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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 throughput for varying PU mean arrival rate μPUL for different levels of standard deviation of PUL σPUL.
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Related In: Results  -  Collection


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fig14: SU throughput for varying PU mean arrival rate μPUL for different levels of standard deviation of PUL σPUL.

Mentions: When the standard deviation of PUL is low σPUL = 0.2, dynamic softmax either achieves similar network performance or outperforms the traditional exploration approaches, specifically, up to 17% compared to the exploitation-only approach which incurs the highest packet loss (see Figure 14(a)). The exploitation-only approach exploits the best-possible route at all times and there is lack of load balancing among routes causing network congestion and reduced SU throughput. When σPUL = 0.8, similar trends are observed in network scenario of σPUL = 0.8 in the investigation of SU end-to-end delay (see Section 5.3.3).


Reinforcement learning for routing in cognitive radio ad hoc networks.

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

SU throughput for varying PU mean arrival rate μPUL for different levels of standard deviation of PUL σPUL.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig14: SU throughput for varying PU mean arrival rate μPUL for different levels of standard deviation of PUL σPUL.
Mentions: When the standard deviation of PUL is low σPUL = 0.2, dynamic softmax either achieves similar network performance or outperforms the traditional exploration approaches, specifically, up to 17% compared to the exploitation-only approach which incurs the highest packet loss (see Figure 14(a)). The exploitation-only approach exploits the best-possible route at all times and there is lack of load balancing among routes causing network congestion and reduced SU throughput. When σPUL = 0.8, similar trends are observed in network scenario of σPUL = 0.8 in the investigation of SU end-to-end delay (see Section 5.3.3).

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