Paper Info
Title
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 nondeterministic 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 finitealphabet 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.
Year
Venue
Field
2016
IEEE Global Communications Conference
Mathematical optimization,Algorithm design,Computer science,Matrix (mathematics),Quadratic equation,Convex function,Time complexity,Karush–Kuhn–Tucker conditions,Precoding,Channel state information
DocType
ISSN
Citations 
Conference
2334-0983
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Juening Jin1102.50
Yahong Rosa Zheng288576.15
Wen Chen31242106.63
Chengshan Xiao41719133.78