Title: Diffusion improved multiband-structured subband adaptive filter algorithms with dynamic selection of regressors and subbands over distributed networks

Authors: Mohammad Shams Esfand Abadi; Mohammad Javad Ahmadi; Mohammad Saeed Shafiee

Addresses: Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, 16788-15811, Iran ' Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, 16788-15811, Iran ' Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, 16788-15811, Iran

Abstract: The present study solves the problem of distributed estimation in the diffusion networks based on the family of improved multiband-structured subband adaptive filters (IMSAFs). The diffusion IMSAF (DIMSAF), the DIMSAF with dynamic selection of regressors (DIMSAF-DSR), and the DIMSAF with dynamic selection of subbands (DIMSAF-DSS) are established. The DIMSAF-DSS and the DIMSAF-DSR algorithms, while benefiting from high convergence speed in DIMSAF, have lower computational complexity and lower steady-state error. During the weight coefficients adaptation in DIMSAF-DSR, the input signal regressors are dynamically selected at each subband of different nodes. In DIMSAF-DSS, the subbands are dynamically selected at each node. In the following, the introduced algorithms are established based on a general update equation. Accordingly, the mean-square performance analysis of the algorithms is studied in a unified way. The theoretical results and the good performance of proposed algorithms are justified by several computer simulations in adaptive diffusion networks.

Keywords: diffusion network; distributed estimation; dynamic selection; IMSAFs; improved multiband-structured subband adaptive filter; mean-square performance.

DOI: 10.1504/IJSNET.2019.103500

International Journal of Sensor Networks, 2019 Vol.31 No.4, pp.253 - 264

Received: 28 Jun 2019
Accepted: 01 Aug 2019
Published online: 06 Nov 2019 *

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