scholarly journals A dynamic channel access strategy for underlay cognitive radio networks: Markov modelling and performance evaluation

2015 ◽  
Vol 28 (1) ◽  
pp. e2928 ◽  
Author(s):  
Elmira Jalali ◽  
Indika A. M. Balapuwaduge ◽  
Frank Y. Li ◽  
Vicent Pla
Keyword(s):  
Performance Evaluation ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Channel Access ◽  
Markov Modelling ◽  
Underlay Cognitive Radio ◽  
Dynamic Channel ◽  
And Performance

scholarly journals Interference Aware Routing Protocols for Underlay Cognitive Radio Networks

2021 ◽  
Vol 15 ◽  
pp. 6-9
Author(s):  
Sami Touati ◽  
Hatem Boujemaa ◽  
Nazha Abed
Keyword(s):  
Cognitive Radio ◽  
Outage Probability ◽  
Cognitive Radio Networks ◽  
Routing Protocols ◽  
Radio Networks ◽  
Optimal Routing ◽  
Underlay Cognitive Radio ◽  
And Performance ◽  
The One ◽  
Primary Receiver

In this paper, optimal and sub-optimal routing protocols are proposed for cognitive radio networks. We first investigate optimal routing that consists in searching among all paths, the one that minimizes the end-to-end outage while verifying interference constraint to primary receiver. A sub-optimal one-hop routing is proposed where the best relay is selected in each hop. The last routing protocol consists in decomposing the network in many sub-networks composed of K hops. Then the best route is determined in each of these sub-networks so that the K hops-outage probability is minimized. The proposed K-hops routing allows a good compromise between complexity and performance. The performance of different routing protocols are evaluated through simulation results in terms of outage probability.

Modeling and Performance Evaluation

2015 ◽  
pp. 746-769
Author(s):  
Yuehong Gao ◽  
Zhidu Li ◽  
Guoting Zhang ◽  
He Bai
Keyword(s):  
Markov Chain ◽  
Performance Evaluation ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Queuing Theory ◽  
Deep Understanding ◽  
Radio Networks ◽  
Network Calculus ◽  
And Performance ◽  
Stochastic Network Calculus

Performance evaluation and analysis are of key importance to obtain deep understanding of cognitive radio networks. Some effects have been made to model and analyze the performance of cognitive radio networks. In the literature, there are two methodologies: queuing theory/Markov chain-based analysis and stochastic network calculus-based analysis. These two methodologies rely on different mathematical basics and modeling approaches. Thus, they lead to different output metrics on various viewpoints. This chapter aims to give an overall introduction to both methodologies. First, the fundamental models used in queuing/Markov chain-based analysis are presented, followed by their applications in cognitive radio networks. Then, network calculus basics are introduced with the modeling and application in performance analysis of the cognitive radio network.

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