  Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G)
and beyond wireless communication systems owing to higher spectrum efficiency,
spatial gains, and energy efficiency. Reaping the benefits of MIMO transmission
can be fully harnessed if the channel state information (CSI) is available at
the transmitter side. However, the acquisition of transmitter side CSI entails
many challenges. In this paper, we propose a deep learning assisted CSI
estimation technique in highly mobile vehicular networks, based on the fact
that the propagation environment (scatterers, reflectors) is almost identical
thereby allowing a data driven deep neural network (DNN) to learn the
non-linear CSI relations with negligible overhead. Moreover, we formulate and
solve a dynamic network slicing based resource allocation problem for vehicular
user equipments (VUEs) requesting enhanced mobile broadband (eMBB) and
ultra-reliable low latency (URLLC) traffic slices. The formulation considers a
threshold rate violation probability minimization for the eMBB slice while
satisfying a probabilistic threshold rate criterion for the URLLC slice.
Simulation result shows that an overhead reduction of 50% can be achieved with
12% increase in threshold violations compared to an ideal case with perfect CSI
knowledge.
