Abstract
Many works of literature support the potential of distributed channel estimation resorting to the traditional LMS algorithm and its variants. But these conventional LMS algorithms fail in an impulsive noise environment, which is undeniable in many communication systems. Hence in this paper, we study distributed channel estimation with robust cost functions. Most of the robust adaptive algorithms are less efficient in terms of convergence rate. To deal with this, we propose the use of the window-based Lorentzian norm in a distributed framework to gain the merit of improved convergence rate in terms of both distribution and data reuse. The performance of the proposed algorithm is validated using simulation results. Our contribution in this work is the application of Lorentzian norm in sensor networks with diffusion cooperation and stability analysis of the same.
Published Version
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