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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
SUs' interference to PUs 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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fig16: SUs' interference to PUs 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, most next-hop node (or link) and channel pairs have very similar PU mean arrival rate μPUL. The effect of learning rate on network performance is minimal and so all the learning rate adjustment approaches achieve almost similar network performance (see Figure 16(a)). When σPUL = 0.8, the link and channel pairs have great difference in the levels of PU mean arrival rate μPUL, and the Q-values among the channels generally vary greatly. The counterapproach achieves almost similar SUs' interference to PUs compared to the best empirical approach and the lowest interference level compared to the win-lose and random approaches (see Figure 16(b)). This is because when σPUL is high, the counterapproach chooses a suitable learning rate that reduces fluctuations in Q-values while making routing decisions, while the win-lose and random approaches adjust the SU learning rate very fast resulting in more frequent changes to SU route decision, and hence higher SUs' interference to PUs.


Reinforcement learning for routing in cognitive radio ad hoc networks.

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

SUs' interference to PUs 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

fig16: SUs' interference to PUs 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, most next-hop node (or link) and channel pairs have very similar PU mean arrival rate μPUL. The effect of learning rate on network performance is minimal and so all the learning rate adjustment approaches achieve almost similar network performance (see Figure 16(a)). When σPUL = 0.8, the link and channel pairs have great difference in the levels of PU mean arrival rate μPUL, and the Q-values among the channels generally vary greatly. The counterapproach achieves almost similar SUs' interference to PUs compared to the best empirical approach and the lowest interference level compared to the win-lose and random approaches (see Figure 16(b)). This is because when σPUL is high, the counterapproach chooses a suitable learning rate that reduces fluctuations in Q-values while making routing decisions, while the win-lose and random approaches adjust the SU learning rate very fast resulting in more frequent changes to SU route decision, and hence higher SUs' interference to PUs.

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