Block-aware Power Allocation Based on Q-Learning in Millimeter-Wave Network

Abstract

millimeter-Wave(mm-Wave) communication is expected to provide significant capacity gains in ultra-dense network scenarios of the 5G wireless communication system.To address the complex interference in the mm-Wave communication scenario and the interruption caused by the high block rate of the dynamic links of the cell edge users,this paper proposes a power allocation strategy scheme based on Q-Learning algorithm considering the high intermission rate of mm-Wave communication.Poisson Cluster Process(PCP) is used in the modelling of randomly deployed base station user systems,and the different influences of link block on the useful signals and interference signals are analyzed.Then the egoistic and altruistic strategy is introduced in the design of state and reward function of the Q-Learning algorithm,and the machine learning strategy is used to get the optimal solution to power allocation.Simulation results show that,compared with the CDP-Q scheme that does not consider the link block rate,the proposed algorithm significantly improves the total capacity of the system due to the optimal power allocation based on the dynamic status of links.