Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/142883
Title: Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
Authors: Alessio Zappone;Emil Bj&x00F6;rnson;Luca Sanguinetti;Eduard Jorswieck
Year: 2017
Publisher: IEEE
Abstract: The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.
URI: http://localhost/handle/Hannan/142883
volume: 65
issue: 11
More Information: 2844,
2859
Appears in Collections:2017

Files in This Item:
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7862919.pdf1.06 MBAdobe PDF
Title: Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
Authors: Alessio Zappone;Emil Bj&x00F6;rnson;Luca Sanguinetti;Eduard Jorswieck
Year: 2017
Publisher: IEEE
Abstract: The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.
URI: http://localhost/handle/Hannan/142883
volume: 65
issue: 11
More Information: 2844,
2859
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7862919.pdf1.06 MBAdobe PDF
Title: Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks
Authors: Alessio Zappone;Emil Bj&x00F6;rnson;Luca Sanguinetti;Eduard Jorswieck
Year: 2017
Publisher: IEEE
Abstract: The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to their nonconvex nature in interference channels. The aim of this paper is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome the high complexity, we also propose a framework to compute suboptimal power control strategies with affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.
URI: http://localhost/handle/Hannan/142883
volume: 65
issue: 11
More Information: 2844,
2859
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7862919.pdf1.06 MBAdobe PDF