NDLI: A Gauss-Seidel approach to precoding design for joint transmission of distributed correlated sources

Content Provider	IEEE Xplore Digital Library
Author	Jun Fang Hongbin Li
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA (Hongbin Li) \|\| National Key Laboratory on Communications, University of Electronic Science and Technology of China, Chengdu 610054, China (Jun Fang)
Abstract	We consider the problem of transmitting multiple spatially distributed correlated sources to a common destination (e.g. a fusion center or an access point) in wireless sensor networks (WSNs). The correlated data from multiple sensors are jointly transmitted to the destination via orthogonal channels. We assume that the channel between each sensor and the receiver is multiple-input multiple-output (MIMO), with each sensor and the receiver equipped with multiple transmit/receive antennas. In this framework, we study the problem of joint design of linear precoders for all sensors by assuming the knowledge of the instantaneous channel state information (CSI), with the objective of maximizing the mutual information between the sources and the destination. We propose a Gauss-Seidel iterative approach which successively optimizes the precoding matrix associated with each sensor, while fixing the other precoding matrices. Numerical results show that the proposed algorithm that takes into account the spatial correlations across sensors can achieve higher capacity than conventional methods that neglect the spatial correlations.
Starting Page	1496
Ending Page	1500
File Size	774421
Page Count	5
File Format	PDF
ISBN	9781467350501
ISSN	10586393
e-ISBN	9781467350518
DOI	10.1109/ACSSC.2012.6489276
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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