Low Communication-Parallel Distributed Adaptive Signal Processing (LC-PDASP) Architecture for Processing-Inefficient Low Cost Platforms

Abstract

In order to reduce the extensive processing of the centralized processor, distributed adaptive techniques are used that provide a cooperative solution for high definition adaptive algorithms. The distributed adaptive filtering techniques can be used in those applications, that utilize processing incapable platforms, such as military surveillance, industry, transportation instrumentation, environmental parameters estimation and agriculture development. In this thesis, a Low Communication Parallel Distributed Adaptive Signal Processing (LC-PDASP) architecture for a group of computationally-incapable and inexpensive small platforms is introduced. The proposed architecture is capable of running computationally-expensive procedures like complex adaptive algorithms parallely with minimally low communication overhead. The RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. complexity and communication burden of the proposed LC-PDASP architecture are compared with the complexity and communication burden of conventional PDASP architecture. The comparative analysis shows that the proposed LC-PDASP architecture provides low computational complexity and exhibits minimally reduced communication burden per iteration with an improvement of 85% as compared to the conventional PDASP architecture.