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.
