RSSI-Based Hybrid Beamforming Design with Deep Learning
This addresses the problem of latency and energy consumption in 5G communications for practical MIMO systems, though it is incremental as it builds on existing hybrid beamforming methods.
The paper tackles the challenge of hybrid beamforming in 5G MIMO systems by designing a precoder using only RSSI feedback, reducing signaling overhead and complexity. Results show sum-rates close to full-CSI optimal solutions, with minimal CSI feedback required to increase spectral efficiency.
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.