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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Veröffentlicht in:2016 IEEE Global Communications Conference (GLOBECOM) S. 1 - 6
Hauptverfasser: Juening Jin, Zheng, Yahong Rosa, Wen Chen, Chengshan Xiao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2016
Schlagworte:
Algorithm design and analysis
Convex functions
Mutual information
Optimization
Precoding
Programming
Receivers
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Zusammenfassung: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