Stacked Intelligent Metasurface-Enhanced Wideband Multiuser MIMO OFDM-IM Communications

Leveraging the multilayer realization of programmable metasurfaces, stacked intelligent metasurfaces (SIM) enable fine-grained wave-domain control. However, their wideband deployment is impeded by two structural factors: (i) a single, quasi-static SIM phase tensor must adapt to all subcarriers, and (ii) multiuser scheduling changes the subcarrier activation pattern frame by frame, requiring rapid reconfiguration. To address both challenges, we develop a SIM-enhanced wideband multiuser transceiver built on orthogonal frequency-division multiplexing with index modulation (OFDM-IM). The sparse activation of OFDM-IM confines high-fidelity equalization to the active tones, effectively widening the usable bandwidth. To make the design reliability-aware, we directly target the worst-link bit-error rate (BER) and adopt a max-min per-tone signal-to-interference-plus-noise ratio (SINR) as a principled surrogate, turning the reliability optimization tractable. For frame-rate inference and interpretability, we propose an unfolded projected-gradient-descent network (UPGD-Net) that double-unrolls across the SIM's layers and algorithmic iterations: each cell computes the analytic gradient from the cascaded precoder with a learnable per-iteration step size. Simulations on wideband multiuser downlinks show fast, monotone convergence, an evident layer-depth sweet spot, and consistent gains in worst-link BER and sum rate. By combining structural sparsity with a BER-driven, deep-unfolded optimization backbone, the proposed framework directly addresses the key wideband deficiencies of SIM.

Distributed Multi-Task Learning for Joint Wireless Signal Enhancement and Recognition

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify the properties of the received signal in a no-collaborative manner. However, it is challenging to accurately classify signals in low signal-to-noise ratio (SNR) conditions and distributed network settings. In this paper, we propose a novel distributed multi-task learning framework for joint wireless signal enhancement and recognition (WSER), addressing the crucial need for non-collaborative signal identification in modern wireless networks. Our approach integrates a wireless signal enhancement and recognition network (WSERNet) with FedProx+, an enhanced federated learning algorithm designed for heterogeneous data distributions. Specifically, WSERNet leverages an asymmetric convolution block (ACBlock) to capture long-range dependencies in the input signal and improve the performance of the deep learning model. FedProx+ introduces a proximal term to the loss function to encourage the model updates to be closer to the previous model, enhancing the convergence speed and robustness of federated learning. Extensive experiments demonstrate the effectiveness of the proposed framework for joint WSER, achieving superior performance compared to state-of-the-art methods under both centralized and distributed settings including independent and identically distributed (IID) and non-IID data distributions.

Fundamental Trade-off in Wideband Stacked Intelligent Metasurface Assisted OFDMA Systems

Conventional digital beamforming for wideband multiuser orthogonal frequency-division multiplexing (OFDM) demands numerous power-hungry components, increasing hardware costs and complexity. By contrast, the stacked intelligent metasurfaces (SIM) can perform wave-based precoding at near-light speed, drastically reducing baseband overhead. However, realizing SIM-enhanced fully-analog beamforming for wideband multiuser transmissions remains challenging, as the SIM configuration has to handle interference across all subcarriers. To address this, this paper proposes a flexible subcarrier allocation strategy to fully reap the SIM-assisted fully-analog beamforming capability in an orthogonal frequency-division multiple access (OFDMA) system, where each subcarrier selectively serves one or more users to balance interference mitigation and resource utilization of SIM. We propose an iterative algorithm to jointly optimize the subcarrier assignment matrix and SIM transmission coefficients, approximating an interference-free channel for those selected subcarriers. Results show that the proposed system has low fitting errors yet allows each user to exploit more subcarriers. Further comparisons highlight a fundamental trade-off: our system achieves near-zero interference and robust data reliability without incurring the hardware burdens of digital precoding.

Stacked Intelligent Metasurface Assisted Multiuser Communications: From a Rate Fairness Perspective

Stacked intelligent metasurface (SIM) extends the concept of single-layer reconfigurable holographic surfaces (RHS) by incorporating a multi-layered structure, thereby providing enhanced control over electromagnetic wave propagation and improved signal processing capabilities. This study investigates the potential of SIM in enhancing the rate fairness in multiuser downlink systems by addressing two key optimization problems: maximizing the minimum rate (MR) and maximizing the geometric mean of rates (GMR). {The former strives to enhance the minimum user rate, thereby ensuring fairness among users, while the latter relaxes fairness requirements to strike a better trade-off between user fairness and system sum-rate (SR).} For the MR maximization, we adopt a consensus alternating direction method of multipliers (ADMM)-based approach, which decomposes the approximated problem into sub-problems with closed-form solutions. {For GMR maximization, we develop an alternating optimization (AO)-based algorithm that also yields closed-form solutions and can be seamlessly adapted for SR maximization. Numerical results validate the effectiveness and convergence of the proposed algorithms.} Comparative evaluations show that MR maximization ensures near-perfect fairness, while GMR maximization balances fairness and system SR. Furthermore, the two proposed algorithms respectively outperform existing related works in terms of MR and SR performance. Lastly, SIM with lower power consumption achieves performance comparable to that of multi-antenna digital beamforming.

AI-Empowered Channel Generation for IoV Semantic Communications in Dynamic Conditions

The Internet of Vehicles (IoV) transforms the transportation ecosystem promising pervasive connectivity and data-driven approaches. Deep learning and generative Artificial Intelligence (AI) have the potential to significantly enhance the operation of applications within IoV by facilitating efficient decision-making and predictive capabilities, including intelligent navigation, vehicle safety monitoring, accident prevention, and intelligent traffic management. Nevertheless, efficiently transmitting and processing the massive volumes of data generated by the IoV in real-time remains a significant challenge, particularly in dynamic and unpredictable wireless channel conditions. To address these challenges, this paper proposes a semantic communication framework based on channel perception to improve the accuracy and efficiency of data transmission. The semantic communication model extracts and compresses the information to be transmitted. In addition, the wireless channel is estimated by using a generative diffusion model, which is employed to predict the dynamic channel states, thereby improving the quality of IoV service. In dynamic scenarios, however, the channel estimation performance may be degraded when substantially new scenarios take place, which will adversely affect user experience. To mitigate this limitation, we employ a large model to fine-tune the channel generation model to enhance its adaptability for varying scenarios. The performance and reliability of the proposed framework are evaluated on the two public datasets.

MEF-Explore: Communication-Constrained Multi-Robot Entropy-Field-Based Exploration

Collaborative multiple robots for unknown environment exploration have become mainstream due to their remarkable performance and efficiency. However, most existing methods assume perfect robots' communication during exploration, which is unattainable in real-world settings. Though there have been recent works aiming to tackle communication-constrained situations, substantial room for advancement remains for both information-sharing and exploration strategy aspects. In this paper, we propose a Communication-Constrained Multi-Robot Entropy-Field-Based Exploration (MEF-Explore). The first module of the proposed method is the two-layer inter-robot communication-aware information-sharing strategy. A dynamic graph is used to represent a multi-robot network and to determine communication based on whether it is low-speed or high-speed. Specifically, low-speed communication, which is always accessible between every robot, can only be used to share their current positions. If robots are within a certain range, high-speed communication will be available for inter-robot map merging. The second module is the entropy-field-based exploration strategy. Particularly, robots explore the unknown area distributedly according to the novel forms constructed to evaluate the entropies of frontiers and robots. These entropies can also trigger implicit robot rendezvous to enhance inter-robot map merging if feasible. In addition, we include the duration-adaptive goal-assigning module to manage robots' goal assignment. The simulation results demonstrate that our MEF-Explore surpasses the existing ones regarding exploration time and success rate in all scenarios. For real-world experiments, our method leads to a 21.32% faster exploration time and a 16.67% higher success rate compared to the baseline.

Dynamical ON-OFF Control with Trajectory Prediction for Multi-RIS Wireless Networks

Reconfigurable intelligent surfaces (RISs) have demonstrated an unparalleled ability to reconfigure wireless environments by dynamically controlling the phase, amplitude, and polarization of impinging waves. However, as nearly passive reflective metasurfaces, RISs may not distinguish between desired and interference signals, which can lead to severe spectrum pollution and even affect performance negatively. In particular, in large-scale networks, the signal-to-interference-plus-noise ratio (SINR) at the receiving node can be degraded due to excessive interference reflected from the RIS. To overcome this fundamental limitation, we propose in this paper a trajectory prediction-based dynamical control algorithm (TPC) for anticipating RIS ON-OFF states sequence, integrating a long-short-term-memory (LSTM) scheme to predict user trajectories. In particular, through a codebook-based algorithm, the RIS controller adaptively coordinates the configuration of the RIS elements to maximize the received SINR. Our simulation results demonstrate the superiority of the proposed TPC method over various system settings.

WirelessMathBench: A Mathematical Modeling Benchmark for LLMs in Wireless Communications

Large Language Models (LLMs) have achieved impressive results across a broad array of tasks, yet their capacity for complex, domain-specific mathematical reasoning-particularly in wireless communications-remains underexplored. In this work, we introduce WirelessMathBench, a novel benchmark specifically designed to evaluate LLMs on mathematical modeling challenges to wireless communications engineering. Our benchmark consists of 587 meticulously curated questions sourced from 40 state-of-the-art research papers, encompassing a diverse spectrum of tasks ranging from basic multiple-choice questions to complex equation completion tasks, including both partial and full completions, all of which rigorously adhere to physical and dimensional constraints. Through extensive experimentation with leading LLMs, we observe that while many models excel in basic recall tasks, their performance degrades significantly when reconstructing partially or fully obscured equations, exposing fundamental limitations in current LLMs. Even DeepSeek-R1, the best performer on our benchmark, achieves an average accuracy of only 38.05%, with a mere 7.83% success rate in full equation completion. By publicly releasing WirelessMathBench along with the evaluation toolkit, we aim to advance the development of more robust, domain-aware LLMs for wireless system analysis and broader engineering applications.

Robust Beamforming Design for STAR-RIS Aided RSMA Network with Hardware Impairments

In this article, we investigate the robust beamforming design for a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided downlink rate-splitting multiple access (RSMA) communication system, where both transceivers and STAR-RIS suffer from the impact of hardware impairments (HWI). A base station (BS) is deployed to transmit messages concurrently to multiple users, utilizing a STAR-RIS to improve communication quality and expand user coverage. We aim to maximize the achievable sum rate of the users while ensuring the constraints of transmit power, STAR-RIS coefficients, and the actual rate of the common stream for all users. To solve this challenging high-coupling and non-convexity problem, we adopt a fractional programming (FP)-based alternating optimization (AO) approach, where each sub-problem is addressed via semidefinite relaxation (SDR) and successive convex approximation (SCA) methods. Numerical results demonstrate that the proposed scheme outperforms other multiple access schemes and conventional passive RIS in terms of the achievable sum rate. Additionally, considering the HWI of the transceiver and STAR-RIS makes our algorithm more robust than when such considerations are not included.

Compact Varactor-Integrated RIS for Wideband and Continuously Tunable Beamforming

This letter presents a novel Reconfigurable Intelligent Surface (RIS) that features a low-profile structure, wide operating bandwidth, and continuous phase control. By incorporating a middle patch layer without introducing an additional air gap, the proposed design maintains a thin form factor, while achieving a smooth 310{\deg} phase shift over 10\% bandwidth at 6.1 GHz with excellent reflection. A fabricated 10*10 RIS array exhibits stable performance, enabling precise beam control across a 600 MHz bandwidth. These results highlight the potential of the proposed low-profile, wideband RIS with continuous phase tuning for next-generation wireless communication systems.