Probabilistic Massive MIMO Channel Estimation with Built-in Parameter Estimation

07/29/2020
by   Shuai Huang, et al.
0

In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) system uses low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements y. This poses new challenges to the channel estimation problem, and the sparse prior on the channel coefficients x in the angle domain is used to compensate for the information lost during quantization. By interpreting the sparse prior from a probabilistic perspective, we can assume x follows some sparse prior distribution and recover it using approximate message passing (AMP). However, the distribution parameters are unknown in practice and need to be estimated. Due to the increased computational complexity in the quantization noise model, previous works either use an approximated noise model or manually tune the noise distribution parameters. In this paper we treat both the signal and parameters as random variables and recover them jointly within the AMP framework. This leads to a much simpler parameter estimation method and allows us to work with the true quantization noise model. Experimental results show that the proposed approach achieves state-of-the-art performance under various noise levels and does not require parameter tuning, making it a practical and carefree approach for channel estimation.

READ FULL TEXT

page 3

page 4

research
07/15/2020

1-Bit Compressive Sensing via Approximate Message Passing with Built-in Parameter Estimation

1-bit compressive sensing aims to recover sparse signals from quantized ...
research
08/21/2018

Channel Estimation for One-Bit Massive MIMO Systems Exploiting Spatio-Temporal Correlations

Massive multiple-input multiple-output (MIMO) can improve the overall sy...
research
05/20/2022

Approximate Message Passing with Parameter Estimation for Heavily Quantized Measurements

Designing efficient sparse recovery algorithms that could handle noisy q...
research
10/18/2018

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communi...
research
07/29/2022

Robust Quantitative Susceptibility Mapping via Approximate Message Passing

Purpose: It has been challenging to recover QSM in the presence of phase...
research
12/23/2020

EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization

In this work, we introduce EQ-Net: the first holistic framework that sol...
research
05/23/2023

A Graph-Based Collision Resolution Scheme for Asynchronous Unsourced Random Access

This paper investigates the multiple-input-multiple-output (MIMO) massiv...

Please sign up or login with your details

Forgot password? Click here to reset