Reinforcement Learning Empowered QoS-aware Adaptive Q-Routing in Ad-hoc Networks
Jiacheng Du, Xiaoyan Huang, Fan Wu, Supeng Leng · 2020
With the rapid growth of the network applications, more services with diverse QoS requirements have emerged. Efficient routing technique plays a vital role in supporting the diversified serveries in dynamically changing wireless multi-hop networks. To this end, we propose the reinforcement learning empowered QoS-aware adaptive Q-routing (RL-QAQ) algorithm, so as to provide discriminated transmission for different services with various QoS requirements as well as reduce the delivery delay and routing overhead. In the proposed RL-QAQ algorithm, an adaptive probability is devised to optimize the exploration strategy to reduce the overhead of acquiring the network status. Besides, the QoS-aware reward function and Q-tables for the different services are devised to support multi-QoS transmission requirements. Simulation results demonstrate that the proposed RL-QAQ algorithm can adaptively adjust the routing policy according to the varying network environment to meet the transmission requirements of different services with low delivery delay and routing overhead.