FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems

[Abstract] A method for channel estimation in wideband massive Multiple-Input Multiple-Output systems using hybrid digital analog architectures is developed. The proposed method is useful for Frequency-Division Duplex at either sub-6 GHz or millimeter wave 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.Xunta de Galicia; ED431G2019/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-RAgencia Estatal de Investigación de España; RED2018-102668-TAgencia Estatal de Investigación de España; PID2019-104958RB-C4

Low-Complexity Downlink Channel Estimation in mmWave Multiple-Input Single-Output Systems

This paper tackles the problem of channel estimation in mmWave multiple-input single-output systems, where users are equipped with single-antenna receivers. By leveraging broadcast transmissions in the downlink channel, two novel low-complexity estimation approaches are devised, able to operate even in presence of a reduced number of transmit antennas or limited bandwidth. Numerical results show that the proposed algorithms provide accurate estimates of the channel parameters, achieving at the same time about 50% complexity reduction compared to existing approaches