Khanna, Saurabh and Murthy, Chandra R (2017) Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach. In: IEEE Transactions on Signal and Information Processing over Networks, 3 (1). pp. 29-45. ISSN 2373-7778
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Abstract
This work proposes a decentralized, iterative, sparse Bayesian learning algorithm for in-network estimation of multiple joint-sparse vectors by a network of nodes, using noisy and underdetermined linear measurements. The proposed algorithm, called consensus-based distributed sparse Bayesian learning, exploits the network wide joint sparsity of the unknown sparse vectors to recover them from significantly fewer number of local measurements compared to standalone sparse signal recovery schemes. To reduce the amount of internode communication and the associated overheads, the nodes exchange messages with only a small set of bridge nodes. Under this communication scheme, we separately analyze the convergence of the underlying bridge node-based alternating direction method of multiplier (ADMM) iterations used in our proposed algorithm and establish its linear convergence rate. The findings from the convergence analysis of decentralized ADMM are used to accelerate the convergence of the proposed algorithm. Using Monte Carlo simulations as well as real-world-data-based experiments, we demonstrate the superior performance of our proposed algorithm compared to existing decentralized algorithms: DRL-1, DCOMP, and DCSP.
| Item Type: | Journal Article |
|---|---|
| Publication: | IEEE Transactions on Signal and Information Processing over Networks |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Alternating direction method of multipliers; decentralized estimation; distributed compressive sensing; joint sparsity; sensor networks; sparse Bayesian learning |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 13 Jun 2022 12:09 |
| Last Modified: | 13 Jun 2022 12:09 |
| URI: | https://eprints.iisc.ac.in/id/eprint/73415 |
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