  This letter investigates the problem of providing gigabit wireless access
with reliable communication in 5G millimeter-Wave (mmWave) massive
multiple-input multiple-output (MIMO) networks. In contrast to the classical
network design based on average metrics, a distributed risk-sensitive
reinforcement learning-based framework is proposed to jointly optimize the
beamwidth and transmit power, while taking into account the sensitivity of
mmWave links due to blockage. Numerical results show that our proposed
algorithm achieves more than 9 Gbps of user throughput with a guaranteed
probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More
importantly, there exists a rate-reliability-network density tradeoff, in which
as the user density increases from 16 to 96 per km2, the fraction of users that
achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the
baseline models, respectively.
