An efficient DCA based algorithm for power control in large scale wireless networks

In recent years, power control and resource allocation techniques for cellular communication systems are very active research areas. Power control is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. One of the most po...

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Vydáno v:Applied mathematics and computation Ročník 318; s. 215 - 226
Hlavní autoři: Le Thi, Hoai An, Ta, Anh Son, Pham Dinh, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.02.2018
Elsevier
Témata:
Computer Science
DC programming
DCA
Power control
Quality of service (QoS)
Wireless communications
DC programming
Wireless communications
Quality of service (QoS)
DCA
Power control
ISSN:0096-3003, 1873-5649
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Popis
Shrnutí:In recent years, power control and resource allocation techniques for cellular communication systems are very active research areas. Power control is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. One of the most popular power control problems is based on maximizing the weighted sum of data rates under the peak power constraints for all users. It is a difficult nonconvex optimization problem for which standard approach Geometric Programming is not applicable in large scale setting. In this paper, we propose an efficient method based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), an innovative approach in nonconvex programming framework for solving this problem. The purpose is to develop fast and scalable algorithms able to handle large scale systems. The two main challenges in DC programming and DCA that are the effect of DC decomposition and the efficiency of solution methods to convex subproblems are carefully studied. The computational results on several datasets show the robustness as well as the efficiency of the proposed method in terms of both quality and rapidity, and their superiority compared with the standard approach Geometric Programming.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2017.08.061