  As a promising technique to meet the drastically growing demand for both high
throughput and uniform coverage in the fifth generation (5G) wireless networks,
massive multiple-input multiple-output (MIMO) systems have attracted
significant attention in recent years. However, in massive MIMO systems, as the
density of mobile users (MUs) increases, conventional uplink training methods
will incur prohibitively high training overhead, which is proportional to the
number of MUs. In this paper, we propose a selective uplink training method for
massive MIMO systems, where in each channel block only part of the MUs will
send uplink pilots for channel training, and the channel states of the
remaining MUs are predicted from the estimates in previous blocks, taking
advantage of the channels' temporal correlation. We propose an efficient
algorithm to dynamically select the MUs to be trained within each block and
determine the optimal uplink training length. Simulation results show that the
proposed training method provides significant throughput gains compared to the
existing methods, while much lower estimation complexity is achieved. It is
observed that the throughput gain becomes higher as the MU density increases.
