The performance of modern wireless communications systems depends critically
on the quality of the available channel state information (CSI) at the
transmitter and receiver. Several previous works have proposed concepts and
algorithms that help maintain high quality CSI even in the presence of high
mobility and channel aging, such as temporal prediction schemes that employ
neural networks. However, it is still unclear which neural network-based scheme
provides the best performance in terms of prediction quality, training
complexity and practical feasibility. To investigate such a question, this
paper first provides an overview of state-of-the-art neural networks applicable
to channel prediction and compares their performance in terms of prediction
quality. Next, a new comparative analysis is proposed for four promising neural
networks with different prediction horizons. The well-known tapped delay
channel model recommended by the Third Generation Partnership Program is used
for a standardized comparison among the neural networks. Based on this
comparative evaluation, the advantages and disadvantages of each neural network
are discussed and guidelines for selecting the best-suited neural network in
channel prediction applications are given.

现代无线通信系统的性能在很大程度上取决于发送方和接收方所拥有的可用信道状态信息（CSI）的质量。本文通过比较预测质量的表现，对适用于信道预测的最新神经网络进行了综述和对比分析，并提出了选择最适合信道预测应用的神经网络的指南。

神经网络在无线信道预测中的比较

A Comparison of Neural Networks for Wireless Channel Prediction

In this paper, we propose to utilize intelligent reflecting surfaces (IRSs)
for enhancing the physical layer security of wireless communications systems.
In particular, an IRS-assisted secure wireless system is considered, where a
multi-antenna transmitter communicates with a single-antenna receiver in the
presence of an eavesdropper. To maximize the secrecy rate, both the beamformer
at the transmitter and the IRS phase shifts are jointly optimized. Based on the
block coordinate descent (BCD) and minorization maximization (MM) techniques,
two efficient algorithms are developed to solve the resulting non-convex
optimization problem for small- and large-scale IRSs, respectively. Simulation
results show that IRSs can significantly improve physical layer security if the
proposed algorithms are employed. Furthermore, we reveal that deploying
large-scale IRSs is more efficient than enlarging the antenna array size of the
transmitter for both boosting the secrecy rate and enhancing the energy
efficiency.

本文提出使用智能反射表面（IRS）来增强无线通信系统的物理层安全，并针对一个多天线发射机与单天线接收器在窃听者存在的情况下考虑了辅助安全无线系统。为了最大化保密率，同时优化了发射机的波束形成器和 IRS 相位。本文提出的两个高效算法可以解决结果非凸优化问题，模拟结果表明，如果使用所提出的算法，则 IRS 可以显著提高物理层安全性。此外，我们还发现，部署大规模 IRS 要比扩大发射机天线阵列大小更有效，可以提高保密率并增强能量效率。

智能反射表面实现安全无线通信

Enabling Secure Wireless Communications via Intelligent Reflecting  Surfaces

Coordinated Multi-Point (CoMP) is known to be a key technology for next
generation mobile communications systems, as it allows to overcome the burden
of inter-cell interference. Especially in the uplink, it is likely that
interference exploitation schemes will be used in the near future, as they can
be used with legacy terminals and require no or little changes in
standardization. Major drawbacks, however, are the extent of additional
backhaul infrastructure needed, and the sensitivity to imperfect channel
knowledge. This paper jointly addresses both issues in a new framework
incorporating a multitude of proposed theoretical uplink CoMP concepts, which
are then put into perspective with practical CoMP algorithms. This
comprehensive analysis provides new insight into the potential usage of uplink
CoMP in next generation wireless communications systems.

本文提出了一个新的框架，将多种理论上行 CoMP 概念与实际 CoMP 算法结合起来，从而综合分析了 uplink CoMP 在下一代无线通信系统中的潜在用途，并解决了需大量附加基础设施和不完美信道知识等问题。