Spectrum Sensing on High Density Cognitive Radio Vehicular Ad Hoc Network

Abstract

Intelligent Transport System (ITS) has emerged as the most probable technology for improved transport experience more so in environments with high vehicular density. Effective vehicular communication is however hindered by spectrum scarcity due to the already crowded licensed spectrum. This has led to the emergence of Cognitive Radio (CR) systems as a solution to the spectrum scarcity problem. A crucial component in CR is spectrum sensing. Various spectrum sensing techniques including Cyclostationary, Matched Filter, and Energy detection have been proposed and applied with varied outcomes. In this paper, the above mentioned detection techniques are discussed and an improved energy detection based cooperative spectrum sensing scheme is proposed for improved communication in vehicular ad hoc networks (VANET). The proposed scheme showed an improvement in the performance of a network which in turn could lead to more efficient utilization of spectrum.

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems

“…However, number of samples when SNR < < 1 (less than 0 dB). In such a scenario, detection of signal becomes difficult with a fixed threshold for all SNR levels [ 40 ]. With noise uncertainty and fixed threshold In previous section, the general model for spectrum sensing assumed constant noise with zero mean and no uncertainty.…”

“…of samples N S → ∞ when SNR < < 1 (less than 0 dB). In such a scenario, detection of signal becomes difficult with a fixed threshold for all SNR levels [40].…”

“…It was re-modeling of the existing mathematical system to achieve desirable performance metrics without changing the sensing algorithm. The concept of cooperative communication has been used for spectrum sensing in [ 40 ] to enhance the detection performance of an energy detector for VANET. The region of uncertainty is addressed with the help of a Fusion center that makes a cooperative decision on the presence of an available band.…”

A collaborative spectrum sensing algorithm for cognitive radio based on related vector machine

“…  (8) Thus, the marginal likelihood distribution representation of the hyperparameters is obtained…”

“…With the development of emerging wireless communication technologies, people's increasing share of the operating bandwidth of the system has made the limited spectrum resources more and more tense [1][2][3][4][5] . Cognitive radio energy effectively solves the problem of shortage of spectrum resources and insufficient utilization of frequency bands, so it has been widely concerned by researchers at home and abroad [6][7][8][9][10] . Bkassiny 11 used a machine learning approach to solve the spectrum sensing problem in a certain signal-to-noise environment.…”