Recent advances in the fabrication and experimentation of Reconfigurable
Intelligent Surfaces (RISs) have motivated the concept of the smart radio
environment, according to which the propagation of information-bearing
waveforms in the wireless medium is amenable to programmability. Although the
vast majority of recent experimental research on RIS-empowered wireless
communications gravitates around narrowband beamforming in quasi-free space,
RISs are foreseen to revolutionize wideband wireless connectivity in dense
urban as well as indoor scenarios, which are usually characterized as strongly
reverberant environments exhibiting severe multipath conditions. In this
article, capitalizing on recent physics-driven experimental explorations of
RIS-empowered wave propagation control in complex scattering cavities, we
identify the potential of the spatiotemporal control offered by RISs to boost
wireless communications in rich scattering channels via two case studies.
First, an RIS is deployed to shape the multipath channel impulse response,
which is shown to enable higher achievable communication rates. Second, the
RIS-tunable propagation environment is leveraged as an analog multiplexer to
localize non-cooperative objects using wave fingerprints, even when they are
outside the line of sight. Future research challenges and opportunities in the
algorithmic design and experimentation of smart rich scattering wireless
environments enabled by RISs for sixth Generation (6G) wireless communications
are discussed.

本文介绍了可重构智能表面（RIS）在改善复杂多路径环境下的宽带无线连接中的应用，提出了两种案例研究，即通过控制多径信道脉冲响应来提高通信速率和利用 RIS 调谐的传播环境作为模拟复用器进行物体定位，同时讨论了 6G 无线通信中由 RIS 实现的智能场景中的算法设计和实验的未来挑战和机遇。

智能可重构表面用于丰富散射无线通信：近期实验、挑战和机遇

Reconfigurable Intelligent Surfaces for Rich Scattering Wireless  Communications: Recent Experiments, Challenges, and Opportunities

Large-scale MIMO systems are well known for their advantages in
communications, but they also have the potential for providing very accurate
localization thanks to their high angular resolution. A difficult problem
arising indoors and outdoors is localizing users over multipath channels.
Localization based on angle of arrival (AOA) generally involves a two-step
procedure, where signals are first processed to obtain a user's AOA at
different base stations, followed by triangulation to determine the user's
position. In the presence of multipath, the performance of these methods is
greatly degraded due to the inability to correctly detect and/or estimate the
AOA of the line-of-sight (LOS) paths. To counter the limitations of this
two-step procedure which is inherently sub-optimal, we propose a direct
localization approach in which the position of a user is localized by jointly
processing the observations obtained at distributed massive MIMO base stations.
Our approach is based on a novel compressed sensing framework that exploits
channel properties to distinguish LOS from non-LOS signal paths, and leads to
improved performance results compared to previous existing methods.

该研究提出了一种基于压缩感知框架的直接定位方法，通过联合处理分布式大规模 MIMO 基站获得的观测值来区分 LOS 和非 LOS 信号路径，以实现用户位置的高精度定位。