Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments

El uso eficiente del espectro se ha convertido en un área de investigación activa, debido a la escasez de este recurso y a su subutilización. En un escenario en el que el espectro es un recurso compartido como en la radio cognitiva (CR), los espacios sin uso dentro de las bandas de frecuencias con licencia podrían ser detectados y posteriormente utilizados por un usuario secundario a través de técnicas de detección y sensado del espectro. Generalmente, estas técnicas de detección se utilizan a partir de un conocimiento previo de las características de canal. En el presente trabajo se propone un enfoque de detección ciega del espectro basado en análisis de componentes independientes (ICA) y análisis de espectro singular (SSA). La técnica de detección se valida a través de simulación, y su desempeño se compara con metodologías propuestas por otros autores en la literatura. Los resultados muestran que el sistema propuesto es capaz de detectar la mayoría de las fuentes con bajo consumo de tiempo, un aspecto que cabe resaltar para aplicaciones en línea con exigencias de tiempo.The efficient use of spectrum has become an active research area, due to its scarcity and underutilization. In a spectrum sharing scenario as Cognitive Radio (CR), the vacancy of licensed frequency bands could be detected by a secondary user through spectrum sensing techniques. Usually, this sensing approaches are performed with a priori knowledge of the channel features. In the present work, a blind spectrum sensing approach based on Independent Component Analysis and Singular Spectrum Analysis is proposed. The approach is tested and compared with other outcomes. Results show that the proposed scheme is capable of detect most of the sources with low time consumption, which is a remarkable aspect for online applications with demanding time issues

Blind separation of convolutive mixtures of cyclostationary sources using an extended natural gradient method

An on-line adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary source signals is proposed. The algorithm is derived by applying natural gradient iterative learning to the novel cost function which is defined according to the wide sense cyclostationarity of signals. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures

Blind Identification of Underdetermined Mixtures Based on the Hexacovariance and Higher-Order Cyclostationarity

International audienceIn this work, we consider the problem of blind identification of underdetermined mixtures in a cyclostationary context relying on sixth-order statistics. We propose to exploit the cyclostationarity at higher orders by taking into account the knowledge of source cyclic frequencies in the sample estimator of the observation hexacovariance. Two blind identification algorithms based on the proposed estimator are considered and their performances are tested by means of computer simulations. Our simulation results show that significant improvements can be obtained when both second and fourth-order cyclo-stationarities are exploited

ICAR, a tool for Blind Source Separation using Fourth Order Statistics only

International audienceThe problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in general good results and even outperforms classical methods in several situations: ICAR ~(i) succeeds in separating sources with low signal to noise ratios, ~(ii) does not require sources with different SO or/and FO spectral densities, ~(iii) is asymptotically not affected by the presence of a Gaussian noise with unknown spatial correlation, (iv) is not sensitive to an over estimation of the number of sources

Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

A sequential algorithm for the blind separation of a class of periodic source signals is introduced in this paper. The algorithm is based only on second-order statistical information and exploits the assumption that the source signals have distinct periods. Separation is performed by sequentially converging to a solution which in effect diagonalizes the output covariance matrix constructed at a lag corresponding to the fundamental period of the source we select, the one with the smallest period. Simulation results for synthetic signals and real electrocardiogram recordings show that the proposed algorithm has the ability to restore statistical independence, and its performance is comparable to that of the equivariant adaptive source separation (EASI) algorithm, a benchmark high-order statistics-based sequential algorithm with similar computational complexity. The proposed algorithm is also shown to mitigate the limitation that the EASI algorithm can separate at most one Gaussian distributed source. Furthermore, the steady-state performance of the proposed algorithm is compared with that of EASI and the block-based second-order blind identification (SOBI) method

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike classical SCSS approaches, we consider a setting where only examples of the sources are available rather than their models, inspiring a data-driven approach. For source models with underlying cyclostationary Gaussian constituents, we establish a lower bound on the attainable mean squared error (MSE) for any separation method, model-based or data-driven. Our analysis further reveals the operation for optimal separation and the associated implementation challenges. As a computationally attractive alternative, we propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator. We demonstrate in simulation that, with suitable domain-informed architectural choices, our U-Net method can approach the optimal performance with substantially reduced computational burden

Separation of instantaneous mixtures of cyclostationary sources and application to digital communication signals

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200