Efficient Neural Network DPD Architecture for Hybrid Beamforming mMIMO
نویسندگان
چکیده
This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared on their ability to compensate nonlinear distortion of power amplifiers in a single user (SU) multiuser (MU) Fully-Connected (FC) HBF transmitters. The proof-of-concept is provided with 64 × FC system, 2 RF chains. complexity solution reduced by using Real-Valued Time-Delay two hidden layers (RVTDNN2L) instead as many blocks there chains the transmitter it shown that proposed architecture better compensates traditional memory polynomial DPD. Two RVTDNN2L developed linearization MU systems, also efficiently linearizes transmitters terms Normalized Mean-Squared Error (NMSE) Vector Magnitude (EVM).
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12030597