Decentralized Regression with Asynchronous Sub-Nyquist Sampling
When capturing data on a sensor field to uncover its latent structure, there are often nuisance parameters in the observation model that turn even linear regression problems into non-convex optimizations. One common case is the lack of common timing source in ADCs, therefore samplings are done with time offsets. Motivated by the desire of estimating jointly the sensor field and nuisance parameters in a wide area deployment, this paper derives a new decentralized algorithm that combines alternating optimization and gossip-based learning. The proposed algorithm is shown to converge to the neighborhood of a local minimum, both analytically and empirically.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
- The following copyright notice applies to all of the above items that appear in IEEE publications: "Personal use of this material is permitted. However, permission to reprint/publish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE."
- The following copyright notice applies to all of the above items that appear in ACM publications: "© ACM, effective the year of publication shown in the bibliographic information. This file is the author’s version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the journal or proceedings indicated in the bibliographic data for each item."
- The following copyright notice applies to all of the above items that appear in IFAC publications: "Document is being reproduced under permission of the Copyright Holder. Use or reproduction of the Document is for informational or personal use only."