Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
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DOI：
10.1109/ieeeconf51394.2020.9443430
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Yu Zhang;Muhammad Alrabeiah;A. Alkhateeb
中科院分区：
文献类型：
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作者：
Yu Zhang;Muhammad Alrabeiah;A. Alkhateeb
部署大规模天线阵列是当前和未来无线通信系统的关键特征。然而，由于一些非理想的实际条件，如未知的阵列几何或可能的硬件损伤，准确的信道状态信息变得难以获取。这阻碍了波束成形/组合向量的设计，而波束成形/组合向量对于充分利用大规模MIMO系统的潜力或对抗毫米波（mmWave）通信中的高路径损耗至关重要。在本文中，我们提出了一种新的解决方案，该解决方案利用深度强化学习（DRL）来学习针对一组用户优化的波束模式，而无需明确了解信道。仿真结果表明，该方法能够在只需要用户反馈波束形成增益的情况下，通过量化移相器找到接近最优的波束方向图。
Deploying large scale antenna arrays is a key characteristic of current and future wireless communication systems. However, due to some non-ideal practical conditions, such as the unknown array geometry or possible hardware impairments, the accurate channel state information becomes hard to acquire. This impedes the design of beamforming/combining vectors that are crucial to fully exploit the potential of the large-scale MIMO systems or to combat the high path-loss in millimeter wave (mmWave) communications. In this paper, we propose a novel solution that leverages deep reinforcement learning (DRL) to learn the beam pattern that is optimized for a group of users without the explicit knowledge of the channels. Simulation results show that the developed solution is capable of finding the near optimal beam pattern with quantized phase shifters and with only requiring the beamforming gain feedback from the users.