Combination of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) is a win-win strategy which can significantly enhance the coverage performance. However, eavesdroppers may enjoy similar performance gains as the legitimate users. To solve this problem, an artificial noise (AN) assisted secure communication strategy is proposed to maximize the secrecy rate. An alternating optimization (AO) based iterative algorithm leveraging the classical successive convex approximation (SCA) and the semidefinite relaxation (SDR) techniques is proposed to derive the optimal AN model and the RIS parameters. It is found that the proposed algorithm provides better secrecy performance with less AN power compared with the benchmark schemes. More RIS elements help reducing the AN power, while this effect shrinks when the number of RIS elements is sufficiently large. Increasing the number of transmit antennas reduces the AN power if the eavesdropper is quite close to the transmitter, while improves it when the eavesdropper is far away.

This letter investigates a fluid antenna (FA)-assisted integrated sensing and communication (ISAC) system, with joint antenna position optimization and waveform design. We consider enhancing the sum-rate maximization (SRM) and sensing performance with the aid of FAs. Although the introduction of FAs brings more degrees of freedom for performance optimization, its position optimization poses a non-convex programming problem and brings great computational challenges. This letter contributes to building an efficient design algorithm by the block successive upper bound minimization and majorization-minimization principles, with each step admitting closed-form update for the ISAC waveform design. In addition, the extrapolation technique is exploited further to speed up the empirical convergence of FA position design. Simulation results show that the proposed design can achieve state-of-the-art sum-rate performance with at least 60% computation cutoff compared to existing works with successive convex approximation (SCA) and particle swarm optimization (PSO) algorithms.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users’ computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Conventionally, the sensing and communication stages for edge intelligence systems are executed sequentially, leading to an excessive time of dataset generation and uploading. To combat the weakness, this paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC), where the sensing and communication stages are merged to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. For the proposed ISAC-accelerated edge intelligence system, the resource allocation and beamforming should be jointly optimized to exploit the underlying ISAC benefits. We formulate a joint resource allocation and beamforming optimization problem. Despite the non-convexity, we obtain globally optimal solutions assuming that the constant maximal transmits power, and devise an alternating optimization algorithm for the original problem without such assumption. Furthermore, we analyze the ISAC acceleration gain of the proposed system over that of the conventional edge intelligence system. Both theoretic analysis and simulation results show that ISAC accelerates the conventional edge intelligence system when the duration of generating a sample is more than that of uploading a sample. Otherwise, the ISAC acceleration gain vanishes or even is negative. In this case, we derive a sufficient condition for positive ISAC acceleration gain.

This paper investigates a dynamic game framework for physical layer security (PLS)-aware wireless network with third-party collaborative users (TCUs). In the considered system, legitimate users (LUs) aim to transmit secret signals under the threat of eavesdroppers (EVs). There exists TCUs rationally selecting to form coalitions with LUs or EVs, strategically enhancing the secrecy transmission rates for LUs or increasing eavesdropping rates for EVs to obtain rewards. Due to the unpredictability of wireless systems (e.g., time-varying channel conditions, user mobility), coalitions among the three parties may be unfixed and dynamically changing. A hierarchical game integrating a matching subgame with a coalition formation subgame is formulated to model the interactions among LUs, EVs, and TCUs. In order to derive the optimal long-term strategies while solving the equilibrium of the hierarchical game, we first introduce a constrained Gale-Shapley algorithm which determines the optimal power, mode, and channel selections of TCUs with the known coalition partition, and then propose a coalition selection algorithm for reaching the stability of coalition partitions. Moreover, we develop a deep reinforcement learning (DRL)-based solution to concatenate the aforementioned algorithms, producing the long-term optimal strategies for LUs, EVs and TCUs over multiple time slots. Simulations evaluate the performance of the proposed scheme, and demonstrate its superiority over counterparts.

In this paper, we study the energy-efficient unmanned aerial vehicle (UAV) swarm assisted mobile edge computing (MEC) with dynamic clustering and scheduling. In the considered system model, UAVs are divided into multiple swarms, with each swarm consisting of a leader UAV and several follower UAVs. These UAVs serve as mobile edge servers, providing computing services to their covered ground end-users. Unlike existing works, we allow UAVs to dynamically cluster into different swarms, in other words, each follower UAV can change its leader based on the time-varying spatial positions, updated application placement, etc. in a dynamic manner. With the objective of maximizing the long-term energy efficiency of the UAV swarm assisted MEC system, a joint optimization problem of UAV swarm dynamic clustering and scheduling is formulated. Considering the inherent cooperation and competition among intelligent UAVs, we further reformulate this problem as a combination of a series of strongly interconnected multi-agent stochastic games, and theoretically prove the existence of the corresponding Nash Equilibrium (NE). Then, we propose a novel reinforcement learning based UAV swarm dynamic coordination (RLDC) algorithm for obtaining such an equilibrium. Furthermore, the convergence and complexity of the RLDC algorithm are analyzed. Simulations are performed to evaluate the performance of RLDC and illustrate its superiority compared to existing approaches.