Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning
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DOI：
10.1109/access.2021.3064073
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Alkhateeb, Ahmed
中科院分区：
文献类型：
--
作者：
Taha, Abdelrahman;Alrabeiah, Muhammad;Alkhateeb, Ahmed
采用大型智能表面（LISs）是提高未来无线系统覆盖率和速率的一个有前途的解决方案。这些表面包含大量的近无源元件，这些元件与入射信号相互作用，例如通过反射信号，以一种智能的方式提高无线系统的性能。先前的工作集中在假设全通道知识的LIS反射矩阵的设计上。然而，在美国评估这些渠道是一个关键的挑战性问题。由于LIS元件数量庞大，如果所有LIS元件都是无源的（未连接到基带），则信道估计或反射波束训练将与(i)巨大的训练开销相关；如果所有元件都通过全数字或混合模拟/数字架构连接到基带，则与（ii）令人望而却步的硬件复杂性和功耗相关。本文通过利用压缩感知和深度学习的工具，提出了这些问题的有效解决方案。首先，提出了一种基于稀疏信道传感器的LIS结构。在此体系结构中，除了少数活动元素（连接到基带）外，所有LIS元素都是被动的。然后，我们开发了两个解决方案来设计LIS反射矩阵，其训练开销可以忽略不计。在第一种方法中，我们利用压缩感知工具从仅在活动元素上看到的通道构建所有LIS元素上的通道。在第二种方法中，我们开发了一种基于深度学习的解决方案，在该解决方案中，LIS学习如何与给定的通道中的事件信号进行交互，这些通道表示环境状态和发射器/接收器位置。我们表明，所提出的解决方案的可实现率接近上界，它假设了完美的通道知识，训练开销可以忽略不计，只有少数活动元素，使它们对未来的LIS系统有希望。
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers 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 channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. 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). 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. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.