Off-Grid Aware Channel and Covariance Estimation in mmWave Networks
Chethan Kumar Anjinappa, Ali Cafer Gurbuz, Yavuz Yapici, and Ismail, Guvenc

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…
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