Multi-Cell Multi-Beam Prediction Using Auto-Encoder LSTM for mmWave Systems

Millimeter wave (mmWave) systems rely on communication in narrow beams for directional and spatial multiplexing gains. A key challenge in realizing these systems is beam tracking, particularly in environments with high mobility and blockage. Additionally, in wide-area mmWave cellular systems, user equipment (UE) devices must often simultaneously track signals from multiple cells, since links to individual cells can be unreliable. Models of the channel dynamics across multiple cells and multiple beams are difficult to derive from first principles. In this work, we propose a fully data-driven approach based on a novel auto-encoder integrated long short term memory (LSTM) network, which predicts multiple beams from multiple cells, one time step in the future. The key innovation is to use an auto-encoder pre-processing step, which reduces the dimensionality of the input- the main challenge in multi-cell, multi-beam tracking. The prediction capability of the proposed network is verified and compared to common baseline predictors as well as popular machine learning (ML) based neural network predictors in realistic system-level simulations using a commercial ray-tracer. We observe that predictions from the proposed network, which utilizes auto-encoders for dimensionality reduction, offers significantly better best beam accuracy and lower beam misalignment loss than common baseline approaches. We also discuss outage prediction and proactive beam switching as applications of the multi-cell multi-beam prediction.