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 exc...

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Bibliographic Details
Published in2024 26th International Conference on Digital Signal Processing and its Applications (DSPA) pp. 1 - 6
Main Authors Ivanov, Andrey, Osinsky, Alexander, Bychkov, Roman, Kalinin, Vladimir, Lakontsev, Dmitry
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.03.2024
Subjects
Array signal processing
Convex functions
hybrid beamforming
Hybrid power systems
OFDM
PAPR
Peak to average power ratio
Signal processing algorithms
Simulation
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Summary: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.
DOI:10.1109/DSPA60853.2024.10510068