论文标题
在线梁学习与毫米波莫莫系统的干扰无效
Online Beam Learning with Interference Nulling for Millimeter Wave MIMO Systems
论文作者
论文摘要
使用大型天线阵列是毫米波（MMWave）和Terahertz通信系统的关键特征。由于硬件限制和缺乏通道知识，通常采用基于代码书的光束形成/组合来实现所需的数组增益。但是，大多数现有的代码手册仅着重于改善目标用户的增益，而无需考虑干扰。这会在密集网络中引起关键的性能降解。在本文中，我们提出了一种基于样本的在线增强学习梁图案设计算法，该算法学习如何塑造光束图案以将干扰方向无效。所提出的方法不需要任何明确的渠道知识或与干扰器的任何协调。仿真结果表明，开发的解决方案能够学习形状良好的光束图案，从而显着抑制干扰，同时牺牲所需用户的可耐受性光束形成/梳子增益。此外，在现实情况下，建立并用于实现和评估开发的在线光束学习解决方案的硬件概念验证原型。学习的光束图案以态室内测量，显示了开发框架的性能增长，并突出了MMWave和Terahertz系统的基于机器学习的有前途的基于机器学习的优化方向。
Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems. Due to the hardware constraints and the lack of channel knowledge, codebook based beamforming/combining is normally adopted to achieve the desired array gain. However, most of the existing codebooks focus only on improving the gain of their target user, without taking interference into account. This can incur critical performance degradation in dense networks. In this paper, we propose a sample-efficient online reinforcement learning based beam pattern design algorithm that learns how to shape the beam pattern to null the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. Simulation results show that the developed solution is capable of learning well-shaped beam patterns that significantly suppress the interference while sacrificing tolerable beamforming/combing gain from the desired user. Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios. The learned beam patterns, measured in an anechoic chamber, show the performance gains of the developed framework and highlight a promising machine learning based beam/codebook optimization direction for mmWave and terahertz systems.
