Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
This addresses the energy efficiency challenge in massive MIMO systems for wireless communications, but it is incremental as it builds on existing hybrid beamforming techniques with a new learning method.
The paper tackles the complex problem of designing hybrid beamforming vectors for massive MIMO subarray systems, which involves discrete connections and quantized phase-shifters, by proposing an unsupervised learning approach that achieves higher sum-rates than existing methods in simulations.
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect CSI is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.