International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 72 - Number 23 |
Year of Publication: 2013 |
Authors: Amardip Kumar, Manoranjan Rai Bharti, Sandeep Kumar Jain |
10.5120/12685-9481 |
Amardip Kumar, Manoranjan Rai Bharti, Sandeep Kumar Jain . An Adaptive and Efficient Local Spectrum Sensing Scheme in Cognitive Radio Networks. International Journal of Computer Applications. 72, 23 ( June 2013), 38-45. DOI=10.5120/12685-9481
A proliferation in wireless applications is growing the demand of radio spectrum which is limited as a result one of the major issues faced in wireless communication technology is spectrum scarcity. Cognitive radio (CR) solves the problem of spectrum scarcity by dynamically accessing the spectrum holes in the radio spectrum created by absence of the licensed primary user while bringing no interference to primary users. To achieve fast sensing speed and precise accuracy, cooperative spectrum sensing is usually employed but at the cost of cooperation overhead among CR users which can be reduced by improving local spectrum sensing accuracy. Well-known local spectrum sensing schemes are matched filter detection (MFD), energy detection (ED) and cyclostationary feature detection (CFD). An adaptive local spectrum sensing scheme is proposed in this paper. First, a number of channels available in a bandwidth of interest are sensed serially. The scheme determines a better matched filter, or a combination of energy and cyclostationary detectors based on the available information of the signal present in the channel. If information about PU waveform in a channel is not sufficient, then the combined energy and cyclostationary detection, spectrum sensing is done on the basis of estimated SNR which is calculated in advance for available channels. A concept of SNR wall is discussed for energy detection. One-order cyclostationary detection is performed in time domain in place of cyclostationary detection in frequency domain so that the real-time operation and low-computational complexity can be achieved. To evaluate the scheme's performance, the results are compared with conventional single-stage detector like MFD, ED and CFD. The performance comparison is made based on the probability of detection(P_d ), probability of false alarm(P_fa ) and overall detection time(T_0 ).