# Autoencoder for Position-Assisted Beam Prediction in mmWave ISAC Systems

arXiv: https://arxiv.org/abs/2511.18594 · HTML: https://mlsanity.com/paper/2511.18594/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 23 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Integrated Sensing and Communications: Recent Advances and Ten Open Challenges](https://mlsanity.com/paper/2305.00179/) (2023)
- [Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?](https://mlsanity.com/paper/2205.09054/) (2022)
- [Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles](https://mlsanity.com/paper/2208.02157/) (2022)
- [Seventy Years of Radar and Communications: The Road from Separation to Integration](https://mlsanity.com/paper/2210.00446/) (2022)
- [Hybrid-Layers Neural Network Architectures for Modeling the Self-Interference in Full-Duplex Systems](https://mlsanity.com/paper/2110.09997/) (2021)
- [Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and Communications](https://mlsanity.com/paper/2203.06324/) (2022)

## Abstract (arXiv)

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.