFWeave, a learned wireless propagation model designed to generalize across scenes, transceiver locations, and transceiver configurations. To accommodate varying array structures, RFWeave incorporates variable-length transceiver tokens that represent antenna elements individually. Furthermore, to capture the geometry between transmit and receive antenna elements, RFWeave aggregates their pairwise spatial relations into propagation tokens. A configuration-conditioned decoder predicts the spatial spectrum and received power. RFWeave is trained on a large-scale simulated dataset spanning 10,000 indoor scenes and more than 5 million channel records under diverse transceiver configurations. Extensive evaluations show that RFWeave achieves state-of-the-art performance across all evaluated generalization settings, improving spatial spectrum structural similarity (SSIM) by up to 15.7% and reducing received power mean absolute error (MAE) by up to 35.4% over the strongest baselines on unseen scenes. Additional experiments on measured DICHASUS data and in beam management further demonstrate RFWeave's broader applicability to real-world channels and downstream wireless tasks.

Ultrasound image quality depends on both the probe geometry and the transmit and receive processing, but engineers usually design the two separately. Physics-based simulators (Field II, k-Wave, MUST/SIMUS) compute the radio-frequency channel data but give no gradients, so design studies must use grid or black-box searches that become slow as the number of parameters increases. We present a fully differentiable end-to-end pipeline for plane-wave ultrasound imaging, with synthetic speckle phantoms, an analytical frequency-domain RF simulator, delay-and-sum beamforming and an extensible image-quality objective. The element pitch, the transmit and receive apodization and the global speed of sound are differentiable parameters of one computation graph, so one reverse-mode pass gives their exact joint gradients. Studies of each parameter rank the design parameters: the optimized pitch decreases the grating lobes, and the speed-of-sound autofocus gives clear contrast and resolution gains. Over 15 test phantoms, the optimized pitch increases the cyst gCNR from 0.55 to 0.66 (about 20%), and the speed-of-sound correction adds a further 0.05. The lateral point-spread function agrees with Field II within about 10%, and on 49 real CUBDL plane-wave acquisitions from five institutions, the framework finds the global speed of sound with a mean absolute difference of 16.6 m/s from the published corrections and phantom speed of sound.

Reconfigurable intelligent surfaces (RISs) have emerged as a key enabler for next-generation wireless networks, offering control over the propagation environment through nearly passive reflecting elements. When combined with spatial modulation (SM), an RIS shapes a finite set of noise-free signal points at the receiver, and the error rate performance under maximum-likelihood (ML) detection is governed by the smallest pairwise Euclidean distance among these points. In this paper, we propose a low-complexity RIS phase shift design algorithm for RIS-assisted SM under practical discrete phase shift control. The proposed method targets the minimum Euclidean distance directly through a reduced greedy search over the discrete set of RIS phase shifts, in contrast to existing approaches that align RIS phase shifts to maximize the effective channel gain and therefore do not directly address the dominant error events. The search effort is controlled by a small set of integer parameters, providing a tunable trade-off between bit error rate (BER) performance and computational cost. We evaluate the proposed method against continuous and quantized phase-alignment baselines, including a complexity-matched comparison against the most directly comparable benchmark. Numerical results show that the proposed method achieves a significant SNR gain over quantized phase alignment under the same discrete hardware model, and this advantage persists under imperfect channel state information.

In unmanned aerial vehicle (UAV) networks, integrated communication and sensing (ISAC) is emerging as a key enabler to support extended and continuous communication and sensing capabilities for sixth-generation (6G) services among UAVs operating in swarms. In this paper, a comprehensive framework is presented to investigate the ISAC coverage probability of a reference UAV-base station (BS) in a UAV network. A fixed number of UAV user equipment (UAVUEs) and UAV-BSs are deployed in an aerial corridor modeled as a finite cylinder. The three-dimensional (3D) binomial point process (BPP) is used to model both the 3D spatial locations of the UAV-UEs and the UAV-BSs. By adopting realistic 3D third generation partnership project (3GPP) antenna patterns for both the UAV-UEs and UAV-BSs, the UAVs' boresight direction of the maximum antenna gain is modeled as a random unit vector. All UAV-BSs have sensing capabilities, so an arbitrary yet fixed reference UAV-BS acts as a monostatic radar to acquire sensing measurements associated with a target UAV-UE at a given time snapshot while the target UAVUE communicates with the reference UAV-BS. Consequently, considering both clutter and communication interference, exact analytical expressions for ISAC coverage probability are derived. Low complexity baseline analytical expressions are also derived by ignoring clutter interference and directional characteristics of the UAV-UEs. Finally, motivated by the vision for intelligent UAV networks, a DeepSets-based model is developed and trained to estimate the optimal location of the reference UAV-BS that maximizes ISAC performance given a spatial deployment of the UAVs. To the best of our knowledge, this is the first work to rigorously investigate ISAC performance in UAV networks while incorporating the directionality characteristics of the UAV antennas.

Electromagnetic information theory (EIT) brings electromagnetic physics into wireless channel analysis and offers a physical view of channel structures hidden by conventional models. This paper studies channel instability near electromag- netic singular states, where small environmental perturbations can cause large variations in the end-to-end channel. Using resolvent-based spectral analysis, we develop an electromag- netic multiple-input multiple-output (MIMO) channel model that decomposes the end-to-end channel into a smooth regular component and singularity-associated components. The analysis shows that the singularity-associated components become more sensitive to environmental perturbations near singular states. The proposed model also reveals a limitation of conventional water filling (WF): power allocation based only on instan- taneous channel gains does not account for such instability. We therefore estimate singularity information from broadband MIMO channel state information (CSI) and develop a singularity- aware transmit covariance optimization method. Simulations demonstrate numerical stability of the estimated pole frequencies and illustrate the environmental sensitivity of pole-associated channel components. The proposed allocation reduces weighted singular-channel exposure by 90.32% while retaining 95% of the nominal WF rate.

This paper studies near-field massive MIMO channel estimation based on an electromagnetic channel model with limited pilots. Starting from Maxwell's equations, we derive the homogeneous background response and the scattering response in the spatial and wavenumber domains. It is found that a compact scattering cluster produces a localized receive–transmit transverse-wavenumber block across finite apertures, thereby yields a block-sparse NLOS channel coefficient matrix in the wavenumber domain. Considering the spatial non-stationarity of large-scale antenna arrays, we introduce visibility factors at the transmit and receive sides of the channel model. To solve the non-stationary sparse channel estimation problem in the wavenumber domain, we propose a visibility-aware block-sparse Bayesian channel estimation algorithm. Simulation results demonstrate that the proposed method achieves lower channel-estimation NMSE than benchmark schemes.

The upcoming sixth generation (6G) wireless networks are designed to support advanced applications that the current air interface based on orthogonal frequency division multiplexing (OFDM) cannot accommodate, necessitating innovations in waveform design. Orthogonal time frequency space (OTFS) has emerged as a promising alternative, offering enhanced performance particularly in high-mobility channel environments. However, deploying fundamentally different waveforms over the same frequencies fails to exploit the mutual relationships between them. In this paper, a forward-compatible yet backward-compatible coexistence framework is proposed, where various OFDM waveform options are incorporated while seamlessly integrating OTFS within the same network. The proposed coexistence paradigm involves utilizing a specially designed spreading matrix in the delay-Doppler (DD) domain to process OTFS data. The matrix is tailored to establish orthogonality between OTFS and OFDM waveforms, effectively reducing cross-interference when both coexist within the same time-frequency (TF) resources. Our results demonstrate up to a 6 dB improvement in bit error rate (BER) performance for the OTFS waveform compared to other coexistence schemes. The proposed approach also shows greater interference resilience and approaches the upper bounds of achievable rates. By utilizing the full bandwidth and time duration, it enhances range and velocity resolutions, leading to improved sensing performance. These findings are supported by mathematical analysis and extensive simulations for uplink scenarios.

A quickest change detection problem is studied where the decision-maker has access to multiple experiments, but can choose only one experiment at a time to detect the change. The true post-change distribution under each experiment is unknown, but known to belong to a family of distributions. It is assumed that each of these families of distributions has an element that is least favorable in a well-defined sense. An algorithm is proposed that is designed using the least favorable distributions. It is shown that the algorithm is first-order asymptotically optimal, as the rate of false alarms goes to zero. This algorithm minimizes the worst-case average detection delay over all post-change distributions within the assumed families, subject to a constraint on the average run length to false alarm and constraints on the average fraction of time each experiment is performed before change.

The Zak-OTFS carrier waveform is a pulse in the delay-Doppler (DD) domain, and given the input-output (I/O) relation at one DD point in a frame, we can predict the I/O relation at all other points. While it is simple to acquire the effective wireless channel with Zak-OTFS, the cost of embracing inter-carrier interference is equalization complexity. The cost of linear minimum mean-square error (LMMSE) equalization grows cubically with the frame size. We describe how to use channel knowledge to reduce equalization complexity. For any pulse shaping filter, and for fractional delays and Dopplers, we show that the DD channel matrix is unitarily similar, through the discrete Zak transform, to a time-domain (TD) matrix that is a weighted sum of cyclic delay and Doppler operators, where the weights are the taps of the effective channel. We also obtain a dual frequency-domain (FD) form through an additional Fourier transform. The TD matrix is banded with a width set by the delay spread that is independent of the Doppler spread, whereas the FD matrix is banded with a width set only by the Doppler spread. We design a receiver that reads off the two widths from the channel estimate, selects the cheaper domain, and solves the banded system exactly, using a bordered Cholesky factorization that treats the quasi-periodic wrap-around without sacrificing degrees of freedom. Filters that decay slowly need a few preconditioned conjugate gradient steps. We show that the complexity of equalization becomes linear in the frame size. Simulations match direct DD-LMMSE error rates with three to four orders of magnitude fewer multiplications. A single receiver handles channels with very different delay and Doppler characteristics.

A printed tapered-slot coupling probe with a partially inserted 12-mm wire element is characterized for short-range microwave coupling through a brain-mimicking agar–gelatin–saline phantom and ex vivo brain. Simulated and measured free-space $S_{11}$ responses agree over a measured $-10$-dB band spanning 5.78 to 6.86~GHz, corresponding to a 1.08-GHz bandwidth and approximately 17.1~percent fractional bandwidth. In a controlled two-probe experiment, the phantom was introduced and removed while the probe arrangement and measurement conditions were retained. This comparison was repeated under the same retained arrangement, with the same general $S_{21}$ differential behavior observed across repetitions. Near 6.3~GHz, measured $S_{21}$ was approximately $-25$~dB with the phantom present and approximately $-40$~dB in air, corresponding to an $\approx 16~\mathrm dB$. Ex vivo goat brain measurements repeated more than five times showed reproducible behavior. Across 5.78–6.86~GHz, the ex vivo goat-brain $S_{21}$ response closely followed that of the phantom, with an approximately figure-derived maximum operating-band difference of about 1.5~dB. The observed increase in $|S_{21}|$ is interpreted as a property of the complete probe–medium–probe system, including loading, mutual coupling, interfaces, geometry, propagation, and material loss. The measurement does not uniquely identify a tissue-intrinsic mechanism.

Layer-wise post-training quantization methods let each layer compensate for the quantization error inherited from earlier layers. We ask under what assumptions such compensation guarantees a network output error no larger than without it. For linear networks of any depth, with the required calibration Gram matrices invertible, if the rounding error of each layer has zero conditional mean and a deterministic second-moment map common to both schemes, as under subtractive dither on an unbounded grid, then compensation with strengths in $[0,1]$ fixed in advance gives a mean-square output error no larger than no compensation, each scheme being followed along its own trajectory. The exact gain separates error removed directly from reduced excitation of later rounding noise. Outside this setting the comparison can fail. With rectified linear activations it fails for two independent reasons: noise changes activation states, and, even when no state changes, the correction moves error from samples on which a unit is inactive to samples on which it is active. With stable activations, commutators between the calibration projection and the activation patterns bound the excess. With a bounded codebook, even unique exact layer-wise minimizers with no overload can give a larger output error. For fixed rounding errors it already fails for two scalar linear layers, refuting a published universal guarantee. An exact residual identity gives the necessary and sufficient condition on a common layer input. All these comparisons hold the calibration input fixed. On two 8B models at 4 bits, fixed half-strength compensation beat uncompensated rounding in every model, text and draw tested, yet no rule for choosing the strength beat a layer-wise rule consistently, and lowering the full-network loss on the data used to choose did not reliably lower it on new text.

A reading departs from what a monitoring system expects for one of three reasons: the physical process changed, the instrument is failing, or the value was corrupted between the instrument and the software. The three call for different responses and look alike in a single sample. We state the three-cause problem, prove which pairs of causes one channel cannot separate, and propose a bank of online detectors - a Kalman tracker with CUSUM and EWMA charts on its innovation, a Hampel median with generalized-ESD confirmation, a robust noise statistic with ADWIN, exact-value checks and an optional two-channel complementary filter - combined by a deterministic, inspectable attribution rule. The detectors were merged into a public algorithm library. On 2,942 injected events of thirteen fault types the method detects 96.3% (95% interval 95.7-97.0) at 0.22 false alarms per hour and attributes 91.2% (90.6-91.8) of detected events correctly from one channel and 99.0% (98.6-99.4) with a second, rate channel; from one channel only 2 of 207 detected sensor drifts are attributed to the instrument, as the problem statement predicts. Injected into recorded industrial signals, the same catalogue is detected at 80.8% and attributed correctly at 84.5%, at 3.6 false alarms per hour. On the Skoltech Anomaly Benchmark the method detects 32 of 33 experiments with a median delay of 15 s, at 46 false alarms per hour against 11 for a rolling z-score.

This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security triage of Open Radio Access Network (O-RAN) testing. Unlike approaches relying on static rule sets or privileged access to raw packet captures, AutoSec-RAN fine-tunes Mistral-7B-Instruct through two Low-Rank Adaptation (LoRA) stages over a curated corpus of 69 O-RAN, 3GPP, and adjacent standards documents, then selectively augments inference with retrieval-augmented generation (RAG) over the same specification index. At inference, the model receives only an investigator-style question and a scenario metadata block, simulating an automated analyst operating without raw packet evidence. AutoSec-RAN is validated on a purpose-built 5G/O-RAN testbed across labeled fronthaul distributed denial of service replay scenarios. A Gemma-4-31B judge LLM scores each candidate's answer on faithfulness, correctness, and actionability relative to a reference authored from full packet capture and log evidence. AutoSec-RAN achieves a mean judge overall score of 0.713 across all 313 evaluation items (0.755 restricted to the five supervised taxonomy classes), clearing the 0.70 reporting threshold, and a blended score of 0.778 (0.808 supervised-only) when cosine similarity over Beijing Academy of Artificial Intelligence (BAAI) General Embedding (BGE) is incorporated as a secondary semantic signal. The framework performs strongest on mitigation and threat-model reasoning (with a mean overall of 0.849 and 0.848, respectively) and identifies a measurable gap on the held-out attack-direction class (0.515), a controlled result that exposes a specific training data requirement. These results establish specification-grounded LoRA fine-tuning combined with RAG retrieval as a viable foundation for automated security validation of AI-native O-RAN.

Radio map estimation (RME), also known as spectrum cartography, aims to recover radio signal strength across multiple domains (e.g., spatial and spectral domains) from sparse measurements, naturally leading to a tensor completion problem. Recent advances show that deep learning-based generative models can provide data-driven structural constraints for RME, yielding substantial performance improvements over traditional model-based approaches. Diffusion models (DMs) represent the current state of the art in generative modeling and hold strong promise for advancing RME. However, existing diffusion-based RME methods often require extensive training data, necessitate retraining when scenarios or measurement formats change, and lack recoverability guarantees. This paper proposes a latent factor diffusion framework for spatio-spectral radio map tensor completion. By training two independent DMs on single-emitter's spatial loss field and spectral signature and embedding them into the factors of the radio map tensor, the proposed approach enables diffusion posterior sampling-based recovery with reduced training complexity as well as enhanced measurement format/sensing pattern flexibility. Recoverability and sample complexity guarantees under the proposed approach are also established. Numerical experiments validate the effectiveness of the proposed method, showing strong generalization across city environments unseen in training data.

Open Radio Access Networks (O-RAN) empower operators with choice and customization through their disaggregated and interoperable architecture. However, ensuring interoperability in O-RAN presents challenges due to complex testing environments and evolving specifications. This paper addresses these challenges by proposing the Generative AI and Graph Neural Network-based Open RAN Testing and Automation (GNG-OTA) framework. GNG-OTA utilizes state-of-the-art AI techniques including generative AI, natural language processing, and graph neural networks to enable accurate, reliable, efficient, and automatic O-RAN interoperability and performance testing.

A quantum-dot array prepared in a timed Dicke state exhibits directional radiation asymmetry, which has often been interpreted as evidence of reciprocity breaking through quantum-state preparation. We show that this interpretation is incorrect: reciprocity concerns transmit-receive response, not necessarily pattern symmetry. Moreover, the first-order coherence of any single-excitation state is exactly equivalent to the power pattern of a classical phased array driven by conjugate weights, making the observed asymmetry classically reproducible. The genuine quantum signature instead appears in second-order correlations, where a single-excitation state violates the classical lower bound on photon coincidences. We therefore propose a quantum-antenna certificate combining nonzero directional asymmetry with photon antibunching at the beam peak. We further derive a tight asymmetry bound, establish the correct mirror operation for planar arrays, correct the collective decay rate with dipole-dipole coupling, and derive a saturation law for direction-finding precision. All theoretical results are validated through independent numerical implementations.

This paper investigates how bistatic delay information improves the observability and localization performance of 3D moving targets in distributed Wi-Fi sensing. Motivated by emerging IEEE 802.11bf WLAN sensing, we consider one transmitter, multiple distributed receivers, and a single target with unknown position and velocity. We compare Doppler-only sensing with delay-Doppler sensing, in which delay-capable receivers provide additional bistatic path-length information for target localization. We derive the Fisher information matrix (FIM) and the position equivalent FIM (EFIM) for both sensing formulations. The resulting observability analysis shows that Doppler-only localization generically requires six receivers, whereas delay-Doppler localization can become locally identifiable with three receivers under non-degenerate geometry. We further study mixed receiver architectures in which only a subset of receivers provides delay estimates. Simulations with Wi-Fi-like bandwidths confirm the transition and quantify the roles of bandwidth, Doppler noise, receiver geometry, and delay-capable receiver availability.

Movable antenna (MA) can enhance the physical layer security (PLS) performance of integrated sensing and communications (ISAC). However, most existing works assume either near-field (NF) or far-field (FF) propagation models for both communication users (CUs) and targets. This paper considers a more practical scenario where multiple CUs, an eavesdropper (Eve), and multiple targets are in a mixed near- and far-field environment due to the changes in the propagation field caused by antenna position updates in MA with a unified array response and continuous NF/FF weights. To evaluate the PLS performance of MA-aided ISAC in mixed-field environments, we formulate an optimization problem to maximize the sum secrecy rate (SSR) of CUs by jointly designing the transmit beamforming and antenna positions at the transmitter. The problem is highly non-convex, so a two-layer algorithm is proposed to iteratively solve the problem using the projected gradient ascent (PGA) method and the Lagrangian dual transform. Since the abrupt switch between the NF and the FF models during antenna movement can cause the objective function to oscillate during iteration, the unified array response is combined with the Barzilai-Borwein step, Armijo line search, and precise projection to ensure that the SSR does not decrease during the iteration and the objective function is differentiable on the Rayleigh boundary. Numerical results show that the MA-ISAC using the mixed-field model achieves almost identical PLS performance to that of using the full NF model in terms of SSR, and both outperform the FF model. Using the mismatched FF model during antenna position updates results in a loss of MA gain, potentially making MA inferior to the FA-based system using the correct model.

Radio frequency fingerprinting aims to identify transmitters by exploiting small differences in the emitted signal waveform. Common approaches exploit hardware-dependent imperfections for that purpose. This work instead investigates whether differences between digital signal processing implementations of a transmitter can be identified from their radiated signals. As a practical case study, five different software-based DSP implementations for generating a 2-FSK RTTY communication waveform are considered. In a monitoring receiver, a classifier can identify these DSP implementations using convolutional neural networks that are trained on augmented software-generated signals represented as raw IQ, power spectrum or instantaneous frequency. The trained networks are evaluated on over-the-air recordings obtained using a shortwave transceiver and a software-defined radio receiver. The best neural network combines IQ and the power spectrum representation and achieves an accuracy of up to 90%. The study further examines the influence of the signal SNR and the observation bandwidth on the classification accuracy. The results demonstrate that DSP implementations leave distinguishable characteristics in the transmitted signals and suggest that DSP-dependent fingerprinting can provide complementary information for specific emitter identification systems.

Designing a massive multiple-input multiple-output (MIMO) detector that is tailored to each specific deployment site can improve detection performance, yet most existing detectors, including deep-unfolding methods, adapt mainly within human-prescribed algorithmic structures. This letter proposes SiteEvo, a site-adaptive agentic discovery framework that extends adaptation from parameter tuning to detector-algorithm discovery. Rather than selecting or recombining predefined architectures, the large language model (LLM) agent synthesizes complete executable detector programs under a common design contract by combining its pretrained communication knowledge, site-specific wireless evidence, and experience accumulated during the search. Each proposal defines its own processing structure and trainable parameterization, and the parameters are optimized by a communication-system backend. The resulting high-performance detector is further structurally refined to reduce block-level computational cost under a bit error rate (BER)-preservation constraint and is finally frozen for deployment without online LLM inference. Simulations across three sites demonstrate BER reductions by factors of 2.6 to 6.0 over the best-performing competing detector at each site at 6 dB, while structural refinement reduces block-level computational cost by 16.7-34.1% under a validation-BER constraint.

In multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, conventional receivers based on linear channel estimation and subband-averaged interference covariance reconstruction suffer serious performance degradation under strong interference. To address this issue, we propose a probabilistic framework for joint channel estimation and interference suppression using score-based prior learning. Specifically, we formulate a variational inference problem that exploits the bilinear structure of the interference by explicitly separating the interference channels from the interference symbols. To solve the resulting highly nonconvex bilinear inference problem, we develop a score-based variational inference algorithm that combines score-based turbo message passing for target-channel estimation with a reverse mean propagation framework for interference-channel inference, while the interference symbols are updated via linear minimum mean square error (LMMSE) estimation. We further design an interference rejection combining (IRC) detector with subcarrier-level covariance reconstruction based on the estimated interference channels, thereby avoiding the performance loss caused by conventional subband averaging. Numerical results demonstrate that the proposed algorithm is able to effectively suppress the interference in channel estimation, as well as to accurately reconstruct the interference subspace for interference rejection in data detection, and thus significantly outperforms existing baselines.

Due to the directional antenna pattern, the received signal power decreases when the target deviates from the array boresight, degrading direction-sensing accuracy. Although rotatable arrays can alleviate this problem, repeated array rotations will induce an incremental long sensing latency. To break the dilemma of harvesting the directive gain from element and making a low-latency sensing, a multi-armed array comprising multiple subarrays with different orientations is proposed to provide wide spatial coverage encompassing low-altitude and terrestrial regions while enabling multi-view direction sensing. However, the different views lead to different direction-sensing accuracies. To fully exploit the complementary information from multiple views, two fusion methods, called SNR-weighted fusion and logistic-weighted fusion, are developed. The former directly assigns fusion weights according to the estimated received SNRs, whereas the latter employs an exponential SNR mapping to further emphasize high-SNR subarrays. Furthermore, the Cramer-Rao lower bound (CRLB) is derived to provide a theoretical benchmark for the proposed multi-view direction-sensing system. Simulation results show that logistic-weighted fusion achieves better accuracy in the very low-SNR regime, while both proposed methods approach the CRLB at medium and high SNRs and maintain stable sensing performance.

Monocular depth estimation (MDE) has emerged as a cost-effective alternative to hardware-dependent 3D sensing modalities, yet predicting precise metric depth remains a persistent challenge due to intrinsic scale ambiguity, edge bleeding, and limited zero-shot generalization in unseen environments. Recent multi-modal approaches incorporate 1D/3D Single-Photon Avalanche Diode (SPAD) data to resolve these ambiguities, but they mainly treat SPAD noise as a harmful artifact required for denoising or leave the noise ignored. In this paper, we present an essentially different insight: native SPAD noise is an informative prior that implicitly encodes deterministic environmental characteristics. Driven by this paradigm shift, we introduce a novel, lightweight Test-Time Gated Attention framework by Harnessing SPAD Noise, therefore dubbed HSN. Tailored for indoor scenarios, HSN leverages the synergistic combination of 1D SPAD histograms and Gate Adapters (GAs). Through three complementary optimization phases, it generates channel-wise gated attention vectors to dynamically adjust encoder feature maps at inference time, effectively resolving ambiguous scale and boundary challenges without the heavy computational overhead or dense 3D modalities that other TTA methods suffer from. Extensive experiments on challenging real-world indoor benchmarks show that HSN achieves state-of-the-art metric depth accuracy in in-domain settings, while exhibiting even greater potential under zero-shot generalization to unfamiliar environments. Furthermore, interpretability analysis reveals that the proposed gated attention mechanism adaptively shifts attention toward deeper semantic layers under intensifying SPAD noise. Code will be made publicly available.

The AI-RAN paradigm makes the next-generation cellular network more versatile by co-locating AI and RAN workloads on the same commodity hardware. While the GPU is a promising platform for AI-RAN realization, a generic concurrent AI application interferes with the latency-sensitive RAN on GPU compute resources. In this paper, we propose Eunoia, a dynamic real-time AI-RAN GPU scheduler that is transparent to both AI applications and the RAN workload and operates at the granularity level of the RAN slot. We deploy Eunoia onto NVIDIA Aerial AI-RAN and evaluate Eunoia on both an over-the-air 5G private network and an emulated multi-cell network. We demonstrate that in a 27-cell AI-RAN, Eunoia keeps the RAN uplink P99 latency under 3 ms while delivering higher AI performance than other RAN-safe static partitioning methods.

Multi-frequency electrical impedance tomography (EIT) often requires specialized hardware. This challenge is especially pronounced in biomedical applications. We developed a 16-electrode EIT system using an Arduino GIGA R1 WiFi for generation and acquisition, with a discrete current source, three analog multiplexers, and basic signal conditioning. Dual cores separate waveform timing from switching and acquisition. A 3D-printed tank with silver-wire electrodes was tested at 11 frequencies (1-180 kHz). Across the 10-180 kHz analysis band, median repeat-frame signal-to-noise ratio was 48 dB, acquisition rate was ~0.18 frames/s per frequency (~60 s per sweep), and cross-session baseline drift was ~6%. KCl phantoms contained an insulating ABS cylinder or KCl-filled dialysis bags (0.001-1.0 M) spanning positive and negative conductivity contrasts. Boundary voltages were analyzed without reconstruction to estimate inclusion position, contrast polarity, and relative magnitude. Leave-one-session-out matching located ABS inclusions in 29/30 runs at 160 kHz (87-97% across the band; chance 33%). For dialysis bags, polarity was correctly identified in all 36 nonzero-contrast runs. Signed voltage projections correlated with the cylindrical contrast factor (R^2=0.68 across runs; R^2=0.89 for the aggregate fit). Concentration was ordered (Spearman rho=0.85), despite saturation above ~10-fold contrast. Bag position accuracy ranged from 6/6 at the highest contrasts to 2/6 at matched conductivity. The matched-conductivity bag produced a ~1.7% RMS perturbation against 0.4-0.6% repeat-frame variability. These results demonstrate that microcontroller-based multi-frequency EIT can support selected detection and identification tasks through direct boundary-voltage analysis, supporting future studies of muscle electrochemistry.

Semantic sensing extends integrated sensing and communication (ISAC) beyond physical parameter estimation by exploiting task-oriented representations from radio echoes. Nevertheless, existing multimodal sensing remains largely limited to vision-assisted radio physical parameter estimation or typically processes the visual and radio modalities separately, leaving radio representations underutilized for visual task enhancement. Feature-level fusion enables closer cross-modal interaction, while echo interference complicates the learning of user-specific radio representations and its semantic consistency with the visual entities. To address these challenges, this paper proposes a multimodal semantic sensing framework for orthogonal frequency-division multiplexing (OFDM)-ISAC systems. We propose a radio encoder to learn radio representations oriented toward visual localization from superimposed pilot echoes, together with Variational Radio-Visual Grounding (VRVG) to promote semantic consistency between the learned radio representations and visual spatial features. The radio representations are then formulated as semantic probes to highlight the corresponding entities in the visual scene through cross-modal fusion. Experiments using real-world RCooper dataset show that the resulting synergistic radio-visual embeddings enable accurate radio-visual user matching and significantly improve both user-specific visual localization and physical parameter estimation performance. The results substantiate the benefits of radio representation learning and semantically consistent fusion for multimodal semantic sensing.

Continuous-aperture arrays (CAPAs) provide a new electromagnetic transmission paradigm by directly controlling the source-current distribution over a continuous radiating aperture. This paper studies CAPA-enabled integrated sensing and communication (ISAC) with spatially extended sensing targets, for which the electromagnetic response over each target region is characterized by an aperture-to-region operator. Unlike point-target models, the resulting illumination operator may have infinite rank, precluding a priori finite-dimensional reduction of the transmit design. We formulate the joint communication and target-illumination beamforming problem in the continuous electromagnetic domain and develop an operator semidefinite relaxation (SDR). A key structural result is that the optimal transmit covariance operators remain finite rank even when the aggregate illumination operator has infinite rank. We further establish the tightness of the operator SDR and provide a constructive rank-one recovery of the information beamformers. Since the finite electromagnetic subspace supporting the optimum is generally unknown, we develop a dual-slack-driven adaptive mode-enrichment method that discovers the required modes from the full-space optimality conditions. The dual-slack violation simultaneously identifies missing modes and yields a computable certificate of the remaining full-space optimality gap. Global convergence of the enrichment sequence to the full-space optimum is established. Numerical results validate the finite-rank optimal structure and show that the proposed method approaches the full-space optimum with substantially fewer electromagnetic modes than truncated-SVD and spatial-Fourier representations.

Ultra-reliable and low-latency communication (URLLC) is essential for mission-critical cellular Internet of Things (IoT) applications requiring reliable short-packet transmission and efficient radio-resource allocation. However, finite-blocklength (FBL)-aware beamforming becomes computationally demanding as the numbers of users and antennas increase. Existing successive convex approximation (SCA) methods typically rely on general-purpose convex solvers without exploiting the structure of the resulting beamforming problem. This paper develops a structure-exploiting consensus alternating direction method of multipliers (ADMM) framework for low-complexity FBL-aware beamforming in downlink multi-user multiple-input single-output (MU-MISO) cellular IoT networks. The FBL sum rate is maximized subject to transmit-power and per-user minimum-rate constraints. Each SCA iteration yields a convex quadratically constrained quadratic program (QCQP), reformulated into an equivalent consensus problem. For an M-antenna base station serving N IoT nodes, the global update exploits Kronecker structure to replace an MN-by-MN factorization with a shared M-by-M factorization, while rank-one channel matrices enable low-complexity local projections. CVX-SCA serves as a general-purpose benchmark, whereas standard ADMM-SCA isolates the gains from structural exploitation. Numerical results demonstrate close agreement with CVX-SCA in objective value, feasibility, and convergence. Under user scaling, the proposed method achieves more than 4-fold end-to-end speedup over CVX-SCA and approximately 6-fold per-iteration speedup over standard ADMM-SCA, demonstrating its computational efficiency for cellular IoT resource allocation.