International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) | IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 33| Single User Eigenvalue Based Detection For Spectrum Sensing In Cognitive Radio Network Sheetal Jain 1 , Madhukar Deshmukh 2 1, 2 (Assoc. Professor, Department of electronics and telecommunication, Trinity College of Engg. and Research University of Pune, Pune) I. INTRODUCTION With new technologies and services in wireless communication, demand for spectrum has been evolved as major issue to deal with. Spectrum should be efficiently used and spectrum allocation should be dynamic to meet growing demand. A way out is to sense the spectrum and act as secondary user when primary user is not in picture. Figure (1) below demonstrates concept of opportunistic sharing. First image shows primary user is using the resource (laptop) while second one show when primary user is not utilizing the resource (laptop) secondary user can temporarily use it. This management is done by special system. In the same way to reduce and utilize unused spectrum a special system is been designed which can intelligently manage sharing of spectrum. Cognitive radio is one way to handle this issue. Cognitive radio is a transceiver having spectrum sensing capability to find temporarily holes in the spectrum and accordingly changes its transmission or reception parameters so that spectrum is never vacant and meets our demand for spectrum usage. This process is what called as dynamic spectrum management. Spectrum sensing is the main functionality in cognitive radio. [4]Cognitive radio is responsible for quality of service to primary user. Sensing the spectrum has many challenges first is finding exact noise model which is very difficult, secondly low signal to noise ratio and lastly to avoid hidden node problem. The hidden terminal issue occurs when a node is in coverage area of primary transmitter, but hidden by larger object so that it cannot receive the signal from primary transmitter or receiver. In this study it is assumed that the primary system is a broadcast system and primary receivers are passive (unable to transmit). Main challenge in spectrum sensing lies in low SNR. In the figure (2) shown below is the primary and secondary network. PU is the primary user and CR1 and CR2 are the cognitive radio 1 and 2 in coverage area of PU. Thus CR1 and CR2 can sense the spectrum used by primary user PU and if it is not active then cognitive radio 1 or 2 in secondary network can opportunistically use the spectrum. Once the primary user occupies the spectrum then secondary user discontinues its use and searches for another vacant space. Main issue to discuss with is if say cognitive radio 3 (CR3) is not in coverage area of primary user PU, CR3 may be behind big buildings or topologically crowded places then CR3 may erroneously decide that channel is vacant and use the channel causing interference in the primary user. [6][10]This particular situation is called as hidden node terminal and can be avoided by cooperative sensing. Thus decision whether channel is used by primary user or not is made by cooperation of cognitive radios. In the above eg CR1 and CR2 will inform CR3 that spectrum is occupied or not thereby avoiding interference with primary user. Taking all these into consideration we have to find a robust way of spectrum sensing wherein noise uncertainty makes no difference on the performance. Various detection methods include [1][9] i) Matched filter ii) Energy detection iii) Feature Abstract: Scarcity of spectrum is the issue that wireless communication technology has to deal with. Primary user is the licensed user of the spectrum. When primary user is idle or not using the spectrum secondary user can utilize the spectrum. This sharing of spectrum can be enabled by cognitive radio (CR) technology. The heart of enabling this technology is spectrum sensing. Spectrum sensing involves detection of primary signal at very low SNR (in the range of -20 dB), under noise and interference uncertainty. This requirement makes spectrum sensing, a hard nut to crack. Another major issue in detection is hidden node problem wherein the node in vicinity of primary transmitter also indicates absence of the primary signal since it is hidden behind the large object. There are various algorithms for detection viz. energy detection, matched filter detection, feature detection (cyclostationary detection, eigen-value based detection etc.) These algorithms have limitations of complexity, requirement of signal knowledge and detection performance. In this paper the performance of eigenvalue based detection (EBD) method in presence of noise and low SNR of the received signal has been evaluated for a single user. Keywords-: Cognitive radio, eigenvalue based detection, energy detector, spectrum sensing.