Beamforming optimization can enhance the next-generation wireless networks. However, finding the optimal beamforming in real-time is hard due to the need for large beam training overhead. The problem can be more challenging in dynamic environments with small coherence channel time. In this paper, we propose an accurate beam prediction solution using light detection and ranging (LiDAR)-assisted radio frequency (RF) system. More specifically, we propose a deep-learning model based on long-sort term memory (LSTM) to predict future beam indices from a set of pre-defined beam steering codebook. In addition to solving the beamforming overhead problem, the proposed deep learning approach is a model-free approach that can be applied with no required knowledge of the channel state information. We compare the proposed model with the cross-validation results in addition to other benchmarks using the top-k accuracy metric. The numerical results show that the proposed scheme has achieved a top-1 accuracy of 84.6% compared to an 80.3% for cross-validation while the other benchmarks have achieved a 57.5% using the same data set.
O. Rinchi et al., "Deep-learning-based Accurate Beamforming Prediction Using LiDAR-assisted Network," IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/PIMRC56721.2023.10293949
Electrical and Computer Engineering
Keywords and Phrases
beamforming; deep-learning; light detection and ranging (Li-DAR); long-sort term memory (LSTM)
International Standard Book Number (ISBN)
Article - Conference proceedings
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01 Jan 2023