Low-power wake-up radio (LP-WuR) is a key technology for the sixth-generation (6G) Release 20 roadmap for enabling energy-efficient devices. However, its reliance on non-coherent envelope detection (ED) fundamentally limits detection reliability under multipath fading and low signal-to-noise ratio (SNR), restricting detection probability at the cell edge. This letter proposes a non-coherent rake receiver with blind phase compensation for On-Off keying (OOK)-based LP-WuR, operating without channel state information (CSI). The proposed receiver resolves effective channel taps at the LP-WuR filter bandwidth, detects reliable fingers via a constant false alarm rate (CFAR) threshold, and blindly compensates the per-tap phase through a quantized phasor bank, using a locally generated reference, before coherently combining the taps using equal gain combining (EGC). A closed-form bit error rate (BER) analysis confirms diversity order equal to the number of resolved fingers, yielding a diversity gain with a slight quantization loss. Simulation results validate the gain in BER while incurring a bounded, roughly twelvefold operation-count overhead over ED.
Federated learning in Open Radio Access Networks (O-RAN) enables privacy-preserving distributed training, yet regulatory mandates such as the General Data Protection Regulation (GDPR) require verifiable erasure of client data influence from trained models. Existing federated unlearning schemes assume ideal channels, rendering them impractical under real wireless conditions. We propose Channel-Aware Federated Unlearning (CAFU), which reconceives fading channel noise as a constructive gradient perturbation resource, eliminating the need for full model retraining. Orchestrated via a dedicated xApp on the near-Real-Time RAN Intelligent Controller (near-RT RIC) using real-time channel state information, CAFU provably achieves ε-certified forgetting with substantially reduced communication overhead compared to baselines, while preserving post-unlearning model utility.
This letter investigates a two-stage near-field covert communication for wideband millimeter-wave (mmWave) systems. During the sensing stage, we propose a novel sensing scheme exploiting controllable near-field beam squint, enabled by phase shifters (PSs) and true-time delays (TTDs), to acquire the channel state information (CSI) of a passive warden Willie. The sensing accuracy is analytically characterized by the Cramér-Rao bounds (CRBs), which establish a CRB-driven uncertainty model for Willie’s channel. During the communication stage, relying on this imperfect CSI, we maximize the achievable covert capacity by jointly optimizing the sensing duration and wideband analog beamforming, subject to a Kullback-Leibler divergence-based covertness constraint. To tackle the intractable problem, we develop a robust fully-digital approximation scheme coupled with a one-dimensional search, followed by an alternating optimization algorithm to extract the practical TTD and PS configurations. Finally, simulation results validate the proposed framework, revealing significant covert capacity gains over TTD-free baselines and highlighting a fundamental sensing-communication trade-off.
This letter addresses the challenges of spectrum scarcity and co-channel interference in visible light communication (VLC) multiple-input multiple-output (MIMO) systems by introducing over-the-air computation (AirComp) technology. Under VLC-specific transmission constraints, the precoding matrices, aggregation matrix, and DC bias vectors are tightly coupled, which makes the resulting optimization problem highly non-convex. To solve this problem, we propose a Stiefel manifold-based alternating optimization (SMAO) method designed for VLC-MIMO AirComp systems to minimize the aggregation mean squared error (MSE). Simulation results show that the proposed SMAO method achieves a lower aggregation MSE than the baseline methods under both perfect and imperfect channel state information (CSI).
In multiple-input multiple-output (MIMO) systems, beamforming is a core enabler for concentrating signal power toward targeted users to enhance spectral efficiency and network capacity. However, realizing the full potential of beamforming critically relies on accurate channel state information (CSI). Conventional CSI acquisition requires extensive pilot transmissions and continuous feedback, incurring high overhead. To overcome this limitation, inspired by the novel concept of channel knowledge map (CKM), we propose an environment-aware beamforming prediction (EA-BFP) framework that directly maps environmental topology and transceiver locations to optimal beamforming vectors. The framework employs a dual-branch neural network: a modified ResNet extracts spatial features from the environmental map, while fourier position encoding (FoPE) provides precise high-dimensional coordinate embedding, which enables zero-shot beam decisions. Simulation results demonstrate that our approach achieves lower prediction errors and higher achievable rates than baseline models, showcasing superior main lobe alignment, data efficiency, and cross-scenario generalization under limited fine-tuning samples.
This letter proposes a diffusion model-based signal restoration framework to enhance signal quality in wireless communication systems. In this study, the received complex-valued signals are transformed as image representations to learn spatial structural characteristics. In addition, the SDEdit strategy is introduced to mitigate the distribution mismatch between the Gaussian noise distribution assumed during diffusion model training and the distribution of actual input signals. To validate the practical applicability of the proposed method, a DMRS (Demodulation Reference Signal) dataset was constructed in a real wireless communication environment using a self-implemented 5G spec-compliant testbed. Experimental results show that the proposed framework consistently outperforms traditional denoising techniques as well as deep learning-based models across all SNR environments.
Pinching-antenna systems (PASS) can reshape an integrated sensing and communication (ISAC) channel through radiating-point placement, but one carrier state generally cannot simultaneously condition a multiuser channel and coherently combine a target echo. This letter studies movable-signal multiple access (MSMA) for this mismatch. The data streams share one block-frozen communication state and are separated by joint feed precoding, whereas a coded sensing reference occupies a second block-frozen state over the same PASS geometry. An attenuation-aware guided-plus-spherical channel model is developed with an explicit point-target coefficient and finite receive-filter overlap between the two state components. For the resulting target-dependent disturbance covariance, an exact conditional Fisher-information matrix and Cramér–Rao lower bound are derived, followed by a local curvature law that relates state resolution to PASS-induced delay variance. A three-block method screens finite state pairs, jointly selects structured precoding directions and allocates stream powers, and updates ordered pinching positions. Every reported iterate is feasible for the original SINR and power constraints and is accepted only when the exact sensing objective does not decrease. Numerical results show the gain over fixed-carrier, single-state, and fixed-geometry designs.
Accurate time difference of arrival (TDOA) localization depends on reliable measurements. In cluttered indoor and urban environments, non-line-of-sight (NLOS) propagation is common. It introduces positive excess delays and biases TDOA observations. This paper studies receiver subset selection for TDOA localization under NLOS conditions. Conventional minimum cost fitting may select a subset with a low residual but a large localization error. To improve robustness, we propose a subset expansion detection framework. Each original four receiver subset is augmented with additional receivers to form multiple expanded configurations. This allows the same original subset to be examined repeatedly.We further develop a validation scheme that combines a balancing parameter test with a position consistency test. The former exploits the one-sided nature of NLOS delays. The latter checks whether position estimates remain consistent across different expanded subsets. Simulation results show that the proposed method achieves lower root mean square error than several baselines under a range of NLOS conditions.
Quantum annealer decoders have been proposed to unlock new potential by addressing channel decoding problems in quadratic unconstrained binary optimization (QUBO) form. However, the variables required by the existing node constraints in polar-QUBO decoding increase rapidly with the code length, resulting in prohibitively high computational complexity. In this paper, we propose reduced-variable QUBO (RV-QUBO) for short systematic polar codes, based on three key steps: 1) applying equivalent generator matrix constraints to replace the node constraints; 2) introducing a common subexpression elimination approach to identify and replace recurring XOR subexpressions with new variables; 3) imposing an extra length restriction on each XOR expression to balance the error-correction performance and the variable count. Numerical results indicate that the proposed scheme achieves a 73.60%reduction in variable count for P(32, 28), while reducing the required iterations for convergence by 66.7%.
Carrier frequency offset (CFO) severely degrades parameter estimation in orthogonal frequency-division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems because it is coupled with target Doppler shifts. In this letter, we propose a clutter-aided (CA)-space-alternating generalized expectation-maximization (SAGE) algorithm that exploits dominant scattering components to decouple the common frequency offset from target Doppler shifts and jointly estimate target ranges and velocities. The CFO is inferred from the frequency group supported by multiple scattering clusters and refined via maximum likelihood optimization. A closed-form Cramér-Rao lower bound (CRLB) is derived to characterize the performance limits. Simulations show that the proposed method resolves the CFO–Doppler coupling, achieves near-CRLB performance, and outperforms baseline schemes.
Conventional near-field codebooks uniformly cover a prescribed angle–distance region, which can produce redundant codewords and excessive pilot overhead in scene-specific environments. This letter proposes a distribution-aware variational codebook learning (DAVCL) framework that jointly learns uplink (UL) sensing, finite codebook construction, and direct beam selection from unlabeled channel samples. A learned phase-only sensing matrix maps T UL pilots into a Gaussian-mixture latent space, whose decoded component means form K constant-modulus (CM) codewords and whose posterior directly selects a codeword without beam sweeping. A decoder-only rate-refinement stage further improves the prototype-to-beam mapping while preserving the learned posterior selection rule. Simulations demonstrate that DAVCL improves effective rate and reduces redundant codewords compared with geometry-based and learning-based baselines.
Pinching antennas (PAs) reconfigure wireless channels by adjusting the positions of radiating elements along dielectric waveguides. In this paper, we propose a PA-assisted index modulation multiple-access (PA-IMMA) scheme for 6G uplink communications, where each user conveys information through both modulation symbols and time-slot indices. We further develop a PA-position optimization algorithm that adapts the PA deployment to the user distribution by maximizing the minimum equivalent channel gain, thereby improving the BER performance of the weakest user and enhancing user fairness. Simulation results show that PA-IMMA outperforms conventional PA-based time-division multiple access and validate the effectiveness of the proposed optimization algorithm.
In this letter, we investigate uplink optical wireless random access in a symmetric four-photodiode (PD) receiver cell, where each PD is allowed to move along the ceiling diagonals and is jointly optimized in terms of its position, orientation, and field of view (FOV). To guarantee full observability of the coverage area, we first derive an analytical bound that characterizes the relationship among PD displacement, pointing direction, and the minimum FOV required for complete coverage. Leveraging this result, we propose a coverage-critical boundary optimization strategy that substantially reduces the search space for identifying the optimum. Based on a Lambertian channel model and an signal-to-interference plus noise ratio (SINR)-driven successive interference cancellation (SIC) decoding rule, simulation results confirm the existence of an optimal position–orientation–FOV triplet. In unobstructed environments, the optimal geometry places the PDs near the cell corners, and enabling orientation control—rather than adjusting the FOV alone with fixed PD positions—achieves up to a 37.5% throughput improvement over conventional designs.
Unmanned Aerial Vehicles (UAVs) are envisioned as key enablers for time-sensitive data collection in 6G cognitive networks. Semantic communication offers a promising solution to overcome bandwidth scarcity by transmitting only essential information. However, the heavy computational burden of semantic extraction is often ignored, which can lead to significant processing delays and stale information. In this paper, we investigate a semantic-aware Age of Information (AoI) minimization problem in UAV-assisted networks, explicitly accounting for the trade-off between transmission latency and computation latency. We formulate a joint optimization problem for UAV trajectory planning and semantic compression level selection. To solve this problem with a hybrid action space (continuous trajectory and discrete compression levels), we propose a Hybrid-Action Proximal Policy Optimization (HA-PPO) deep reinforcement learning algorithm. Simulation results demonstrate that the proposed scheme significantly reduces the Semantic-aware AoI compared to traditional bit-based and greedy semantic baselines. Crucially, our results reveal that intelligent switching between semantic extraction and raw transmission based on the UAV’s computing capacity is essential for maintaining information freshness.
This letter proposes an adaptive multi-link operation (MLO) traffic-to-link allocation scheme (ATLAS) to reduce the latency of multi-link devices (MLDs) by allocating the traffic to links under enhanced distributed channel access (EDCA) uplink scenarios. A Markov chain is constructed to analyze EDCA, and the transmission probability of each access category (AC) is derived to estimate the expected access delay and queueing delay. A min-max problem is established to effectively allocate traffic of each AC to multiple links, so that the link layer delay (including the upper MAC holding delay) is minimized.
To address the high energy consumption in massive multiple-input multiple-output (MIMO) systems, this letter proposes a two-stage quantization framework that jointly optimizes analog-to-digital converter (ADC) bit allocation (BA) and baseband bitwidth assignment. By leveraging the fact that ADC resolutions can be dynamically reconfigured whereas baseband bitwidths are typically fixed at design time, the joint problem is decoupled. The ADC BA is determined via theoretical energy efficiency (EE) and bit error rate (BER) analysis, enabling real-time adaptation, while the baseband bitwidths are optimized via a simulated annealing (SA) based search. Simulation results show that with less than 0.5 dB signal-to-noise ratio degradation, the proposed method are able to reduce the average baseband bitwidth by 47.77% compared with unified quantization, and improves EE by 13.4% over uniform BA.
In orthogonal time-frequency space (OTFS) modulation, the discrete Zak transform (DZT) is employed to map information symbols onto the delay-Doppler (DD) domain, effectively mitigating the severe Doppler shifts encountered in low Earth orbit (LEO) satellite channels. However, the fixed discrete sampling structure inherent to DZT-based mapping is not well suited for handling fractional delays and fractional Doppler shifts in such channels. In this work, we first analyze and establish the feasibility of OTFS modulation based on the finite fractional Zak transform (FFrZT). Building on this foundation, we propose an FFrZT-based OTFS modulation scheme and apply it to LEO satellite communication systems, evaluating its performance in coping with fractional delay and fractional Doppler effects in LEO channels. Simulation results demonstrate that, compared with conventional DZT-based OTFS modulation, the proposed FFrZT-OTFS scheme not only more effectively mitigates fractional delay and fractional Doppler impairments and improves the bit error rate (BER) performance without increasing receiver complexity but also achieves a reduced peak-to-average power ratio (PAPR).
Cell-Free MIMO integrated sensing and communication (CF-ISAC) systems can improve the capabilities of communication and sensing by utilizing distributed access points (APs). However, the imperfect channel state information (CSI) weakens the efforts of beamforming designs, resulting in performance degradation of CF-ISAC systems. To address this issue, this letter proposes a robust beamforming design algorithm for CF-ISAC systems. Specifically, we first formulate an optimization problem that maximizes the weighted sum rate (WSR) of user equipments (UEs) and leverages thresholds to fine-tune the communication performance of UEs and the sensing performance of the target, respectively. To deal with the non-convexity of the problem, the Lagrangian dual transform and the quadratic transform are utilized to convert the objective function into a convex form. Additionally, the S-procedure, Schur’s complement, and Taylor’s expansion are employed to convert the constraints into convex forms. Finally, the original optimization problem is transformed into a set of tractable semidefinite programming (SDP) subproblems. Simulation results demonstrate that the proposed robust algorithm exhibits a higher probability of satisfying each UE’s communication rate constraint under imperfect CSI.
In millimeter-wave (mmWave) multi-subarray systems, the beamforming space is limited by hardware constraints, and the anti-jamming performance degrades due to direction-of-arrival (DoA) estimation errors. To tackle these challenges, this paper proposes two robust hybrid beamforming (HBF) approaches, namely interior-point (IP)-based HBF and deep unfolding network (DUN)-based HBF. The IP-based HBF leverages Riemannian manifold optimization to design the analog beamforming matrix under hardware constraints, and employs the interior-point algorithm to maximize the signal-to-interference-plus-noise ratio (SINR) under strict jamming suppression constraints. The DUN-based HBF unfolds the gradient ascent algorithm into a deep network, significantly reducing computational complexity with slight performance degradation. Simulation results demonstrate that the IP-based HBF achieves superior anti-jamming performance, while the DUN-based HBF achieves a favorable trade-off between anti-jamming performance and computational complexity. Both proposed methods outperform the baseline schemes, providing flexible and effective anti-jamming solutions for practical mmWave multi-subarray systems.
A fluid antenna system (FAS) can improve both energy harvesting and data transmission in wireless powered communication (WPC) by exploiting spatial diversity with a single RF chain. In this paper, we study the outage probability of a FAS-assisted WPC network under the port selection criterion that maximizes the product of the channel gains of the two links. Adopting the block-correlation (BC) decomposition fitted to the Jakes’ autocorrelation function, we derive a semi-analytical outage expression via nested Gauss–Chebyshev quadrature with singularity-removing variable substitutions. Numerical results under the exact Jakes’ correlation confirm the analytical predictions and show that exploiting both links in port selection yields much lower outage probabilities than single-link selection. Moreover, the advantage becomes more pronounced as the aperture increases, and for sufficiently large apertures it further increases with the number of ports. For the considered settings, significant diversity gains from increasing the number of ports are observed at W = 4λ.
