4.7 Article

Joint Transceiver Design for Linear MMSE Data Fusion in Coherent MAC Wireless Sensor Networks

期刊

INFORMATION FUSION
卷 37, 期 -, 页码 37-49

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.inffus.2016.12.008

关键词

Wireless sensor network; Data fusion; Signal estimation; Linear transceiver; Optimization

资金

  1. National Science Foundation [1133027, 1343372]
  2. Directorate For Engineering
  3. Div Of Electrical, Commun & Cyber Sys [1343372] Funding Source: National Science Foundation
  4. Office of Advanced Cyberinfrastructure (OAC)
  5. Direct For Computer & Info Scie & Enginr [1133027] Funding Source: National Science Foundation

向作者/读者索取更多资源

This paper considers the design of minimal mean square error (MMSE) transceivers in a wireless sensor network. The problem is nonconvex and challenging, and previous results (with partial solutions and/or with convergence unproved) left much to be desired. Here we propose several approaches - 2 block coordinate descent (BCD), essentially cyclic multi-block method and its variants and distributive method to solve this problem. The proposed 2-BCD approach formulates the subproblem of joint beamformer optimization as a general second-order cone programming problem, which lends itself to standard numerical solvers and which requires no extra assumptions like previous works do. The proposed essentially cyclic multi-block approach further decomposes the joint beamformer design subproblem into multiple blocks, and rigorously solves each with semi-closed-form solution. The distributive algorithm optimizes transmitters in a decentralized manner and has never been considered in existing literature. The distributive algorithm has time complexity independent of number of sensors and is especially suitable for large-scale networks. All the previous BCD-based approaches left some singularity issue unattended as well as the convergence property unaddressed, our proposals are the first to provide a complete and provably converging analytical solution. Extensive analysis and simulations demonstrate the merits of the novel approaches relative to existing alternatives. (C) 2017 Elsevier B.V. All rights reserved.

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