Massive MIMO systems are typically designed assuming linear power amplifiers
(PAs). However, PAs are most energy efficient close to saturation, where
non-linear distortion arises. For conventional precoders, this distortion can
coherently combine at user locations, limiting performance. We propose a graph
neural network (GNN) to learn a mapping between channel and precoding matrices,
which maximizes the sum rate affected by non-linear distortion, using a
high-order polynomial PA model. In the distortion-limited regime, this
GNN-based precoder outperforms zero forcing (ZF), ZF plus digital
pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from
the state-of-the-art. At an input back-off of -3 dB the proposed precoder
compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two
and four users respectively. Radiation patterns show that these gains are
achieved by transmitting the non-linear distortion in non-user directions. In
the four user-case, for a fixed sum rate, the total consumed power (PA and
processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and
ZF plus DPD respectively. A complexity analysis shows six orders of magnitude
reduction compared to DAB precoding. This opens perspectives to operate PAs
closer to saturation, which drastically increases their energy efficiency.

在扭曲受限的情况下，使用图形神经网络来学习通道和预编码矩阵之间的映射，以最大程度地提高受非线性畸变影响的总速率，增加能效。