Blind Spectrum Sensing for Cognitive Radio Based on Model Selection

Abstract

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 1 , 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 Eurécom RF Agile Platform. Simulations show promising performance results of the proposed technique in terms of sensing vacant sub-bands in the spectrum.

Spectrum sensing for cognitive radio exploiting spectrum discontinuities detection

“…More recently, a detector based on the signal space dimension based on the estimation of the number of the covariance matrix independent eigenvalues has been developed [6][7][8]. It was presented that one can conclude on the nature of this signal based on the number of the independent eigenvectors of the observed signal covariance matrix.…”

Subspace-Based Cooperative Spectrum Sensing for Cognitive Radios

“…A new class of spectrum sensing technique called the blind spectrum sensing method is introduced in [13] - [16]. As discussed in section 2.1, for conventional spectrum sensing techniques, some kind of information about the primary user or the accurate knowledge of the noise floor is needed.…”

“…In blind spectrum sensing techniques, there is no need for any kind of information that has to be predetermined. [13] uses Akaike weights as a decision metric for the presence of a PU signal. The Akaike weights can be interpreted as an estimate of the probability that the received signal distribution fits the Gaussian one.…”

On the Capacity of Wi-Fi System in TV White Space with Aggregate Interference Constraint

“…The major challenges for operating Wi-Fi system in TVWS are the restrictions on permissible transmit power and number of available channels as the TV reception must be protected from any harmful secondary interference. Unlike the former research focus on the operation of autonomous secondary user (SU) based on sensing [7], a new geo-location database method is proposed in [8], where a SU would only need to report its location to the database and in return receive information regarding the spectrum availability and associated constraints. [9] extends the regulation framework to a multiple secondary user case considering the random deployment of SUs, TV receiver antenna directivity, and the cumulative effect of adjacent channel interference (ACI).…”