A Gridless Compressive Sensing Based Channel Estimation for Millimeter Wave MIMO OFDM Systems with One-Bit Quantization

TL;DR

This paper introduces a gridless convex approach using Binary Atomic Norm Minimization for estimating sparse mmWave MIMO OFDM channels from one-bit measurements, improving accuracy over grid-based methods.

Contribution

It proposes a novel gridless convex method for one-bit compressed sensing channel estimation using atomic norm minimization techniques.

Findings

Binary Atomic Norm Minimization accurately recovers channels.

Reweighted Binary Atomic Norm improves estimation precision.

Simulation results confirm effectiveness of the proposed methods.

Abstract

This paper considers the problem of estimating the sparse mmWave massive multiple input - multiple output (MIMO) OFDM channel from one-bit quantized measurements. Unlike previous grid-based one-bit compressive sensing approaches, we present a gridless convex method to recover sparse channel form one-bit measurements via Binary Atomic Norm Minimization (BiANM) and Reweighted Binary Atomic Norm Minimization (ReBiANM). Simulation results verify the accuracy of the binary and reweighted binary atomic norm minimization techniques.