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Title: Incremental Modified Leaky LMS
Authors: Modalavalasa, Sowjanya
Sahoo, Upendra Kumar
Sahoo, Ajit Kumar
Keywords: Parameter Drift
Finite precision effects
Incremental MLLMS
Incremental Leaky LMS
Issue Date: Dec-2017
Citation: 14th IEEE India Council International Conference (INDICON-2017), Roorkee, Uttarkhand, India, 15 - 17 December, 2017
Abstract: Incremental Least means squares algorithm(ILMS) is one of the simplest algorithm for parameter estimation in distributed wireless networks, which find a wide range of applications from monitoring environmental parameters to satellite positioning. Digital implementation of adaptive filters results in quantization errors and finite precision errors, which makes the ILMS algorithm to suffer from drift problem. Incremental Leaky LMS algorithm(ILLMS) introduces a leakage factor in the update equation and overcomes the drift problem. But the overall performance of ILLMS is similar to ILMS in terms of convergence speed. To overcome this, an Incremental Modified Leaky LMS(IMLMS) is proposed based on MLLMS algorithm which in turn derived from the Least Sum of Exponentials(LSE) algorithm. LSE algorithm employs sum of exponentials of errors in its cost function and it results in convex and smooth error surface with more steepness, which results in faster convergence rate. Simulation results prove that the proposed IMLLMS outperforms the ILLMS.
Description: Copyright of this document belongs to proceedings publisher.
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