Trainable Least Squares to Reduce PAPR in OFDM-based Hybrid Beamforming Systems
<|reference_start|>Trainable Least Squares to Reduce PAPR in OFDM-based Hybrid Beamforming Systems: In this paper, we propose a trainable least squares (LS) approach for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals in a hybrid beamforming (HBF) system. Compared to digital beamforming (DBF), in HBF technology the number of antennas exceeds the number of digital ports. Therefore, PAPR reduction capabilities are restricted by both a limited bandwidth and the reduced size of digital space. The problem is to meet both conditions. Moreover, the major HBF advantage is a reduced system complexity, thus the complexity of the PAPR reduction algorithm is expected to be low. To justify the performance of the proposed trainable LS, we provide a performance bound achieved by convex optimization using the CVX Matlab package. Moreover, the complexity of the proposed algorithm can be comparable to the minimal complexity of the digital ``twin'' calculation in HBF. The abovementioned features prove the feasibility of the trained LS for PAPR reduction in fully-connected HBF.<|reference_end|>
@article{ivanov2024trainable,
    title={Trainable Least Squares to Reduce PAPR in OFDM-based Hybrid Beamforming
  Systems},
    author={Andrey Ivanov, Alexander Osinsky, Roman Bychkov, Vladimir Kalinin and
  Dmitry Lakontsev},
    journal={arXiv preprint arXiv:2404.02160},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02160},
    primaryClass={cs.OH cs.IT math.IT}
}