MULTI-CHANNEL COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS

Abstract

A cognitive radio by virtue of its ability to sense and adapt to the dynamic spectrum scenario, can increase the spectral efficiency. In order to be non-invasive, a cognitive radio must adhere to strict benchmarks in the quality of spectrum sensing for primary users of a band. Thus, spectrum sensing has a major role to play in cognitive radio. IEEE 802.22, the first standard for cognitive radio devices, imposes strict requirements for the detection and false alarm probability on all spectrum sensing devices at SNR up to -20 dB. Energy detection is the simplest and near optimum technique that is widely used for spectrum sensing. However, its performance is drastically affected by uncertainty in noise variance due to SNR wall [1]. Energy detection works well in Gaussian noise scenarios, which, however, is not appropriate to be directly utilized in wireless fading environments. To that end, cooperative sensing strategies have been studied to combat the wireless fading in [2], where multiple secondary users (SU) independently detect the licensed primary channel using an energy detector and report their initial detection results to a fusion center (FC). In the past, most of research in cooperative spectrum sensing has focused on single channel systems where all SUs sense the same channel together. However, with the popularity of multi-channel systems, such as the orthogonal frequency division multiplexing (OFDM) systems, improving sensing performance of one channel is not sufficient. It is important to find more channels satisfying the required sensing performance by cooperative spectrum sensing. Thus, the study of multi-channel cooperative spectrum sensing is necessary for cognitive radio (CR) networks. Multi-band joint detection using a set of narrowband energy detectors using cooperative spectrum sensing is evaluated. This thesis also presents a comparative analysis of multi-channel cooperative spectrum sensing in Rayleigh fading and Gaussian noise environment. Both hard combining and soft combining of data at the fusion centre is considered. Algorithms to determine the optimal sensing time durations have been developed and analysed. The throughput deterioration while going from soft combining to hard combining and from AWGN to Rayleigh Fading environment has been studi