Cognitive Radio Channel Selection Strategy Based on Experience-Weighted Attraction Learning

Author(s):  
Sun Yong ◽  
Qian Jiansheng
2021 ◽  
Author(s):  
◽  
Yu Ren

<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>


Author(s):  
Yong Yao ◽  
Alexandru Popescu ◽  
Adrian Popescu

Cognitive radio networks are a new technology based on which unlicensed users are allowed access to licensed spectrum under the condition that the interference perceived by licensed users is minimal. That means unlicensed users need to learn from environmental changes and to make appropriate decisions regarding the access to the radio channel. This is a process that can be done by unlicensed users in a cooperative or non-cooperative way. Whereas the non-cooperative algorithms are risky with regard to performance, the cooperative algorithms have the capability to provide better performance. This chapter shows a new fuzzy logic-based decision-making algorithm for channel selection. The underlying decision criterion considers statistics of licensed user channel occupancy as well as information about the competition level of unlicensed users. The theoretical studies indicate that the unlicensed users can obtain an efficient sharing of the available channels. Simulation results are reported to demonstrate the performance and effectiveness of the suggested algorithm.


2012 ◽  
Vol 10 (8) ◽  
pp. 1682-1689
Author(s):  
R. Kaniezhil ◽  
C. Chandrasekar

2021 ◽  
Author(s):  
◽  
Yu Ren

<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>


2013 ◽  
Vol 36 (10-11) ◽  
pp. 1172-1185 ◽  
Author(s):  
Mubashir Husain Rehmani ◽  
Aline Carneiro Viana ◽  
Hicham Khalife ◽  
Serge Fdida

2008 ◽  
Vol 26 (1) ◽  
pp. 156-167 ◽  
Author(s):  
Ala Al-Fuqaha ◽  
Bilal Khan ◽  
Ammar Rayes ◽  
Mohsen Guizani ◽  
Osama Awwad ◽  
...  

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