Correlated Sparse Recovery Using Compressive Covariance Estimation for the mmWave Channel Estimation Problem

Date and Time: June 25, 2021 (Friday), 5:45 PM to 6:30 PM

Talk Recording YouTube Link: https://youtu.be/GahzatcWNwc

Abstract:

In this talk, we focus on the recovery of sparse vectors with correlated non-zero entries from their noisy low dimensional projections. Specifically, we consider the technique of covariance matching, where we first estimate the covariance of the signal from compressed measurements, and then use the obtained covariance matrix as a plug-in to the linear minimum mean squared estimator to obtain an estimate for the sparse vector. We present a novel parametric Gaussian prior model, inspired by sparse Bayesian learning (SBL), which captures the underlying correlation in addition to sparsity. Based on this prior, we develop a novel Bayesian learning algorithm called Corr-SBL, using the expectation-maximization procedure. We then apply Corr-SBL to the channel estimation problem in mmWaveMIMO systems employing a hybrid analog-to-digital architecture. We use channel measurements in the pilot transmission phase to estimate the channel across multiple coherence blocks. We show the efficacy of the Corr-SBL prior by analyzing the error in the channel estimates. Our results show that, compared to genie-aided estimators and other existing sparse recovery algorithms, exploiting both sparsity and correlation results in significant performance gains, even under imperfect covariance estimates obtained using a limited number of samples.This talk is based on joint work with Prof. Chandra R. Murthy.

Biography:

Dheeraj Prasanna received the B. E. degree in Electronics and Communication Engineering from Rashtreeya Vidyalaya  College of Engineering (RVCE), Bangalore in 2018.  He recently defended his M.Tech  (Research) thesis from the Department of Electrical Communication Engineering at the Indian Institute of Science, Bangalore under  the guidance of Prof. Chandra R. Murthy. Currently he is working  as a modem  systems engineer in Qualcomm’s Bangalore Wireless R&D division. His research interests are in the areas of compressed sensing, Bayesian learning and wireless communications.