标题： 基于RIS辅助的近场通信的分段波束训练和信道估计 显示英文标题

标题： Piecewise Beam Training and Channel Estimation for RIS-Aided Near-Field Communications

摘要： 本文研究了可重构智能表面（RIS）辅助近场通信系统中的信道估计问题。现有的信道估计技术需要大量的导频开销和计算复杂度，尤其是在RIS元素数量极大的情况下。为了解决这个问题，我们引入了一种双时间尺度的信道估计策略，利用了RIS基站（BS）信道和用户-RIS信道之间的非对称相干时间。我们推导出一个时间尺度属性，表明对于较长相干时间内的任意两个有效信道，其中一个有效信道可以表示为一个向量（称为小时间尺度的有效信道）与另一个有效信道的乘积。通过将初始时间块中估计的有效信道与分段波束训练的观测结果相结合，我们提出了一种高效的方法来估计后续的小时间尺度有效信道。我们在理论上验证了所提出的RIS设计的有效性，并通过仿真表明，我们的信道估计方法在各种现实信道模型下，在导频开销和计算复杂度方面都优于现有方法。 显示英文摘要

摘要： In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techniques require substantial pilot overhead and computational complexity, especially when the number of RIS elements is extremely large. To address this issue, we introduce a two-timescale channel estimation strategy that leverages the asymmetric coherence times of both the RIS-base station (BS) channel and the User-RIS channel. We derive a time-scaling property indicating that, for any two effective channels within the longer coherence time, one effective channel can be represented as the product of a vector, termed the small-timescale effective channel, and the other effective channel. By integrating the estimated effective channel from the initial time block with observations from our piecewise beam training, we present an efficient method for estimating subsequent small-timescale effective channels. We theoretically verify the efficacy of the proposed RIS design and demonstrate, through simulations, that our channel estimation method outperforms existing methods in pilot overhead and computational complexity across various realistic channel models.