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Communication-Efficient Decentralized Sparse Bayesian Learning of Joint Sparse Signals

Khanna, Saurabh and Murthy, Chandra R (2017) Communication-Efficient Decentralized Sparse Bayesian Learning of Joint Sparse Signals. In: IEEE Transactions on Signal and Information Processing over Networks, 3 (3). pp. 617-630. ISSN 2373-7778

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Official URL: https://doi.org/10.1109/TSIPN.2016.2632041


We consider the problem of decentralized estimation of multiple joint sparse vectors by a network of nodes from locally acquired noisy and underdetermined linear measurements, when the cost of communication between the nodes is at a premium. We propose an iterative, decentralized Bayesian algorithm called fusion-based distributed sparse Bayesian learning (FB-DSBL) in which the nodes collaborate by exchanging highly compressed messages to learn a common joint sparsity inducing signal prior. The learnt signal prior is subsequently used by each node to compute the maximum a posteriori probability estimate of its respective sparse vector. Since the internode communication cost is expensive, the size of the messages exchanged between nodes is reduced substantially by exchanging only those local signal prior parameters which are associated with the nonzero support detected via multiple composite log-likelihood ratio tests. The average message size is empirically shown to be proportional to the information rate of the unknown vectors. The proposed sparse Bayesian learning (SBL)-based distributed algorithm allows nodes to exploit the underlying joint sparsity of the signals. In turn, this enables the nodes to recover sparse vectors with significantly lower number of measurements compared to the standalone SBL algorithm. The proposed algorithm is interpreted as a degenerate case of a distributed consensus-based stochastic approximation algorithm for finding a fixed point of a function, and its generalized version with Robbins-Monro-type iterations is also developed. Using Monte Carlo simulations, we demonstrate that the proposed FB-DSBL has superior mean squared error and support recovery performance compared to the existing decentralized algorithms with similar or higher communication complexity.

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 Institute of Electrical and Electronics Engineers Inc.
Keywords: Compressed sensing; Distributed estimation; Joint sparsity; Sensor networks; Sparse Bayesian learning
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 14 Jun 2022 04:56
Last Modified: 14 Jun 2022 04:56
URI: https://eprints.iisc.ac.in/id/eprint/73416

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