Robust and Efficient Millimeter-Wave Massive MIMO Hybrid Precoding
Architecture based on the Perceiver Neural Network
Abstract
Hybrid precoding has been envisaged as an attractive alternative to
fully digital precoding in massive multiple-input-multiple-output
(mMIMO) systems, where it can ultimately reduce the cost and the power
consumption while maintaining an acceptable sum rate. Within this
context, deep learning (DL) is proposed as an optimization tool to
realize this. However, most of the existing DL based hybrid precoding
solutions are based on the convolutional neural networks (CNNs), which
have inductive bias that limits the learning capability of the highly
stochastic massive MIMO channel. Conversely, the present contribution
proposes a novel DL based hybrid precoder that can achieve an improved
performance at a reduced computation time compared to the CNN based
techniques. This is achieved through the design of a Perceiver Neural
Network (PNN) architecture for hybrid precoding, where the PNN accepts a
noisy channel matrix as an input and produces the analog precoder and
combiners as an output. Also, the proposed architecture learns to
reshape the input data in order to achieve the best accuracy through a
novel trainable reshaping module. Our design involves an offline phase,
in which we build our realistic ray tracing based massive MIMO dataset
to train our Perceiver-based hybrid precoder (PBHP). In this context, we
conduct extensive experiments to compare the PBHP with a CNN-based
hybrid precoder (CNN-HP). It is extensively shown that the proposed PBHP
outperforms the CNN-HP in terms of both accuracy and inference time.
Moreover, the PBHP is more robust when the transmit power is low and the
number of antennas is large. Finally, the offered results demonstrate
that the PBHP exhibits a drastically less inference time (by nearly an
order of magnitude) than the CNN-HP, especially for higher number of
antennas, rendering it a promising candidate for the next-generation
wireless networks.