Deep learning (DL) has shown the great potentials to break the bottleneck of
communication systems. This article provides an overview on the recent
advancements in DL-based physical layer communications. DL can improve the
performance of each individual block in communication systems or optimize the
whole transmitter/receiver. Therefore, we categorize the applications of DL in
physical layer communications into systems with and without block structures.
For DL-based communication systems with block structures, we demonstrate the
power of DL in signal compression and signal detection. We also discuss the
recent endeavors in developing end-to-end communication systems. Finally, the
potential research directions are identified to boost the intelligent physical
layer communications with DL.

本文概述了基于深度学习的物理层通信的最新进展，将 DL 在通信系统中的应用分为具有块结构和没有块结构的系统，探讨了 DL 在信号压缩和信号检测中的应用，以及最近为开发端到端通信系统而做的努力，并指出了提升具有 DL 的智能物理层通信的潜在研究方向。

物理层通讯中的深度学习

Deep Learning in Physical Layer Communications

Compressed Sensing (CS) is a novel technique for simultaneous signal sampling
and compression based on the existence of a sparse representation of signal and
a projected dictionary $PD$, where $P\in\mathbb{R}^{m\times d}$ is the
projection matrix and $D\in\mathbb{R}^{d\times n}$ is the dictionary. To
exactly recover the signal with a small number of measurements $m$, the
projected dictionary $PD$ is expected to be of low mutual coherence. Several
previous methods attempt to find the projection $P$ such that the mutual
coherence of $PD$ can be as low as possible. However, they do not minimize the
mutual coherence directly and thus their methods are far from optimal. Also the
solvers they used lack of the convergence guarantee and thus there has no
guarantee on the quality of their obtained solutions. This work aims to address
these issues. We propose to find an optimal projection by minimizing the mutual
coherence of $PD$ directly. This leads to a nonconvex nonsmooth minimization
problem. We then approximate it by smoothing and solve it by alternate
minimization. We further prove the convergence of our algorithm. To the best of
our knowledge, this is the first work which directly minimizes the mutual
coherence of the projected dictionary with a convergence guarantee. Numerical
experiments demonstrate that the proposed method can recover sparse signals
better than existing methods.

本研究提出通过直接最小化投影词典的互不一致性来找到最优投影，并证明了算法的收敛性。该算法在实验中表现优于现有方法，能更好地恢复稀疏信号。