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Reinforcement learning for routing in cognitive radio ad hoc networks.

Al-Rawi HA, Yau KL, Mohamad H, Ramli N, Hashim W - ScientificWorldJournal (2014)

Bottom Line: This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation.New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing.Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Networked Systems, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor, Malaysia.

ABSTRACT
Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

Show MeSH
SU end-to-end delay for varying weight factor ω.
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Related In: Results  -  Collection


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fig7: SU end-to-end delay for varying weight factor ω.

Mentions: Figure 7 shows that the SU end-to-end delay increases with the weight factor ω of WCRQ-routing. Hence, a higher value of weight factor ω improves the PUs' network performance; while a lower value of ω improves SUs' network performance. In addition, with respect to PUL and PER, Figure 7 shows that PER has greater effects on SUs' network performance compared to PUL because PER has a direct effect on the packet length and a SU's packet can be transmitted successfully during a PU-SU packet collision. With a higher value of PER (or a noisier channel), SU network congestion increases due to increasing number of SU packet retransmissions leading to higher SU end-to-end delay.


Reinforcement learning for routing in cognitive radio ad hoc networks.

Al-Rawi HA, Yau KL, Mohamad H, Ramli N, Hashim W - ScientificWorldJournal (2014)

SU end-to-end delay for varying weight factor ω.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4128325&req=5

fig7: SU end-to-end delay for varying weight factor ω.
Mentions: Figure 7 shows that the SU end-to-end delay increases with the weight factor ω of WCRQ-routing. Hence, a higher value of weight factor ω improves the PUs' network performance; while a lower value of ω improves SUs' network performance. In addition, with respect to PUL and PER, Figure 7 shows that PER has greater effects on SUs' network performance compared to PUL because PER has a direct effect on the packet length and a SU's packet can be transmitted successfully during a PU-SU packet collision. With a higher value of PER (or a noisier channel), SU network congestion increases due to increasing number of SU packet retransmissions leading to higher SU end-to-end delay.

Bottom Line: This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation.New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing.Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Networked Systems, Sunway University, No. 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor, Malaysia.

ABSTRACT
Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.

Show MeSH