Fluid antenna systems (FAS) have emerged as a promising technology to achieve high spatial diversity by dynamically reconfiguring multiple closely spacedNantenna ports. However, the inherent spatial correlation among these ports poses significant challenges for accurate performance analysis. Traditional block-correlation modeling algorithms, which partition theN×NToeplitz-structured correlation matrix into independentDblocks with constant correlation coefficients, often yield substantial approximation errors to block-correlation models, especially in scenarios with limited ports. In this paper, we revisit the spatial block-correlation model for FAS and introduce a novel block-correlation modeling algorithm in tuning the model parameters, which realizes the variable block-correlation model in practice. Our proposed approach derives closed-form expressions for the optimal block-specific correlation coefficients and develops a low-complexity heuristic algorithm that reduces the computational complexity from exponentialDN–Dto linear (N–D) ×Dsearches,thereby achieving significantly lower approximation error compared to constant correlation models. To validate the effectiveness of our variable block-correlation modeling algorithm, we first apply it to point-to-point FAS communications with closely spaced ports, deriving analytical expressions for the joint probability density function (PDF) of channel amplitudes and outage probability. Our analysis shows that the proposed algorithm offers tractable performance evaluation and superior accuracy, particularly when the number of ports is small (NThese results underscore the practical value of our approach for the design and optimization of next-generation FAS-based wireless networks.

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence. To enhance the efficiency and scalability of AirComp systems, this paper proposes a comprehensive dual-approach framework that systematically transitions from traditional mathematical optimization to deep reinforcement learning (DRL) for resource allocation under execution uncertainty. Specifically, we establish a rigorous system model capturing execution uncertainty via Gamma-distributed computational workloads, resulting in challenging nonlinear optimization problems involving complex Gamma functions. For single-user scenarios, we design advanced block coordinate descent (BCD) and majorization-maximization (MM) algorithms, which yield semi-closed-form solutions with provable performance guarantees. However, conventional optimization approaches become computationally intractable in dynamic multi-user environments due to inter-user interference and resource contention. To this end, we introduce a Deep Q-Network (DQN)-based DRL framework capable of adaptively learning optimal policies through environment interaction. Our dual methodology effectively bridges analytical tractability with adaptive intelligence, leveraging optimization for foundational insight and learning for real-time adaptability. Extensive numerical results corroborate the performance gains achieved via increased edge server density and validate the superiority of our optimization-to-learning paradigm in next-generation AirComp systems.

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

To overcome the challenge of limited bandwidth, client selection has been considered an effective method for optimizing Federated Learning (FL). However, since the volatility of the learning environment, the available clients exhibit some volatility over the training process in terms of client population, client data, training status, and transmitting status, which greatly increases the difficulty of client selection. To find a practical solution, we explore a client selection problem in volatile federated learning (Volatile FL). Specifically, we first derive the convergence analysis for non-convex and strongly convex cases to illustrate the main factors affecting the convergence speed. Then, we introduce the client utility to quantify the client’s contribution to model training and discuss the key problems of client selection in Volatile FL. For an efficient settlement, we propose CU-CS, a Combinatorial Multi-Arm Bandit (C2MAB) based decision scheme for the proposed selection problem. Theoretically, we prove that the regret of CU-CS is strictly bounded by a finite constant, justifying its theoretical feasibility. The experimental results demonstrate that our method significantly boosts FL by speeding up model convergence, promoting model accuracy, and reducing energy consumption.

In this paper, we investigate a secure mobile edge computing (MEC) network in the presence of multiple eavesdroppers, where multiple users can offload parts of their tasks to the computational access point (CAP). The multiple eavesdroppers may overhear the confidential task offloading, which leads to information leakage. In order to address this issue, we present the minimization problem of the secrecy outage probability (SOP), by jointly taking into account the constraints from the latency and energy consumption. With the aim to improve the system secrecy performance, we then introduce three user selection criteria to choose the best user among multiple ones. Specifically,criterion Imaximizes the locally computational capacity, whilecriterion IIandIIImaximize the secrecy capacity and data rate of main links, respectively. For these criteria, we further analyze the system secrecy performance by deriving analytical and asymptotic expressions for the SOP, from which we can conclude important insights for the system design. Finally, simulation and analytical results are provided to verify the proposed analysis. The results show that the three criteria can efficiently safeguard the MEC networks, compared to the traditional local computing and fully offloading, especially with a large value of user number.

Incorporating short-packet communications with non-orthogonal multiple access (NOMA) networks is able to achieve both low communication delay and high spectral efficiency. In this article, a wireless NOMA downlink short-packet communication system is studied. The system includes a base station, an entrusted central user, and an untrusted cell-edge user. The untrusted cell-edge user may eavesdrop the signals from the base station to the central user. We theoretically derive the average secure block error rate (BLER) of the central user in flat Rayleigh fading channels by utilizing the linear approximations on BLER and secure BLER. We also present the asymptotic average secure BLER at high signal-to-noise ratio (SNR). From numerical results, it is shown that our derived analytical average secure BLER matches the simulated one. Numerical results also illustrate that at high SNR, error floors occur. Furthermore, at high SNR, it is found that when the average secure BLER is not one, the power allocation factors for the central and cell-edge users have almost no impact on the average secure BLER.

In this paper, we study decode-and-forward relaying networks in the presence of direct links, where they are used by the eavesdropper to overhear the confidential message from the source and relay. The secure data transmission can go through from either the direct or the relaying branch, and we focus on the practical communication scenarios, where the main and eavesdropper channels are correlated. Although traditional opportunistic selection techniques can choose one better branch to ensure the secure performance, it needs to continuously know the channel state information (CSI) of both branches and may result in a high branch switching rate. To overcome these limitations, we propose a distributed secure switch-and-stay combining (DSSSC) protocol, where only one between direct and relaying branches is activated to assist the secure data transmission, and the switching occurs when the branch cannot support the secure communication any longer. The DSSSC protocol uses either the instantaneous or the statistics of the eavesdropping CSI. For both cases, we quantify the impact of correlated fading on secure communication by deriving an analytical expression for the secrecy outage probability (SOP) as well as an asymptotic expression for the high main-to-eavesdropper ratio region. From the asymptotic SOP, we can conclude that the DSSSC can achieve the optimal secure performance of opportunistic selection with less implementation complexity, and the channel correlation can further enhance the transmission security.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

In this study, the authors study an energy harvesting relaying system with multiuser, in which the relay node is capable of harvesting radio‐frequency energy and retransmitting data with harvested energy by employing the power splitting strategy. The joint impact of direct and relaying links is taken into account in the process of user selection, and furthermore exploited for data transmission by using the selection combing receiver. To evaluate the system performance, analytical outage probabilities regarding both amplify‐and‐forward (AF) and decode‐and‐forward (DF) relaying have been derived under Rayleigh fading channel. Furthermore, they calculate the asymptotic expressions of outage probability, and they confirm that AF relaying as well as DF relaying approaches the system diversity order of , where N denotes the user number. Numerical results are demonstrated to validate the proposed analysis as well.