Cyclostationary-based Spectrum Sensing In Cognitive Radio Systems

Over the last decade, wireless communications have grown rapidly, and more spectrum resources are needed to support numerous emerging wireless services. As the issue of spectrum scarcity becomes more obvious, cognitive radio technology was recently proposed. It can improve the spectrum utilization by allowing secondary networks (users) to borrow unused radio spectrum from primary licensed networks (users) or to share the spectrum with the primary networks (users). One of the most critical components of cognitive radio technology is spectrum sensing. This thesis deals with the problem of spectrum sensing by exploiting the cyclostationarity of primary signals. The main contributions are as follows:(1) Many existing cyclostationarity-based spectrum sensing methods have employed the Dandawate-Giannakis’algorithm due to its constant false alarm rate (CFAR) property and robustness in the low signal-to-noise ratio (SNR) regime. The Dandawate-Giannakis algorithm has assumed that the distribution of cyclic auto-correlation estimations has the same asymptotic covariance under the null hypothesis and the alternative hypothesis. It was derived in this thesis that this does not hold true for the spectrum sensing application and a generalized likelihood ratio test based on a different hypotheses formulation has been derived too. The asymptotic distributions of the proposed test statistics, which proves the CFAR property of the proposed algorithm, is also be given during the derivation. The simulation results show that the proposed GLRT algorithm is superior to the Dandawate-Giannakis algorithm in detection performance.(2) Moreover, the proposed GLRT algorithm for multi-antenna cyclostationary spectrum sensing has been extended in the following study. The proposed multi-antenna spectrum sensing algorithm achieves a significant performance gain over its single-antenna counterpart by taking into account the cyclic auto-correlations obtained from all the receiver antennas and the cyclic cross-correlations obtained from all pairs of receiver antennas.(3) For most modulated communication signals like BPSK and QPSK, the (conjugate) cyclic autocorrelations at positive and negative cycle frequencies are conjugated. In practical wireless environment, the (conjugate) cyclic autocorrelations at positive and negative cycle frequencies are affected by noise and multipath fading differently, and this property could be used to improve the performance of spectrum sensing. This thesis analyzes the cyclic autocorrelations at dual cycle frequencies in multipath fading model and extend Dandawate’s test to dual cycle frequencies for spectrum sensing. It is shown that the proposed dual cycle sensing scheme achieves certain frequency diversity. The simulation results demonstrate that the proposed scheme performs much better than single cycle detector.(4) This thesis proposes a novel method to generate the cyclostationary signature for Orthogonal Frequency Division Multiplexing (OFDM) signals, which adopts a hopping subcarrier. The method has a low overhead and good performance of the signature detection under frequency-selective fading channel.