An Optimal Channel Selection Strategy via Cognitive Radio Nodes

2012 ◽  
Vol 10 (8) ◽  
pp. 1682-1689
Author(s):  
R. Kaniezhil ◽  
C. Chandrasekar
Keyword(s):  
Cognitive Radio ◽  
Channel Selection ◽  
Selection Strategy

scholarly journals Analysis and Implementation  of Reinforcement Learning  on a GNU Radio Cognitive  Radio Platform

2021 ◽  
Author(s):  
◽  
Yu Ren
Keyword(s):  
Cognitive Radio ◽  
Success Rate ◽  
Large Scale ◽  
Channel Selection ◽  
Packet Transmission ◽  
Selection Strategy ◽  
Gnu Radio ◽  
Selection Scheme ◽  
Q Learning ◽  
Transmission Success

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

scholarly journals Analysis and Implementation  of Reinforcement Learning  on a GNU Radio Cognitive  Radio Platform

2021 ◽  
Author(s):  
◽  
Yu Ren
Keyword(s):  
Cognitive Radio ◽  
Success Rate ◽  
Large Scale ◽  
Channel Selection ◽  
Packet Transmission ◽  
Selection Strategy ◽  
Gnu Radio ◽  
Selection Scheme ◽  
Q Learning ◽  
Transmission Success

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

scholarly journals SURF: A distributed channel selection strategy for data dissemination in multi-hop cognitive radio networks

2013 ◽  
Vol 36 (10-11) ◽  
pp. 1172-1185 ◽  
Author(s):  
Mubashir Husain Rehmani ◽  
Aline Carneiro Viana ◽  
Hicham Khalife ◽  
Serge Fdida
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Data Dissemination ◽  
Channel Selection ◽  
Radio Networks ◽  
Selection Strategy

Opportunistic Channel Selection Strategy for Better QoS in Cooperative Networks with Cognitive Radio Capabilities

2008 ◽  
Vol 26 (1) ◽  
pp. 156-167 ◽  
Author(s):  
Ala Al-Fuqaha ◽  
Bilal Khan ◽  
Ammar Rayes ◽  
Mohsen Guizani ◽  
Osama Awwad ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Channel Selection ◽  
Cooperative Networks ◽  
Selection Strategy

scholarly journals Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2970
Author(s):  
Dejan Dašić ◽  
Nemanja Ilić ◽  
Miljan Vučetić ◽  
Miroslav Perić ◽  
Marko Beko ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Channel Selection ◽  
Spectrum Management ◽  
Radio Networks ◽  
Selection Strategy ◽  
Distributed Spectrum Sensing ◽  
The Individual

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

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