Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems
This work addresses the high training overhead in beam alignment for 6G networks, offering a lightweight solution for improved efficiency, though it is incremental as it builds on existing methods with a focus on complexity reduction.
The paper tackles the problem of reducing computational complexity in position-assisted beam prediction for mmWave ISAC systems in 6G networks, achieving an 83% complexity reduction while maintaining similar beam prediction accuracy compared to a baseline deep neural network.
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.