A Practical Channel Estimation Scheme for SubConnected Hybrid Massive MIMO Systems In massive multipleinput multipleoutput (MIMO) system, the increasing number of antennas causes high power consumption and hardware complexity. Subconnected hybrid system structure can effectively mitigate integration difficulty and reduce the number of radio frequency (RF) chains. Despite losing little degrees of freedom, this structure makes massive MIMO achieved in practice. In this paper, we proposes a uplink channel estimation scheme where a subconnected hybrid massive MIMO base station (BS) communicates with a singleantenna user. Since the sparsity caused by severe path loss, channel state information can be reconstructed by estimating the angles and gains of paths. We apply joint beam selection and sumdifference beam (JBSSDB) method to estimate path angles and utilize least square (LS) method to estimate gains information. Simulation results demonstrate that the proposed scheme achieves lower NMSE than conventional method in the case of same overhead.

FDD Channel Estimation Via Covariance Estimation in Wideband Massive MIMO Systems A method for channel estimation in wideband massive MultipleInput MultipleOutput systems using hybrid digital analog architectures is developed. The proposed method is useful for FrequencyDivision Duplex at either sub6 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 meansquared error solution.