Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks

Efficient Recursive Least Square Technique for Spectrum Sensing in Cognitive Radio Networks

Bommidi Sridhar (JNTUH, Hyderabad, India) and Srinivasulu Tadisetty (Kakatiya University, Warangal, India)
DOI: 10.4018/IJBDCN.2019070101


Cognitive radio-based systems rely on spectrum sensing techniques to detect whitespaces to exploit. Cognitive radio (CR) is an attractive approach to face the shortage in the electromagnetic spectrum resources and improve the overall spectrum utilization. However, Energy detectors perform far from optimally by affecting the performance of the underlying system. In this article, two spectrum-sensing techniques are considered for CR networks; one based on energy detection and the other based on multi-taper spectral estimation (MSE). This article proposes a new method to optimize the overall performance in cooperative spectrum sensing in cognitive radio (CR) networks. An efficient recursive least square (ERLS)-based approach is proposed in order to optimize the overall performance to monitor the primary user active or inactive stage with use of secondary user while receiving data. An energy detector (ED) and multi-taper (MTM) spectrum sensing techniques are examined as local spectrum sensing techniques. Finally, a genetic algorithm is compared with the proposed system to show the system effectiveness.
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1. Introduction

The electromagnetic radio frequency (RF) spectrum is a scarce natural resource, the use of which by transmitters and receivers is typically licensed by governments. Static spectrum access is the main policy for the current wireless communication technologies (Ali, and Hamouda, 2017). A Smart wireless communication system which familiar in surrounding environmental activities and tackle the change by edifice to accept the variation in upcoming Radio Frequency which cause by certain deviation in operating modules like modulation schemes, power-transmit and frequency of carrier in real scenario. To facilitate economical green wireless networks and energy efficient CRNs reduces the environmental impact and also cuts deployment costs. To assure that the unlicensed user can recognize the unoccupied spectrum quick and precisely without impeding the licensed users, cooperative sensing is considered to improve the performance of sensing by leveraging spatial diversity (Chaudhary et al., 2016).

The concept of cognitive radio (CR) for designing wireless communications systems has emerged since last decade to mitigate the scarcity problem of limited radio spectrum by improving the utilization of the spectrum. (Thilina et al., 2013) Cognitive Radio (CR) technology has been presented as one of the most tempted solutions for spectrum scarcity problem by permitting the unlicensed users to access the idle frequency band opportunistically without causing interference to licensed users. This frequency band of unused spectrum, licensed to PU, at any given time in a specified region is termed as spectrum hole or white space. CR technology is to find the vacant spectrum for usage, providing sufficient protection to PUs. (Jaglan et al., 2015). Cognitive radio (CR) facilitates efficient spectrum use of current licensed spectrum that is highly underutilized and is considered as a potential solution to the problem of spectrum scarcity problem of spectrum scarcity (Kulkarni, and Banerjee, 2017).

There are multiple technique for realization of spectrum sensing. As described in, the matched filter detection (coherent detection by way of gain in the signal-to-noise ratio (SNR)), the cyclo-stationary feature detection (oppression of the intrinsic periodicity of primary signals), entropy-based detection (require a preceding knowledge of primary signal) can solve the noise uncertainty of the spectrum sensing through information entropy, and energy detection is the most popular method. It is consider being a most competent technique among all others. (Chaudhary et al., 2016) The spectrum prediction is a process in which the future state of the channel is forecasted on the bases of the historical information of the channel. The prediction technique requires the information about the pre-status of channel, whereas, in the spectrum monitoring no such pre-requisite because the decision is taken only on the current received packet’s statistics (Thakur et al., 2017).

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