PERFORMANCE ANALYSIS OF COLLABORATIVE SPECTRUM SENSING FOR OFDM SIGNAL BASED ON DISTRIBUTED DETECTION WITH 2-BIT DECISION

One of the major components of cognitive radio is its ability to detect the availability of unused spectrum. In a cognitive radio, it is necessary to guarantee the quality of the spectrum detection about the presence or absence of the spectrum hole before the frequency spectrum is used. Previous research in the field of spectrum detection for cognitive radio in the paper titled Collaborative Spectrum Sensing in Cognitive Radio Using Hard Decision Combining with Quality Information explain about the signal detected by the distributed detection using energy detector method and its application has not been used for OFDM (Orthogonal Frequency Division Multiplexing) signal. A new method of signal detection was introduced in the paper titled Autocorrelation-Based Decentralized Sequential Detection of OFDM Signals in Cognitive Radios. This paper introduces a simple and computationally efficient spectrum sensing method for detection of OFDM signal using autocorrelation coefficients of OFDM signal. In this thesis, the author will examine the spectrum detection method for OFDM cognitive radio with distributed detection using autocorrelation-based detector method with 2-bit decision information. This study will propose a model and design of the threshold in each cognitive radio user to generate optimum fusion rule based on specific criteria in the fusion center to increase the probability of detection. The results of this study will show that by using an autocorrelation-based detector with 2-bit decision information in spectrum detection for OFDM cognitive radio, the probability of detection will be increased when compared to using an autocorrelation-based detector with 1-bit decision information

Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering

The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed

Compressive Identification of Active OFDM Subcarriers in Presence of Timing Offset

In this paper we study the problem of identifying active subcarriers in an OFDM signal from compressive measurements sampled at sub-Nyquist rate. The problem is of importance in Cognitive Radio systems when secondary users (SUs) are looking for available spectrum opportunities to communicate over them while sensing at Nyquist rate sampling can be costly or even impractical in case of very wide bandwidth. We first study the effect of timing offset and derive the necessary and sufficient conditions for signal recovery in the oracle-assisted case when the true active sub-carriers are assumed known. Then we propose an Orthogonal Matching Pursuit (OMP)-based joint sparse recovery method for identifying active subcarriers when the timing offset is known. Finally we extend the problem to the case of unknown timing offset and develop a joint dictionary learning and sparse approximation algorithm, where in the dictionary learning phase the timing offset is estimated and in the sparse approximation phase active subcarriers are identified. The obtained results demonstrate that active subcarrier identification can be carried out reliably, by using the developed framework.Comment: To appear in the proceedings of the IEEE Global Communications Conference (GLOBECOM) 201

Performance analysis of spectrum sensing techniques for cognitive radio

Spectrum sensing is a key element for cognitive radio and is process of obtaining awareness about the radio spectrum in order to detect the presence of other users. In this paper we study the performance of different spectrum sensing techniques in terms of detection performance and required SNR, based on theoretical expressions. Keywords- cognitive radio; spectrum sensing; energy detection; matced filter detection; cyclostationary feature detectio