Spectrum sensing for cognitive radio is studied, using an electronically steerable parasitic antenna receptor (ESPAR), which relies on a single RF front end, and therefore meets the demanding low cost, power and size requirements of modern wireless terminals. We develop a strategy whereby the angular domain is divided into sectors, that are accessed via beamforming on a time division basis, to detect signals from primary users. We study the performance of detection metrics based on energy received per beam, and also eigenvalue-based detection statistics through considering the covariance matrix across the various directional beams. The ESPAR is able to achieve over 6dBi SNR improvement due to its beamforming capability. Further, once primary users' signals have been detected, directional transmit opportunities which do not interfere with active PUs become available, and we develop an adaptive beamforming algorithm to capitalise on these to efficiently utilise the spatial domain, which numerically optimises the beampattern and antenna efficiency using a convex formulation. The resulting beampatterns give between -20dB and -30dB nulls in the primary user direction.

The essential tradeoff between sensing capability and achievable throughput of the secondary network is one of the active research topics for researchers working on cognitive radio. In this paper, noise uncertainty which has a great impact on sensing methods is taken into account in the maximization of throughput using eigenvalue based spectrum sensing schemes. This issue has not been tackled in the throughput associated studies before. First, the theoretical and empirical distributions of the decision statistics and the detection performances for eigenvalue based sensing techniques are studied in the presence of noise uncertainty. The computed detection probabilities of maximum-minimum eigenvalue (MME) detector and maximum eigenvalue detector (MED) are compared with the most widely used energy detector (ED). Then, in the light of the obtained results, the throughput of the secondary network is maximized in order to find out the sensing duration for each scheme using multiple receive antennas. It is shown that, under low signal to noise ratio (SNR) regime, the designed sensing slot duration achieves the best sensing throughput tradeoff.

In this paper, we study the eigenvalue-based spectrum sensing techniques for multiple-antenna cognitive radio networks. First, we study the extreme eigenvalue distributions of a complex Wishart matrix and then, in contrast to the asymptotic analysis reported in the literature, we derive the exact distribution of the test statistics of (i) maximum eigenvalue detector (MED) (ii) maximum-minimum eigenvalue (MME) detector and (iii) energy with minimum eigenvalue (EME) detector for finite number of samples (n) and finite number of antennas (m). These distributions are represented by complex hypergeometric functions of matrix argument, which can be expressed in terms of complex zonal polynomials. We also describe the method to compute these complex hypergeometric functions. Based on these exact distribution of the test statistics we find the exact decision thresholds as a function of the desired probability of false-alarms for MED, MME and EME. Simulation results show superior performance compared to the decision thresholds obtained from asymptotic (i.e, n,m → ∞) distributions.

In this paper, we propose exact decision thresholds for “blindly combined energy detection (BCED)” in the case of multiple receiver collaborative spectrum sensing. As opposed to the decisions thresholds estimation based on an asymptotic analysis in the sense of large samples and/or large collaborative antennas presented in the literature, the proposed mathematical formulation can be used to calculate exact thresholds for finite number of samples and collaborative antennas. The proposed formulation is based on our recent progress in the exact eigenvalue distributions of complex Wishart matrices with finite sizes. Moreover, the proposed thresholds valid for both correlated and uncorrelated Gaussian noise cases. Finally, we show that the probability of detection performance with the proposed exact decision thresholds performs better than the performance achieved with the decision thresholds calculated based on the asymptotic analysis, thus validate the importance of this work.