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

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 poly...

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Bibliographic Details
Published in:	2016 IEEE Global Communications Conference (GLOBECOM) pp. 1 - 6
Main Authors:	Juening Jin, Zheng, Yahong Rosa, Wen Chen, Chengshan Xiao
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.12.2016
Subjects:	Algorithm design and analysis Convex functions Mutual information Optimization Precoding Programming Receivers
Online Access:	Get full text
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Summary:	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.
DOI:	10.1109/GLOCOM.2016.7841860