Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

Youheng Tan,Xiaojun Jing

doi:10.3390/app11073083

Abstract

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

Highlights

With the deep integration of the emerging information technology and wireless communication represented by mobile internet, the internet of things, and cloud computing, radio technology is widely used under the broadband, ubiquitous, and mobile application scenarios, which leads to the explosive growth of spectrum demand, resulting in an extreme shortage of spectrum resources [1]
We provide an effective approximation of the N-out-of-K rule for heterogeneous cognitive radio networks and give the closed-form solution of the detection probability and its corresponding false alarm probability
The N-out-of-K rule-based cooperative spectrum sensing (CSS) scheme is approximate to the Poisson distribution for the closed-form expression of the optimal sensing threshold at the fusion center (FC) under the heterogeneous cognitive radio networks in this paper, where the closed-form solutions of the false alarm probability and the detection probability are provided

Summary

Introduction

With the deep integration of the emerging information technology and wireless communication represented by mobile internet, the internet of things, and cloud computing, radio technology is widely used under the broadband, ubiquitous, and mobile application scenarios, which leads to the explosive growth of spectrum demand, resulting in an extreme shortage of spectrum resources [1]. Cognitive radio (CR), despite being universal, is one of the enabling technologies for the current internet of things (IoT) [2,3,4] because it is devoted to searching idle spectrum of the registered user and providing opportunistic access at any time if the radio spectrum of the registered user is unoccupied [5,6]. CR has made great contributions to improving spectrum utilization and has developed into an emerging technology. Once the PU is back, SU will immediately release the corresponding spectrum resources

Results

Discussion

Conclusion