PROJECT TITLE :

On the Amount of Downlink Training in Correlated Massive MIMO Channels - 2018

ABSTRACT:

During this Project, we have a tendency to study the effect of downlink (DL) training on the achievable add rate of multiuser large MIMO channels with spatial correlations, and derive sufficient conditions of the DL channel estimation error covariance matrices that maintain the total multiplexing gain at high information signal-to-noise-ratios (SNRs). Given the derived conditions, a easy asymptotic higher sure on the average total rate loss thanks to channel estimation is obtained. We tend to derive, in closed-type, training sequences of limited length that satisfy these conditions. The training duration is variable and will increase with the information SNR, whereas the sequences lie in a subspace spanned by a variable range of user spatial covariance matrices' eigenvectors. We have a tendency to additionally study the problem of sequence codebook design and notice solutions to the current drawback for uniform linear and rectangular arrays using asymptotic results. For the aforementioned training structure, the designed codebooks are observed numerically to be near-optimal for a moderate variety of base station antennas. Because of their ability to identify a sufficient restricted variety of channel directions to train, the proposed solutions will substantially cut back DL training overheads while providing achievable rates that are comparable with the rates achieved with excellent channel state information.