Deep Learning for Multi-User Proactive Beam Handoff: A 6G Application

TL;DR

This paper explores deep learning techniques, specifically LSTM networks, to predict proactive beam handoffs in 6G networks using time series data from user equipment, showing that position data's importance diminishes with more lookbacks.

Contribution

It introduces LSTM-based methods for proactive beam handoff prediction and analyzes the impact of including UE position data versus solely time series measurements.

Findings

LSTM models can effectively predict beam handoffs using time series data.

UE position data improves prediction only up to a certain number of lookbacks.

At large lookbacks, LSTMs learn optimal beams without explicit position information.

Abstract

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of lookbacks of the beam measurements to study the performance of the prediction used for the proactive beam handoff. Simulations show that while UE positions can improve the prediction performance, it is only up to a certain point. At a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.