Neural network-based medium access control (MAC) protocol models (NPMs) improve goodput through site-specific operations but are vulnerable to shifts from their training network environments, such as changes in the number of user equipments (UEs) severely degrading goodput. To enhance resilience against such environmental shifts, we propose three novel token-based MAC protocol frameworks empowered by large language models (LLMs). First, we introduce a token-based protocol model (TPM), where an LLM generates MAC signaling messages. By editing LLM instruction prompts, TPM enables instant adaptation, which can be further enhanced by TextGrad, an LLM-based automated prompt optimizer. TPM inference is fast but coarse due to the lack of real interactions with the changed environment, and computationally intensive due to the large size of the LLM. To improve goodput and computation efficiency, we develop T2NPM, which transfers and augments TPM knowledge into an NPM via knowledge distillation (KD). Integrating TPM and T2NPM, we propose T3NPM, which employs TPM in the early phase and switches to T2NPM at a later stage. To optimize this phase switching, we design a novel metric of meta-resilience, which quantifies resilience to unknown target goodput after environmental shifts. Simulations corroborate that T3NPM achieves 20.56% higher meta-resilience than NPM with 19.8 & times; lower computation cost than TPM in FLOPs.

In this paper, we investigate a channel estimation problem in a downlink millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, which suffers from impulsive interference caused by hardware non-idealities or external disruptions. Specifically, impulsive interference presents a significant challenge to channel estimation due to its sporadic, unpredictable, and high-power nature. To tackle this issue, we develop a Bayesian channel estimation technique based on variational inference (VI) that leverages the sparsity of the mmWave channel in the angular domain and the intermittent nature of impulsive interference to minimize channel estimation errors. The proposed technique employs mean-field approximation to approximate posterior inference and integrates VI into the sparse Bayesian learning (SBL) framework. Simulation results demonstrate that the proposed technique outperforms baselines in terms of channel estimation accuracy.

In this letter, we propose a novel expected sum rate maximization algorithm called robust beam split and angular uncertainty (RoBA) for a terahertz (THz) hybrid precoding downlink system. To mitigate the effects of beam split and angular uncertainty that occur in a practical wideband THz system, we introduce an array covariance matrix at each subcarrier. This matrix captures the impact of frequency-dependent array responses and statistical angular errors, enabling tractable optimization of the expected sum rate. The resulting constant modulus analog precoder design problem is solved using the Riemannian conjugate gradient (RCG) algorithm. Simulation results show that the proposed RoBA algorithm significantly improves the expected sum rate compared to existing benchmarks.

Continuous efforts have been devoted to integrate millimeter wave (mmWave) and terahertz (THz) bands into future communication standards in order to overcome the bandwidth shortage problem and achieve high data rates, primarily through developing accompanying technologies that can overcome the severe propagation loss and blockage associated with increased carrier frequency. One of the most notable accompanying technologies is reconfigurable intelligent surface (RIS), which uses a large number of low-cost passive reflecting elements to reconfigure the propagation environments for improved communication performance and coverage. Despite its numerous benefits, RIS can make channel estimation more difficult due to its lack of radio frequency (RF) chains that can perform baseband signal processing. In addition, the cascaded channel structure of RIS-aided communication systems, which differs from that in conventional systems, brings about significant challenges in both channel estimation and beamforming. In this paper, we propose the joint channel estimation and beamforming optimization algorithm for RIS-aided multiple-input multipleoutput (MIMO) communication systems. By carefully exploiting the angular sparsity of mmWave/THz channels, our proposed algorithm successfully designs the RIS matrices that not only facilitate the channel estimation process but also achieve the passive beamforming gain through increased channel capacity. Simulation results demonstrate that our proposed algorithm provides the systems of interest with significant improvement in spectral efficiency.

In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidths, designing mmWave systems with low-resolution ADCs is beneficial. To tackle this issue, we propose a channel estimation design using task-based quantization that considers the underlying hybrid analog and digital architecture in order to improve the system performance under finite bit-resolution constraints. Our goal is to accomplish a channel estimation task that minimizes the mean squared error distortion between the true and estimated channel. We develop two types of channel estimators: a cascaded channel estimator for an RIS with purely passive elements, and an estimator for the separate RIS-related channels that leverages additional information from a few semi-passive elements at the RIS capable of processing the received signals with radio frequency chains. Numerical results demonstrate that the proposed channel estimation designs exploiting task-based quantization outperform purely digital methods and can effectively approach the performance of a system with unlimited resolution ADCs. Furthermore, the proposed channel estimators are shown to be superior to baselines with small training overhead.

This paper proposes a channel estimation technique using a Bayesian framework for reconfigurable intelligent surface (RIS)-empowered millimeter-wave (mmWave) multi-user equipment systems, where a few semi-passive RIS elements with baseband processing capability are integrated into the RIS. Here, low-resolution analog-to-digital converters are considered to minimize the power consumption of the RIS. Based on additional information sampled from the RIS semi-passive elements, we propose a separate RIS channel estimation technique using the sparse Bayesian learning (SBL) framework, where we use a complex adaptive Laplace prior to effectively capture the angular domain sparsity normally present in mmWave channels. Simulation results demonstrate that the proposed technique provides superior estimation performance to baselines, particularly an approach using a Student’s-t prior.

In millimeter wave (mmWave) systems, precise channel information is critical for effective beamforming. However, channel estimation problems typically involve inferring high-dimensional channels from limited measurements, resulting in underdetermined systems that make accurate estimation challenging. Conventional compressed sensing (CS) algorithms address this challenge by exploiting sparsity assumptions, while recent approaches utilizing generative models (GMs) leverage generative priors instead. Despite their advantages, existing GM-based approaches rely on gradient-based algorithms for non-convex objectives or require large amounts of channel data for training. We propose a novel GM-based CS algorithm that avoids purely gradient-based optimization and eliminates the need for channel data by training on compressed path channels and sequentially reconstructing each path. Numerical results show that our proposed algorithm outperforms conventional CS algorithms under various scenarios.

In this paper, we configure a cell-free massive multiple-input multiple-output (MIMO) systems with reconfigurable intelligent surface (RIS). Considering dominant line-of-sight (LOS) paths and array structures of antennas of access points (APs) and elements of RIS, spatially correlated Rician fading is employed for channel models. We analyze channel estimation and data transmissions to compute statistics of aggregate channels and signals. Based on the statistical CSI, we derive closed-form expressions of uplink and downlink spectral efficiencies to evaluate system performance. Simulation results verify the analytical expressions accord with numerical experiments. Effects with a large number of system parameters are examined for several channel scenarios, e.g., full links, no direct links, or correlated Rayleigh fading, and utility of optimizing RIS phase shifts is also analyzed.

As a key technology for 6G, integrated sensing and communication (ISAC) is receiving considerable attention, and deploying a reconfigurable intelligent surface (RIS) can enhance both communication performance and sensing capability of ISAC by providing additional degrees of freedom. In this paper, we investigate a beam training framework for RIS-aided ISAC systems where beam alignment for a communication user equipment (UE) is conducted while simultaneously detecting a single target through its echo signal. Using codebooks constructed according to the principles of the 5G standard, we propose a partial search procedure that achieves low training overhead and mathematically show that this strategy is sufficient to identify a suitable codeword combination to serve the UE. By applying the auxiliary beam pair method, the target's angle information from the perspectives of the base station and RIS is obtained. Then, a high-accuracy closed-form localization is proposed based on the angle estimates, and we further extend the proposed technique to multi-target localization scenarios. Numerical results highlight the advantages of the proposed technique in the ISAC context, showing that the training procedure can effectively find a codeword combination and that the target localization technique outperforms the benchmarks.

In this paper, we study the optimization of subband precoder for an orthogonal frequency division multiplexing rate-splitting multiple access (OFDM-RSMA) system in a multi-user multiple-input single-output (MU-MISO) downlink scenario. In conventional OFDM-space division multiple access (SDMA) systems, each subband consisting of multiple subcarriers is generally precoded using a single precoder. This can lead to a performance degradation if the channel is frequency selective within a subband. To address this issue, leveraging RSMA’s robustness to multi-user interference and imperfect channel state information (CSI), we introduce RSMA into the OFDM system where subband precoding is applied. For the proposed RSMA-based subband precoding system, the sum rate and minimum rate are maximized by jointly optimizing the subband precoders and the portion of the common rates using the successive convex approximation (SCA) technique. We also propose low-complexity algorithms that exploit the structure of the achievable rate expressions derived for proposed RSMA-based subband precoding system and leverage the characteristic of the subcarrier channels in OFDM systems. Simulation results show that the proposed RSMA-based subband precoding system outperforms the SDMA-based subband precoding system, verifying the robustness of RSMA. Furthermore, we demonstrate that the proposed low-complexity algorithms can achieve satisfactory performance compared to the proposed SCA-based algorithms and can significantly outperform the practical zero-forcing-based subband precoder with comparable complexity.

As wireless networks continue to evolve, stringent latency, ultra reliability, and channel dynamics reveal limitations of gNB-centric massive multiple-input multiple-output (mMIMO) architectures, motivating a rethinking of the user equipment (UE) role. The UE is transitioning from a passive transceiver to an active contributor to system-level performance. This article examines the evolution of UE functionalities in mMIMO systems from fifth-generation to sixth-generation, bridging third generation partnership project standardization with practical device implementation and architectural innovation. Through a chronological analysis of Releases 15 to 19, we trace the progression from conventional channel state information (CSI) reporting to artificial intelligence and machine learning-based CSI processing and UE-initiated beam management. We also discuss key implementation challenges, including multi-panel UE architectures, on-device intelligence, and energy-efficient operation, and then discuss corresponding architectural innovations under practical constraints. Digital-twin-based evaluations demonstrate that UE-initiated beam reporting improves throughput under mobility, while multi-panel architectures enhance link robustness compared to single-panel designs.

In this paper, the millimeter wave (mmWave) wireless power transfer (WPT) system is considered where the wireless charging station (WCS) operates without prior knowledge of the wireless charging device (WCD)’s location. A hierarchical beam selection algorithm is proposed, which employs hierarchical beams with varying beamwidths to determine the optimal beam for the WCD while mitigating error propagation (EP) across levels. The proposed algorithm consists of the beam identification process and the verification process. In the beam identification process, the WCS identifies the beam that achieves the highest received signal strength indicator (RSSI). In the verification process, the WCS validates the selected beam using the verification beam set, which is constructed based on the beam with the second-highest RSSI. This process mitigates EP across hierarchical levels. The proposed algorithm is implemented on the commercial mmWave RF module, and its performance is evaluated through experiments in the indoor laboratory environment.

This letter investigates a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided frequency division duplexing (FDD) communication system. Since downlink (DL) and uplink (UL) signals are transmitted simultaneously in FDD, the transmission and reflection coefficients of the STAR-RIS should be optimized to support both transmissions. Considering when a single base station with multi-antenna supports multiple users, we formulate a weighted sum-rate maximization problem to consider the relative priority between the DL and UL, as well as among users. To effectively tackle the formulated non-convex optimization problem, we adopt an alternating optimization framework that decomposes it into tractable sub-problems, where the beamformers at the base station, the UL power allocation for users, and the transmission and reflection coefficients of the STAR-RIS are iteratively updated. Within this framework, a successive convex approximation-based technique is proposed to optimize the transmission and reflection coefficients of the STAR-RIS. Simulation results demonstrate that the proposed optimization framework achieves superior performance compared to benchmarks and effectively balances transmission priority.

This paper investigates a practical precoding scheme for rate-splitting multiple access (RSMA) in multi-user multiple-input single-output (MU-MISO) systems where the nonlinear characteristics of power amplifiers are explicitly taken into account. To enhance performance in the presence of distortion caused by nonlinear power amplifiers, we propose an iterative accelerated gradient-based precoding scheme. The proposed precoding scheme is further integrated with a novel power scaling function that dynamically adjusts transmit power to satisfy practical power consumption constraints while maximizing an achievable sum rate. The gradient of the achievable sum rate for the considered system is derived to support efficient gradient-based optimization. We also prove that the proposed iterative precoding scheme converges to a critical point. Simulation results demonstrate that the proposed precoding scheme consistently outperforms modified conventional schemes including distortion-aware and weighted minimum mean square error beamforming, across both overloaded and underloaded scenarios. In particular, the proposed scheme demonstrates consistent performance in high-power regimes, where conventional schemes exhibit severe degradation due to distortion. Furthermore, the proposed precoding scheme exhibits faster convergence compared to the conventional benchmark.

Low Earth orbit (LEO) satellite networks are envisioned as a promising solution for providing ubiquitous connectivity and narrowing the digital divide. The extensive footprint of LEO satellite constellations enables broad coverage, resulting in spatially non-uniform traffic demand across the serviced areas. Meanwhile, stringent on-board power constraints make power-intensive transmission architectures less attractive and motivate energy-efficient transmission strategies that effectively exploit scarce satellite network resources. To this end, this paper proposes a cooperative transmission framework that jointly accounts for non-uniform traffic demand and network-wide power consumption. Each LEO satellite integrates hybrid precoding (HPC), radio frequency (RF) chain activation, and hardware quantization, while user-equipment (UE)-centric satellite clusters are organized using statistical channel state information (sCSI) and traffic demands. A framework for joint optimization of cooperative transmission architecture and resource allocation is designed to maximize demand-aware energy efficiency (EE), resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable. Accordingly, a two-stage algorithm is developed under a distributed linear precoding structure, in which a modified cross-entropy (CE) method searches over discrete variables, while fractional programming is employed for transmit power allocation. Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems require accurate channel estimation for effective beamforming. Conventional compressed sensing (CS) algorithms utilize the inherent sparsity information of channels but suffer from basis mismatch problems for on-grid methods and high computational complexity for off-grid methods. Recently, generative model-based CS frameworks have shown promising results by directly learning the distribution of channel realizations, eliminating the need for prior sparsity information. However, these approaches rely on non-convex optimization in latent space that may converge to suboptimal solutions and exhibit generalization problems across diverse channel environments. Moreover, existing generative approaches have not adequately addressed the quantization effects of practical finite-resolution analog-to-digital converters (ADCs). To address these limitations, we propose a novel generative CS (GCS) algorithm using a conditional variational autoencoder (CVAE) that learns fundamental path components of mmWave MIMO channels rather than complete channel realizations while considering finite-resolution quantization. The CVAE is trained on single-path measurements with spatial frequency conditioning variables, enabling generalization across diverse channel environments without requiring environment-specific training data. Our algorithm directly encodes quantized received pilot signals and angular conditioning variables into latent representations, which are then fed into a decoder to output reconstructions conditioned on the angular parameters, with the decoder trained to be robust to perturbations from additive and quantization noise. The algorithm sequentially identifies path components by searching through spatial frequency codebooks with different conditioning values and applying gradient-based refinement for off-grid accuracy. Numerical results demonstrate that the proposed GCS algorithm outperforms conventional CS algorithms across various scenarios.

Federated learning (FL) and federated distillation (FD) are distributed learning paradigms that train UE models with enhanced privacy, each offering different trade-offs between noise robustness and learning speed. To mitigate their respective weaknesses, we propose a hybrid federated learning (HFL) framework in which each user equipment (UE) transmits either gradients or logits, and the base station (BS) selects the per-round weights of FL and FD updates. We derive convergence of HFL framework and introduce two methods to exploit degrees of freedom (DoF) in HFL, which are (i) adaptive UE clustering via Jenks optimization and (ii) adaptive weight selection via a damped Newton method. Numerical results show that HFL achieves superior test accuracy at low SNR when both DoF are exploited.

The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, traditional centralized artificial intelligence (AI) paradigms are ill-suited to the decentralized, resource-constrained, and dynamic nature of 6G ecosystems. This article explores knowledge distillation (KD) and collaborative learning as promising techniques that enable the efficient and scalable deployment of lightweight AI models across distributed communications and sensing (C&S) nodes. We begin by providing an overview of KD and highlight the key strengths that make it particularly effective in distributed scenarios characterized by device heterogeneity, task diversity, and constrained resources. We then examine its role in fostering collective intelligence through collaborative learning between the central and distributed nodes via various knowledge distilling and deployment strategies. Finally, we present a systematic numerical study demonstrating that KD-empowered collaborative learning can effectively support lightweight AI models for multi-modal sensing-assisted beam tracking applications with substantial performance gains and complexity reduction.

Precise channel state knowledge is crucial in future wireless communication systems, which drives the need for accurate channel prediction without additional pilot overhead. While machine-learning (ML) methods for channel prediction show potential, existing approaches have limitations in their capability to adapt to environmental changes due to their extensive training requirements. In this paper, we introduce the channel prediction approaches in terms of the temporal channel prediction and the environmental adaptation. Then, we elaborate on the use of the advanced ML-based channel prediction to resolve the issues in traditional ML methods. We also analyze the training process, dataset characteristics, and the influence of source tasks and pre-trained models on channel prediction performance, demonstrating their effects under different adaptation samples and environments. Furthermore, we propose practical model selection criteria based on latency constraints, data availability, and environmental dynamics to support effective deployment in wireless communications. Finally, we discuss open challenges and possible future research directions of ML-based channel prediction.

Integrated sensing and communication (ISAC) and the Frequency Range 3 (FR3) (upper mid-band) spectrum are among the key enablers of future wireless systems. ISAC promises new sensing functionalities for networks historically designed for communications, while the FR3 spectrum, approximately from 7 to 24GHz, offers large bandwidths and diverse propagation characteristics that significantly extend deployment possibilities. Motivated by the potential synergy between these two paradigms, this work presents an experimental investigation of a multiband ISAC channel in the FR3 range under realistic conditions. Using the Pi-Radio software-defined radio (SDR) platform and superresolution parameter estimation methods, we design a multiband testbed that measures sensing metrics such as the probability of detection (PD), probability of false alarm (PFA), and localization root mean-squared error (RMSE) across sub-bands at 6.5, 8.75, 10, 15, and 21.7 GHz. To analyze how communication performance reacts to environmental dynamics, we introduce the channel update rate gain (CURG), a new metric that quantifies achievable data-rate gains induced by target-dependent channel variations.