Supervised-Learning-Aided Communication Framework for MIMO Systems with Low-Resolution ADCs

TL;DR

This paper introduces a supervised learning-based framework for data detection in MIMO systems with low-resolution ADCs, replacing traditional channel estimation with empirical system learning to improve detection accuracy.

Contribution

It proposes a novel supervised learning approach that learns the nonlinear input-output system for data detection, reducing training overhead and detection complexity in low-resolution ADC MIMO systems.

Findings

Improved detection accuracy over conventional methods.

Effective system learning with empirical conditional PMFs.

Analytical vector-error-rate expression for one-bit ADCs.

Abstract

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel communication framework that is inspired by supervised learning. The key idea of the proposed framework is to learn the non-linear input-output system, formed by the concatenation of a wireless channel and a quantization function used at the ADCs, for data detection. In this framework, a conventional channel estimation process is replaced by a system learning process, in which the conditional probability mass functions (PMFs) of the nonlinear system are empirically learned by sending the repetitions of all possible data signals as pilot signals. Then the subsequent data detection process is performed based on the empirical conditional PMFs obtained during the system learning. To reduce both the training overhead and the detection complexity, we also develop a supervised-learning-aided successive-interference-cancellation method. In this method, a data signal vector is divided into two subvectors with reduced dimensions. Then these two subvectors are successively detected based on the conditional PMFs that are learned using artificial noise signals and an estimated channel. For the case of one-bit ADCs, we derive an analytical expression for vector-error-rate of the proposed framework under perfect channel knowledge at the receiver. Simulations demonstrate the detection error reduction of the proposed framework compared to conventional detection techniques that are based on channel estimation.