Off-Grid Aware Channel and Covariance Estimation in mmWave Networks

TL;DR

This paper introduces a novel parameter-perturbation framework that improves off-grid channel and covariance estimation in mmWave networks, addressing inaccuracies caused by discretization in compressed sensing techniques.

Contribution

The work proposes a new perturbation-based algorithm combined with SOMP to jointly estimate off-grid parameters and weights, enhancing mmWave channel estimation accuracy.

Findings

Significant performance improvement over traditional methods

Effective handling of off-grid effects in compressed sensing

Enhanced accuracy in channel and covariance estimation

Abstract

The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave) frequency band a key component of the next-generation wireless networks. While mmWave spectrum offers extremely large transmission bandwidths to accommodate ever-increasing data rates, unique characteristics of this new spectrum need special consideration to achieve the promised network throughput. In this work, we consider the off-grid problem for mmWave communications, which has a significant impact on basic network functionalities involving beam steering and tracking. The off-grid effect naturally appears in compressed sensing (CS) techniques adopting a discretization approach for representing the angular domain. This approach yields a finite set of discrete angle points, which are an approximation to the continuous angular space, and hence degrade the accuracy of related parameter estimation. In order to cope with the off-grid effect, we present a novel parameter-perturbation framework to efficiently estimate the channel and the covariance for mmWave networks. The proposed algorithms employ a smart perturbation mechanism in conjunction with a low-complexity greedy framework of simultaneous orthogonal matching pursuit (SOMP), and jointly solve for the off-grid parameters and weights. Numerical results show a significant performance improvement through our novel framework as a result of handling the off-grid effects, which is totally ignored in the conventional sparse mmWave channel or covariance estimation algorithms.