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

References in corpus (6)

• Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems
• Throughput Analysis of Massive MIMO Uplink with Low-Resolution ADCs
• Massive MIMO with Non-Ideal Arbitrary Arrays: Hardware Scaling Laws and Circuit-Aware Design
• Uplink Performance of Wideband Massive MIMO with One-Bit ADCs
• Massive MIMO with 1-bit ADC
• On Deep Learning-Based Channel Decoding

Cited by in corpus (20)

• Deep Learning Based MIMO Communications
• SVM-based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems
• Deep Learning for Wireless Physical Layer: Opportunities and Challenges
• Reliable OFDM Receiver with Ultra-Low Resolution ADC
• An Introduction to Deep Learning for the Physical Layer
• Deep Learning based Channel Estimation for Massive MIMO with Mixed-Resolution ADCs
• Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining
• Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
• Pseudo-Random Quantization Based Two-Stage Detection in One-Bit Massive MIMO Systems
• Uplink Multiuser Massive MIMO Systems with Low-Resolution ADCs: A Coding-Theoretic Approach
• A Low-Complexity Soft-Output wMD Decoding for Uplink MIMO Systems with One-Bit ADCs
• One-Bit Sphere Decoding for Uplink Massive MIMO Systems with One-Bit ADCs
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems
• LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs
• Learning based signal detection for MIMO systems with unknown noise statistics
• Supervised-Learning for Multi-Hop MU-MIMO Communications with One-Bit Transceivers
• Noncoherent OOK Symbol Detection with Supervised-Learning Approach for BCC
• Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
• Semi-Supervised Learning Detector for MU-MIMO Systems with One-bit ADCs
• Soft-Output Detection Methods for Sparse Millimeter Wave MIMO Systems with Low-Precision ADCs