强化学习框架下移动自组织网络分步路由算法

移动自组织网络是一种无基础设施、由移动通信节点组成的无线网络,具有高动态特性。传统的路由协议并不能适应节点移动性带来的频繁拓扑变化,简单的洪泛路由也会因开销过大降低网络的性能。针对如何在移动自组织网络中自适应地进行路由选择,提出强化学习框架下的分步路由选择算法。该算法以最小链路总往返时延为目标,基于强化学习进行路由搜寻,在筛选出符合目标需求节点集合的基础上,结合置信度选择路由。在链路变得不可靠时,数据包被广播给筛选出的邻居节点集以提升路由可靠性并降低开销。对提出的算法在分组到达率和路由开销等主要性能指标进行数值仿真分析。仿真结果表明,提出的分步路由算法相比于基于强化学习的智能鲁棒路由,在降低开销的同时,保持着相当的吞吐率。

Stepwise routing algorithm in mobile ad hoc network under reinforcement learning framework

Mobile ad hoc network is a communication network formed by mobile nodes with non-infrastructure, which has highly dynamic characteristics. Conventional routing protocols cannot adapt to the frequent topology changes brought by node mobility, and the flooding routing also causes the network performance degradation due to the excessive routing overhead. A stepwise routing algorithm based on reinforcement learning was proposed for adaptive routing in mobile ad hoc networks. This algorithm aims at total round trip time minimization and uses the reinforcement learning algorithm to select the next hop. After selecting the set of nodes that meet the requirements of the target, it combines the confidence parameters to select the route. When the link becomes unreliable, packets are broadcasted to filtered neighbor nodes to improve the reliability and reduce the routing overhead. The main property indication of the proposed algorithm, such as throughput and routing overhead, were analyzed theoretically. The simulation results show that, compared with the reinforcement learning based smart robust routing, the proposed routing algorithm reduces the overhead and maintains a competitive throughput.