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Wireless Communications and Mobile Computing
Volume 2017, Article ID 1248796, 7 pages
https://doi.org/10.1155/2017/1248796
Research Article

Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

Department of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan

Correspondence should be addressed to Hasan Raza; moc.ciwra@nasah

Received 7 December 2016; Revised 14 February 2017; Accepted 20 February 2017; Published 31 May 2017

Academic Editor: Lei Zhang

Copyright © 2017 Noor M. Khan and Hasan Raza. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP) operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS) algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of and decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of and decreased processing time compared to the Kalman and RLS algorithms, respectively.