  An exponential increase in the data rate demand prompted several technical
innovations. Multi User Multiple Input Multiple Output (MU-MIMO) is one of the
most promising schemes. This has been evolved into Massive MIMO technology in
5G to further stretch the network throughput. Massive MIMO tackles the rising
data rate with the increase in the number of antenna. This comes at the price
of a higher energy consumption. Moreover the high velocity users in MU-MIMO
scheme experiences a frequent unpredictable change in the channel condition
that degrade its downlink performance. Therefore a proper number of antenna
selection is of paramount importance. This issue has been addressed using
machine learning techniques and Channel State Information (CSI) but only for
static users. In this study we propose to introduce antenna diversity in
spatial multiplexing MU-MIMO transmission scheme by operating more number of
reception antenna compare to the number of transmission antenna. The diversity
improves the downlink performance of high velocity users. In general our
results can be interpreted for large scale antenna systems like Massive MIMO.
The proposed method can be easily implemented in the existing network
architectures with minimal complexity. Also it has the potential for solving
real-life problems like call drops and low data rate to be experienced by
cellular users traveling through high-speed transportation systems like Dhaka
MRT project
