Reconfigurable intelligent surface (RIS) has aroused a surge of interest in recent years. In this paper, we investigate the joint phase alignment and phase quantization on discrete phase shift designs for RIS-assisted single-input single-output (SISO) system. Firstly, the phenomena of phase distribution in far field and near field are respectively unveiled, paving the way for discretization of phase shift for RIS. Then, aiming at aligning phases, the phase distribution law and its underlying degree-of-freedom (DoF) are characterized, serving as the guideline of phase quantization strategies. Subsequently, two phase quantization methods, dynamic threshold phase quantization (DTPQ) and equal interval phase quantization (EIPQ), are proposed to strengthen the beamforming effect of RIS. DTPQ is capable of calculating the optimal discrete phase shifts with linear complexity in the number of unit cells on RIS, whilst EIPQ is a simplified method with a constant complexity yielding sub-optimal solution. Simulation results demonstrate that both methods achieve substantial improvements on power gain, stability, and robustness over traditional quantization methods. The path loss (PL) scaling law under discrete phase shift of RIS is unveiled for the first time, with the phase shifts designed by DTPQ due to its optimality. Additionally, the field trials conducted at 2.6 GHz and 35 GHz validate the favourable performance of the proposed methods in practical communication environment.
翻译:可重构智能表面(RIS)近年来引起了广泛的关注。本文研究了在离散相位移位设计中对RIS辅助单输入单输出(SISO)系统进行关节相位对准和相位量化。首先,分别揭示了远场和近场中相位分布的现象,为RIS的相位移位铺平了道路。然后,针对相位对齐,对相位分布规律及其潜在的自由度进行了表征,作为相位量化策略的指导。随后,提出了两种相位量化方法:动态阈值相位量化(DTPQ)和等间隔相位量化(EIPQ),以加强RIS的波束成形效果。DTPQ能够以线性复杂度计算出RIS单位单元数目的最优离散相位移位,而EIPQ是一种简化的方法,具有恒定复杂度,提供次优解。仿真结果表明,两种方法都在功率增益、稳定性和鲁棒性方面取得了大幅改进,优于传统量化方法。首次揭示了在RIS的离散相移下的路径损耗(PL)缩放定律,由于其最优性,相位转移由DTPQ设计。此外,在2.6 GHz和35 GHz进行的现场实验验证了所提出方法在实际通信环境中的良好性能。