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Spectrum sensing is a fundamental component of cognitive radio. How topromptly sense the presence of primary users is a key issue to a cognitiveradio network. The time requirement is critical in that violating it will causeharmful interference to the primary user, leading to a system-wide failure. Themotivation of our work is to provide an effective spectrum sensing method todetect primary users as soon as possible. In the language of streaming basedreal-time data processing, short-time means small-sized data. In this paper, wepropose a cumulative spectrum sensing method dealing with limited sized data. Anovel method of covariance matrix estimation is utilized to approximate thetrue covariance matrix. The theoretical analysis is derived based onconcentration inequalities and random matrix theory to support the claims ofdetection performance. Comparisons between the proposed method and othertraditional approaches, judged by the simulation using a captured digital TVsignal, show that this proposed method can operate either using smaller-sizeddata or working under lower SNR environment.

Spectrum Sensing with Small-Sized Datasets in Cognitive Radio: Algorithms and Analysis