Papers

In this work we propose a region of interest (ROI)-aware mode selection and task assignment (RAMSTA) framework for distributed integrated sensing and communication (ISAC) systems. In realistic ISAC use-cases, the sensing performance requirement can be location dependent. Vehicular targets at a traffic intersection, unmanned aerial vehicle approaching a restricted airspace or a sparsely populated areas may require different levels of detection or localization accuracy. This warrants the design of location sensitive resource allocation to account for the varying sensing performance requirements. Thus, we design a sigmoid-based ROI proximity parameter to tune the communication-sensing trade-off in a weighted sum rate and position posterior Cramér-Rao lower bound minimization problem. The resulting mixed-integer non-linear program is solved by applying a penalized convex-concave procedure. We show that the ROI sensitivity thresholds allows the adaptive variation of the communication-sensing trade-off with respect to the predicted location of the target and relative to a predefined ROI.

Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, their continuously deformable structure makes accurate configuration estimation challenging, particularly when external vision systems are unavailable or obstructed. In this work, we present an embedded pose sensing framework that combines inertial measurement units (IMUs) and active magnetic fields to estimate the robot configuration without relying on external cameras. The angular measurements from the IMU and magnetic-field references are fused to improve local orientation estimation and reduce accumulated orientation error during operation. This pose sensing scheme achieves an update rate of 16.7~Hz, allowing real-time feedback. The proposed system is experimentally validated through closed-loop control, where the estimated robot configuration is used to maintain the end-effector at a desired position while interacting with an object. These results demonstrate the potential of distributed magnetic–inertial sensing for real-time pose estimation and closed-loop control of continuum robots.

High-altitude platform stations (HAPSs) are expected to coexist with terrestrial networks (TNs) to provide seamless wide-area wireless connectivity in future sixth-generation (6G) systems. However, the strong line-of-sight (LoS) propagation of HAPS links introduces severe cross-tier interference under spectrum sharing. This paper proposes a reverse time division duplexing (RTDD)-based transmission framework for integrated HAPS–TN systems, where the HAPS and terrestrial base station (BS) operate in opposite transmission directions to mitigate cross-tier interference while preserving TDD channel reciprocity. Standardized 3GPP channel models are adopted for both HAPS and terrestrial links. To enable tractable performance analysis, an analytical Kronecker channel model is developed to approximate the empirical 3GPP terrestrial BS channel, based on which a random matrix theory (RMT)-based large-system analysis is established to derive deterministic approximations for the uplink and downlink signal-to-interference-plus-noise ratios (SINRs). Numerical results demonstrate an excellent agreement between the proposed analysis and Monte Carlo simulations. Moreover, the proposed RTDD framework effectively suppresses cross-tier interference, achieves near interference-free performance.

This paper develops a cooperative integrated sensing and communication (ISAC) framework in which a multi-antenna base station (BS) simultaneously localizes a target and serves multiple communication users, a subset of which is equipped with reconfigurable intelligent surfaces (RISs). The Fisher information matrix (FIM) for target positioning is derived, explicitly characterizing its dependence on the BS beamforming coefficients, RIS phase profiles, and bistatic sensing geometry. Under the stated scaling assumptions, coherent RIS phase alignment yields a Fisher-information gain that scales quadratically with the number of RIS elements, whereas independent random phases provide a linear gain in expectation. We formulate a sensing-centric joint active and passive beamforming problem that minimizes the position error bound (PEB) subject to per-user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. The resulting non-convex problem is addressed through an iterative successive convex approximation (SCA) procedure that solves a sequence of convex subproblems. We further develop a target localization estimator that fuses one direct time-of-arrival (ToA) measurement, one angle-of-arrival (AoA) measurement, and multiple RIS-assisted indirect ToA measurements. Under small measurement errors and asymptotically efficient first-stage ToA/AoA estimation, the estimator covariance approaches the Cramér–Rao bound (CRB) to first order. Numerical results validate the analytical scaling laws and demonstrate the localization gains enabled by cooperative RIS-equipped users.

We present an Integrated Sensing And Communication (ISAC) framework for the joint estimation of the Doppler frequency of a passive mobile target in a bistatic scenario with clock-asynchronous nodes and where the Receiver (RX) is static but the Transmitter (TX) is mobile. In such a setup, previous geometric solutions are integrated with an Inertial Measurement Unit (IMU) device at the TX, coming up with a truly joint data fusion and estimation algorithm based on extended Kalman filtering. The approach jointly estimates the target Doppler frequency, along with the speed and direction of motion of the TX and it is robust to the unavailability of static paths between the TX and RX pair, a condition that makes previous solutions ineffective. The developed extended Kalman filter strikes a balance between the accuracy of ISAC and the IMU reliability. The proposed solution is validated via numerical simulations, obtaining a median Doppler error of 1.2% with a smartphone-grade IMU under realistic operating conditions.

This work investigates the spatially wideband (SWB) array factor (AF) of linear thinned antenna arrays. The SWB AF is formulated as a matched filter and expressed as a spatially variant convolution of a spatially narrowband (SNB) AF and a SWB kernel. Originally derived for uniform arrays, the SWB AF approximation is revisited and extended to thinned arrays. The approximation is shown to remain accurate across a wide range of parameters. Representing the SWB AF as a convolution offers an intuitive interpretation of how the bandwidth-aperture product modifies the SNB AF. Specifically, the SWB kernel acts as an averaging window for the SNB AF, suppressing the sidelobe levels (SL) of the SNB AF. In thinned arrays, the sidelobes away from the mainlobe can be treated as random, noise-like, making averaging and suppression particularly effective. Next, the analytical formulas for the expected SL and peak-to-sidelobe level (PSL) in SNB thinned arrays are first introduced and subsequently extended to the SWB regime. Finally, simple closed-form expressions for the expected SL and PSL levels of the uniformly thinned arrays with a uniform spectrum are provided. The expected SWB SL and PSL are formulated as functions of the bandwidth-aperture product and are shown to steadily decrease with distance from the mainlobe.

In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused prediction then guides each operator's transmission design subject to communication QoS, power, and tracking constraints. We further establish exact rank one recovery for the communication covariances and finite interval bounds on local tracking uncertainty. Simulations show improved consistency of tracking uncertainty under substantial motion model mismatch. CI avoids overconfidence under correlated errors, while using the current fused prediction reduces RMSE by about 31% relative to transmission designed for communication alone, with less than 2% additional power. Ablation further shows that the power difference between current and outdated predictions is driven mainly by predictive covariance rather than predictive mean.

Wireless split learning (SL) reduces on-device computation by offloading upper layers to a server, yet transmitting high-dimensional intermediate features at each iteration remains a major communication bottleneck. Existing methods select features at the client side using task-agnostic criteria such as magnitude, statistics, or clustering, which increases client-side processing and often degrades accuracy under non-independent and identically distributed (non-i.i.d.) client data. We propose importance-aware class-balanced sparsification (ICS), a lightweight approach in which the server ranks feature channels using Grad-CAM-based scores obtained from the true-class logit during backpropagation. The per-class scores are aggregated into a class-balanced, label-agnostic importance vector that mitigates head-class bias under label skew, and each client reuses this vector in the next round to retain the top-$N$ feature channels, incurring no additional client-side forward or backward passes. We further derive a non-asymptotic convergence bound that isolates the sparsification-induced error and characterizes how the sparsification ratio and mini-batch size jointly affect convergence under a fixed communication budget, and we analyze the communication and computational overhead of ICS against representative baselines. Beyond sequential CNN-based SL, we extend ICS to parallel split learning and to transformer-based models. Experiments show that ICS consistently outperforms the baselines, with larger gains under severe non-i.i.d. partitions.

Over-the-air (OTA) radar target emulation is an important method for evaluating the sensing capabilities of integrated sensing and communication (ISAC) and radar devices. Conventional single-feed compact antenna test ranges (CATRs) are generally limited to a single target direction, while multi-feed angular-synthesis methods commonly used in radar testing still require far-field illumination over the device-under-test (DUT) aperture. To overcome these limitations, this paper proposes a radar target OTA emulation framework based on multi-feed plane wave generator (PWG). The key capability is the simultaneous emulation of spatially close targets from different directions with joint angle–range–Doppler control. Target range is emulated through the corresponding delay, while Doppler and the complex echo coefficient are controlled by a channel emulator. Meanwhile, a multi-input amplitude and phase matrix (APM) maps each independent target-signal path to a dedicated PWG excitation vector that synthesizes the corresponding incident plane wave over the same test zone. This enables multiple target directions to be reproduced as local plane waves at a test distance substantially shorter than the conventional far-field requirement. The framework is experimentally validated at 3~GHz using a real $1\times11$ phased-array DUT. The PWG–DUT separation is 1.6~m, substantially shorter than the 7.2~m Fraunhofer distance of the DUT. At this separation, the synthesized $0^{\circ}$ and $10^{\circ}$ incident waves each exhibit an amplitude peak-to-peak error of 1.0~dB. Across two four-target angle–delay cases and one two-target angle–velocity case, the estimated target parameters show angular deviations within $1^{\circ}$, interpolated delay errors within 2.4~ns, and velocity errors within 0.15~m/s.

Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, with conventional hybrid analog-digital precoding yields tri-hybrid multiple-input multiple-output (MIMO) precoding, which can substantially improve the spectral efficiency of wideband multi-user MIMO orthogonal frequency-division multiplexing (OFDM) systems. However, the joint design of EM, analog, and digital precoding remains challenging. To address this challenge, we propose a tri-hybrid precoding network (Tri-PNet) based on Conformer, an emerging neural architecture that combines the local modeling strength of convolutional neural networks with the global dependency modeling of Transformers. Furthermore, two representative radiation-pattern modes, i.e., the non-regular mode and the 3rd Generation Partnership Project (3GPP) Technical Report (TR) 38.901 mode, are investigated. Tri-PNet is trained in an unsupervised manner to jointly learn EM, analog, and digital precoding by maximizing the average sum spectral efficiency. Its radiation-pattern selection network (RPSNet) employs a Conformer encoder to capture both local and global frequency-domain correlations, whereas its hybrid analog-digital precoding network (HPNet) combines cross-attention and dual-path processing with singular-value-decomposition (SVD) and zero-forcing (ZF) priors. Simulation results under both radiation-pattern modes demonstrate that Tri-PNet outperforms random EM precoding and conventional hybrid MIMO without EM precoding, approaches the greedy EM precoding search scheme with substantially lower online complexity, and remains robust to imperfect channel state information (CSI).

Radar hardware faults threaten automated perception, motivating accurate, compact diagnosis and understandable maintenance guidance. We introduce SCORE-LM, which couples a small scatterer-conditioned operator-response encoder (SCORE) to an adapted local language model. SCORE combines self-referenced complex trajectories, physical descriptors, and a selective state-space branch, with source-only self-supervision and directional fault inference. On eight capture-excluded Rad-R fault recordings, it achieves state-of-the-art performance within the evaluated nine-model comparison: 88.39% mean capture recall and 88.20% four-fault macro-F1 at ten frames. Its 39,520 radar inference coefficients are 119.7 times fewer than RadrNet-DS-CI's, while recall is 15.56 percentage points higher than this strongest competitor. In a separate low-label protocol, SCORE reaches 71.58% recall with one labeled source window per class. A nonlinear projector converts four frozen fault similarities into five soft tokens, linking compact diagnosis to class-conditioned maintenance guidance. On 75 development questions covering 24 radar windows, language adaptation raises correct-fault answers from 45 to 62 (60.0% to 82.7%) relative to removing the co-trained adapters, while retaining the same projector. SCORE-LM thus combines a compact radar specialist with a language interface for communicating fault-specific inspection guidance.

Two non-geostationary orbit (NGSO) mega-constellations over shared spectrum leads to inter-constellation interference. Existing mitigation mainly relies on frequency/time partitioning, spatial isolation and satellite selection. However, these approaches may sacrifice resource reuse or be constrained by spatial geometry. To enable interference mitigation without centralized coordination, this letter investigates distributed power control for independent, equal-priority NGSO operators. We formulate the interaction as a non-cooperative game and propose a distributed water-filling best response using local serving-channel estimates and aggregate interference measurements. Pure-strategy Nash equilibrium existence and a weighted spectral-norm sufficient condition for uniqueness and convergence are established. Simulation results demonstrate utility gains of 3.91% over uncoordinated transmission with 20 active beams.

This paper proposes a novel blind beamforming strategy for signal enhancement aided by an intelligent reflecting surface (IRS), using only samples of the received signal power. Unlike existing conditional sampling mean (CSM) based approaches, which rely solely on comparing the CSM values, the proposed algorithm fully exploits the information in the sampled data by constructing a least squares problem. This enables the estimation of the phase difference between the IRS-reflected channel and the direct channel, ultimately yielding the optimal IRS phase configuration. The simulation results verify that, by fully leveraging the collected data, the proposed scheme outperforms the CSM-based method, especially when the number of IRS phase quantization levels is larger than 2, while incurring the same level of computational complexity.

The application of intelligent reflecting surfaces (IRSs) to suppress interference in wireless communication systems has recently attracted significant research attention. Most existing approaches rely on complete or partial channel state information (CSI) to configure the IRS. However, acquiring accurate CSI in IRS-assisted systems involves considerable pilot overhead and introduces non-negligible delays. This issue is further exacerbated under strong interference conditions, where interfering sources are typically non-cooperative, making CSI acquisition even more challenging. As a result, existing CSI-dependent interference suppression methods become difficult to deploy in practice. To address these limitations, we propose a novel blind interference suppression strategy that combines a proportional phase-inversion (PPI) algorithm with the conditional sample mean (CSM) method. The proposed approach determines the IRS configuration using only the received signal power, without requiring any prior CSI. We conduct a comprehensive performance evaluation by deriving the theoretical performance of the proposed scheme, which is subsequently verified through numerical simulations. Furthermore, simulation results across various parameter settings demonstrate that the proposed blind interference suppression scheme reduces the interference power to the level of noise, thereby achieving a marked signal-to-interference-plus-noise ratio (SINR) improvement and outperforms existing benchmark schemes.

This paper addresses the problem of suppressing non-cooperative interference in intelligent reflecting surface (IRS)-aided wireless links without any channel state information (CSI) or cooperation from the interferer. We propose a fully blind framework that relies solely on received signal power measurements. A key insight is that nulling the aggregate interference channel requires only the complex ratios between the IRS-reflected paths and the direct interference link, rather than absolute CSI. We develop a novel estimation algorithm that obtains unbiased estimates of these channel ratios using only power samples collected under random IRS configurations. Theoretically, we prove that unbiased estimation is feasible when the number of discrete phase levels $K\geq 3$, and establish the Cramer-Rao lower bounds (CRLBs) for both the phase offset and amplitude ratio estimates, thus providing design guidance. Based on the estimated ratios, we propose two low-complexity IRS phase optimization algorithms: a one-shot greedy method and an iterative variant that mitigates error propagation from weakly reflecting elements. Simulations demonstrate that the proposed schemes can suppress strong interference to within a few dB of the interference-free upper bound, offering a practical, CSI-free solution for robust wireless communications in contested spectral environments.

A single-tone large-signal operating point of a nonlinear two-port is a periodic orbit of a periodically forced circuit. When the device has memory, the Floquet exponents of that orbit separate into fast (electrical) and slow (thermal and trapping) modes, and long-term memory is motion on the invariant manifold attached to the slow modes. Existence and uniqueness of that manifold follow from the parameterization method of Cabré, Fontich and de la Llave, applied to the stroboscopic map at the orbit; the manifold is the spectral submanifold of Haller and Ponsioen, without a small-forcing parameter. The manifold is a bundle over the circle of drive phase, its fiber dimension the number of slow exponents, and the dynamic X-parameter kernel of Verspecht et al. identifies its reduced dynamics from the transient after a step in drive amplitude. An exact reduced model has one memory state per slow exponent, the memoryless X-parameter surface is the fixed-point family of the reduced dynamics, and the envelope-domain model is the reduced dynamics driven by the envelope. The hypotheses are verified and the manifold constructed for a GaN HEMT compact model with thermal and trap memory: the trap's time constant at the operating point, $14 \mu$s, is set by the linearization, not by its $6$ ms emission time, and the trap's nonlinearity confines a polynomial representation of the manifold to a few millivolts of drive, so trap memory must be represented over the operating range, by tables or a fitted network, not by an expansion about the operating point.

The cochlear implant is a neural prosthetic that uses an array of electrodes to directly stimulate auditory nerve fibers with electrical impulses. One factor that is known to impact outcomes is the quality of the electro-neural interface. In this work, we propose a pipeline that permits creating accurate and patient-specific digital twins that model the cochlear implant electro-neural interface. We leverage medical images and electrophysiological measurements from the patient's device for digital twin construction. We validate our digital twins by comparing predictions of neural response measurements to clinical measurements and speech recognition outcomes. Neural health estimated by the digital twin correlates strongly with word recognition rates (R=0.75, p<1e-5) in an analysis of 29 cases and sentence in noise recognition rates (R=0.78, p<1e-4) in an analysis of 21 cases. We also report preliminary findings with digital twin-based patient remapping with 6 subjects, where word recognition rates improved on average by 10 percentage points. These digital twins have the potential to provide clinicians with an unprecedented window into the electro-neural interface for individual cochlear implant recipients, which could lead to improved hearing outcomes when used as a foundation for program customization.

Alcohol intoxication is a leading contributor to fatal and severe-injured e-scooterist crashes. Current countermeasures, such as temporal restrictions or pre-ride cognitive screening, cannot continuously assess an e-scooterist's physical motor control or impairment in real time. We conducted a controlled experiment in which 25 participants rode an instrumented e-scooter through a test track while sober and at two targeted blood alcohol concentration levels (0.05% and 0.08%). The e-scooter was instrumented with a six-axis inertial measurement unit (IMU), and throttle and brake lever position sensors, all sampled at 100 Hz. Two complementary signal features were computed: normalised permutation entropy, which quantifies temporal complexity, and standard deviation, which quantifies signal amplitude. Repeated measures correlation identified seven kinematic features (all IMU and throttle signals) whose entropy decreased (p < 0.001) while standard deviation increased (p < 0.01) with increasing intoxication, indicating that intoxicated riders shift from continuous, low-amplitude micro-corrections to fewer, high-amplitude reactive corrections. An entropy based multi-class logistic regression classifier, evaluated through leave-one-participant-out cross-validation, achieved 85% overall accuracy and a weighted one-vs-rest area under the receiver operating characteristic curve (AuROC) of 0.94, with a sober-vs-high AuROC of 1.00. Steering rate and lateral acceleration were the most important predictive features, indicating that alcohol induces a distinct collapse in lateral equilibrium during riding. Ultimately, these results demonstrate that onboard kinematic sensing combined with entropy-based signal analysis can reliably distinguish sober from intoxicated e-scooter riding, providing a foundation for automatic intoxication detection systems that preserve mobility for sober riders.

This work introduces innovation-based sampling of continuous stochastic processes, in which a sample is generated only when the process reveals “sufficiently new” information relative to all previous samples. The resulting innovation process admits a lattice structure and a three-dimensional state representation. For a Wiener process, we characterize the direction, timing, and frequency of innovations. We show that direction reversals become increasingly rare and establish limit theorems for their number. Innovations also become progressively sparser: the expected number of innovations grows only as the square root of time, and hence the sampling rate vanishes asymptotically. We then study remote estimation from sparsely received innovations. Notably, the absence of an innovation carries information: the minimum mean-square error (MMSE) estimate evolves with the age of information (AoI) and achieves a substantial MSE reduction over the conventional silence-ignorant zero-order hold (ZOH) estimator. Finally, we develop tractable affine-age and exponential-age approximations for practical use. Overall, information is conveyed not only by the content of innovations, but also by their direction and timing.

We address joint continuous localization and data detection in ambient backscatter communication, where weak observations can leave competing geometry hypotheses plausible and a staged point-estimate interface can discard information about this ambiguity. We develop the Geometry-aware Frame Network (GeoFrameNet), which projects channel frequency responses onto a physics-derived delay–angle-of-arrival lattice and aggregates sign-robust evidence across orthogonal frequency-division multiplexing symbols to form a shared geometry posterior over physically feasible candidates. Localization uses the full posterior with candidate-specific refinement; detection combines candidate-conditioned differential logits using posterior weights renormalized over selected candidates. Bit supervision guides geometry scoring. For a fixed device, the Cross-Frame Geometry Consensus Network (CFGC-Net) fuses frozen GeoFrameNet candidate logits and features across frames for localization while preserving frame-wise detection outputs. On an independent test set, GeoFrameNet achieves lower localization root-mean-square error (RMSE) than an all-symbol multiple-measurement-vector sparse Bayesian learning (MMV-SBL) baseline at all eight evaluated signal-to-noise ratio (SNR) points, with simultaneous bit error rate and RMSE reductions from $-30$ to $-22.5$~dB. At $-30$~dB SNR, the respective RMSEs are 3.1573 and 18.8859~m. CFGC-Net achieves the lowest aggregate RMSE among the evaluated fusion strategies for two, four, and eight frames.

Accurate models of programmable metasurfaces based on multiport-network theory (MNT) account for mutual coupling (MC) through a configuration-dependent matrix inversion. The latter's high computational cost during gradient-based optimization (GBO) can be alleviated via a finite-order Neumann approximation. We show that the accuracy of this approximation depends strongly on the (tacitly) chosen MNT parametrization. For 1-bit-programmable meta-elements (e.g., meta-elements based on PIN diodes), we identify a closed-form Möbius reparametrization that depends only on the meta-element's two load states and requires neither training data nor numerical optimization. For an experimentally estimated proxy MNT model of a fabricated 96-element 19-GHz dynamic metasurface antenna with strong MC, a second-order Neumann approximation with our reparametrization achieves forward and control-gradient accuracies of 21.68 and 21.70 dB, respectively. Relative to the full proxy MNT, it reduces the runtime of a combined forward and control-gradient evaluation by a third and the saved-tensor memory by a factor of ten. In a prototypical GBO problem of end-to-end optimization for DMA-based scene classification, it achieves 94.97% test accuracy vs. 95.57% with the full model. We further note that MC-strength metrics and MC-unaware benchmarks should be parametrization-invariant, motivating, for instance, definitions based on the best directly fitted zeroth-order model.

Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an opportunity to trade bit-level fidelity for greater transmission reach and efficiency. We propose an end-to-end optical semantic communication system for joint image classification and reconstruction over a nonlinear wavelength-division multiplexed (WDM) fiber channel. The system maps each image directly into a fixed-length sequence of channel symbols that preserves task-relevant information, without explicit source compression or channel coding. Experiments on the MNIST dataset cover launch powers from -9 to +3 dBm, fiber lengths up to 800 km, and 16-, 64-, and 256-Quadrature Amplitude Modulation (QAM) formats. At 0 dBm, classification accuracy remains between 98.92% and 99.31% across all tested link lengths and modulation orders, while requiring fewer transmitted symbols than a Low-Density Parity-Check (LDPC)-coded JPEG baseline at every tested modulation order. These results show that semantic communication can simultaneously extend optical reach and reduce transmission resources by conveying only task-relevant information.

Dynamic sparse attention reduces long-context attention computation by selecting only a subset of tokens, but still requires access to the full KV cache, leaving serving memory-bound. Offloading the KV cache to host memory reduces device memory pressure but places H2D transfers on the decoding critical path. In DSA, substantial overlap in selected KV entries across decoding steps creates an opportunity for device-resident reuse. Exploiting this reuse efficiently, however, presents three critical challenges: costly matching of selected tokens against entries retained in the HBM buffer, uneven distribution of the remaining H2D transfers across accelerator cores, and retaining frequently accessed KV entries within limited HBM capacity. We present AVSG, an operator that addresses these challenges for DSA while preserving its exact token selections. AVSG uses vectorized hash matching to identify reusable HBM-buffer slots and reserve slots for entries requiring transfer, then evenly partitions these H2D transfers across accelerator cores. Lifetime-based buffer management retains frequently accessed KV entries in the device buffer across decoding steps, and shared slot reservations extend reuse across tokens within a multi-token prediction iteration. On a single NPU, vectorized hash matching is 2.80x faster than scalar dual-pointer matching, miss-only transfer raises effective H2D bandwidth by up to 34.44x over request-level assignment, and an 8K-entry buffer reaches a 94.83% hit rate on real requests. These gains translate into end-to-end improvement: on a serving stack processing a real-world production dataset, AVSG reduces time per output token by 39% and increases output throughput by 1.27x relative to the same KV offload layout without HBM-buffer reuse, demonstrating the benefit of efficient matching, balanced transfer, and effective HBM residency in production-scale serving.

This letter proposes a faster-than-Nyquist (FTN) weighted affine Fourier transform (WAFT) transmission scheme, termed FTN-WAFT, which combines the peak-to-average power ratio (PAPR) flexibility of WAFT with FTN time compression to improve spectral efficiency and power amplifier (PA) efficiency over high-mobility channels. However, operating the PA at a reduced input back-off (IBO) inevitably introduces nonlinear distortion, which further couples with FTN-induced inter-symbol interference (FTN-ISI) and doubly selective channel dispersion. To address this practical impairment, a multilayer message-passing (MLMP) receiver aided by PA interference cancellation (PA-IC), termed PA-IC-MLMP, is proposed. The proposed receiver reconstructs the dominant PA distortion from soft symbol estimates, cancels the channel-propagated nonlinear interference, and performs MLMP over the WAFT, FTN, channel, and constellation layers. Simulation results show that the proposed receiver improves the bit error rate (BER) and effective throughput under nonlinear PA constraints while maintaining low complexity.

Complex-valued optimal and adaptive filters are widely used to identify unknown systems in a broad range of practical applications. Recently, a number of complex-valued bilinear filters, such as the complex-valued bilinear Wiener filter (C-BWF), and the complex-valued bilinear least mean squares (C-BLMS) filter, have been proposed to model and identify complex-valued systems, which are bilinear with respect to their coefficients. However, if the input signals are contaminated with noise, the performance of these complex-valued bilinear filters degrades significantly due to an additional noise-induced bias. To overcome this issue, we propose novel bias-compensated complex-valued bilinear filters that estimate and account for input-noise statistics. Specifically, a bias-compensated C-BWF and a bias-compensated C-BLMS filter are derived in this work. We further include a convergence analysis for the latter. Since practical applications require knowledge of the input-noise variance, we briefly present an adapted method from literature to estimate this quantity. Finally, simulation results demonstrate the superior performance of the proposed filters compared to their standard (non-bias-compensated) counterparts and several state-of-the-art methods.

Space-air-ground integrated networks (SAGINs) are expected to support ubiquitous three-dimensional (3D) communication and sensing services in future sixth-generation (6G) wireless networks. However, heterogeneous mobility and service requirements across the space, air, and ground segments challenge fixed-boresight or fixed-sector antennas in supporting wide-area coverage, maintaining directional alignment over time-varying communication links, and flexibly adjusting observation directions for sensing tasks. To address these limitations, non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) technology has emerged as a promising solution by enabling mechanical or electronic boresight adjustment while keeping the antenna positions fixed. This article investigates the roles of RA technology in enabling communication and sensing in SAGINs. Specifically, we first explain how RA boresight control complements platform mobility and supports wide-area coverage, dynamic directional transmission, cross-segment cooperative operation, and cooperative sensing. We then discuss the main design challenges, potential approaches, and future research directions, including channel acquisition and predictive tracking, joint orientation control and access/handover coordination, cross-segment coordination and resource management, as well as deployment tradeoffs and practical implementation. Finally, an RA array prototype and two representative simulation studies are presented to illustrate the feasibility and potential performance gains of RA-enabled SAGINs.

This paper studies predictive coefficient tracking for digitally generated self-interference cancellation (SIC) in vehicle-mounted monostatic integrated sensing and communication (ISAC) systems. In such systems, the canceller must suppress strong transmitter leakage and nearby unintended reflections while preserving delayed sensing echoes. On mobile platforms, mechanical vibration from engines, rotors, or nearby structures can induce quasi-periodic variation in the self-interference (SI) channel. The optimal cancellation coefficients may then vary within one adaptation period, causing the applied coefficients of a conventional adaptive filter to lag behind the moving SI channel and increasing the residual SI. To address this problem, we propose a Koopman-predictive SIC (KP-SIC) module that augments a conventional adaptive filter with a predicted coefficient trajectory. KP-SIC uses recent effective SI-channel coefficient snapshots to identify structured vibration-induced dynamics and predict a sample-varying coefficient sequence for the next adaptation period. The predicted coefficients place the canceller closer to the expected time-varying optimum, while the adaptive filter corrects residual prediction errors and unstructured variation. For evaluation, the module is integrated with a widely linear normalized least mean-square (WL-NLMS) filter under a flexible analog SIC (FASIC) framework. Simulations show that KP-SIC improves SI suppression by $6.3$–$10.2$~dB over conventional FASIC-WL-NLMS once the observation window spans at least about $1.5$ vibration cycles. Compared with a non-causal performance bound, KP-SIC achieves $60$–$74\%$ of the available prediction gain.

This letter investigates the joint impact of array curvature and mutual coupling (MC) on holographic curvature-reconfigurable apertures. An analytical framework is developed to characterize spatial correlation, coupling-aware reference directivity, and spectral efficiency. Under the small-curvature regime, we establish a relation linking inter-element distances to coupling perturbations and spatial-mode eigenvalues. Simulation results reveal asymmetric spectral-efficiency variations between transmit- and receive-side array curvature with MC, while their no-MC difference is consistent with zero under hemispherical isotropic scattering. Results based on the 3rd Generation Partnership Project clustered delay line-B channel angular/power profile, together with colored-noise sensitivity analysis, further show that the effects of curvature and receive-side coupling depend on the propagation angular distribution and receiver-noise covariance. These findings highlight the importance of jointly accounting for array geometry, mutual coupling, and receiver noise when evaluating curved holographic multiple-input multiple-output links.

Pinching antenna (PA) systems provide macroscopic spatial reconfigurability by enabling signal radiation from favorable locations along a dielectric waveguide. Exploiting this flexibility, however, typically requires channel knowledge for PA configuration, which can introduce substantial overhead in dynamic propagation environments. This paper proposes PA-assisted differential spatial modulation (PA-DSM), enabling data transfer with spatial index selection and non-coherent data detection without instantaneous channel state information (CSI) at the receiver. To exploit PA reconfigurability while preserving this CSI-efficient operation, we introduce a slow-timescale received signal strength indicator (RSSI)-assisted selection strategy that identifies a favorable subset of PAs from scalar power measurements. We further specialize a two-stage low-complexity maximum-likelihood (LC-ML) detector to phase shift keying (PSK) signaling, obtaining closed-form symbol decisions that remove the joint modulation-combination search while preserving the minimizer of exhaustive differential detection. Furthermore, a moment generating function (MGF)-based pairwise error analysis and the corresponding bit error probability (BEP) bound are developed under normalized common-K Rician fading, revealing how the difference rank and spectrum, number of receive antennas, and previous-state-dependent line-of-sight (LoS) alignment affect the error performance. Numerical results across representative indoor-office, indoor-factory, and street-canyon sixth-generation (6G) Frequency Range 3 (FR3) scenarios validate the analytical trends and quantify when PA-DSM outperforms coherent benchmark schemes whose channel estimates age between pilot transmissions.

The acquisition of high-dimensional channel state information (CSI) in wireless MIMO-OFDM communications usually requires high pilot overhead, or relies on accurate and complete positional or environmental information. In this paper, we propose a geometry-aided channel deduction (GCD) approach, which utilizes an uncalibrated digital twin (DT) with only approximate environmental geometry and positions to assist the channel acquisition. The key rationale behind is that, even imprecise geometric information, which can be easily obtained in advance through radio sensing technologies or existing geographic databases, provides certain structural features about the current channel; meanwhile, the coarse instantaneous channel estimates using only a small amount of pilots provide dedicated information that aligns with the channel structure and further compensates for the geometry inaccuracy and other channel unknowns. To this end, we first extract geometric features from the DT, which contain only simple structural information of the channel. Then we propose random prompt augmentation, a novel method to generate an appropriate prompt that converts geometric multi-path structure into a CSI-like representation while suppressing the disturbance of other unknown channel parameters. The prompt is then fused with the pilot-based instantaneous channel estimate via a channel deduction network. To further enhance the network's versatility, we incorporate pilot configurations into the existing learning architecture to support variable pilot patterns. Comprehensive experiments validate the superiority of the proposed method, which demonstrates high channel acquisition quality, low pilot overhead, and strong robustness. Furthermore, the structural prompt also serves as scenario-related context, enabling our approach to generalize well in new scenarios.