计算机科学
强化学习
计算机网络
服务质量
无线路由协议
路由协议
优化链路状态路由协议
自适应服务质量多跳路由
分布式计算
车载自组网
无线自组网
adhoc无线分发服务
区域路由协议
布线(电子设计自动化)
人工智能
无线
电信
作者
Thong Anh Tran,Toan T. Nguyen,Kyusung Shim,Daniel Benevides da Costa,Beongku An
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2022-12-01
卷期号:71 (12): 13165-13181
被引量:2
标识
DOI:10.1109/tvt.2022.3196046
摘要
In this paper, we propose a novel deep reinforcement learning-based quality-of-service (QoS) routing protocol, namely DRQR, exploiting cross-layer design to establish efficient QoS (EQS) routes in cognitive radio mobile ad hoc networks. An EQS route is a route with minimum end-to-end (E2E) queuing delay subject to QoS constraints such as link stability, residual energy, number of hops and avoiding licensed channels of primary users. Particularly, we propose an NP-complete optimization problem which has a feasible solution as an EQS route. To tackle this problem, we design a new deep reinforcement learning model which supports the DRQR protocol to establish EQS routes in real time by offline training instead of online training like most of literature studies. Moreover, the DRQR protocol guarantees to have high system performance. A mathematical analysis of the E2E queuing delay with random waypoint mobility model also provides to verify simulation results. Numerical results show that the DRQR protocol outperforms state-of-the-art routing protocols in terms of routing delay, queuing delay, control overhead, PDR and energy consumption.
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