Jiazi Gao

Low earth orbit (LEO) satellite-assisted edge computing is a promising paradigm for providing seamless connectivity to remote areas and enabling reliable services in dense urban scenarios. By establishing virtual line-of-sight (LoS) links and coherently combining reflected signals, the reconfigurable intelligent surface (RIS) can significantly enhance the channel conditions of satellite-terrestrial communication links. This paper integrates the novel simultaneously transmitting and reflecting beyond-diagonal RIS (STAR-BD-RIS) technology into the LEO satellite collaborative edge computing (LSCEC) system, where each ground user equipment (GUE) can simultaneously offload task bits to multiple satellites via a hybrid non-orthogonal multiple access (NOMA) scheme with the aid of STAR-BD-RIS. To minimize the weighted sum energy consumption of GUEs and LEO satellites, the transmit power, CPU frequency, offloading strategy, bandwidth allocation, and STAR-BD-RIS beamforming are jointly optimized by an iterative algorithm based on the successive convex approximation (SCA) technique and the penalty dual decomposition (PDD) method. Simulation results illustrate that: 1) the STAR-BD-RIS scheme achieves lower energy consumption than traditional RIS (T-RIS), simultaneously transmitting and reflecting RIS (STAR-RIS) and beyond-diagonal RIS (BD-RIS) schemes in LSCEC systems; 2) the proposed hybrid NOMA demonstrates superior scalability and performance over both NOMA and orthogonal frequency division multiple access (OFDMA); 3) the proposed satellite collaboration scheme significantly reduces the energy consumption compared to the case without collaboration, while exhibiting diminishing energy-saving gains as the number of satellites increases.

This article investigates the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted nonorthogonal multiple access (NOMA) systems with one cooperative jammer and dual eavesdroppers. To guarantee the uplink secure transmission, we maximize the sum secrecy rate under both the perfect and imperfect channel state information (CSI) by jointly optimizing the channel allocation, transmit power, and coefficient matrices. For the problem with perfect CSI, a deep reinforcement learning algorithm is proposed based on the deep deterministic policy gradient (DDPG) framework. Then, by introducing the arbitrary distorted noise to the state space, the proposed algorithm is extended to solve the problem under imperfect CSI without causing additional computational complexity. Simulation results illustrate that: 1) the symmetry of STAR-RIS results in severe information leakage and the sum secrecy rate further degrades when the dual eavesdroppers collaborate with each other; 2) the STAR-RIS with independent phase shift can achieve higher sum secrecy rate than that with coupled phase shift, while the performance gap is trivial when there are fewer STAR-RIS elements; and 3) our proposed algorithm can compensate for the impacts of the imperfect CSI, and the sum secrecy rate decreases with the increase of CSI uncertainty.

Single-station target tracking is a technology that uses only one observation station to track the moving targets. At present, the target motion model of most single-station tracking algorithms is the constant velocity model, which is not suitable for high-dynamic targets. In this paper, a high-dynamic target tracking algorithm based on Unscented Kalman Filter (UKF) is proposed. Firstly, the 2D-MUSIC algorithm is used to estimate the azimuth and pitch angles of the target relative to the observation station. Then the time stamp information is used to obtain the propagation delay. Finally, the UKF algorithm is used to estimate the position, velocity and acceleration of the target. Simulation results show that the proposed algorithm can achieve higher estimation accuracy for the target state than the Extended Kalman Filter (EKF) algorithm and the UKF algorithm without acceleration estimation.