A Low Complexity Near-Maximum Likelihood MIMO Receiver with Low Resolution Analog-to-Digital Converters

TL;DR

This paper introduces a low complexity MIMO receiver that effectively mitigates nonlinear distortion from low-resolution ADCs, achieving near-ML performance with reduced computational demands.

Contribution

It presents a novel equivalent model of quantizer distortion that simplifies mitigation techniques and demonstrates a pseudo-ML detection method with high accuracy.

Findings

Performance comparable to true ML receiver

Significant reduction in computational complexity

Validation of the new distortion model

Abstract

Based on a new equivalent model of quantizer with noisy input recently presented in [23], we propose a new low complexity receiver that takes into account the nonlinear distortion (NLD) generated by Analog to Digital converter (ADC) with insufficient resolution. The strength of new model is that it presents the NLD as a function of only the desired part of input signal (without noise). Therefore it can easily be used in a variety of NLD mitigation techniques. Here, as an illustration of this, we use a pseudo-ML approach to detect the original QAM modulation based on the equivalent transfer function and exhaustive search. Simulation results for a single user QAM under flat fading show performance equivalent to a true ML receiver, but with much lower computational complexity. The excellent performance of our receiver is an independent validation of the model [23].