Modelo de muestreo comprimido multiespectral para radio cognitiva

Cognitive Radio is one of the most promising techniques for optimizing the use of spectrum. However, the large amount of spectral information that must be processed to identify and assign spectral components makes the channel assignment’s times to be increased due to the previous processing of this data and therefore cannot provide service to the devices that require it. Meanwhile, the compressed sampling is a technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required by the Nyquist criterion. This paper presents a new model that uses compressed multispectral sampling for radio-electric spectrum sensing in cognitive radio that improves sensing and channel assignment times, decreasing the number of data required for reconstructing the power spectral information in different bands. This model is based on architectures that use a compressed sample to analyze multispectral images. The operation of a centralized manager is presented to select the power data of different software defined radios (SDR) by binary patterns. These SDRs are in different geographical positions. The centralized manager reconstructs a data cube with the transmitted power and operation’s frequency of all the users based on the samples taken and applying multispectral sensing techniques. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices, and can be stored using only a 6.25% of the original data.