Massive MIMO As an Extreme Learning Machine

TL;DR

This paper demonstrates that massive MIMO systems with low-resolution ADCs naturally implement an extreme learning machine, enabling effective hardware impairment mitigation and adaptive signal processing.

Contribution

It introduces a novel perspective linking massive MIMO with ELM, proposing an adaptive receiver design that improves performance under hardware impairments.

Findings

ELM-based receiver outperforms conventional methods in simulations

Massive MIMO with low-res ADCs functions as an ELM

Adaptive ELM can handle time-varying channel effects

Abstract

This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM). The receive antennas at the base station serve as the hidden nodes of the ELM, and the low-resolution ADCs act as the ELM activation function. By adding random biases to the received signals and optimizing the ELM output weights, the system can effectively tackle hardware impairments, such as the nonlinearity of power amplifiers and the low-resolution ADCs. Moreover, the fast adaptive capability of ELM allows the design of an adaptive receiver to address time-varying effects of MIMO channels. Simulations demonstrate the promising performance of the ELM-based receiver compared to conventional receivers in dealing with hardware impairments.