In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS)
multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in …

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 …

The frequent movement of nodes in cognitive radio mobile ad hoc networks (CRAHNs)
causes challenges in scalability, stability, channel sensing and channel access problems …

In this paper, we discussed the issues of QoS multicast routing in cognitive radio ad hoc
networks. The problem of our concern was: given a cognitive radio ad hoc network and a …

Purpose End-to-end latency in network affects the overall performance in number of ways. It
is one of the major tasks to minimize the end-to-end latency in cognitive radio ad hoc …

This paper proposes a reliable multicast routing protocol based on Q-learning for wireless
ad-hoc networks. The proposed protocol has two goals: 1) enhance the reliability of data …

Addressing delay caused by unstable and dynamic Military Cognitive Radio Ad Hoc
Networks (MCRAHNs) is one of the major challenges in routing. In MCRAHNs nodes can be …

In this paper, we propose a novel deep reinforcement learning-based quality-of-service
routing (DQR) protocol to establish the best route with minimum end-to-end queuing delay …

Cognitive radio (CR) is viewed as the empowering innovation of the dynamic spectrum
access (DSA) model which is imagined to take care of the present spectrum scarcity issue by …

Abstract Routing in Cognitive Radio Ad Hoc Network (CRAHN) is a challenging task due to
limited spectrum availability. To overcome this problem several researchers have proposed …