Robust Incremental Least Mean Square Algorithm With Dynamic Combiner

• https://ieeexplore.ieee.org/document/9832595

Distributed networks, incremental least mean squares algorithm, decentralized
estimation, steady-state analysis, noisy link

In distributed wireless networks, the adaptation process depends on the
information being shared between various nodes. The global minimum, is therefore,
likely to be affected when the information shared between the nodes gets
corrupted. This could happen due to several reasons namely link failure, noisy
environment and erroneous data etc. In this research, we propose
a computationally efficient robust incremental least mean square (RILMS)
algorithm to resolve the aforementioned issues. Essentially, a fusion step is
introduced in the framework of the incremental least mean square (ILMS). Prior to
adaptation at a node, the information shared by the neighbouring node is fused
with the temporally preceding information of the node using an efficient
combiner. An adaptive fusion strategy is proposed resulting in dynamic weight
assignment for the fusion step. Closed form expression for the steady-state
excess mean square error (EMSE) is derived and the performance of the proposed
algorithm is evaluated for the noisy link environments and compared to the
existing algorithms. Extensive experiments show the efficacy of the proposed
approach compared to the contemporary methods. The proposed algorithm is found to
be robust against the link failure and local node divergence problems. The
improved performance of the proposed RILMS algorithm comes with a significant
reduction in computational complexity compared to the convex combination based
ILMS (CILMS) approach.