Papers

Patient responses to dental pulp testing, ranging from no sensation to intense pain, provide important information for assessing pulp status in endodontic diagnosis. However, pain is a subjective sensory and emotional experience that varies considerably across individuals and can be difficult to communicate. We investigated whether complementary autonomic signals could support objective assessment of responses during dental examination. Forty-nine patients underwent cold pulp testing, yielding no-response, mild-response, and intense-response conditions. The framework integrated ECG-derived skin nerve activity (SKNA) and R-R intervals (RRI), together with electrodermal activity (EDA), using temporal convolutional network encoders with attention-based mid-level fusion. Individual baseline signals and subject-level covariates, including anxiety scores and biological sex, were also incorporated. The framework achieved 80.2% balanced accuracy, 75.2% sensitivity, and 85.2% specificity for binary classification of no response versus mild or intense response. For three-class classification, it achieved 60.0% balanced accuracy and a 58.8% macro-averaged F1 score. Ablation and attention-weight analyses indicated that EDA contributed most strongly to model performance, followed by RRI, while SKNA improved balanced accuracy by approximately five percentage points. Age was significantly associated with model performance. These findings support the feasibility of multimodal autonomic sensing for objective, non-invasive assessment of responses to dental pulp stimulation.

This paper investigates rotatable antenna (RA)-enabled low-altitude economy (LAE) networks, where multiple base stations (BSs) are deployed to serve both terrestrial and aerial users. Unlike conventional fixed orientation antennas, RAs can adjust their boresights to serve users at diverse azimuth and elevation angles, thereby providing three-dimensional (3D) coverage. To improve the performance of LAE networks, we formulate a network sum-rate maximization problem that jointly optimizes user association, transmit beamforming, and RA orientations. However, in such LAE networks, aerial users are often visible to multiple BSs through line-of-sight (LoS) links, making user association and interference management particularly challenging. Moreover, user association and RA orientations are closely coupled: user association affects the RA pointing directions, while the RA orientations in turn reshape the channel conditions for user association. To solve this problem, we propose an alternating optimization (AO) algorithm that integrates weighted minimum mean-square error (WMMSE) for transmit beamforming, projected gradient ascent updates for user association, and Frank-Wolfe updates for RA orientations. Furthermore, we develop a discrete candidate-scanning scheme that replaces continuous orientation optimization with a finite search to reduce computational complexity. We also propose an antenna block-based scheme to reduce the number of orientation variables. Simulation results show that the proposed joint design achieves a higher sum-rate than the fixed antenna orientation and nearest BS association benchmarks. Moreover, the candidate-scanning and block-based schemes achieve approximately 89.9% and 94.3% of the performance with high transmit power, respectively, while reducing the complexity of orientation updates.

The dynamic control of the wireless propagation environment enabled by reconfigurable intelligent surfaces (RISs) makes them a promising technology for sixth-generation (6G) wireless networks. Beyond conventional fixed-position deployments, integrating a RIS with an unmanned aerial vehicle (UAV) provides additional spatial flexibility, facilitating favorable Line-of-Sight (LoS) conditions and broadening the range of potential applications. Although UAV-mounted RIS systems have attracted significant research interest, existing studies rely predominantly on numerical simulations and often overlook the impact of practical constraints and real-world disturbances. To address this gap, the authors recently presented the first UAV-mounted RIS prototype and experimentally validated its feasibility through real-world experiments. By using the UAV's extended Kalman Filter (EKF) for real-time RIS reconfiguration, the proposed system effectively mitigates disturbance effects and preserves the performance gains of the RIS-enabled link. To further advance UAV-mounted RIS research and facilitate broader access to real-world experimental insights, this work provides a comprehensive dataset collected from multiple flight experiments. The dataset includes EKF state estimates, ground-truth measurements, corresponding RIS configurations, and S21 channel data.

Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying cycle phase. Corruption from motion, poor contact, and physiological interference obscures morphology needed for diagnosis, making signal restoration essential. Existing diffusion approaches condition on corrupted observations alone and must learn cyclic structure implicitly. We instead propose two inductive biases which encode cyclostationarity: a shift-covariant wavelet representation and dense per-sample phase conditioning inferred from the corrupted input. We further introduce a training-free cyclostationarity index that quantifies phase structure and predicts when phase conditioning will help. Finally, we propose antithetic coupling of reverse trajectories to reduce sampling variance while achieving comparable performance with fivefold fewer network evaluations. Across modalities, our results show that explicitly encoding measurable cyclic structure improves physiological time-series restoration.

Nowadays, over-the-air computation (AirComp) exploits the waveform superposition property of the wireless multiple access channel to directly compute a function of distributed data from simultaneously transmitted signals. In this paper, we propose Histogram-State Coded Multiple Access Computing (HiCoMAC), a fundamentally new convolutional-coded method for digital AirComp that directly recovers the arithmetic-sum sequence from the superposition of coded and modulated user signals. HiCoMAC exploits the permutation invariance of the sum function to represent the multi-user convolutional encoder state by the numbers of users occupying the individual encoder states. This central idea enables log-max maximum a posterior (MAP) histogram-state Viterbi decoding with substantially reduced state complexity compared to joint full-state decoding. We further introduce the computational free distance to distinguish histogram paths that produce different arithmetic-sum sequences and develop a weighted product graph for its shortest path evaluation. This distance is then used to optimize the rectangular ratio of a fixed labeled quadrature amplitude modulation (QAM) constellation to improve the computational accuracy. Simulation results show that the computation-oriented modulation design increases the computational free distance and reduces the normalized mean square error (NMSE), while HiCoMAC outperforms the considered multi-symbol digital AirComp baselines under equal transmission and energy budgets.

Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. This distinction matters for brain-computer interfaces, where every new user must be calibrated. We evaluated personal adaptation of three frozen EEG foundation models (CBraMod, REVE and LaBraM) in 235 held-out subjects from three motor-imagery datasets, comparing each subject's adapter with the population model and with adapters fitted to other subjects. Using all first-half session labels, personal adapters improved mean balanced accuracy over the population model by 1.5-5.4 percentage points and outperformed exchanged adapters by 2.3-7.3 points in all nine model-dataset combinations. The size of this benefit depended on population training: with four times the original budget, median gains remained positive (1.0-2.0 points) but were smaller for every model, and no population model reached a confirmed plateau. Acquiring the benefit cheaply was unreliable: few-label calibration was consistently non-negative on only one dataset, and in CBraMod neither unlabeled context nor meta-learned initialization outperformed matched controls. Personalization should therefore be evaluated against both a population reference and exchanged parameters, across population-training budgets.

Indoor bistatic WiFi sensing must often operate across rooms, where intervening walls block the direct line-of-sight (LOS) path between the transmitter (Tx) and receiver (Rx). Recovering a human trajectory from channel-state-information (CSI)-derived delay, angle-of-arrival (AoA), and Doppler estimates is difficult because the Tx position is often unknown and NLOS propagation is inconsistent with direct-path geometry. This paper presents CoTrack, a self-calibrating passive WiFi tracking scheme that does not require the Tx position. CoTrack extracts one dominant human-induced response and represents its transmitter-side propagation by a virtual Tx, which it estimates jointly with the human trajectory. Because similar measurements can be explained by different virtual-Tx–trajectory pairs, CoTrack uses multi-start nonlinear least squares and accepts an initialization only when the best-fitting candidates concentrate around a common virtual-Tx position. It then applies online alternating optimization for continuous tracking. We analyze the local identifiability of the joint estimation problem and derive the corresponding Cramér–Rao bound. Across six LOS and cross-room NLOS experiments, CoTrack achieves a median trajectory error of 1.14m(80th percentile: 1.42m) and a median virtual-Tx discrepancy of 0.47m(80th percentile: 0.54m).

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) provides abundant spatial and time-frequency degrees of freedom for integrated sensing and communication (ISAC), while movable antennas (MAs) further introduce reconfigurable spatial freedom through array geometry adjustment. However, how the array geometry and information bearing MIMO-OFDM waveform jointly shape the three dimensional (3D) ambiguity response remains insufficiently understood, and conventional two dimensional (2D) range-Doppler metrics cannot fully characterize this coupling. This paper investigates MA-enhanced MIMO-OFDM ISAC with joint design of the transmit MA positions and symbol-level precoding (SLP) waveform. The discrete periodic angle-range-Doppler ambiguity function is first derived, and its structure is characterized in terms of waveform rank. It is proved that a rank one waveform yields a 3D ambiguity response that is separable between the angular dimension and the range-Doppler plane, whereas a higher rank waveform combines multiple spatial components with different weights across candidate angles, allowing the range-Doppler response to vary with the candidate angle and enabling the MA positions to further shape the 3D ambiguity response. Based on this analysis, a 3D integrated sidelobe level ratio (ISLR) is defined, the joint optimization of the MA positions and SLP waveform is formulated, and a penalty dual decomposition based alternating optimization algorithm is developed. For the radar-only case, the existence of at least one rank one globally optimal solution is established, enabling a lossless reduction from the original high dimensional MIMO-OFDM waveform design to a low dimensional joint design of the spatial beam and MA positions.

Downlink communications from base stations (BSs) to unmanned aerial vehicles (UAVs) in sixth-generation (6G) networks require precise beam alignment to overcome severe mobile communication path loss. However, traditional exhaustive beam sweeping relies on discrete codebooks and consumes valuable air-interface resources for online measurements, rendering it inefficient for highly dynamic aerial environments. In this paper, we propose BeamCKMDiff, a physics-informed generative diffusion framework designed to construct high-fidelity continuous beam-aware channel knowledge maps (BeamCKMs). Unlike existing empirical approaches, BeamCKMDiff employs a diffusion transformer (DiT) architecture featuring a dual-path conditioning mechanism. It integrates an analytical line-of-sight (LoS) beam prior with environmental topologies, while simultaneously embedding continuous beamforming vectors through an adaptive layer normalization (adaLN) mechanism. Building upon this differentiable generative mapping, we propose a CKM-based end-to-end continuous beamforming and proactive dual-BS handover algorithm. By analytically propagating gradients through the reverse diffusion process, the proposed framework enables successive convex approximation (SCA)-based beamforming optimization in the computational domain, effectively bypassing physical pilot scanning. Simulation results demonstrate that BeamCKMDiff achieves a normalized mean square error (NMSE) of –23.96 dB, establishing a highly reliable spatial prior. Compared to discrete beam sweeping and LoS-assumed baselines, the proposed CKM-based beamforming adapts to non-LoS (NLoS) conditions, providing accurate beam alignment and higher spectral efficiency. Furthermore, the integrated dual-BS handover mechanism eliminates blockage-induced link outages, ensuring robust connectivity for high-mobility aerial platforms.

In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation characterizes both the connectivity and the edge orientation of the digraph. Based on the spectral decomposition of the Hermitian matrix, a digraph clustering algorithm with $k$-means is introduced to produce a partition on the graph. Applying this algorithm (bottom-up) recursively to a digraph with partially labeled vertices yields a spectral hierarchical digraph clustering (\myproj) algorithm that produces consistent nested partitions of the digraph, or equivalently, a tree structure. Furthermore, based on the in-degree and out-degree of each cluster in the digraph clustering, a pair of hierarchical interval partitions (filtrations) can be derived in a top-down manner to produce a pair of nested knot sequences. These knot sequences facilitate the construction of multilevel spline quasi-interpolants, enabling a noisy graph signal to be decomposed into a coarse approximation and inter-level details, followed by adaptive thresholding and reconstruction. Experiments on synthetic and real-world digraphs demonstrate the superiority of our {\myproj} algorithm for digraph clustering across diverse graph structural properties (homophily and heterophily) and supervision settings. Moreover, experiments on digraph signal processing using multilevel spline quasi-interpolants further demonstrate the effectiveness of signal recovery on digraphs in terms of RMSE and SNR.

We study the problem of learning directed acyclic graphs (DAGs) with nonnegative edge weights from observational data. We propose the Nonnegative and Concomitant (NoCo) DAG estimator, which jointly recovers the weighted graph structure and the exogenous noise variances in the linear structural equation model for the observations. Different from prior art, this noise-adaptive formulation blends a smoothed concomitant lasso criterion with a simpler log-determinant acyclicity constraint that exploits nonnegativity and yields a more benign optimization landscape. Specifically, nonnegative weights allow us to impose acyclicity directly on the adjacency matrix without elementwise squaring of its entries, thus avoiding the well-documented degeneracy of the Karush-Kuhn-Tucker conditions. Computationally, we develop a method of multipliers' algorithm that accommodates both homoscedastic and heteroscedastic noise profiles. Within each iteration, we use block successive convex approximation to minimize the augmented Lagrangian, alternating between proximal gradient steps for the adjacency matrix and closed-form updates for the noise scales. Simulated experiments demonstrate NoCo's improved structural and edge-weight recovery relative to competing methods in a variety of settings, highlighting the benefits of exploiting nonnegativity along with noise adaptivity.

This letter investigates a satellite-terrestrial collaborative inference system for remote sensing information processing, where a ground station (GS) serves multiple low Earth orbit (LEO) satellites running heterogeneous deep neural network (DNN) inference tasks. By partitioning the DNN between the satellite and the GS, each satellite executes the front-end layers locally, transmits the resulting intermediate features to the GS, thereby offloading the remaining layers to the GS for inference completion. We jointly optimize DNN partitioning, satellite and GS computing resources, satellite-ground bandwidth allocation, and satellite transmit power to minimize the average task completion latency under per-satellite energy constraints. To address the coupling between discrete partitioning decisions and continuous communication-computing variables, we propose a two-layer optimization algorithm. The inner layer performs closed-form updates and efficient nested bisection searching to update the continuous resource variables, while the outer layer employs random-restart coordinate descent to refine the partitioning decisions. Simulation results show that the proposed algorithm effectively reduces task completion latency compared with conventional binary offloading.

Neural audio codecs can achieve similar objective quality at lower bitrates than conventional codecs, but their decoder-side computational cost may offset this bitrate advantage on battery-powered client devices. This paper presents a measurement-based rate–energy–quality analysis of four neural audio codecs–EnCodec, DAC, HILCodec, and SNAC–and two conventional baselines, AAC-LC and Opus, on laptop and phone platforms. Speech and music are evaluated separately using the original-reference ViSQOL protocol. For the main comparison, operating points are matched by selecting the measured point nearest to the midpoint of the common overlap in treatment-level mean ViSQOL. A secondary analysis includes only explicitly evaluated bitrate settings. Decoder energy is measured as idle-subtracted energy per second of audio (J/s), and results are summarized as the median of three repeated runs for execution-valid runtime and device paths. Pairwise break-even transmission-energy thresholds are then derived analytically from the measured decoder energy and bitrate. At the matched-quality operating points, the evaluated neural codecs achieved similar ViSQOL scores at lower bitrates but generally required more decoder-side energy than the applicable conventional codecs. EnCodec produced the lowest neural break-even thresholds in both matched-quality cohorts on both platforms. By contrast, several DAC and SNAC comparisons on the Phone XNNPACK CPU path exceeded 200 mJ/kbit, and their full-band Phone configurations required more than 1 s to decode 1 s of audio. These results show that lower bitrate alone does not guarantee an energy benefit: deployment efficiency also depends on decoder complexity, the effective runtime and device mapping, execution validity, accelerator availability, and the transmission-energy coefficient.

Real-time terrain classification directly from Level-0 raw Synthetic Aperture Radar (SAR) data remains restricted by traditional digital-centric paradigms, where the processing of noisy, high-dimensional patches is hindered by computationally intensive processors and significant downlink latency. To address these fundamental limitations, this work establishes a new research paradigm for autonomous on-board sensing by proposing a Stacked Intelligent Metasurface-Diffractive Deep Neural Network (SIM-D$^2$NN). This architecture leverages the physical wave-propagation medium to offload inference tasks from digital processors to a physical device. By executing in-wave feature mapping, the SIM-D$^2$NN facilitates a move toward an integrated `compute-while-transmitting' framework, providing an alternative to the traditional `digitize-then-process' sequence. The multi-layer metasurface is positioned at the forefront of the satellite communication module. The initial layer modulates the raw SAR data through both amplitude and phase adjustments, where a 90$^\circ$ phase rotation is introduced as a lightweight but effective augmentation strategy to enhance robustness against noise and Doppler distortions. Subsequent layers learn variable phase shifts, enabling advanced feature mapping for the classification task. The classification results at the terrestrial station can be directly obtained based on the signal amplitude received at each antenna. This design reduces reliance on downlink bandwidth and high-power terrestrial computing, achieving performance around 90% in the binary task directly from real raw SAR data in terms of accuracy, precision, recall, and F1 Score. Therefore, our method helps bridge the gap between next-generation remote sensing tasks and in-orbit processing needs, paving the way for computationally efficient remote sensing applications.

We present a deterministic CRT-based sparse Fourier architecture for exact recovery of noiseless, on-grid, at-most-\(k\)-sparse spectra with an engineered transform length. The transform length \(N\) is selected as an exact product of small pairwise-coprime stage primes, allowing each stage to use transforms whose size scales with the sparsity rather than the ambient dimension. Three co-prime-increment time shifts provide a one-sided singleton-consistency screen: every genuine singleton passes, while passing collision bins remain provisional candidates. Each candidate bin is mapped by a total label-emission rule to at most one global frequency label, and per-stage label sets are intersected, yielding a deterministic \(O(k)\) candidate bound for every input. Candidate construction uses \(O(k\log N)\) samples. In a comparison real-RAM model with exact arithmetic and exact phase evaluation but without unit-cost root-index decoding, it requires \(O(k\log^2 N/\log k)\) arithmetic operations; in a stronger root-index-oracle model the arithmetic cost is \(O(k\log N)\). Under all-stage genuine-singleton survival, the candidate set contains the true support. Exactness, however, does not depend on that survival condition: a consecutive-sample residual verifier accepts a sparse reconstruction only when it is exact, and all failures route to a dense FFT. The resulting hybrid algorithm therefore returns the exact spectrum for every input in the stated noiseless, on-grid, at-most-\(k\)-sparse model, with \(O(N\log N)\) worst-case runtime. The verified sparse path additionally incurs an \(O(k^2)\) verifier cost in the root-index-oracle model. Neither computational model is a bit-complexity model.

Practical assistive and rehabilitative brain–computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preprocessing, model selection, or ensemble selection. We present ThinkNet, a validation-controlled framework that combines train-only normalization, validation-guided evolutionary search, and validation-gated inference to identify compact decoders and inference policies for held-out subjects. We evaluate four-class BCI Competition IV-2a (session T) decoding with nine Leave-One-Subject-Out (LOSO) folds, three seeds, seven fixed decoder entries, and a broader search over ten representative decoder families; the held-out subject is never used for normalization, hyperparameter, architecture, or ensemble-policy selection. In the fixed benchmark, the validation-selected compact decoder achieved 44.35$\pm$15.41% accuracy with 4.9K parameters, 19 KB FP32 weights, and 0.99 ms batch-1 Orin CUDA inference. Across the broader search, compact models ($\leq$25K parameters) achieved higher mean held-out accuracy than mid-size and large alternatives after selected retraining (40.10% vs. 35.09% and 34.78%). Validation-gated ensembling improved over validation-selected single-model inference, reaching 43.98$\pm$16.25% in the fixed benchmark and 43.31$\pm$15.88% for the compact six-family ensemble. A non-deployable oracle analysis revealed a 6.1-point family-selection gap and near-zero validation–test correlation, showing that validation reliability remains a key bottleneck under subject shift. Thus, ThinkNet is a validation-controlled framework for compact MI-EEG model and inference-policy selection, rather than a single-architecture benchmark.

Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder can become an overconfident near-one-class predictor. This is especially problematic in source-free deployment, where target-user labels are unavailable during adaptation and expert selection. We present EEG-Fusion, a failure-informed decision-level fusion framework that treats source-free MI decoding as label-free reliability estimation over heterogeneous experts. EEG-Fusion applies subject-wise Euclidean alignment and normalization-only test-time adaptation, then routes each target subject to a neural, covariance-based, or physiological-feature expert using a reliability gate trained on source-held-out folds to predict expert performance and collapse risk from label-free stream diagnostics. The gate uses confidence, entropy, prediction diversity, expert agreement, and predicted class balance; collapse is measured as the maximum predicted class fraction. In 9-fold leave-one-subject-out (LOSO) evaluation with three seeds, relative to a no-alignment raw EEGNet source-free anchor, EEG-Fusion improves subject macro-F1 from 0.417 to 0.529 on BCI IV-2a local protocol, from 0.314 to 0.482 on BNCI2014-001, and from 0.607 to 0.708 on BNCI2014-004; corresponding collapse-index reductions are 0.199, 0.227, and 0.169. In a 9-subject Cho2017 external subset, EEG-Fusion improves macro-F1 from 0.516 to 0.630. These results suggest that label-free reliability estimation can reduce subject-level failure modes in source-free MI-EEG deployment.

Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where the loss could decrease the most, but whether they improve compared to a reference width is untested on EEG. Here, we grow three convolutional backbones on 12 motor-imagery datasets under three protocols and compare each with its reference model per subject. The growing ShallowFBCSPNet scores 2.9 points above its reference model with only half the parameters (0.57x), SCCNet changes by at most 1.2 points. Deep4Net growing models show decreased accuracy, but they require adaptation that prevent to compare faithfully the results. These differences follow the selection step, which keeps a candidate neuron relying on a dynamic threshold from singular values decomposition. Overall, these results suggest that growth helps when its criterion can rank the candidate neurons, and that the rate of skipped neuron addition tells where a decoder can be grown small from scratch.

This study investigates the feasibility of coexistence between an incumbent fixed service (FS) and a 6G network in the same location under spectrum sharing. For coexistence to be possible, a protection criterion must be met. Regulatory agencies define this criterion as the ratio of inter-network interference to noise at the FS receiver. Simulation results indicate that this coexistence is not possible without the implementation of some interference mitigation strategy. To this end, a distance-based protection zoning and two user selection strategies were investigated from the fixed service perspective, assuming the 6G network is in uplink. The results indicate that meeting the protection criterion can be achieved when only few users in uplink are selected, while still providing optimistic capacity performance for such selected users in the shared band.

Target-speaker automatic speech recognition (TS-ASR) aims to recognize a designated speaker while suppressing interfering speech in multi-talker environments. Conventional TS-ASR typically relies on an enrollment utterance, whereas text-guided methods use known lexical content, such as a wake word, to identify the target speaker from the observed mixture. These two cues provide complementary information but are usually studied separately. We propose a Unified Dual-Cue TS-ASR framework that supports text cues, enrollment speech, or both within a single model. Text cues interact with the mixture representation to extract target-speaker information conditioned on known lexical content, while an independent enrollment utterance provides complementary speaker information. Cross-attention cue-conditioning modules are integrated into shared Conformer blocks, and negative-cue sampling provides cue-validity supervision during dual-cue training. Experiments on 30,000 two-speaker mixtures across five recording/domain conditions and four oracle text-cue lengths show that, with five-character text cues, the concatenated dual-cue method achieves 8.80% CER, compared with 17.32% for text-only and 29.06% for enrollment-only inference. It also outperforms parallel dual-cue fusion (9.49% CER) and yields lower dual-cue CER across all five evaluation subsets. These results demonstrate the benefit of jointly exploiting complementary lexical and speaker information for target-speaker ASR.

Signal-multiplexing network ranging (SM-NR) shares broadcasts across node pairs, but its sequential operation leads to a ranging cycle that grows linearly with network size. This paper proposes a concurrent coded SM-NR (CC-SM-NR) framework for asynchronous half-duplex networks. Firstly, the CC-SM-NR protocol coordinates concurrent transmissions through binary transmit-listen codewords. The transmit-listen schedule defined by these codewords ensures reciprocal observations subject to a finite concurrency limit. Then, we derive the exact minimum number of transmit-listen rounds without a concurrency limit, which reveals that the minimum grows logarithmically with network size. To account for practical scenarios, we establish the necessary and sufficient conditions for the constant-weight feasibility of codewords under a finite concurrency limit. Subsequently, we propose a low-complexity scheduling algorithm that achieves the minimum round count within the constant-weight codeword class. To support higher observation redundancy, this scheduling design is extended through a greedy construction. Finally, simulation results demonstrate the effectiveness of the proposed schemes for network ranging.

Rate-splitting sparse code multiple access (RS-SCMA) superimposes QAM-modulated common messages and SCMA-encoded private messages over the same resource elements. This paper analyzes the bit error rate (BER) performance of orthogonal frequency division multiplexing (OFDM)-based RS-SCMA in the presence of carrier frequency offset (CFO) over frequency-selective Rayleigh fading channels. A finite-alphabet BER framework is developed for common-layer detection, successive interference cancellation (SIC), and private-layer SCMA detection. The proposed analysis explicitly retains the common and private-layer signal components and models the unresolved intra- and inter-block CFO leakage as conditionally Gaussian interference using its conditional second-order statistics. Residual interference caused by erroneous common-layer cancellation and the effect of a tunable message-splitting factor are also incorporated. The results show that CFO produces a high-SNR BER floor that increases with the normalized frequency offset. Under the considered conditions, the overloaded SCMA private layer is more severely affected and dominates the overall BER. Simulations with perfect SIC further show that eliminating common-layer decision errors has only a marginal impact on the private-layer BER floor, indicating that the dominant degradation arises from CFO-impaired private-layer detection rather than SIC error propagation. Monte Carlo simulations validate the derived BER expressions and demonstrate their accuracy in capturing the layer-wise and overall BER performance over the considered CFO range.

Semantic mismatch between independently trained AI-native agents in heterogeneous networks can impair semantic communications. Hybrid analog-digital semantic equalization can align the incompatible latent representations without retraining the semantic transceivers. We study a practical realization of this approach based on a fabricated dynamic metasurface antenna (DMA), an emerging low-cost, low-power, ultracompact technology for hybrid analog-digital beamforming. We model the DMA-assisted channel using multiport-network theory (MNT), accounting for mutual coupling (MC), structural scattering, and binary lossy tuning states. We use the experimentally estimated MNT parameters of our fabricated 19-GHz DMA prototype with strong MC. We jointly optimize digital pre- and post-equalizers and the DMA at a reference receiver geometry for latent-space alignment, then freeze the digital stages and adapt only the DMA after receiver motion. In our CIFAR-10 image-classification task at the receiver, the jointly optimized system achieves 94.5% accuracy, while DMA-only adaptation restores a median accuracy of 90.3% after receiver motion. Our semantic-aware wave-domain adaptation substantially outperforms semantic-unaware benchmarks. We further observe that varying the DMA configuration across channel uses provides little additional benefit for either semantic-aware or semantic-unaware optimization.

Decoding signals over unknown channels with minimal pilot overhead is a critical challenge in next-generation communications. Existing deep learning approaches typically rely on generic encoders that struggle to model long-range temporal dependencies or efficiently capture the channel's physical properties from scarce data. We argue that standard architectures suffer from agnostic estimation gaps, as they must implicitly learn the constellation geometry that is already known. We introduce the Constellation-Aware Transformer (CAT), a novel architecture that explicitly injects geometric inductive biases into the equalization process. CAT is composed of a stack of custom TransFIRmer blocks, which use an "early interaction" paradigm to co-process received signals and ideal constellation symbols. Each block features a split Feed-Forward Network that applies a Finite Impulse Response (FIR)-inspired filter for deconvolution and a parallel MLP for geometric refinement. We show that this design is structurally aligned with the optimal linear (MIMO Wiener) receiver: its attention can implement a matched-filter bank, and its bidirectional FIR branch provides the non-causal filtering that block MMSE equalization requires. In the semi-supervised setting, CAT needs fewer pilots than VAE and standard Transformer baselines: on two of our three ISI channels, it reaches a lower SER with 64 pilots than they do with 128.

The combination of ultra-wide bandwidth and large-scale multiple-input multiple-output arrays with frequency-independent phase shifters induces beam squint, causing frequency and distance dependent array gain variations across subcarriers and degrading integrated sensing and communications performance. Transceiver beamforming and group index modulation (GIM) can alleviate the impact of beam squint on communication and sensing performance. However, their designs are coupled in the frequency domain, as beamforming shapes the subcarrier responses on which GIM grouping and activation depend. To address this coupling, we propose a joint transceiver beamforming and GIM design that maximizes a weighted combination of normalized worst-case communication and sensing distances. An alternating optimization algorithm jointly refines the transceiver beamformers and GIM configuration. Simulation results show that the proposed joint design reduces bit error rate in the high signal-to-noise ratio region and achieves a more favorable communication-sensing trade-off, while maintaining an average sensing AUC comparable to that of the considered GIM based benchmark schemes.

In this study, the coexistence between a multiple-input and multiple-output (MIMO) primary network (PN) and a distributed MIMO (D-MIMO) secondary network (SN) is analyzed when both networks operate in time division duplex (TDD) and their uplink (UL) and downlink (DL) phases are not synchronized. The impact of the interference is observed when the networks operate with different 3GPP slot patterns, which vary in terms of UL/DL proportion and slot position, resulting in a significant impact on their overall spectral efficiency (SE). Given that, we propose a slot pattern coordination to reduce the perceived interference on the PN network by controlling the features of the pattern used by the SN. The results show that this strategy helps to avoid loss of performance at the PN.

Underlay spectrum sharing enables flexible coexistence between a primary network (PN) and a secondary network (SN), but inter-network interference remains a critical limiting factor. Existing mitigation strategies often rely on instantaneous channel state information (CSI), incurring high signaling overhead. This work proposes a zoning-based interference management framework that leverages large-scale channel statistics to partition the PN into interference zones and classify secondary APs (S-APs) into risk tiers. The zone-level characterization enables selective power-reduction policies with reduced coordination complexity. Simulation results confirm that the proposed framework protects the PN while preserving SN spectral efficiency (SE) in distributed cell-free deployments.

Emerging scenarios, including low-altitude economy and intelligent transportation, require ultra-reliable communi- cation and high-precision sensing, making integrated sensing and communication (ISAC) a key enabler for sixth-generation (6G) networks. The ISAC signal serves as the carrier for both communication and sensing, and its design directly affects the overall performance of ISAC system. This article presents a progressive roadmap of ISAC signal design. First, we establish a multi-dimensional performance metric framework of ISAC signal covering communication, sensing, and radio-frequency (RF) performance. Second, we introduce a three-stage ISAC signal design paradigm, namely UNLEASH POTENTIAL, EXPAND DIMENSIONS, and DEEPEN COLLABORATION. This paradigm progresses from the exploitation of existing signals, through the design of new waveform bases and multi-dimensional signal structures, to the ISAC signal design with multi-node cooperation. Finally, we propose a corresponding standardization roadmap that evolves from compatibility with existing communication sys- tems toward native and cooperation-enabled ISAC. The resulting framework provides practical guidance for ISAC signal design and the standard evolution toward 6G.

Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient zeroth-order optimization (MeZO) sidesteps this cost by estimating gradients from forward passes only. However, for BitNet architectures, a family of LLMs with ternary {-1,0,1\} weights and 8-bit activations, fine-tuning requires updating full-precision latent weights, and thus the memory footprint of MeZO no longer matches that of inference. A promising solution is to finetune only a subset of the latent weights, but existing sparse zeroth-order (ZO) methods either ignore the ternary structure or require first-order gradient information to build a sparse mask, which is at odds with the purpose of ZO fine-tuning. We propose TerMeZO, a sparse MeZO scheme that exploits the geometry of the ternary quantizer itself to identify the latent weights that are more likely to change values during fine-tuning, at no additional data or memory cost. Our convergence analysis shows that TerMeZO can converge faster than full-parameter MeZO, owing to its optimized reduction of the fine-tuning effective dimension. We run extensive experiments on BitNet models ranging from 1B to 3B parameters, spanning classification, instruction-following, and mathematical reasoning tasks. TerMeZO matches or exceeds the performance of full-parameter MeZO while substantially reducing the fine-tuning memory footprint.

Driven by ongoing standardization efforts, Wi-Fi sensing has recently emerged as a promising functionality within wireless local area networks (WLANs). However, due to the use of standard null-data packets (NDPs) to estimate the channel, conventional Wi-Fi sensing systems are susceptible to spoofing attacks. To counter this threat, we propose a lightweight tag-embedding scheme that enables physical layer authentication (PLA) of sensing signals. Specifically, a secret tag is superimposed onto the orthogonal frequency division multiplexing (OFDM) channel sounding signal, allowing legitimate WLAN receivers to verify signal authenticity and detect spoofed transmissions. A unique tag is generated for each transmission to avoid replay attacks, and the subcarrier occupancy of each tag is randomized to minimize the mean channel estimation error at each subcarrier. Simulation results demonstrate reliable authentication performance with a 95% correct authentication rate at a signal-to-noise ratio (SNR) of 10dB, while introducing an additional channel estimation error of only 0.52dB at legacy receivers that are unaware of the embedded tag. Thus, our proposed scheme provides an effective and backward-compatible countermeasure against spoofing attacks, enhancing the trustworthiness of future Wi-Fi sensing systems.