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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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fig19: 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, the effect of learning rate is minimal and so all the learning rate adjustment approaches achieve almost similar network performance (see Figure 19(a)), and the reasons are similar to those observed in the investigation of SU packet loss (see Section 6.3.3). When σPUL = 0.8, the counterapproach achieves almost similar SU throughput compared to the best empirical approach, and the highest SU throughput compared to the win-lose and random approaches, specifically up to 7% higher compared to win-lose (see Figure 19(b)), and the reasons are similar to those observed in the investigation of SU packet loss (see Section 6.3.3).


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

fig19: 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, the effect of learning rate is minimal and so all the learning rate adjustment approaches achieve almost similar network performance (see Figure 19(a)), and the reasons are similar to those observed in the investigation of SU packet loss (see Section 6.3.3). When σPUL = 0.8, the counterapproach achieves almost similar SU throughput compared to the best empirical approach, and the highest SU throughput compared to the win-lose and random approaches, specifically up to 7% higher compared to win-lose (see Figure 19(b)), and the reasons are similar to those observed in the investigation of SU packet loss (see Section 6.3.3).

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