Abstract
Wideband signals are expected to be used to achieve the required quality of service (QoS) in the next generation of wireless communications, civil and military radar, and many wireless sensor network (WSN) scenarios. Wideband signal detection has been identified as one of the most challenging problems in the proliferation of the cognitive radio technology. Moreover in many applications, spectrum sensing in cognitive radio (CR) is expected to be performed with limited resources in terms of time, computation, and complexity. This paper is dedicated to the detection of a wideband signal with small sample size. Aiming at using small sample size, a statistical model of samples is given based on Student’s t distribution. However, the limited number of channel observations brings a reduction of confidence in the decision. A set of new basic probability assignments associated with the hypothesis of the occupied or vacant channel are then proposed to perform the Dempster-Shafer (D-S) decision process. Simulation results show that the proposed method has much higher sensitivity to sense an occupied channel than the traditional energy detection method (ED) and the decision fusion method when small sample size is used.
Highlights
With the evolution and development of various wireless technologies, spectrum resources are becoming scarce due to the increasing need for spectral bandwidth and number of users
In order to evaluate the credibility of the collected samples in the kth subband, we propose two new basic probability assignment (BPA) functions mk(H0) and mk(H1) for H0 and H1 hypotheses in Eqs. (6) and (7), respectively mk(H0) = 1 − F0(Yk) mk(H1) = F1(Yk) where mk(H0) and mk(H1) are related to the cumulative distribution function (CDF) F0 (y) and F1 (y) of Yk, respectively
The advantage of the proposed technique compared to the traditional detection methods is that only small number of samples is required
Summary
With the evolution and development of various wireless technologies, spectrum resources are becoming scarce due to the increasing need for spectral bandwidth and number of users. In order to overcome these shortcomings, eigenvalue-based spectrum sensing methods have been proposed [14, 15], which are mainly based on the asymptotic or limiting distribution of extreme eigenvalues in order to overcome the noise uncertainty problem. They cannot be extended to a more general dimensional setting due to their daunting computational cost. Different from the GoF test mentioned above, both hypotheses of the presence and absence of the wideband signal would be considered in the proposed method in order to make full use of the statical information of the binary hypotheses and improve the detection performance.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
