Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems

TL;DR

This paper introduces a lightweight autoencoder model for mmWave beam prediction in 6G systems, significantly reducing computational complexity while maintaining high accuracy by leveraging position information.

Contribution

It presents a novel three-layer undercomplete autoencoder that efficiently predicts beams with lower complexity compared to traditional neural networks.

Findings

Achieves similar accuracy to baseline models

Reduces computational complexity by 83%

Effective in position-assisted beam prediction

Abstract

Integrated sensing and communication and millimeter wave (mmWave) have emerged as pivotal technologies for 6G networks. However, the narrow nature of mmWave beams requires precise alignments that typically necessitate large training overhead. This overhead can be reduced by incorporating the position information with beam adjustments. This letter proposes a lightweight autorencoder (LAE) model that addresses the position-assisted beam prediction problem while significantly reducing computational complexity compared to the conventional baseline method, i.e., deep fully connected neural network. The proposed LAE is designed as a three-layer undercomplete network to exploit its dimensionality reduction capabilities and thereby mitigate the computational requirements of the trained model. Simulation results show that the proposed model achieves a similar beam prediction accuracy to the baseline with an 83% complexity reduction.