Low-Complexity Model-Based Deep Learning for MIMO Digital Predistortion via Neural Network Array Modeling

Abstract

This work presents a direct-learning architecture of a neural network (NN) digital predistortion (DPD) linearizer for a multiple-input multiple-output (MIMO) system. The array-propagated neural network (APNN) technique accounts for nonlinear cross-modulation distortion due to antenna coupling in MIMO systems while maintaining low complexity compared to a single-input single-output (SISO) system. The behavior of the MIMO antenna array is modeled using a NN to train the DPD NNs through memory backpropagation. The linearization methods were evaluated across two test cases using 65 nm complementary metal-oxide-semiconductor (CMOS) power amplifiers (PAs). The first case utilized two PAs under a worst case 4 dB coupling scenario, transmitting 20 MHz 802.11ac Wi-Fi signals with a 10 dB peak-to-average power ratio (PAPR) at a 1 GHz center frequency and an average power of 8.5 dBm. Results demonstrate an error vector magnitude (EVM) improvement from −15.1 to −40.6 dB and from −16.2 to −39.4 dB for the two chains, respectively, with computational complexity increases of 35% and 32% compared to the SISO model. The second test case employed a three-antenna array, in which mutual coupling was introduced according to element proximity. In this scenario, 80 MHz 802.11ax Wi-Fi signals with an 11 dB PAPR were transmitted at an average power of 9 dBm. The proposed approach yielded EVM improvements from −22.1 to −39.7 dB, from −19.1 to −40.4 dB, and from −20.5 to −40.2 dB. In this configuration, only cross samples from adjacent channels were included, demonstrating the scalability of the APNN method.