Beamforming Training In Tdd Mu-Massive-Mimo With Optimal Transmission Interval

In this paper, we consider the beamforming training (BFT) technique, which is performed prior to the downlink (DL) data transmission in a time-division duplexing multi-user massive multiple-input multiple-output system. The BFT provides the estimates of effective channel state information (CSI) to all users rather than the statistic CSI in the case without the BFT, such that a more reliable DL data detection is facilitated. However, in a realistic scenario where the channel aging is present, the gain of BFT can be destroyed and a loss in the spectral efficiency (SE) may occur, if the CSI is not promptly updated. As a result, we target at maximizing the SE over the transmission interval, whose solution can be obtained via solving a fixed point equation. In simulations, three linear precoders are evaluated in both cases with and without the BFT. It is illustrated that applying the BFT in the aging channel is not always beneficial even with the optimal transmission interval, as it also depends on the system parameters, e.g. the type of precoder and the mobility of users.