FAS-enabled cooperative non-orthogonal multiple access (FAS-CoNOMA) systems capture the potential of fluid antenna systems in enhancing network performance. In this system, a base station (BS) transmits a superposition signal to a central user (CU) and a cell-edge user (EU), both equipped with FAS. Specifically, the CU decodes the signal intended for the EU and cooperatively relays it to improve the EU’s communication performance. The EU employs selective combining (SC) or maximum ratio combining (MRC) to receive signals from both the BS and CU. By leveraging the dynamic properties of FAS to improve user differentiation, the CoNOMA system effectively enhances network performance compared to traditional NOMA, OMA, and fixed position antenna (FPA) systems. To address the challenging spatial correlation properties in FAS, we utilize the block-diagonal matrix approximation (BDMA) model to calculate the outage probabilities for both the CU and EU. We then derive upper bound, lower bound, and asymptotic approximation of the outage probabilities to gain deeper insights. Furthermore, we optimize the EU’s outage probability under the CU’s outage constraint and total transmit power limits by adjusting the power allocation coefficient for the CU and the transmit powers for both the BS and CU. To simplify the optimization process, we reduce the number of variables and apply the alternating optimization (AO) algorithm to break down the problem into two sub-problems. Each sub-problem is solved using the bisection search method and gradient descent algorithm (GDA). Simulation results demonstrate that FAS significantly improves outage performance, especially for the EU, and that CoNOMA notably captures the potential of FAS beyond NOMA and OMA, offering a promising solution for future wireless networks.

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are analytically intractable, while oversimplified constant-correlation models fail to capture the true behavior. In this work, we address these challenges by applying the variable block-correlation model (VBCM) -- originally proposed by Ramírez-Espinosa \textit{et al.} in 2024 -- to FAS security analysis, and by developing comprehensive optimization methods to enhance analytical accuracy. We derive new closed-form expressions for average secrecy capacity (ASC) and secrecy outage probability (SOP), demonstrating that the VBCM framework achieves simulation-aligned accuracy, with relative errors consistently below $5\%$ (compared to $10$--$15\%$ for constant-correlation models). To maximize ASC, we further design two algorithms: a grid search (GS) method and a gradient descent (GD) method. Numerical results reveal that the VBCM-based approach not only provides reliable insights into FAS security performance, but also yields substantial gains -- ASC improvements exceeding $120\%$ in high-threat scenarios and $18$--$19\%$ performance enhancements for compact antenna configurations. These findings underscore the practical value of integrating VBCM into FAS security analysis and optimization, establishing it as a powerful tool for advancing 6G communication systems.

In this paper, we exploit an emergency mobile crowdsensing (MCS) framework that utilizes unmanned aerial vehicles (UAVs) in collaboration with uncrashed base stations (BSs) to enhance sensing and communication efficiency. In the proposed framework, mobile users (MUs) equipped with sensors collect data, while UAVs, deployed as aerial relays, collaborate with uncrushed BSs to facilitate the transmission and aggregation of the sensed data from all MUs. However, the limited resources significantly affect the deployment of UAVs and the design of the UAV-relay-assisted MCS system. Moreover, selecting MUs for sensing tasks and allocating bandwidth among them are crucial factors that determine MUs’ sensing capabilities and the data transmitting policies. Incorporating with foregoing essential factors, we formulate a comprehensive problem that jointly optimizes the MU selection, bandwidth allocation, UAV deployment, as well as strategies for sensing and transmitting data, aiming to improve the total reward of agent. The formulated problem poses high challenges due to the coupling between the sensing, transmission, as well as the UAVs deployment policies. To deal with this problem, we first derive the optimal transmission power and sensing data size under given MU selection, bandwidth allocation, and UAVs deployment strategy. The original optimization problem is subsequently decomposed into three folds, corresponding to finding the optimal MU selection, bandwidth allocation solution, as well as the deployment of UAVs. Meanwhile, a joint dynamic programming and a swap-then-compare enabled algorithm is proposed to obtain the optimal MU selection and bandwidth allocation policies. Next, the successive convex approximation (SCA) techniques are used to find the optimal locations for the UAVs. Extensive numerical results verify that the proposed joint algorithm can significantly outperform several benchmark approaches.

In this paper, we conduct a theoretical analysis of how to integrate reconfigurable intelligent surfaces (RIS) with cooperative non-orthogonal multiple access (NOMA), considering URLLC. We consider a downlink two-user cooperative NOMA system employing short-packet communications, where the two users are denoted by the central user (CU) and the cell-edge user (CEU), respectively, and an RIS is deployed to enhance signal quality. Specifically, compared to CEU, CU lies nearer from BS and enjoys the higher channel gains. Closed-form expressions for the CU’s average block error rate (BLER) are derived. Furthermore, we evaluate the CEU’s BLER performance utilizing selective combining (SC) and derive a tight lower bound under maximum ratio combining (MRC). Simulation results are provided to our analyses and demonstrate that the RIS-assisted system significantly outperforms its counterpart without RIS in terms of BLER. Notably, MRC achieves a squared multiple of the diversity gain of the SC, leading to more reliable performance, especially for the CEU. Furthermore, by dividing the RIS into two zones, each dedicated to a specific user, the average BLER can be further reduced, particularly for the CEU.

The multiple-input-multiple-output (MIMO) scheme enhances the capacity and reliability of secure Internet of Things (IoT) communication systems. In this article, an MIMO secure IoT communication system which includes a multiantenna transmitter, a multiantenna legitimate receiver, and a multiantenna eavesdropper is investigated. Considering that the transmitter and receiver are deterministic whereas the eavesdropper is either deterministic or randomly distributed, we theoretically derive the secrecy outage probabilities (SOPs) in independent and identically distributed (i.i.d.) as well as full-correlated Rayleigh fading channels. The i.i.d. or full-correlated Wishart matrices are introduced to express the mutual information rates from the transmitter to the legitimate receiver and eavesdropper. By analyzing the joint probability density functions of unordered eigenvalues of Wishart matrices, we obtain the moment generating functions (MGFs) of mutual information rates. Employing inverse Laplace transforms of the MGFs, the SOPs are obtained. Simulation results show the close agreement between the simulated and analytical results.

Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, where the trajectories and communication services are jointly optimized for image transmission of ground users. We aim to minimize the transmission delay while considering constraints such as the UAV’s energy threshold, collision avoidance, and the bandwidth of the multi-UAV system. We propose a value decomposition based multi-agent deep reinforcement learning (VD-MADRL) algorithm to solve this problem, which explores the joint optimization scheme of UAV trajectory, channel allocation, and semantic information selection. Simulations demonstrate that the proposed algorithm greatly reduces system energy consumption and transmission delay compared to other traditional UAVs’ trajectory planning algorithms.

Caching popular contents at edge networks (such as small-cell base stations) has been proposed to deal with the ever-growing mobile traffic. At the meantime, recommendation system is able to shape user demands for further prompting caching gain. In this paper, we study a multi-antenna multi-cell edge network employing transmit beamforming with caching-aware recommendation and user association. We first establish a framework for the joint problem of beamforming, user association, content caching and recommendation to minimize the content transmission delay of mobile users, by specifying a set of necessary conditions for all four component functions of the network. The resulting optimization problem corresponds to a non-convex, multi-timescale, and mixed-integer programming problem, which is hard to handle. To deal with the difficulty in solving the joint optimization problem by the direct formulation, we equivalently decompose it into three sub-problems. Then, we develop a computationally-efficient iterative algorithm to obtain the sub-optimal solution, where the three subproblems are tackled iteratively. Simulation results are conducted to demonstrate that the proposed algorithm can obtain lower transmission delay than baseline schemes.

Driven by the growing popularity of mobile applications, mobile cloud computing has been envisioned as a promising approach to enhance computation capability of mobile devices and reduce the energy consumptions. In this paper, we investigate the problem of multi-user computation offloading for mobile cloud computing under dynamic environment, wherein mobile users become active or inactive dynamically, and the wireless channels for mobile users to offload computation vary randomly. As mobile users are self-interested and selfish in offloading computation tasks to the mobile cloud, we formulate the mobile users' offloading decision process under dynamic environment as a stochastic game. We prove that the formulated stochastic game is equivalent to a weighted potential game which has at least one Nash Equilibrium (NE). We quantify the efficiency of the NE, and further propose a multi-agent stochastic learning algorithm to reach the NE with a guaranteed convergence rate (which is also analytically derived). Finally, we conduct simulations to validate the effectiveness of the proposed algorithm and evaluate its performance under dynamic environment.

Dual-connectivity (DC), an emerging paradigm in the recent 3GPP specification, is envisioned as a promising solution to enhance mobile users' (MUs') traffic offloading by aggregating radio resources at both macro and small cells. In this paper, we investigate the energy-efficient DC-enabled traffic offloading through small cells which are powered by the on-grid power supply and harvesting renewable energy from nature. In spite of reducing the on-grid power consumption, powering traffic offloading by energy harvesting (EH) leads to the offloading outage due to the intermittency in EH power-supply, which degrades the offloading throughput. Therefore, to reap both the advantages of DC and the EH-supply, we propose a joint traffic scheduling and power allocation scheme that aims at minimizing the total on-grid power consumption, while accounting for the offloading outage and guaranteeing the MU's quality of service (QoS) requirement. In spite of the non-convexity nature of the joint optimization of traffic scheduling and power allocation, we propose an algorithm to efficiently compute the optimal offloading solution. Numerical results are provided to validate our proposed algorithm and the performance gain of the proposed DC-enabled traffic offloading scheme.

This paper investigates the problem of dynamic spectrum access for canonical wireless networks, in which the channel states are time-varying. In the most existing work, the commonly used optimization objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical quality-of-service index), by taking into account not only the expectation but also other high-order statistic. Therefore, we formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. Based on an approximated utility function, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.

Traffic learning and prediction is at the heart of the evaluation of the performance of telecommunications networks and attracts a lot of attention in wired broadband networks. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In this paper, we first collect a significant amount of application-level traffic data from cellular network operators. Afterward, with the aid of the traffic “big data,” we make a comprehensive study over the modeling and prediction framework of cellular network traffic. Our results solidly demonstrate that there universally exist some traffic statistical modeling characteristics at a service or application granularity, including α-stable modeled property in the temporal domain and the sparsity in the spatial domain. But, different service types of applications possess distinct parameter settings. Furthermore, we propose a new traffic prediction framework to encompass and explore these aforementioned characteristics and then develop a dictionary learning-based alternating direction method to solve it. Finally, we examine the effectiveness and robustness of the proposed framework for different types of application-level traffic. Our simulation results prove that the proposed framework could offer a unified solution for application-level traffic learning and prediction and significantly contribute to solve the modeling and forecasting issues.

In this paper, we study the power control problem for interference management in the ultra-dense small cell networks, which is formulated to maximize the sum-rate of all the small cells while keeping tolerable interference to the macrocell users. We investigate the problem by proposing a novel game with dynamic pricing. Theoretically, we prove that the Nash equilibrium (NE) of the formulated game coincides with the stationary point of the original sum-rate maximization problem, which could be locally or globally optimal. Furthermore, we propose a distributed iterative power control algorithm to converge to the NE of the game with guaranteed convergence. To reduce the information exchange and computational complexity, we propose an approximation model for the original optimization problem by constructing the interfering domains, and accordingly design a local information-based iterative algorithm for updating each small cell's power strategy. Theoretic analysis shows that the local information-based power control algorithm can converge to the NE of the game, which corresponds to the stationary point of the original sum-rate maximization problem. Finally, simulation results demonstrate that the proposed approach yields a significant transmission rate gain, compared with the existing benchmark algorithms.

This paper proposes a distributed cooperative framework for improving the energy efficiency of green cellular networks. Based on the traffic load, neighboring base stations (BSs) cooperate to optimize the BS switching (sleeping) strategies so as to maximize the energy saving while guaranteeing users' minimal service requirements. The inter-BS cooperation is formulated following the principle of ecological self-organization. An interaction graph is defined to capture the network impact of the BS switching operation. Then, we formulate the problem of energy saving as a constrained graphical game, where each BS acts as a game player with the constraint of traffic load. The constrained graphical game is proved to be an exact constrained potential game. Furthermore, we prove the existence of a generalized Nash equilibrium (GNE), and the best GNE coincides with the optimal solution of total energy consumption minimization. Accordingly, we design a decentralized iterative algorithm to find the best GNE (i.e., the global optimum), where only local information exchange among the neighboring BSs is needed. Theoretical analysis and simulation results finally illustrate the convergence and optimality of the proposed algorithm.

This paper proposes a novel base station (BS) coordination approach for intercell interference mitigation in the orthogonal frequency-division multiple access based cellular networks. Specifically, we first propose a new performance metric for evaluating end user's quality of experience (QoE), which jointly considers spectrum efficiency, user fairness, and service satisfaction. Interference graph is applied here to capture and analyze the interactions between BSs. Then, a QoE-oriented resource allocation problem is formulated among BSs as a local cooperation game, where BSs are encouraged to cooperate with their peer nodes in the adjacent cells in user scheduling and power allocation. The existence of the joint-strategy Nash equilibrium (NE) has been proved, in which no BS player would unilaterally change its own strategy in user scheduling or power allocation. Furthermore, the NE in the formulated game is proved to lead to the global optimality of the network utility. Accordingly, we design an iterative searching algorithm to obtain the global optimum (i.e., the best NE) with an arbitrarily high probability in a decentralized manner, in which only local information exchange is needed. Theoretical analysis and simulation results both validate the convergence and optimality of the proposed algorithm with fairness improvement.