Distributed Incremental Leaky LMS

Abstract

Adaptive algorithms are applied to distributed networks to endow the network with adaptation capabilities. Incremental Strategy is the simplest mode of cooperation as it needs less amount of communication between the nodes. The adaptive incremental strategy which is developed for distributed networks using LMS algorithm, suffers from drift problem, where the parameter estimate will go unbounded in non ideal or practical implementations. Drift problem or divergence of the parameter estimate occurs due to continuous accumulation of quantization errors, finite precision errors and insufficient spectral excitation or ill conditioning of input sequence. They result in overflow and near singular auto correlation matrix, which provokes slow escape of parameter estimate to go unbound. The proposed method uses the Leaky LMS algorithm, which introduces a leakage factor in the update equation, and so prevents the weights to go unbounded by leaking energy out.