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

Data Dissemination and Channel Selection in Cognitive Radio Networks

2013 ◽  
pp. 30-49 ◽  
Author(s):  
Mubashir Husain Rehmani ◽  
Yasir Faheem
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Data Dissemination ◽  
Channel Selection ◽  
Radio Networks ◽  
Selection Strategies ◽  
Detailed Classification ◽  
Pros And Cons ◽  
In The Beginning

In this chapter, the authors provide a comprehensive review of broadcasting and channel selection strategies for wireless cognitive radio networks. In the beginning, some applications of the data dissemination in wireless cognitive radio networks are discussed to highlight their importance and utility. Next, the authors provide a detailed classification of broadcasting protocols in light of the existing literature, and the pros and cons of each classified category are discussed. Afterwards, the data dissemination is briefly discussed in the context of multi-channel environments and the related issues are highlighted. Then, the authors discuss the challenges of data dissemination in cognitive radio networks, followed by the classification of channel selection strategies along with their advantages and disadvantages for various classes of applications. In the last part, the authors conclude this chapter with open research issues that need to be addressed to provide efficient channel selection and data dissemination strategies in cognitive radio networks.

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.

On Fuzzy Logic-Based Channel Selection in Cognitive Radio Networks

2015 ◽  
pp. 255-274 ◽  
Author(s):  
Yong Yao ◽  
Alexandru Popescu ◽  
Adrian Popescu
Keyword(s):  
Fuzzy Logic ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Environmental Changes ◽  
Radio Channel ◽  
New Technology ◽  
Channel Selection ◽  
Decision Criterion ◽  
Radio Networks ◽  
Competition Level

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.

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