Abstract-In this letter, we investigate 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, we propose a distributed risk-sensitive reinforcement learning-based framework 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 km 2 , the fraction of users that achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the baseline models, respectively.
For instance, a risk-sensitive reinforcement learning is studied in REF in millimeter-wave communications to optimize both the bandwidth and transmit power.
Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive Approach
IEEE Communications Letters
IEEE Communications Letters
Computer Science