Open Access Open Access  Restricted Access Subscription or Fee Access

Comparison of Energy Detection & Matched Filter Methods for Spectrum Sensing in Cognitive Radio

Priyanka Lineswala, Hojiwala Robin, Divyesh Shalwala, Abhishek Rupawala, Nikunj Bhutwala

Abstract


Now a days numbers of users increases for communication. A frequency band are limited for communication. So, it requires to sense the primary user of spectrum for this problem the cognitive radio(CR) communication outcome. Cognitive radio is able to detect the spectrum holes in the spectrum bands. It is represent the potential opportunities for unused spectrum which requires three main tasks-spectrum sensing, spectrum analysis and spectrum allocation. Spectrum sensing involves obtaining the spectrum usage characteristics across multiple dimensions such as time, space, frequency and code. In this paper, Spectrum Sensing methods namely Energy Detection & Matched filter are compare by some performance criteria like sensing accuracy, sensing time & sensing performance.

Keywords


Cognitive Radio, DVB-T Signal, Energy Detection Method, Matched Filter Method

Full Text:

PDF

References


Bruce A. Fette, 2006. Cognitive Radio Technology, 1st ed, ELSEVIER, USA.

J. Hwang and H. Yoon, “Dynamic Spectrum Management Policy for Cognitive Radio: An Analysis of Implementation Feasibility Issues,” In Proc. IEEE DySPAN ’08, pp:1-9, 2008.

S. M. Haykin, Cognitive radio and radio networks. INFWEST seminar in Helsinki, 27-28 June 2007.

R. W. A. DaSilva, and A. B. MacKenzie, "Cognitive networks: Adaptation and learning to achieve end-to-end performance objectives," IEEE Communications Magazine, pp. 51-57, December 2006.

Dr. Mary Ann Ingram In August, 2000 in"Smart Antenna Research Laboratory".

J. Model-Based Competence for Software Radio Licentiate Thesis (Stockholm: KTH), 1999.

A. Sahai and D. Cabric, “Spectrum sensing: fundamental limits and practical challenges,” inIEEE International Symposium on New Frontiers Dynamic Spectrum Access Networks, November 2005.

S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, New Jersey: Prentice-Hall, 1998.

S. J. Shellhammer, S. Shankar, R. Tandra, and J. Tomcik, “Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs,” in Proceedings of the First International Workshop on Technology and Policy for Accessing Spectrum (TAPAS), August 2006.

M. Sami Fadali Professor of Electrical Engineering

Nevada, Reno Signal Detection.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.