论文标题
基于RSSI的混合横梁形成设计，深度学习
RSSI-Based Hybrid Beamforming Design with Deep Learning
论文作者
论文摘要
Hybrid Beam Forming是5G毫米波通信的有前途的技术。但是，在实用的多输入多输出（MIMO）系统中，其实施具有挑战性，因为必须解决非凸优化问题，从而引入额外的延迟和能源消耗。此外，必须从试点信号估算频道状态信息（CSI），或者通过专用通道进行后退，并引入大型信号开机。在本文中，Hybrid预编码器仅基于每个用户的接收信号强度指示器（RSSI）反馈而设计。提出了一种深度学习方法，以合理的复杂性执行相关的优化。结果表明，获得的总和率非常接近使用全CSI最佳但复杂的解决方案获得的总和。最后，与现有技术相比，提出的解决方案允许大大提高系统的光谱效率，因为需要最小的CSI反馈。
Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.
