Incremental voting based spectrum sensing model for cognitive radio networks

Incremental voting based spectrum sensing model for cognitive radio networks

Sufyan Muhammad Khan Waqas Muhammad Khan Fazeel Uddin Faraz Sajjad Muhammad Khan  

Department of Computing and Technology, Iqra University, Islamabad 44000, Pakistan

Department of Physics, International Islamic University, Islamabad 44000, Pakistan

Department of Computer Science, International Islamic University, Islamabad 44000, Pakistan

National Center of Excellence in Geology, University of Peshawar, Peshawar 25000, Pakistan

Corresponding Author Email: 
waqaskhanrwp@yahoo.com
Page: 
27-33
|
DOI: 
https://doi.org/10.18280/rces.050201
Received: 
28 April 2018
| |
Accepted: 
30 June 2018
| | Citation

OPEN ACCESS

Abstract: 

Cognitive Radio Networks offers solution of spectrum insufficiency, In CRNs licensed spectrum channels are used by licensed user (Primary users) and unlicensed users (Secondary users) such that secondary user (SU) does not harm and interfere activities of primary user (PU). Spectrum Decision System is required for intelligent spectrum sensing, access and distribution between PU and SU. Cooperative spectrum sensing (CSS) can reduce the overhead on signal processing techniques and enable the SUs for reliable detection of PUs activity. Spectrum availability in CSS approach node makes a binary decision based on its local observation and then forwards its decision to the fusion center (FC). At the FC, all these decisions are merged together according to some fusion rule.

In this paper, a frameork for cooperative SUs is presented, which have to decide about the presence or absence of the PU in target spectrum. The performance of the framework is then evaluated and it is observed that it outperforms different commonly used routing scheme. It ensures to sufficiently increase the probability of correct detection and decrease in the probability of false alarm.

Keywords: 

opportunistic spectrum sensing, poling scheme for cognitive radio, voting based spectrum sensing, Cognitive Radio Networks (CRN)

1. Introduction
2. Related Work
3. Proposed Method
4. Results
5. Conclusion
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