This letter investigates the physical layer security of reconfigurable intelligent surface (RIS) aided integrated-sensing-and-communication (ISAC) systems, in which the high-power radar signal is used as the interference to disturb the eavesdropper. We formulate a joint beamforming optimization problem of active beamforming of base station and passive beamforming of RIS to minimize the maximum eavesdropping signal-to-interference-plus-noise (SINR). To find its solution, we decompose the original problem into two subproblems and then use the alternating optimization (AO) technique based on the fraction programming (FP) and semi-definite programming (SDP) methods. Simulation results show that the proposed FP-SDP-AO algorithm has good convergence performance and can effectively suppress the SINR of eavesdroppers while maintaining a satisfactory quality of service for legitimate users.

Reconfigurable intelligent surface (RIS) is considered as a promising technology, which can effectively control the phase of signals with low-cost reflective elements. In this paper, we investigate the RIS-assisted D2D communication system under Nakagami-m fading. Overlay mode and underlay mode are discussed respectively. To characterize the performance of proposed communication system, we propose novel closed-form analytical expressions for outage probability under two modes, respectively. Based on the analytical results, we derive the diversity order of outage probability. We show that under overlay D2D mode, the diversity order depends on the number of elements and fading parameters. For the underlay D2D case, 0 diversity order is obtained due to the existence of interference. Furthermore, we study the ergodic achievable rate for each mode and the corresponding tight bounds are derived in closed form. High SNR slopes achieved by users under two modes are also discussed. We observe a high SNR slope of order 1 for overlay D2D mode and 0 for underlay D2D mode. In addition, we also investigate the energy efficiency (EE) of the considered system. Numerical results show that the optimal phase shift proposed in our paper enhance system performance effectively comparing to the traditional D2D communication and random phase shift scheme. Significant EE gains can be obtained for low to middle SNR region by passive reflectors with reconfigurable phase. Unlike relay system, the nearer RIS to the D2D transmitter/receiver, the better performance is obtained.

Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computation services. It is attractive for emergency search, traffic control, disaster response, as well as other applications with desperate requirements for information freshness. In this work, we investigate a three-layer multi-UAV assisted MEC system with stochastic task arrival. In order to minimize the age of information (AoI), we jointly optimize the computation offloading, UAV trajectory and communication resources. In particular, we propose a heterogeneous federated multi-agent reinforcement learning algorithm based on a heterogeneous actor-critic framework. All entities in the three-layer MEC system act as both the actor to interact with the environment and the critic to learn optimal policies. Since the training of each agent in the framework is independent, a novel federated updating method is further adopted to enable cooperation among entities and solve the problem of non-stationary environment. Extensive simulation results validate that the proposed scheme outperforms the baseline schemes in terms of both the convergence and resulted AoI in the multi-UAV assisted MEC system.

Reconfigurable intelligent surface (RIS) is a promising paradigm for implementing intelligent reconfigurable wireless propagation environments in the 6G era. However, most of the existing studies focus on utilizing RIS deployed on buildings to provide services to users or constructing a RIS-assisted system framework for static users, which greatly limited application in real-time changing vehicular communication environments. As a result, combining unmanned aerial vehicles (UAVs) with RIS (RIS-UAV) plays a crucial role in various wireless networks due to their high mobility. To maximize the communication rate between base station (BS) and mobile vehicle, we propose a position prediction strategy for vehicles that facilitates real-time adjustment of UAV trajectories and RIS phase shifts, enhancing communication in dynamic environments. Deep reinforcement learning (DRL) algorithm is utilized to solve the above question, which achieves a good effect on convergence in continuous action space. Simulation results demonstrate that compared with benchmark schemes, the algorithm we suggested has significant performance gains, that is to maximize the communication rate under system constraints and guarantee the reliability of the communication.

Reconfigurable intelligent surface (RIS) has been attracted lots of attention due to the fact that it can reconfigure propagation environment with low cost for resource-constrained systems. In this paper, RIS is considered to support wireless communication system by taking both reflection path and direct path into consideration and investigate the system performance under general Nakagami- $m$ fading. A novel analytical closed-form expression is first derived for outage probability. To get more insights, we provide simplified approximations for the high SNR regime and obtain the diversity order and coding gain in closed form. We demonstrate that $\sum _{l = 1}^{N + 1} {m_{l}^*}$ diversity order which can be obtained by RIS-assisted system, where $N$ is the number of reflect elements in the RIS and ${m_{l}^*}$ refers to the channel fading parameters. Furthermore, we investigate the performance of ergodic achievable rate and provide tight bounds for it in closed form. Based on these results, high SNR slope is discussed. Notably, our analytical results are suitable for any integer fading parameters and arbitrary topologies, transmit power. Numerical results validate the accuracy of our analytical results and show the great performance improvement provided by the RIS-assisted system.