Particle Swarm Optimization Assisted B-spline Neural Network Based Predistorter Design to Enable Transmit Precoding for Nonlinear MIMO Downlink

Abstract

For the multiple-input multiple-output (MIMO) downlink employing high-order quadrature amplitude modulation signaling and with nonlinear high power amplifiers (HPAs) at base station transmitter, the existing precoding designs relying on the linear MIMO channel can no longer work. We propose an efficient and accurate predistorter design to enable transmit precoding for nonlinear MIMO downlink. Specifically, we obtain the closed-form least squares estimates of the nonlinear HPA's amplitude and phase response using two B-spline neural networks during training. The estimated HPA's phase response automatically yields the estimate of the predistorter's phase response. Based on the B-spline neural network estimate of the HPA's amplitude response, we construct a B-spline neural network model for the predistorter amplitude response, and we adopt a particle swarm optimization (PSO) algorithm to solve this highly nonlinear optimization problem. Using our accurate predistorter estimate to pre-compensate for the nonlinear distortions of the transmit HPAs, a standard full-digital transmit precoding design can readily be adopted to combat the MIMO channel interference. A simulation study is conducted to demonstrate the effectiveness of our proposed PSO assisted predistorter design.

More information

Identifiers

ORCID for Soon Xin Ng: orcid.org/0000-0002-0930-7194

Catalogue record

Export record

Altmetrics