Generalized Quadratic Matrix Programming: A Unified Approach for Linear Precoder Design

Abstract

This paper investigates a new class of nonconvex optimization, which provides a unified framework for linear precoder design. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the non-deterministic polynomial time (NP)-hardness of GQMP problems, we provide a polynomial time algorithm that is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point. In terms of application, we consider the linear precoder design problem for spectrum-sharing secure broadcast channels. We design linear precoders to maximize the average secrecy sum rate with finite- alphabet inputs and statistical channel state information (CSI). The precoder design problem is a GQMP problem and we solve it efficiently by our proposed algorithm. A numerical example is also provided to show the efficacy of our algorithm.

Meeting Name

2016 IEEE Global Communications Conference , GLOBECOM (2016: Dec. 4-8, 2016, Washington, DC)

Department(s)

Electrical and Computer Engineering

Sponsor(s)

National Science Foundation (U.S.)

Comments

This work was supported in part by US National Science Foundation under grants ECCS-1231848, ECCS-1408316 and ECCS-1539316

Keywords and Phrases

Channel state information; Communication channels (information theory); Matrix algebra; Optimization; Polynomial approximation; Finite-alphabet inputs; Generalized quadratic matrices; Karush kuhn tuckers; Linear precoder designs; Matrix programming; Nonconvex optimization; Polynomial-time algorithms; Statistical channel state informations; Problem solving

International Standard Book Number (ISBN)

978-1-5090-1328-9

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Dec 2016

Link to Full Text

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