The goal of this letter is to propose an adaptive and distributed approach to cooperative sensing for wireless small cell networks. The method uses a basis expansion model of the power spectral density (PSD) to be est...
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The goal of this letter is to propose an adaptive and distributed approach to cooperative sensing for wireless small cell networks. The method uses a basis expansion model of the power spectral density (PSD) to be estimated, and exploits spectral sparsity to improve estimation accuracy and adaptation capabilities. An estimator of the model coefficients is developed based on sparse diffusion strategies, which are able to exploit and track sparsity while at the same time processing data in real-time and in a fully decentralized manner. Simulation results illustrate the advantages of the proposed sparsity-aware strategies for cooperative spectrum sensing applications.
In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed ...
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In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrumestimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrumestimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions.
This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrumestimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modif...
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This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrumestimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modified CG (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least-mean square-based algorithms and a performance that is close to recursive least-squares algorithms. In comparison with existing centralised or distributedestimation strategies, key features of the proposed algorithms are: (i) more accurate estimates and faster convergence speed can be obtained and (ii) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications.
The goal of this paper is to study the learning abilities of adaptive networks in the context of cognitive radio networks and to investigate how well they assist in allocating power and communications resources in the...
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The goal of this paper is to study the learning abilities of adaptive networks in the context of cognitive radio networks and to investigate how well they assist in allocating power and communications resources in the frequency domain. The allocation mechanism is based on a social foraging swarm model that lets every node allocate its resources (power/bits) in the frequency regions where the interference is at a minimum while avoiding collisions with other nodes. We employ adaptive diffusion techniques to estimate the interference profile in a cooperative manner and to guide the motion of the swarm individuals in the resource domain. A mean square performance analysis of the proposed strategy is provided and confirmed by simulation results. The proposed approach endows the cognitive network with powerful learning and adaptation capabilities, allowing fast reaction to dynamic changes in the spectrum. Numerical examples show how cooperative spectrum sensing remarkably improves the performance of the resource allocation technique based on swarming.
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