Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

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 95.83% and 82.29% 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 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.

Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

“…In the PDASP architecture, let the processing time taken by filter weight matrix W k , estimation error e k , Kalman gain g k , and error covariance matrix Ψ k be T W , T e , T g , and T Ψ , respectively. Therefore, the time taken by the MIMO-based sequential RLS algorithm, T seq , when it executes in cascade fashion [17], can be written as…”

“…In this technique, the parts of the RLS adaptive algorithm are assigned to different nodes in the network and each respective node waits until information is not collected from the previous node. In [17], PDASP architecture is introduced which executes the RLS adaptive algorithm in parallel distributed fashion even with the timenon-aligned indexes over the low-cost wireless sensor nodes. The PDASP architecture provides parallelly lesser computational cost and processing time in each node involved as compared to the above-mentioned techniques [8][9][10][11][12][13][14][15][16].…”

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

“…In this technique, the communication burden reduced by initializing the covariance matrix at each node in the distributed network; however, all the distributed nodes still entail the complex computational complexity of the adaptive algorithm and each node in the network is being idle for K − 1 iterations, where K is the total number of iterations required for the complete convergence of the adaptive filtering algorithm. Furthermore, in [14], a novel processing-efficient parallel distributed adaptive signal processing (PDASP) architecture is introduced. The PDASP architecture entails lesser computational cost as compared to sequentially operated algorithms [15,16]; however, the communication burden among the participating nodes is very high which makes a very critical impact on overall execution time of adaptive filtering algorithm.…”

“…The proposed architecture utilizes only two nodes for the complete communication setup which provides the best utilization of low cost devices than the proposed LC RLS scheme [12,13]. Furthermore, the proposed LC-PDASP scheme exhibits reduced multiplication complexity and communication burden than the conventional PDASP architecture [14]. Moreover, the proposed architecture provides an improvement in mean square error (MSE) than the PDASP architecture.…”