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The spectrum scarcity at sub-6 GHz spectrum has made millimeter-wave (mmWave)frequency band a key component of the next-generation wireless networks. WhilemmWave spectrum offers extremely large transmission bandwidths to accommodateever-increasing data rates, unique characteristics of this new spectrum needspecial consideration to achieve the promised network throughput. In this work,we consider the off-grid problem for mmWave communications, which has asignificant impact on basic network functionalities involving beam steering andtracking. The off-grid effect naturally appears in compressed sensing (CS)techniques adopting a discretization approach for representing the angulardomain. This approach yields a finite set of discrete angle points, which arean approximation to the continuous angular space, and hence degrade theaccuracy of related parameter estimation. In order to cope with the off-grideffect, we present a novel parameter-perturbation framework to efficientlyestimate the channel and the covariance for mmWave networks. The proposedalgorithms employ a smart perturbation mechanism in conjunction with alow-complexity greedy framework of simultaneous orthogonal matching pursuit(SOMP), and jointly solve for the off-grid parameters and weights. Numericalresults show a significant performance improvement through our novel frameworkas a result of handling the off-grid effects, which is totally ignored in theconventional sparse mmWave channel or covariance estimation algorithms.

Off-Grid Aware Channel and Covariance Estimation in mmWave Networks