Blind Spectrum Sensing for Cognitive Radio Based on Model Selection

Cognitive radio devices will be able to seek and dynamically use frequency bands for network access. This will be done by autonomous detection of vacant sub-bands in the radio spectrum. In this paper, we propose a new method for blind detection of vacant sub-bands over the spectrum band. The proposed method exploits model selection tools like Akaike information criterion (AIC) and Akaike weights to sense holes in the spectrum band. Specifically, we assume that the noise of the radio spectrum band can still be adequately modeled using Gaussian distribution. We then compute and analyze Akaike weights in order to decide if the distribution of the received signal fits the noise distribution or not. Our theoretical result are validated using experimental measurements captured by Eurecom RF Agile Platform. Simulations show promising performance results of the proposed technique in terms of sensing vacant sub-bands in the spectrum.