In this paper, we introduce a novel approach to user-centric association and feedback bit allocation for the downlink of a cell-free massive MIMO (CF-mMIMO) system, operating under limited feedback constraints. In CF-mMIMO systems employing frequency division duplexing, each access point (AP) relies on channel information provided by its associated user equipments (UEs) for beamforming design. Since the uplink control channel is typically shared among UEs, we take account of each AP's total feedback budget, which is distributed among its associated UEs. By employing the Saleh-Valenzuela multi-resolvable path channel model with different average path gains, we first identify necessary feedback information for each UE, along with an appropriate codebook structure. This structure facilitates adaptive quantization of multiple paths based on their dominance. We then formulate a joint optimization problem addressing user-centric UE-AP association and feedback bit allocation. To address this challenge, we analyze the impact of feedback bit allocation and derive our proposed scheme from the solution of an alternative optimization problem aimed at devising long-term policies, explicitly considering the effects of feedback bit allocation. Numerical results show that our proposed scheme effectively enhances the performance of conventional approaches in CF-mMIMO systems.

Diffusion models (DMs) have achieved remarkable success across various domains owing to their strong generative and denoising capabilities. Meanwhile, semantic communication based on neural joint source-channel coding (JSCC) has emerged as a promising paradigm for robust and efficient image transmission. However, severe channel noise can still distort the transmitted semantic symbols, resulting in significant performance degradation. Applying DMs to digital semantic symbols, particularly in vector quantization (VQ)-based systems, is fundamentally challenging because the Markov assumption does not hold for the symbol transition dynamics. To address this issue, we introduce SSCDM, a semantic symbol correcting diffusion model whose discrete-time transition dynamics are constructed using solutions from continuous-time Markov chain theory. Furthermore, to promote synergy between DMs and JSCC, our DM structure embeds discrete symbols into a latent feature space using a learned VQ codebook, and a self-organizing map-based loss is incorporated during codebook learning to enhance the geometric vicinity between neighboring digital symbols, thereby promoting topology-preserving semantic representations. Experimental results show that the proposed method significantly improves image reconstruction quality and outperforms previous symbol-level denoising techniques under low signal-to-noise ratio scenarios and different datasets.

Recent breakthroughs in reconfigurable intelligent surface (RIS) technology are reshaping next-generation wireless networks. In multi-RIS deployments, however, the received signal becomes a superposition of numerous single- and double-reflection paths. Some arrive from different angles, while others overlap at the same angle, which makes channel estimation challenging. Conventional approaches attempt to separate these paths by sequentially turning each RIS on and off, but the required switching overhead grows rapidly with the number of channels and becomes impractical. To overcome this limitation, we propose a two-stage channel estimation method for multi-RIS systems. In the first stage, the received signal is projected onto angle subspaces to separate components from different directions. In the second stage, the remaining components that share the same angle are resolved by exploiting the reflection coefficient design of the RISs. Both stages operate on the same superposed received signal, enabling path separation without exhaustive switching. The method further extends beyond the two-RIS case to an arbitrary number of RISs. Simulation results demonstrate that the proposed scheme maintains estimation accuracy while reducing switching overhead, thereby providing a practical solution for large-scale multi-RIS deployments in future wireless networks.

This paper addresses the challenge of channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, pivotal for the advancement of 6G communications. XL-MIMO systems, characterized by their vast antenna arrays, necessitate accurate channel state information (CSI) to leverage high spatial multiplexing and beamforming gains. However, conventional channel estimation methods for near-field XL-MIMO encounter significant computational complexity due to the exceedingly high parameter quantization levels needed for estimating the parametric near-field channel. To address this, we propose a low-complexity two-stage on-grid channel estimation algorithm designed for near-field XL-MIMO systems. The first stage focuses on estimating the LoS channel component while treating the NLoS paths as interference. This estimation is accomplished through an alternating subarray-wise array gain maximization (ASAGM) approach based on the piecewise outer product model (SOPM). In the second stage, we estimate the NLoS channel component by utilizing the sensing matrix refinement-based orthogonal matching pursuit (SMR-OMP) algorithm. This approach helps reduce the high computational complexity associated with large-dimensional joint sensing matrices. Simulation results demonstrate the effectiveness of our proposed low-complexity method, showcasing its significant superiority over existing near-field XL-MIMO channel estimation techniques, particularly in intermediate and high SNR regimes, and in practical scenarios involving arbitrary array placements.

Future 6G networks will interconnect not only devices, but autonomous machines that continuously sense, reason, and act. In such environments, communication can no longer be understood solely as delivering bits or even preserving semantic meaning. Even when two agents interpret the same information correctly, they may still behave inconsistently if their internal reasoning processes evolve differently. We refer to this emerging challenge as belief divergence. This article introduces reasoning native agentic communication, a new paradigm in which communication is explicitly designed to address belief divergence rather than merely transmitting representations. Instead of triggering transmissions based only on channel conditions or data relevance, the proposed framework activates communication according to predicted misalignment in agents internal belief states. We present a reasoning native architecture that augments the conventional communication stack with a coordination plane grounded in a shared knowledge structure and bounded belief modeling. Through enabling mechanisms and representative multi agent scenarios, we illustrate how such an approach can prevent coordination drift and maintain coherent behavior across heterogeneous systems. By reframing communication as a regulator of distributed reasoning, reasoning native agentic communication enables 6G networks to act as an active harmonizer of autonomous intelligence.

This paper proposes a differentially private federated learning (FL) framework built upon an FL algorithm with semantic feature reconstruction (FedSFR) for training semantic communication modules for image transmission. By allowing clients with unfavorable uplink capacity to transmit low-dimensional semantic feature vectors extracted from locally trained joint source-channel coding (JSCC) encoders, FedSFR enhances communication efficiency and training stability under heterogeneous wireless conditions. To protect client privacy, we incorporate the oneshot Laplace mechanism and theoretically demonstrate that feature-based transmission achieves strictly stronger differential privacy (DP) guarantees than gradient-based transmission under an identical communication budget. In addition, a model selection mechanism is introduced to alleviate performance degradation caused by privacy-preserving perturbations. Experimental results on multiple datasets show that the proposed DP-aided FedSFR outperforms DP-enabled FedAvg in training stability and image reconstruction quality in heterogeneous wireless systems.

Future wireless networks must deliver high spectral efficiency in dense environments where many users require simultaneous high-rate communication. Higher carrier frequencies offer more bandwidth but suffer from shorter range and lower channel rank, making new capacity enhancing strategies essential. This paper studies joint antenna array design at the base station (BS) and user devices in line-of-sight (LoS) uplink multiuser multiple-input multiple-output (MU-MIMO) systems with the goal of exploiting radiative near-field effects to unlock full multiplexing gains. In particular, we establish a scaling law under which there exist uniform sparse array configurations whose sum rate gap to an interference-free upper bound decays with the number of BS antennas. For dense user deployments beyond this scaling regime, we propose a joint array design algorithm with provable performance guarantees, in which each user array is configured to maximize its single-user achievable rate conditioned on the BS array, followed by non-uniform BS array optimization using a submodular surrogate. Our simulation results verify that uniform sparse arrays achieve the interference-free regime predicted by the scaling law. Outside this regime, the proposed joint design closely tracks the interference-free upper bound, demonstrating the benefit of non-uniform BS array optimization.

Reconfigurable intelligent surfaces (RISs) and six-dimensional movable antennas (6DMAs) have emerged as promising technologies for enhancing wireless transmission by reconfiguring the propagation environment and antenna geometry, respectively. However, conventional RIS-assisted systems usually rely on fixed-position antenna arrays, whose spatial adaptability is limited when users are non-uniformly distributed in three-dimensional space. To address this limitation, this paper investigates a STAR-RIS-assisted six-dimensional movable antenna base station (6DMA-BS) communication system. In the considered system, the 6DMA-BS adjusts the positions and rotations of multiple antenna surfaces to exploit additional spatial degrees of freedom, while the STAR-RIS provides full-space coverage for users in both reflection and transmission zones. To enhance the system sum rate, a joint optimization problem is formulated by considering 6DMA surface positions and rotations, BS active beamforming, and STAR-RIS passive reflection/transmission beamforming under practical constraints. Since the formulated problem is highly non-convex with strongly coupled variables, an alternating optimization framework is developed. Specifically, the position and rotation subproblems are solved by conditional gradient methods, the active beamforming is obtained by MRT-based direction design and water-filling-based power allocation, and the passive beamforming is optimized via SDR and Gaussian randomization. Simulation results show that the proposed method can significantly improve the sum rate of users as compared to benchmark with FPA + Optimized STAR-RIS, 6DMA + Random STAR-RIS, and FPA + Random STAR-RIS.

We propose a communication-efficient optimally structured gradient coding scheme to jointly address straggler resilience and communication efficiency in heterogeneous distributed learning. By establishing a unified framework that simultaneously optimizes gradient coding and quantization, we formulate an optimization problem to minimize residual error subject to an unbiasedness constraint. We rigorously establish the joint global optimum by deriving a closed-form code structure coupled with an optimal bit allocation strategy, while simultaneously proposing a low-complexity bit allocation algorithm that efficiently yields near-optimal performance. We provide rigorous convergence analysis for convex and smooth functions. Experiments on the COCO dataset demonstrate that our joint design significantly accelerates convergence and enhances communication efficiency compared to existing baselines.

Recent advances in deep learning (DL)-based joint source-channel coding (JSCC) have enabled efficient semantic communication in dynamic wireless environments. Among these approaches, vector quantization (VQ)-based JSCC effectively maps high-dimensional semantic feature vectors into compact codeword indices for digital modulation. However, existing methods, including universal JSCC (uJSCC), rely on fixed, modulation-specific encoders, decoders, and codebooks, limiting adaptability to fine-grained SNR variations. We propose an extended universal JSCC (euJSCC) framework that achieves SNR- and modulation-adaptive transmission within a single model. euJSCC employs a hypernetwork-based normalization layer for fine-grained feature vector normalization and a dynamic codebook generation (DCG) network that refines modulation-specific base codebooks according to block-wise SNR. To handle block fading channels, which consist of multiple coherence blocks, an inner-outer encoder-decoder architecture is adopted, where the outer encoder and decoder capture long-term channel statistics, and the inner encoder and decoder refine feature vectors to align with block-wise codebooks. A two-phase training strategy, i.e., pretraining on AWGN channels followed by finetuning on block fading channels, ensures stable convergence. Experiments on image transmission demonstrate that euJSCC consistently outperforms state-of-the-art channel-adaptive digital JSCC schemes under both block fading and AWGN channels.

Mobile edge computing (MEC) and terahertz (THz)enabled unmanned aerial vehicle (UAV) communication systems are gaining significant attention for improving user service delays in future mobile networks. This article introduces a novel multi-UAV-aided MEC system operating at THz frequencies to minimize expected user service delays, including communication and computation latency. We address this challenge by jointly optimizing UAV relay selection, power control, positioning, and user-resource association for task offloading and resource allocation. To tackle the problem's complexities, we decompose it into four subproblems, each solved optimally with our proposed algorithm. An iterative penalty dual decomposition (PDD) algorithm approximates the original problem's solution. Numerical results demonstrate that our PDD-based approach outperforms baseline algorithms in terms of expected user service delay.

Recent advancements in semantic communication have primarily focused on image transmission, where neural network-based joint source-channel coding modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between agents and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we propose FedSFR, a novel federated learning framework that incorporates semantic feature reconstruction (FR). FedSFR introduces an FR step at the parameter server and allows a subset of clients to transmit compact feature vectors in lieu of sending full local model updates, thereby improving training stability and communication efficiency. To enable effective FR learning, we design a loss function tailored for VQ-based image semantic communication and demonstrate its validity as a surrogate for image reconstruction error. We further establish a rigorous convergence analysis of FedSFR. Experimental results on two benchmark datasets validate the superiority of FedSFR over existing baselines, especially in capacity-constrained settings, confirming both its effectiveness and robustness.

Low Earth Orbit (LEO) satellite communication has recently gained significant attention from the industry as it enables fast and seamless connectivity. However, as the number of satellites in LEO networks increases, making the network denser and more dynamic, a scalable routing algorithm is required to efficiently find paths. Many existing routing algorithms are primarily designed for small-scale networks or terrestrial IP networks and may not be suitable for large-scale LEO networks. To address this challenge, we propose an efficient routing algorithm that leverages the grid-like topology by clustering satellites into sub-mesh groups and selecting major routing directions. To accommodate the continuously changing and dynamic topology, we introduce an update period determination method based on a devised correlation metric. Simulation results demonstrate that the proposed algorithm efficiently discovers near-optimal paths in LEO networks designed with various distance-based cost metrics while significantly reducing computational overhead.

From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established. This article presents the first attempt at such an approach by proposing a universal joint source-channel coding (uJSCC) system that utilizes a single-model encoder-decoder pair and trained vector quantization (VQ) codebooks. To support various modulation orders within a single model, the operation of every neural network (NN)-based module in the uJSCC system requires the selection of modulation orders according to signal-to-noise ratio (SNR) boundaries. To address the challenge of unequal output statistics from shared parameters across NN layers, we integrate multiple batch normalization (BN) layers, selected based on modulation order, after each NN layer. This integration occurs with minimal impact on the overall model size. Through a comprehensive series of experiments, we validate that the modulation-agnostic semantic communication framework demonstrates superiority over existing digital semantic communication approaches in terms of model complexity, communication efficiency, and task effectiveness.

Research in semantic communication has garnered considerable attention, particularly in the area of image transmission, where joint source-channel coding (JSCC)-based neural network (NN) modules are frequently employed. However, these systems often experience performance degradation over time due to an outdated knowledge base, highlighting the need for periodic updates. To address this challenge in the context of training JSCC modules for image transmission, we propose a federated learning (FL) algorithm with semantic feature reconstruction (FR), named FedSFR. This algorithm more efficiently utilizes the available communication capacity by allowing some of the selected FL participants to transmit smaller feature vectors instead of local update information. Unlike conventional FL methods, our approach integrates FR at the parameter server (PS), stabilizing training and enhancing image transmission quality. Experimental results demonstrate that the proposed scheme significantly enhances both the stability and effectiveness of the FL process compared to other algorithms. Furthermore, we mathematically derive the convergence rate to validate the improved performance.

Split Learning (SL) has emerged as a promising paradigm for distributed model training in resource-constrained Internet-of-Things (IoT) environments by partitioning deep neural networks between lightweight client devices and a powerful central server. However, SL suffers from substantial communication overhead due to the frequent transmission of high-dimensional intermediate activations. In this paper, we propose a novel sparsification framework based on Gradient-weighted Class Activation Mapping (Grad-CAM) to alleviate this bottleneck. By leveraging the Grad-CAM, our approach quantifies the importance of activations and selectively transmits only the most discriminative intermediate activations. To overcome the limitation that clients lack direct access to Grad-CAM scores during the current forward pass, we utilize the channel importance vectors computed at the server from the previous iteration as surrogates, thereby enabling effective sparsification without incurring additional computational overhead. Extensive experiments demonstrate that our method significantly reduces communication cost compared to baseline schemes. Furthermore, our results reveal a trade-off between mini-batch size and sparsification ratio in SL, emphasizing the importance of careful activation selection for robust learning under communication constraints.

This paper introduces a novel privacy-enhanced over-the-air Federated Learning (OTA-FL) framework using client-driven power balancing (CDPB) to address privacy concerns in OTA-FL systems. In recent studies, a server determines the power balancing based on the continuous transmission of channel state information (CSI) from each client. Furthermore, they concentrate on fulfilling privacy requirements in every global iteration, which can heighten the risk of privacy exposure as the learning process extends. To mitigate these risks, we propose two CDPB strategies—CDPB-n (noisy) and CDPB-i (idle)—allowing clients to adjust transmission power independently, without sharing CSI. CDPB-n transmits noise during poor conditions, while CDPB-i pauses transmission until conditions improve. To further enhance privacy and learning efficiency, we show a mixed strategy, CDPB-mixed, which combines CDPB-n and CDPB-i. Our experimental results show that CDPB outperforms traditional approaches in terms of model accuracy and privacy guarantees providing a practical solution for enhancing OTA-FL in resource-constrained environments.

In distributed computing systems, reducing the communication load during the data shuffling phase is a critical challenge, as excessive inter-node transmissions are a major performance bottleneck. One promising approach to alleviate this burden is Embedded Index Coding (EIC), which exploits cached data at user nodes to encode transmissions more efficiently. However, most prior work on EIC has focused on minimizing code length in wired, error-free environments-an objective often suboptimal for wireless multiple-input multiple-output (MIMO) systems, where channel conditions and spatial multiplexing gains must be considered. This paper investigates the joint design of EIC and transmit beamforming in MIMO systems to minimize total transmission time, an NP-hard problem. We first present a conventional optimization method that determines the optimal EIC via exhaustive search. To address its prohibitive complexity and adapt to dynamic wireless environments, we propose a novel, low-complexity multi-agent reinforcement learning (MARL) framework. The proposed framework enables decentralized agents to act on local observations while effectively managing the hybrid action space of discrete EIC selection and continuous beamforming design. Simulation results demonstrate that the proposed MARL-based approach achieves near-optimal performance with significantly reduced complexity, underscoring its effectiveness and practicality for real-world wireless systems.

Transformer-based large language models (LLMs) have achieved remarkable success across various tasks. Yet, fine-tuning such massive models in federated learning (FL) settings poses significant challenges due to resource constraints and communication overhead. Low-Rank Adaptation (LoRA) addresses these issues by training compact, low-rank matrices instead of fully fine-tuning large models. This paper introduces a wireless federated LoRA fine-tuning framework that optimizes both learning performance and communication efficiency. We provide a novel convergence analysis, revealing how LoRA rank and covariance effects influence FL training dynamics. Leveraging these insights, we propose Sparsified Orthogonal Fine-Tuning (SOFT), an adaptive sparsification method that streamlines parameter updates without expensive matrix multiplications and singular value decomposition (SVD) operations. Additionally, we present a Two Stage Federated Algorithm (TSFA) algorithm that pre-determines key parameters offline and dynamically adjusts bandwidth and sparsification online, ensuring efficient training under latency constraints. Experiments on benchmark datasets show that our approach achieves accuracy comparable to ideal scenario models while significantly reducing communication overhead. Our framework thus enables scalable, resource-efficient deployment of large models in real-world wireless FL scenarios.