基于广电700MHz基站5G网络覆盖的研究

Research on 5G Network Coverage Based on 70oMHz Radio and Television Base Stations

本研究提出了一种基于Q学习的自适应网络覆盖优化算法,专注于移动通信网络的覆盖场景。结合覆盖建模方式对整个通信场景建模,以初始化Q学习的起点位置,并采用优先决策机制来优化通信覆盖,从而降低环境参数设置和建模约束。通过Q学习设置,本研究所提方法能够充分满足设计要求,并且能够提升覆盖率25%,并削减了弱覆盖以及过度覆盖的区域,且提升了整体算法收敛速度,实现了整个优化设计效率的显著提升。

This study proposes an adaptive network coverage optimization algorithm based on Q-learning focusing on coverage scenarios of mobile communication networks.The entire commu-nication scenario is modeled in conjunction with a coverage modeling approach to initialize the starting position of Q-learning and a prioritized decision-making mcchanism is used to optimize the communication coverage,thus reducing the environmental parameter settings and modeling constraints.With the Q-learning setting,the proposed method can fully meet the design require-ments and improve the coverage by 25%,cut down the weak coverage and over-coverage areas,and improve the overall convergence speed of the algorithm,which achieves a significant im-provement in the cfficicncy of the whole optimization design.