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A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1086-1089
Number of pages4
JournalIEEE COMMUNICATIONS LETTERS
Volume22
Issue number5
Early online date7 Mar 2018
DOIs
StatePublished - May 2018

Bibliographical note

Accepted, IEEE Communications Letters 2018

Documents

  • A Successive Optimization Approach_AL-SALIHI_Publishedonline7March2018_GREEN AAM

    Nakhai_CL2018_0198.pdf, 770 KB, application/pdf

    9/03/2018

    Accepted author manuscript

    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

King's Authors

Abstract

In this letter, we introduce a novel pilot design approach that minimizes the total mean square errors of the minimum mean square error estimators of all base stations (BSs) subject to the transmit power constraints of individual users in the network, while tackling the pilot contamination in multi- cell Massive MIMO systems. First, we decompose the original non-convex problem into distributed optimization sub-problems at individual BSs, where each BS can optimize its own pilot signals given the knowledge of pilot signals from the remaining BSs. We then introduce a successive optimization approach to transform each optimization sub-problem into a linear matrix inequality (LMI) form, which is convex and can be solved by available optimization packages. Simulation results confirm the fast convergence of the proposed approach and prevails a benchmark scheme in terms of providing higher accuracy.

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