Study of Distributed Spectrum Estimation Using Alternating Mixed Discrete-Continuous Adaptation

TL;DR

This paper introduces the DAMDC algorithm for distributed spectrum estimation in sensor networks, combining discrete and continuous adaptation to improve accuracy and performance over existing methods.

Contribution

The paper presents a novel distributed alternating mixed discrete-continuous algorithm that adaptively approaches the oracle for spectrum estimation, outperforming existing sparsity-aware and conventional algorithms.

Findings

DAMDC achieves lower mean square deviation

Improved power spectrum estimation accuracy

Numerical results confirm excellent performance

Abstract

This paper proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.