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 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.
J. Jin et al., "Generalized Quadratic Matrix Programming: A Unified Approach for Linear Precoder Design," Proceedings of the 2016 IEEE Global Communications Conference (2016, Washington, DC), Institute of Electrical and Electronics Engineers (IEEE), Dec 2016.
The definitive version is available at https://doi.org/10.1109/GLOCOM.2016.7841860
2016 IEEE Global Communications Conference (GLOBECOM) (2016: Dec. 4-8, 2016, Washington, DC)
Electrical and Computer Engineering
National Science Foundation (U.S.)
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)
Article - Conference proceedings
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