FDD Channel Estimation via Covariance Estimation in Wideband Massive MIMO Systems.

Sensors (Basel)

Department of Computer Engineering & CITIC Research Center, University of A Coruña, Galicia 15001, Spain .

Published: February 2020

AI Article Synopsis

  • A new method for channel estimation in wideband massive MIMO systems is presented, which works effectively for both sub-6 GHz and mmWave frequency bands, taking into account the beam squint effect.
  • The algorithm utilizes the slow variation of the channel's spatial covariance matrix, allowing for short training sequences and leveraging the inherent structure of the channel.
  • The results demonstrate that this method not only reduces computational complexity but also lowers training overhead significantly, outperforming traditional minimum mean-squared error solutions in simulations.

Article Abstract

A method for channel estimation in wideband massive MIMO systems using hybrid digital analog architectures is developed. The proposed method is useful for FDD at either sub-6 GHz or mmWave frequency bands and takes into account the beam squint effect caused by the large bandwidth of the signals. To circumvent the estimation of large channel vectors, the posed algorithm relies on the slow time variation of the channel spatial covariance matrix, thus allowing for the utilization of very short training sequences. This is possibledue to the exploitation of the channel structure. After identifying the channel covariance matrix, the channel is estimated on the basis of the recovered information. To that end, we propose a novel method that relies on estimating the tap delays and the gains as sociated with each path. As a consequence, the proposed channel estimator achieves low computational complexity and significantly reduces the training overhead. Moreover, our numerical simulations show better performance results compared to the minimum mean-squared error solution.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038954PMC
http://dx.doi.org/10.3390/s20030930DOI Listing

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