Cooperative Spectrum Sensing in Cognitive Radios Using Perceptron Learning for IEEE 802.22 WRAN

Abstract

The primary objective of IEEE 802.22 standard is to determine vacant spectrum bands available in Digital Television channel (DTV) and to utilize them for wireless rural broadband connectivity. Cognitive Radio aims at maximizing the utilization of the limited radio bandwidth while accommodating the increasing number of services and applications in Wireless networks. For cognitive radio networks to operate efficiently, Secondary Users (SU) should be able to exploit radio spectrum that is unused by the primary user. A critical component of cognitive radio is thus spectrum sensing. The secondary user should sense the spectrum efficiently, utilize the opportunities for transmission, and vacate the channel once primary user reoccupies it. In this paper, we propose approaches for cooperative spectrum sensing as per the IEEE 802.22 standard. This paper describes several simulation scenarios that can be used to evaluate spectrum sensing by single SU unit (local sensing) and multiple SUs in a cooperative setup. The detection accuracy and performance of the proposed algorithms are described using performance metrics called probability of detection and probability of false-alarm through extensive simulations using Matlab.

A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

“…The state ℋ0 corresponds primary user absence and state ℋ1 corresponds primary user presence. For the sensing decision, several of the previously mentioned spectrum sensing techniques can be used, including energy detection [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], cyclostationary detection [21,22,23,24,25,26,27], matched filter detection [28,29,30,31], covariance-based detection [32,33,34,35,36,37,38,39], and machine-learning based detection [40,41,42,43,44,45,46,47,48,49,50,51] which are discussed below. These techniques are often evaluated using the probabilities of false alarm and probability of detection.…”

Spectrum Hole Identification in IEEE 802.22 WRAN using Unsupervised Learning

“…A survey on state-of-the art machine learning techniques and role of learning in cognitive radio is presented in [19]. In our recent work [20] we have used Perceptron learning module in which the fusion centre collects local sensing results of each SU and makes the final decision based on soft combination of the local decisions (weighted average method). The weights corresponding to each SU is computed using energy values captured by individual SU.…”

Performance comparison of multicast MANET CRN system’s based on POS scheme with different types of router protocol algorithms

“…However, a major chunk of the assigned spectrum has been employed sporadically and the range of geographical variations in terms of the usage of assigned spectrum falls between 15% and 85% accompanied by a high variance in time [4]. In fact, as per a recent study done by the Federal Communications Commission (FCC), majority of such licensed spectrums were still unoccupied for large time periods [5,6]. We have put forward dynamic spectrum access (DSA), also referred to as cognitive radio, as a substitute policy to allow efficient use of the radio spectrum [7,8].…”