Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for
  Mitigating Non-Linear PA Distortion
Thomas Feys Liesbet Van der Perre François Rottenberg
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.