Next generation wireless base stations and access points will transmit and
receive using extremely massive numbers of antennas. A promising technology for
realizing such massive arrays in a dynamically controllable and scalable manner
with reduced cost and power consumption utilizes surfaces of radiating
metamaterial elements, known as metasurfaces. To date, metasurfaces are mainly
considered in the context of wireless communications as passive reflecting
devices, aiding conventional transceivers in shaping the propagation
environment. This article presents an alternative application of metasurfaces
for wireless communications as active reconfigurable antennas with advanced
analog signal processing capabilities for next generation transceivers. We
review the main characteristics of metasurfaces used for radiation and
reception, and analyze their main advantages as well as their effect on the
ability to reliably communicate in wireless networks. As current studies unveil
only a portion of the potential of metasurfaces, we detail a list of exciting
research and implementation challenges which arise from the application of
metasurface antennas for wireless transceivers.

文章介绍了利用元表面作为无线通讯中主动重构天线的有前景技术。通过对元表面的特性进行分析，文章总结了元表面在无线通讯中的主要优势以及它们对可靠通讯的影响，并提出了一系列激动人心的研究和实现挑战。

6G 超大规模 MIMO 通信中的动态元表面天线

Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications

Massive multiple-input multiple-output (MIMO) systems deploying a large
number of antennas at the base station considerably increase the spectrum
efficiency by serving multiple users simultaneously without causing severe
interference. However, the advantage relies on the availability of the downlink
channel state information (CSI) of multiple users, which is still a challenge
in frequency-division-duplex transmission systems. This paper aims to solve
this problem by developing a full transceiver framework that includes downlink
channel training (or estimation), CSI feedback, and channel reconstruction
schemes. Our framework provides accurate reconstruction results for multiple
users with small amounts of training and feedback overhead. Specifically, we
first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP)
algorithm to extract the frequency-independent parameters (i.e., downtilts,
azimuths, and delays) from the uplink. Then, by leveraging the information from
these frequency-independent parameters, we develop an efficient downlink
training scheme to estimate the downlink channel gains for multiple users. This
training scheme offers an acceptable estimation error rate of the gains with a
limited pilot amount. Numerical results verify the precision of the eNOMP
algorithm and demonstrate that the sum-rate performance of the system using the
reconstructed downlink channel can approach that of the system using perfect
CSI.

本研究旨在通过开发全收发机制，包括下行信道训练（或估计），CSI 反馈和信道重建方案，解决大规模 MIMO 系统中多用户 CSI 的问题。我们的框架利用增强的 Newtonized 正交匹配追踪算法提取频率独立参数，然后开发了有效的下行训练方案，以估计多个用户的下行信道增益。数值结果验证了 eNOMP 算法的精度，并证明了使用重构的下行信道的系统的求和速率性能可以接近使用完美 CSI 的系统。

基于高效下行信道重构的 FDD 大规模 MIMO 技术

FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

In this paper, we implement an optical fiber communication system as an
end-to-end deep neural network, including the complete chain of transmitter,
channel model, and receiver. This approach enables the optimization of the
transceiver in a single end-to-end process. We illustrate the benefits of this
method by applying it to intensity modulation/direct detection (IM/DD) systems
and show that we can achieve bit error rates below the 6.7\% hard-decision
forward error correction (HD-FEC) threshold. We model all componentry of the
transmitter and receiver, as well as the fiber channel, and apply deep learning
to find transmitter and receiver configurations minimizing the symbol error
rate. We propose and verify in simulations a training method that yields robust
and flexible transceivers that allow---without reconfiguration---reliable
transmission over a large range of link dispersions. The results from
end-to-end deep learning are successfully verified for the first time in an
experiment. In particular, we achieve information rates of 42\,Gb/s below the
HD-FEC threshold at distances beyond 40\,km. We find that our results
outperform conventional IM/DD solutions based on 2 and 4 level pulse amplitude
modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our
study is the first step towards end-to-end deep learning-based optimization of
optical fiber communication systems.

本论文采用深度学习实现了一种光纤通信系统，可通过单端到端的过程优化收发器，构建出一个灵活的，鲁棒性较好的收发器，成功地将其应用于强度调制 / 直接检测（IM/DD）系统，实现了可靠的传输，并将传输速度提高至 42GBfs。