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|Abstract:||In this work, we propose adaptive link selection strategies for distributed estimation in diffusion-type wireless networks. We develop an exhaustive search-based link selection algorithm and a sparsity-inspired link selection algorithm that can exploit the topology of networks with poor-quality links. In the exhaustive search-based algorithm, we choose the set of neighbors that results in the smallest mean square error (MSE) for a specific node. In the sparsity-inspired link selection algorithm, a convex regularization is introduced to devise a sparsity-inspired link selection algorithm. The proposed algorithms have the ability to equip diffusion-type wireless networks and to significantly improve their performance. Simulation results illustrate that the proposed algorithms have lower MSE values, a better convergence rate and significantly improve the network performance when compared with existing methods.|
|Citation:||Xu, Songcen, Rodrigo C. De Lamare, and H. Vincent Poor. "Adaptive link selection strategies for distributed estimation in diffusion wireless networks." In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, (2013): 5402-5405. doi:10.1109/ICASSP.2013.6638695|
|Pages:||5402 - 5405|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||2013 IEEE International Conference on Acoustics, Speech and Signal Processing|
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