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Reconfigurable Intelligent Surface (RIS) presents a promising solution for achieving a balance between communication quality and power consumption. However, traditional reflecting RIS designs are limited to covering signals within a 180-degree range. Fortunately, the development of Simultaneous Transmitting and Reflecting RIS (STAR-RIS) technology enables the simultaneous transmission and reflection of signals, allowing for 360-degree coverage. In this letter, we investigate the STAR-RIS assisted wireless communications under Nakagami-m fading channels, and the channel state information (CSI) is assumed perfectly obtained. Considering the hardware design complexity, the RIS phase shifts are discretely configured with quantization errors. We derive the closed-form expressions of the outage probability and the ergodic achievable rate under the discrete phase. In addition, the approximations for the special cases including the random phase case, the continuous phase case, the Rayleigh fading case, as well as the high signal-noise ratio (SNR) case are further deduced. Numerical results verify the correctness of the analytical results.

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.

Wireless federated learning (FL) is a distributed learning framework, in which mobile devices (MDs) collaboratively train a model while protecting data privacy. Considering selfishness of MDs, they are unwilling to sacrifice their resources to assist model training without any reward, which may limit performance of FL. In this paper, we propose an incentive mechanism between a base station (BS) and several mobile devices (MDs) modeled as a Stackelberg game. During this game, BS first decides the total reward that can be distributed to MDs, and then each MD determines its number of local iterations according to its utility function. In addition, we derive the closed-form expressions of the optimal reward function of the BS and the number of local iterations of the MDs. Finally, the numerical results validate the efficacy of our proposed scheme.

The utilization of Reconfigurable Intelligent Surface (RIS) in a backscatter communication (BackCom) system can increase the flexibility of equipment deployment while extending the communication distance. We concentrate on reducing the transmitting power through the optimization of active and passive beamforming and power allocation, while ensuring that BackCom performance remains uncompromised. Unlike previous studies on RIS neglecting the relationship between amplitude and phase shift of the signal reflected by RIS, our work takes into account the practical phase shift and amplitude response. In order to get solutions for the optimisation problem, we propose an iterative algorithm based on the alternating optimization (AO) and minimization-maximization approaches. We also suggest a nonlinear function fitting technique and a trust region inspection algorithm to reduce the computational complexity.

Reconfigurable Intelligent Surface (RIS) with the capability to control the propagation environment by manipulating the electromagnetic wave is regarded as a potential technology to enhance the communication quality. In this paper, we investigate the performance of RIS-assisted communication systems with device-to-device users under a general fading channel, i.e., Nakagami-$m$ fading channel. The closed-form expressions of outage probability, ergodic capacity, and symbol error rate are derived. Further, the approximations for high SNR scenario are derived respectively, and the insights are then explored. Simulation results illustrate the correctness of our analytical expressions.

This letter presents an investigation into secure transmission in a downlink rate-splitting-aided cell-free massive multiple-input multiple-output system. By incorporating the low-resolution digital-to-analog converters and imperfect channel knowledge, we derive tight closed-form expressions for the data rate and secrecy rate under Ricean fading channels. Additionally, we formulate a power control problem with the objective of maximizing the weighted sum data rate, while ensuring a target secrecy rate for all user equipments. A path-following optimization algorithm is then proposed to determine its solution numerically. All theoretical analyses and the efficacy of the proposed algorithm are validated through extensive simulations.