Papers

Active radio-frequency (RF) sensing must acquire evidence under receiver limits while following confirmation, recovery, and stopping instructions. \newhl{We propose RF-Agent, a closed-loop architecture combining language supervision, episode memory, deterministic RF execution, perception feedback, and evidence-based reporting.} An independent auditor checks trajectory compliance. We derive acquisition and supervisory-request bounds, characterize full-history token complexity, and bound joint success by output validity and evidence capacity. \newhl{ActiveRF-AD tests instruction changes at fixed states and evaluates joint success, requiring compliance and RF correctness in the same episode.} \newhl{Training on different instructions at shared sensing states raises counterfactual-pair accuracy from 24.03% to 96.22% and Core joint success from 65.13% to 72.12%. Of the 6.99-point joint-success gain, 6.73 points reflect a decrease in the fraction of all episodes that are RF-correct but noncompliant.} \newhl{At Core budget three, paired-training hierarchical control achieves 66.60% joint success versus 9.17% for direct-action control.} \newhl{In a separate comparison at the same budget, RF-Agent improves joint success by 2.31 points and reduces RF acquisitions by 12.3% relative to an initial-state planner, at increased inference cost.} Cross-backbone diagnostics cover three model families. These results show how instruction-responsive control improves task completion beyond what RF accuracy alone reveals.

Quantum conference key agreement (QCKA) enables multiple spatially separated users to establish a common key with composable information-theoretic security by exploiting multipartite quantum correlations. Greenberger–Horne–Zeilinger (GHZ) states are a natural resource for this task, but their distribution over optical fiber is degraded by transmission loss, polarization decoherence, and detector imperfections. We investigate the finite-key performance of an $N$-party GHZ-based QCKA system using the $N$-BB84 protocol, where each optical link is modeled by extending the bipartite erasure–Pauli channel to incorporate detector dark counts. We characterize the impact of this quantum channel on the shared GHZ state and derive closed-form expressions for the error rates. The analytical results reveal that the local bit-error rate depends only on transmissivity and dark counts, while the global phase-error rate compounds over all $N-1$ links and therefore degrades faster with both distance and the number of parties. These error rates are integrated into a finite-key secret-key-rate (SKR) framework, in which we optimize the parameter-estimation probability via Brent's derivative-free method. Numerical results show that increasing fiber distance and network size reduce the achievable SKR. Furthermore, the optimized parameter-estimation probability consistently increases the SKR and extends the maximum secure distance relative to a fixed-allocation baseline, with comparison to the asymptotic limit quantifying the residual finite-key penalty.

Energy efficiency (EE) optimization for cellular links often assumes Shannon-capacity transmission. The 3GPP New Radio (NR) physical downlink shared channel (PDSCH), however, delivers a transport block whose size is fixed by the modulation and coding scheme (MCS), the rank, and the resource allocation chosen by the scheduler. The transport block must also meet a target block error rate (BLER). In this paper, we optimize EE for the NR PDSCH with the standard transport block size as the throughput and the target BLER as the constraint. Exponential effective signal-to-noise ratio (SNR) mapping (EESM) turns the BLER constraint into a convex per-subcarrier power constraint. We derive a closed-form waterfilling-like power allocation and an EE-maximizing MCS selection rule. With a single calibration parameter per MCS, fitted offline over 3GPP tapped delay line C (TDL-C) channels, EESM predicts the BLER without per-realization link-level simulation. Numerical results show that a Shannon-capacity baseline overestimates the EE of equal-power transmission by 1.5 to 1.7 times at high SNR within the transmit power budget.

The CANDECOMP/PARAFAC (CP) decomposition is widely used for revealing the underlying patterns from multiway data (also referred to as a higher-order tensor). When the data is clipped or saturated, however, the linear CP model becomes unreliable. Several remedies exist such as treating the clipped entries as missing or imputation; however, both fall short in different scenarios. We propose a nonlinear CP model (NCP) for pattern discovery in this setting, extending ideas from nonlinear matrix decompositions to tensors, and solve it using a flexible ADMM (Alternating Direction Method of Multipliers)-based framework that can accommodate a variety of nonlinearities. Using synthetic data with known factors and simulated metabolomics data where clipping arises from instrument detection limits, we demonstrate that NCP recovers the underlying factors more accurately than CP fitted to only the observed entries or threshold-imputed data.

Movable antennas (MAs) have emerged as a promising technology for enhancing wireless communication performance. However, in delay-sensitive systems, MA movement incurs non-negligible delay, which may degrade the service process for bursty traffic. This paper investigates a delay-outage problem in an MA-enhanced multiuser multiple-input multiple-output (MU-MIMO) downlink system. Unlike existing studies that mainly focus on physical-layer performance optimization, a cross-layer framework is developed that accounts for the overall delay due to antenna movement, along with service interruptions and queue accumulation at the medium access control (MAC) layer. Considering the different timescales of antenna movement and packet-level queue evolution, we establish a two-timescale transmission protocol and a cross-layer queueing model with Markov-modulated bursty arrivals. A long-term transmit-power minimization problem is then formulated to jointly optimize the destination antenna positions and transmit beamforming under delay-outage and practical MA movement constraints. To tackle the probabilistic delay-outage constraints, a backlog martingale is constructed to derive a deterministic sufficient condition in terms of service rate, thereby transforming the probabilistic constraints into deterministic service rate requirements. An alternating optimization (AO) algorithm based on a short-horizon approximation is further developed to obtain a high-quality suboptimal solution. Numerical results demonstrate that the proposed algorithm outperforms baseline schemes by effectively balancing the MAs' spatial reconfiguration capability and the temporal service loss.

Integrated sensing and communication (ISAC) has emerged as a key paradigm for unifying radar sensing and wireless communications within a shared hardware and spectral framework. In parallel, ambient backscatter communication (AmBC) has gained attention as a low-power solution for enabling passive connectivity by modulating and reflecting existing radio-frequency signals. Motivated by these developments, this paper investigates an integrated sensing and ambient backscatter communication (ISABC) framework in which a dual-functional radar-communication (DFRC) transceiver jointly performs high-resolution target sensing and low-power tag detection in cluttered environments. Within this ISAC and AmBC setting, we develop a comprehensive system model that jointly captures the estimation of target and clutter parameters, including delay, Doppler shift, and complex reflectivity, together with the recovery of embedded AmBC tag symbols. The resulting DFRC signal model reveals strong coupling among direct-path reflections, clutter echoes, and passive backscatter signals, leading to a highly entangled estimation problem. To address this challenge, we propose a robust three-stage signal processing framework that sequentially estimates target and clutter parameters before detecting the AmBC tag message. This structured approach effectively disentangles the superimposed components, improving both sensing accuracy and communication reliability in ISAC systems. Extensive simulations, benchmarked against the Cramer-Rao lower bound (CRLB), demonstrate the superior estimation performance of the proposed DFRC-based solution. The results further highlight the fundamental trade-offs between sensing accuracy and backscatter communication efficiency in ISABC systems.

For a battery-powered UAV base station, every gram and watt devoted to RAN-compute competes with flight-duration margin. We therefore keep the 5G Core (5GC) and central unit (CU) on the ground and design the airborne unit around only the backhaul endpoint, distributed unit (DU), and radio. The key question is then a practical one: can widely available commodity ARM computers sustain a real radio OpenAirInterface (OAI) 5G DU with useful performance over heterogeneous F1? We answer it with Raspberry Pi~5 and Jetson Orin Nano as DUs, a USRP B210, a commercial handset, and Ethernet, Wi-Fi/GRE, and 5G/WireGuard backhaul. Jetson preserves 88.4% of x86 split-DU Ethernet DL throughput and 87.5-89.0% across all three bearers; 5G/WireGuard preserves 76.4-77.1% of each host's wired DL rate. The validated Jetson/B210/RM500Q-GL electronics weigh 657.4 g and draw about 28 W under sustained traffic (757.4 g with integration allowance). A controlled link adaptation intervention raises split DL from 23.4 to 99.4 Mb/s as the dominant Modulation and Coding Scheme (MCS) moves from 3 to 26. Finally, synchronized radio, F1-U, CPU, and UHD evidence narrows the remaining monolithic-split gap to split-path scheduling/timing behavior. Beyond performance, this capability serves the emergency-response use case that motivates the aerial cell: once deployed above an affected area, it can broadcast a PWS warning message. We release the OAI patch that carries the single-segment Write-Replace Warning over F1 from CU to DU, where SIB8 is scheduled to the handset. The public artifact makes the payload, performance, and emergency-broadcast baseline reproducible with laboratory-accessible hardware.

Millimeter-wave (mmWave) radar enables unobtrusive, contactless electrocardiogram (ECG) reconstruction for cardiac monitoring. Time-frequency spectrograms preserve fine cardiac patterns but often require large backbones to separate ECG-relevant features from respiration, motion, multipath, and subject-dependent interference. We propose Kapture, a parameter-efficient Koopman-governed framework that projects radar hidden states into a low-dimensional observable space and identifies a regularized full linear evolution operator from adjacent observable states. The Koopman-predicted observables refine subsequent hidden states for ECG reconstruction. To suppress predictable interference dynamics, a temporal contrastive objective pulls neighboring states together and separates non-neighbors, while reconstruction supervision preserves task relevance. Using approximately 80 minutes of quasi-static radar-ECG recordings containing realistic noise from body movements and other sources, Kapture consistently improves reconstruction across matched backbone widths, with the largest gains under aggressive compression. The compact configuration approaches the full-width reference accuracy with 68.1% fewer parameters and 91.3% fewer profiler-covered floating-point operations (FLOPs), while the full-width configuration delivers the strongest overall reconstruction performance. Our code will be made publicly available after potential publication.

This paper proposes a novel aerial reconfigurable intelligent surface (RIS)-aided secure integrated sensing and communication (ISAC) architecture. An unmanned aerial vehicle (UAV)-mounted RIS establishes virtual line-of-sight links to bypass blockages, serving legitimate users while sensing potential eavesdroppers. To ensure communication security and sensing reliability, we formulate an infinite-horizon dynamic control problem maximizing the long-term average system secrecy rate and minimizing UAV flight energy. This formulation jointly optimizes the UAV trajectory, active beamforming with artificial noise (AN) at the base station, and RIS phase shifts, subject to strict radar signal-to-noise ratio and UAV kinematic constraints. Due to the highly coupled non-convex variables, solving this problem directly is intractable. Therefore, we propose an online joint optimization framework based on model predictive control (MPC). Within each receding horizon, alternating optimization is employed to decouple the problem into three subproblems, efficiently solved via successive convex approximation and difference of convex programming. Extensive simulations demonstrate that our MPC-based algorithm significantly outperforms static baselines in secrecy rate, exhibiting superior online correction capability and robustness against environmental perturbations.

Analog computing with microwave signals can perform linear transformations directly in the analog domain, as the signals propagate through a microwave network. A fundamental question is which transformations can be computed with a given set of microwave components. In our previous work, we answered this question for networks of hybrid couplers and phase shifters by deriving a necessary and sufficient condition on the transformations these networks can compute, and we showed that the discrete Fourier transform (DFT) satisfies it. In this paper, we take a first step toward the formalization of analog computing with microwaves in Lean, a programming language and proof assistant that is increasingly adopted in mathematics. We formalize the considered components, their series and parallel connections, and the class of networks they can implement. Then, we formally prove the necessary and sufficient condition characterizing these networks, as well as the implementability of the DFT of any size power of two. All proofs are checked by the Lean kernel, and the code is openly available at: https://github.com/matteonerini/formalizing-analog-computing.

Future integrated sensing and communication (ISAC) networks require wireless platforms that can adapt not only their signals but also the physical locations from which they radiate and observe. This article presents pinching-antenna systems (PASS) as a spatially adaptive platform for ISAC. PASS adopts dielectric waveguides as signal-transport media and reconfigurable dielectric pinching antennas to realize programmable radiation and observation points along these waveguides. This unique architecture introduces new spatial degrees of freedom, allowing the network to dynamically reconfigure its interaction geometry with users, targets, and the surrounding environment, rather than solely optimizing signals over a fixed antenna geometry. Leveraging a terahertz testbed, we experimentally validate the spatial control and spatially selective reception capabilities of PASS and demonstrate constructive field combining between two coherent radiation points. We further explore how such spatial reconfigurability can enable radio-map-assisted communication, environment division multiple access, and closed-loop active radio perception. Finally, we identify key challenges and research directions toward practical PASS-ISAC networks.

Ultra Reliable Low-Latency Communication (URLLC) demands submillisecond latency and reliability above 99.999%, which are difficult to guarantee under dynamic wireless and atmospheric conditions. Although free space optical (FSO) links provide high-capacity transmission, their performance degrades under turbulence and fog, while radiofrequency (RF) links offer robustness but limited bandwidth. This paper presents a reinforcement learning-based hybrid optical RF framework that adaptively selects transmission modes to satisfy stringent URLLC requirements. The hybrid decision process is modeled as a Markov Decision Process and optimized using a Deep Q Network (DQN) controller. Extensive simulations under varying attenuation and fading scenarios demonstrate up to 35% latency reduction and 25 to 30 % throughput improvement compared to RF-only and static hybrid schemes, while maintaining reliability above 99.999%. A laboratory-scale testbed operating at 1550 nm and 28 GHz further validates the approach, showing less than 6% deviation between simulated and measured results. The proposed framework demonstrates the practical feasibility of intelligent hybrid transmission for mission-critical next generation wireless networks.

Most federated learning (FL) studies implicitly assume that training data is readily available at participating edge devices. In practice, this data must first be acquired through heterogeneous sensors with potentially different measurement modalities, and the quality and cost of this acquisition process can directly affect the learning performance. This dependence creates a coupled relationship among sensing, computation, and communication, necessitating a system model that captures their joint effect on resource efficiency and learning accuracy. This paper develops such a unified treatment for an over-the-air (OTA) FL system in which each device configures its sensing modality, power, resolution, and sample size, with aggregations conducted OTA at the server. We derive models for sensing noise, energy consumption, latency, and OTA aggregation distortion, and establish a non-convex convergence bound quantifying how these factors influence the global model trajectory. Guided by this analysis, we formulate a per-round optimization that balances energy, latency, and learning quality under physical device constraints. The resulting mixed optimization is addressed through an alternating procedure with closed-form and one-dimensional updates for continuous variables and a lightweight discrete search. Numerical experiments corroborate the advantage of jointly optimizing sensing, computation, and communication decisions.

Neural audio codecs compress waveforms into compact discrete tokens that underpin speech language models, real-time communication, and large-scale audio storage. Almost every dominant design, including residual vector quantization, finite scalar quantization, and single-codebook variants, follows the VQ-VAE template by partitioning the encoder latent through a learned codebook or a fixed scalar grid. We ask whether this partition is necessary. We introduce GS-Codec, a neural speech codec whose bottleneck is a parametric signal decomposition rather than a quantizer. We adapt Gaussian splatting from 3D scene reconstruction to one-dimensional latents. An inner optimization loop fits each encoder segment as a weighted sum of 1D Gaussian primitives. The decoder then reconstructs the waveform from the rendered sum. To avoid the cost of this iterative inner loop at inference time, we additionally train a lightweight GS Predictor Net that regresses the primitive parameters in a single forward pass. The encoder and decoder are trained end-to-end through the inner loop, with no quantizer anywhere in the training pipeline: the bottleneck is the decomposition itself, and scalar quantization is applied only post-training to the fitted parameters. Rather than relying on discrete codebook stages for bitrate control, our representation exposes a fine-grained rate-quality tradeoff: a single trained checkpoint supports post-training bitrate control by varying the number of primitives and the per-parameter bit depth, with no retraining required. GS-Codec matches or exceeds well-established open-source codecs such as EnCodec and DAC on speaker similarity (SIM), intelligibility (STOI), and perceptual quality (UTMOS) at comparable bitrates, while achieving comparable semantic performance (WER). Code and audio samples are available at https://ronaluf.github.io/gs-codec/

The escalating energy demands of modern wireless networks make energy-efficient system design a central challenge for future 6G deployments. Relay-assisted communication has long been recognized as a powerful tool for improving coverage and throughput, tracing its roots to early repeater communication systems and the foundational relay channel models in information theory. Meanwhile, modern relay selection and routing strategies are primarily driven by rate-based or transmit-power-centric metrics, which often neglect hardware-level losses that is independent of the transmitted signal. As networks grow denser and more heterogeneous, signal-independent hardware inefficiencies and circuit power consumption increasingly dominate the total energy consumption and fundamentally alter the optimality of relay assignment decisions. To address the challenge of modeling signal-independent power consumption in relay-assisted systems, the Waste Factor (WF) framework is adopted: a hardware-aware, unifying metric that explicitly captures signal-independent power losses across cascaded transceivers and wireless channels. Using WF, the energy consumption of multi-user, multi-relay wireless systems is quantified and minimized under practical hardware and network constraints. The formulation jointly accounts for component inefficiencies, propagation losses, and relay-selection constraints. The proposed framework demonstrates that the WF formulation provides a fundamentally new way to model relay-assisted communication, enabling hardware-aware energy-per-bit minimization and a structured representation that enables computationally efficient analysis. Simulation results confirm that WF-based relay assignment reduces total energy per bit while improving the ability of users to satisfy minimum received-power requirements, outperforming conventional formulations across both metrics.

We consider multiuser downlink beamforming with prescribed signal-to-interference-plus-noise ratio (SINR) requirements under imperfect channel state information and a tight transmit power budget. Robust worst-case designs often require tractable channel-error models and may not yield feasible beamformers that meet the power budget. To alleviate these issues, we propose conformal risk control for beamforming (CRCBeam), which combines the classical fixed-point minimum-power beamforming with conformal risk control to calibrate an SINR margin from calibration sample pairs of estimated and true channels. Under exchangeability, CRCBeam provides finite-sample control over served-outage risk, which is the probability of transmitting when at least one user fails to meet its SINR target. CRCBeam requires no parametric channel-error model and transmits only when the power budget is met. Numerical experiments using a pilot contamination channel error model corroborate that CRCBeam achieves the served-outage risk while meeting the power budget. In contrast, worst-case robust baselines, Gaussian-ball and conformal-ball beamforming, either do not meet the target risk or exceed the power budget.

Integrated sensing and communication (ISAC) has emerged as a promising approach for vehicle-to-everything (V2X) systems by enabling communication and sensing over shared radio resources without additional installation of dedicated sensors. However, candidate resources may experience different communication qualities due to varying channel conditions and resource contention, which should be considered when designing sensing resource patterns. In this letter, we propose a quality-of-service (QoS)-constrained resource pattern selection framework that jointly considers communication reliability and sensing performance. The sensing objective is formulated based on the Fisher information matrix for joint range and velocity estimation, while a minimum packet reception ratio (PRR) is imposed as the communication QoS constraint. To avoid the complexity of exhaustive search, a greedy selection algorithm with one-swap refinement is developed. Simulation results show that the proposed method improves sensing performance over conventional resource patterns while satisfying the PRR requirement and achieves performance close to the exhaustive-search optimum and sensing-only optimum.

Sub-terahertz (sub-THz) low-phase-noise signal sources commonly employ frequency multiplier chains to extend the operating frequency of lower-frequency oscillators. This paper presents the design and simulations of a wideband, high-output-power frequency multiplier implemented in TSMC 65 nm CMOS technology. The proposed design is based on a distributed frequency multiplier with a quarter-wave transmission line termination at the drain side, where the reflected and forward-traveling waves are constructively combined to enhance the output power. The proposed frequency doubler achieves a minimum conversion loss of 12 dB at an input frequency of 115 GHz with an input power of 12 dBm, resulting in an output power of 0 dBm and 12.6 dB fundamental suppression. For the same input power, the output power remains within 3 dB of its peak value over the 188-260 GHz output frequency range. At 230 GHz, the proposed design achieves a saturated output power of 4 dBm while occupying an area of 0.149 mm^2.

To address the dual challenges of limited transmission distance and wireless communication security in maritime communications, a directional modulation network enhanced by an elevatable and rotatable antenna array is investigated in this paper. Specifically, the base station, whose array and antenna elements can be rotated and whose antenna height can be adjusted, transmits information to legitimate users while resisting eavesdropping. Moreover, transceiver hardware impairments are taken into account. Accordingly, a worst-case secrecy rate maximization problem is formulated under the joint optimization of precoding vectors, array orientation, antenna orientations, and array height. To solve this non-convex optimization challenge, an alternating optimization scheme and a Transformer-based learning scheme are proposed for the single-user and multi-user scenarios, respectively. Simulation results validate the effectiveness of the proposed algorithms. Security performance gains can be obtained after optimizing the array height, array orientation, and antenna orientations. In addition, compared with the isotropic antenna benchmark, the Transformer-based learning scheme can achieve approximately 90% power savings, 87% antenna savings, and serve more legitimate users.

Status updates let a receiver track a changing process, but a sender that controls when they are sent can steer the receiver's estimate without misreporting. We study competition between two such senders with opposing preferences, who report on a binary continuous-time Markov chain using state-dependent Poisson rates under individual budgets. The receiver follows the reports through a zero-order-hold estimator only if this yields at least the long-run weighted correct-estimation utility of a prior-based default estimate. We model the interaction as a Stackelberg game in which the sender favored by this default leads, the opposing sender follows, and the receiver then selects its estimator. We characterize the pure-strategy equilibria in closed form. When the follower can obtain positive utility against a silent leader, limited state-$0$ reporting by the leader merely frees part of the follower's budget for state-$1$ reports, so every optimal leader policy is either silence or full-budget state-$0$ reporting, depending on explicit budget thresholds. A single sender with the same total budget leaves the receiver at its default utility, whereas the full-budget intervention outcome makes the receiver's participation constraint slack and strictly improves its utility. At the intervention threshold, silence and full-budget intervention yield identical sender payoffs but different receiver utilities. Numerical results illustrate these regimes and compare equilibrium performance with a receiver-optimal reporting benchmark.

Many wireless control tasks require repeated selection of a single action from a finite feasible set under stringent latency and reliability constraints. While large language models (LLMs) have recently emerged as general-purpose decision engines, their autoregressive generation mechanism is not naturally aligned with such bounded control problems. This paper investigates System-One models, which directly learn probability distributions over explicitly defined decision spaces, as a lightweight alternative for wireless decision-making. We formalize their decision structure and learning objective, identify their applicability across physical-layer control, radio resource management, mobility, network slicing, and network operations, and evaluate their practical behavior through representative wireless case studies. Using Jev as a System-One implementation, we benchmark decision quality and client-observed latency against generative LLMs and conventional baselines. In receive-antenna selection, Jev delivers up to an 8.5x reduction in median response latency relative to the evaluated LLMs, although this gain comes with a loss in decision quality compared with stronger task-specific alternatives. More notably, in intent-conditioned RAN slicing, Jev achieves utility comparable to the evaluated LLMs while providing more than a 3.5x reduction in median response latency. Complementary evidence from edge-service orchestration further shows that faster decisions do not necessarily translate into lower end-to-end service latency. These results expose a fundamental quality/latency tradeoff and position System-One models not as replacements for numerical optimization, but as a promising decision interface for latency-sensitive, bounded, and intent-driven wireless control.

Learning-based models for radio resource management (RRM) are typically built for a single function and deployment, so each new setting repeats the development pipeline. RRM decisions, however, share a common structure: each is assembled from interdependent fields, defined by the standard, whose values are selected in view of the network state. We propose RRM-GPT, an autoregressive framework for RRM foundation models that generate these decisions as a language model generates text. An encoder maps heterogeneous network observations into a common token representation, and a decoder emits the decision one field at a time, each conditioned on the network state and the fields already committed. Pretraining on unannotated network logs teaches the model what makes a decision valid and how controllers choose among valid decisions; post-training then adapts it to deployment-specific operator objectives through imitation or reinforcement learning. The framework targets two forms of reuse: a function-specific model reused across deployments, and a model shared across RRM functions that generates their interdependent decisions as one sequence. In a 5G New Radio (NR) case study, we demonstrate that a single model generates complete scheduling grants spanning user selection, timing, link adaptation, resource allocation, and control signaling. The model captures dependencies among grant fields and transfers learned behavior to an unseen scenario without adaptation.

Wireless foundation models learn representations from unlabeled radio signals for reuse across downstream tasks. Scaling model capacity is a common strategy for learning richer representations and improving downstream performance. However, its gains are less consistent in wireless self-supervised learning when pretraining data are limited. We investigate objective diversity as an alternative scaling axis: different self-supervised objectives emphasize different signal properties, and combining their representations can preserve more information to enable diverse tasks. We develop a fusion framework that combines frozen representations from independent encoders trained through reconstructive, predictive, and contrastive learning. This provides a reference for the benefits of diversity, but requires the maintenance of multiple encoders. To retain these benefits within the parameter budget of a standard single-objective encoder, we introduce a jointly trained multi-branch architecture with a shared trunk and objective-specific branches. We pretrain on a heterogeneous corpus of spectrogram and channel state information data and evaluate six downstream tasks that span communication, sensing, and positioning. At an equal output embedding dimension, fusion outperforms the evaluated single-objective encoders on all six tasks while using roughly one-third of their parameters. The multi-branch architecture retains much of the fusion benefit within a single-encoder parameter budget. Representation analyses indicate complementary contributions across objectives, with much of the added benefit retained in components orthogonal to the reconstruction representation subspace. These findings support objective diversity as an effective strategy for scaling wireless foundation models.

Frequency range 3 (FR3) is being explored for sixth-generation (6G) integrated sensing and communication (ISAC).However, experimental evidence on the feasibility of physiological sensing in this band is limited. This paper presents VitalFR3, an SDR-based frequency-modulated continuous-wave (FMCW) measurement platform that integrates a direct-sampling Ettus USRP X440 with a SignalCraft SC2470 frequency converter. To the best of our knowledge, this is the first calibrated SDRbased FMCW platform evaluated for respiration and cardiac-rate estimation from human measurements across multiple FR3center frequencies. Using a 100 MHz waveform, we operate the platform at six RF center frequencies from 10 to 22.5 GHz.We demonstrate how to characterize and calibrate the completeVitalFR3 transmit and receive path directly from loopback recordings, achieving a magnitude error below 0.061 dB at all six frequencies. We then propose a low-complexity end-to-end processing pipeline for vital sign estimation. This involves the identification of a persistent target-associated response in fast time, respiratory tracking in slow time, followed by cardiac- rate estimation. The system is evaluated across 16 human measurements, achieving a respiratory-rate mean absolute error of 0.758 breaths per minute, and the majority of the cardiac- rate estimates fall within 5 BPM of their reference rates. In a separate breath-hold measurement, the estimated cardiac rate is 84.3 BPM versus approximately 84.5 BPM from the reference. These results establish VitalFR3 as a calibrated and configurable measurement platform for experimental physiological sensing across FR3.

Deep learning precoders, particularly those based on Transformer architectures, offer superior spectral efficiency for Massive MIMO systems but may incur high computational cost. While existing mixed-precision quantization studies on convolutional neural networks establish important energy baselines, traditional discrete search methods fail to scale to the large parameter spaces of modern Transformers. To bridge this gap, we apply a differentiable precision learning technique to massive MIMO precoding. This approach autonomously and jointly optimizes weights, activations, quantization step sizes, and layer-wise bit-widths in a single training loop, directly optimizing for energy efficiency. We further expand this approach by incorporating Neural Architecture Search across various Transformer model sizes and evaluating the impact of random versus floating-point trained initializations on resolution tuning. Ultimately, we demonstrate that the proposed training method enables the deployment of highly compact Transformer precoders, improving energy efficiency by up to 288\(\times\) compared to the classical Weighted Minimum Mean Square Error algorithm at equal sum-rate performance in a dense downtown environment.

Continuous-discrete filtering is a classical problem in signal processing and control theory, whereby the state driving a stochastic differential equation (SDE) must be inferred from noisy, partial discrete-time observations. Corresponding filtering algorithms are often formed as discrete-time methods and applied to a discretization of the SDE, an approach limited by the incurred discretization error, or model a type of Gaussian moment ordinary differential equation (ODE) flow, which is approximate for nonlinear dynamics. We introduce a new method, the Wiener-space cubature Kalman filter (WSC-KF), for Gaussian filtering in this continuous-discrete setting. The WSC-KF combines spatial sigma-point representations with Wiener space cubature integration, effectively approximating iterated integral terms of the driving Brownian motion to higher-order than standard discretization (e.g., Ito-Taylor expansions), or approximate moment-ODE flows (e.g., as in the unscented Kalman filter). Theoretically, we characterize the path-law approximation degree of WSC-KF through a tunable model order parameter. We prove that, depending on the model order chosen, WSC-KF provides weak order bounds comparable to or dominating that of a cubature Kalman filter using a first-order Ito predict step. Numerically, we confirm that rate on a globally Lipschitz Levy-area diffusion. Degree-5 Wiener cubature matches mixed iterated integrals that existing continuous-discrete Kalman predictors omit, while remaining efficiently computable; this is the mechanism enabling higher-order weak path-law approximation.

Although scattering is inherently non-separable, holographic multiple-input multiple-output channels are commonly modeled using separable scattering across the source and receiver. In this paper, we investigate non-separable scattering in a non-line-of-sight holographic line model based on wavenumber-division multiplexing. We model its power spectral factor (PSF) using a bivariate von Mises-Fisher (vMF) distribution and introduce a non-separability parameter. We prove that the marginal densities flatten and their Kullback–Leibler divergence from the uniform distribution decreases monotonically with non-separability. Using majorization theory, we further establish that an upper bound and an approximation at low signal-to-noise ratio (SNR) of the ergodic spectral efficiency increase monotonically. Numerical results validate the analysis and demonstrate the performance gains of non-separable scattering over its separable counterpart.

Upper mid-band spectrum is promising for 5G and 6G cellular networks, yet concrete and low-emissivity glass impose over 20 dB of building penetration loss above 2 GHz, degrading outdoor-to-indoor (O2I) coverage. A reconfigurable intelligent surface (RIS) on a neighboring building bypasses this blockage by focusing energy onto indoor users. This paper introduces PRIMO (Parallel RIS Min-Rate Optimization), a training-free joint optimizer of base-station precoding, RIS phase shifts, and RIS placement, validated by calibrated ray tracing in a MetroTech Commons digital twin (Brooklyn, NY) at 6.75 and 16.95 GHz. At equal physical aperture, half-wavelength spacing packs 6.25 times as many elements per array at FR3 as at FR1(C), so FR1(C) leads at low indoor-user counts while FR3 dominates at higher counts, where the additional spatial degrees of freedom suppress inter-user interference. Above a 1 bps/Hz service floor, PRIMO supports 92% more indoor users at FR3 than at FR1(C), and raises the minimum user rate by up to 90% over a fully converged alternating-optimization baseline while running about 100 times faster.

Magnetometers have numerous applications, one of the most important being heading determination. In practice, magnetometer measurements are affected by sensor manufacturing errors and by hard-iron and soft-iron effects, and therefore calibration is required for reliable heading estimation. Many conventional three-dimensional calibration methods require sufficiently rich multi-axis rotational excitation, which is not feasible in some applications, particularly for in-situ or online calibration. This paper proposes a two-dimensional magnetometer calibration method aided by inertial sensors. The proposed method is simple, closed-form, and non-iterative, and uses a single-axis rotational motion together with measured roll and pitch angles. After calibration, the corrected magnetic measurements are used for heading determination. Simulation and experimental results demonstrate the effectiveness of the proposed method under limited-rotation conditions.

Pinching-antenna systems attach dielectric pinches to a waveguide, creating line-of-sight radiation points within a few meters of any user. The pinches on one waveguide, however, radiate the same signal, unlike a conventional array, where each element transmits a dedicated signal. Separating the signals of multiple users therefore becomes challenging and requires a new perspective on multiple access. Most existing studies rest on an idealized model, and no survey examines the access schemes under realistic impairments. This article surveys multiple access in pinching-antenna systems and organizes fifty-six works into a unified taxonomy by spatial degrees of freedom. The users of one waveguide can be separated only in time, frequency, power, or guided mode, while multiple waveguides add the space domain. The article then identifies the impairments critical to this separation, which perturb the effective channel, leak interference across orthogonal resources, or couple the pinches on the uplink, and summarizes them in an impairment-versus-method map. A unified evaluation of time-, frequency-, power-, and space-domain access under identical channel and impairment models reveals that the degradation is method specific. The analytical and numerical results give insight into multiple access in these systems and provide a basis for its impairment-aware design, a necessary step toward their adoption in practical networks.