This paper demonstrates the use of deep learning and time series data
generated from user equipment (UE) beam measurements and positions collected by
the base station (BS) to enable handoffs between beams that belong to the same
or different BSs. We propose the use of long short-term memory (LSTM) recurrent
neural networks with three different approaches and vary the number of number
of lookbacks of the beam measurements to study the prediction accuracy.
Simulations show that at a sufficiently large number of lookbacks, the UE
positions become irrelevant to the prediction accuracy since the LSTMs are able
to learn the optimal beam based on implicitly defined positions from the
time-defined trajectories.Comment: 22 pages, 9 figures. Submitted to IEEE Transactions on Communication