[论文解读] Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning

Abdelrahman Taha, Muhammad Alrabeiah+1 more|arXiv (Cornell University)|Apr 22, 2019

Advanced Wireless Communication Technologies被引用 139

一句话总结

本文提出了一种稀疏传感器 LIS 架构，并使用压缩感知和深度学习设计反射矩阵，训练开销极小，仅需很小比例的主动元件即可实现接近最优的速率。

ABSTRACT

Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. In the second approach, we develop a deep learning based solution where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.

研究动机与目标

• 激发并设计大尺度智能表面（LIS）以提升覆盖范围和速率。
• 应对 LIS 信道估计中的大规模训练开销和硬件复杂度挑战。
• 提出一个仅有少量主动传感器的能效高的 LIS 架构。
• 开发基于压缩感知和深度学习的解决方案，以极低的训练开销设计 LIS 反射矩阵。

提出的方法

• 提出一个具有 M 个被动元件和在表面随机分布的 Ḿ 个主动信道传感器的 LIS 架构。
• 将 LIS 相互作用表述为对角相位移矩阵和量化波束成形向量的码本。
• 利用压缩感知（CS）和类 OMP 方法从稀疏采样的信道中恢复完整的 LIS 收发信道。
• 利用阵列响应字典和基于网格的方位/仰角方向，通过稀疏表征构建完整信道。
• 通过对码本进行离线搜索，找到在跨子载波上最大化总速率的最优反射向量（无在线波束训练）。
• 分享一种并行的深度学习方法（论文描述），将采样信道映射到最优反射配置，而无需完整信道信息。

实验结果

研究问题

• RQ1 如何通过稀疏、能源高效的硬件架构提升 LIS 性能？
• RQ2 压缩感知是否能够从少量主动传感器中恢复完整的 LIS 通道信息，以实现接近最优的反射设计？
• RQ3 深度学习是否能够在无需完整信道知识的情况下，将部分信道观测映射到接近最优的 LIS 反射矩阵？
• RQ4 与全连接 LIS 架构相比，使用稀疏主动 LIS 元件可以实现哪些训练开销与能效提升？

主要发现

• 一个仅有很小比例主动传感器的 LIS 架构（主动元件不到 1%）可以在几乎没有训练开销的情况下接近上限速率。
• 基于压缩感知的设计可从采样信道恢复完整信道，利用离线反射码本搜索实现近似最优的速率性能。
• 在 DeepMIMO 射线追踪数据（3.5 GHz 和 28 GHz）上展示了性能提升，显示在稀疏感知下接近最优速率。
• 由于大部分 LIS 元件保持被动，该方法带来显著的能效收益。
• 局限性包括对信道稀疏性和阵列几几何知识的依赖，以及在丰富散射环境中的潜在性能下降。