  Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable
user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high
throughput requirements of future networks. Due to the high blockage
susceptibility of mmWave, the connections may drop frequently. Hence efficient
and fast beam management in initial access (IA) is essential. Current cellular
systems use beam sweeping based IA mechanisms. UC UD concept requires all of
its access points (APs) to perform IA. This leads to a shortage of orthogonal
radio resources. Nonorthogonal resource allocation causes interference which
leads to a higher misdetection probability. In this paper, we propose a novel
deep contextual bandit (DCB) based approach to perform fast and efficient IA in
mmWave based UC UD networks. The DCB model uses one reference signal from the
user to predict the IA beam. The reduced use of reference signals improves beam
discovery delay and relaxes the requirement for radio resources. Ray-tracing
and stochastic channel model-based simulations show that the suggested system
outperforms its beam sweeping counterpart in terms of probability of beam
misdetection and beam discovery delay in mmWave based UC UD networks.
