Recent research has focused on reconfigurable intelligent surface (RIS)-assisted cell-free systems with the goal of enhancing coverage and lowering the cost of cell-free networks. However, current research makes the assumption that the perfect channel state information is known. Channel acquisition is, certainly, a difficulty in this case. This work is aimed at investigating RIS-assisted cell-free channel estimation. Toward this end, two unique characteristics are pointed out: 1) For all users, a common channel exists between the base station (BS) and the RIS; and 2) For all BSs, a common channel exists between the RIS and the user. Based on these two characteristics, cascaded and two-timescale channel estimation concerns are studied. Subsequently, two solutions for tackling with the two issues are presented respectively: a three-dimensional multiple measurement vector (3D-MMV)-based compressive sensing technique and a multi-BS cooperative pilot-reduced methodology. Finally, simulations illustrate the effectiveness of the schemes we have presented.
翻译:最近的研究侧重于可重新整合的智能表面(RIS)辅助无细胞系统,目的是扩大无细胞网络的覆盖面并降低其成本,然而,目前的研究假设了解完美的频道状态信息,在这种情况下,获取频道无疑是一个困难。这项工作旨在调查无细胞的系统估计。为此,指出两个独特的特点:(1) 对于所有用户,基础站和RIS之间存在一个共同的渠道;(2) 对于所有BS系统,RIS和用户之间都存在一个共同的渠道。根据这两个特点,研究了分级和两次尺度的频道估计问题。随后,提出了解决这两个问题的两个解决办法:三维多维测量矢量(3D-MMV)基压缩技术,以及一种多基系统合作试点调整方法。最后,模拟说明了我们提出的计划的有效性。