To satisfy the high data transmission rate requirements of high-speed railway (HSR) wireless communication systems, this paper proposes a reconfigurable intelligent surface (RIS) assisted joint beamforming design algorithm. The algorithm aims to maximize the achievable rate for the HSR communication system while complying with per-antenna power constraints (PAPC) at the base station (BS) and uni-modular constraints on the reflection phase shifts at the RIS. Considering the nonconvex nature of the optimization problem, an alternating optimization (AO) algorithm is employed, decomposing it into two sub-problems: at the BS, an accelerated subgradient (AS) algorithm based on gaussian distribution is proposed to determine the closed-form optimal solution of the transmit covariance matrix; at the RIS, an accelerated minorizationmaximization (AMM) algorithm based on square iteration method is proposed to approximate the optimal solution of the reflection phase shift matrix. Simulation results show that the proposed joint beamforming algorithm effectively addresses the beamforming design challenges in large-scale multiple-input multiple-output (MIMO) HSR communication systems, significantly enhancing the system's achievable rate.
In the heterogeneous ultra-dense 5G network environment, a more flexible network selection algorithm is needed to improve user quality of service (QoS) due to the intensive network deployment and the diversity of user terminal business types. In this paper, a network selection algorithm based on fuzzy-gray ideal solution is proposed, and the decision parameters are dynamically selected according to service QoS requirements. At the same time, without fuzzy reasoning, a gray ideal solution network scoring method based on threshold judgment is designed by combining gray correlation with ideal sorting algorithm. Simulation results show that the proposed algorithm can effectively reduce switching delay and improve the accuracy of network selection.
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