LTE-V下基于深度强化学习的基站选择算法

eNB selection for LTE-V using deep reinforcement learning

针对长期演进-车辆(long term evolution-vehicle, LTE-V)下的车辆随机竞争接入网络容易造成网络拥塞的问题,提出基于深度强化学习(deep reinforcement learning,DRL)为LTE-V下的车辆接入最佳基站(evolved node B,eNB)的选择算法。使用LTE核心网中移动管理单元(mobility management entity,MME)作为代理,同时考虑网络侧负载与接收端接收速率,完成车辆与eNB的匹配问题,降低网络拥塞概率,减少网络时延。使用竞争-双重深度Q网络(dueling-double deep Q-network,D-DDQN)来拟合目标动作-估值函数(action -value function,AVF),完成高维状态输入-低维动作输出的转化。仿真表明,D-DDQN训练完成参数收敛后,LTE-V网络拥塞概率大幅下降,整体性能有较大提升。

The source allocation scheme for long term evolution-vehicle (LTE-V) is based on random selection, which will cause serious network congestion easily. Based on deep reinforcement learning (DRL), an best access evolved node B (eNB) selection algorithm for the vehicle type communication under LTE-V network is proposed. In order to reduce both the blocking probability and communication delays of LTE-V network, the mobility management entity (MME) is used as an agent, also the receiving rate at user side and network loading at network side are taking into consideration. Meanwhile, dueling-double deep Q-network (D-DDQN) is adopt to fit the target action-value function (AVF). D-DDQN can convert the high dimension state inputs to the low dimension action outputs. The simulation shows that the blocking probability of LTE-V network is reduced significantly after the convergence of DQN’s parameters and the properties of the entire network is improved greatly.