Low communication parallel distributed adaptive signal processing (LC-PDASP) architecture for processing-inefficient platforms

Abstract

In this paper, 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 high adaptive filtering algorithms parallely with minimally low communication overhead. A recursive least square (RLS) adaptive algorithm based on the application of multiple-input multiple-output (MIMO) channel estimation is implemented on the proposed LC-PDASP architecture. Complexity and Communication burden of proposed LC-PDASP architecture are compared with that of conventional PDASP architecture. The compar- ative analysis shows that the proposed LC-PDASP architecture exhibits low computational complexity and provides an improvement more than of 85% reduced communication burden than the conventional PDASP architecture. Moreover, the proposed LC-PDASP architecture provides fast convergence performance in terms of mean square error (MSE) than the PDASP architecture.