Energy Efficiency in Wireless Networks via Fractional Programming Theory

By Alessio Zappone, Eduard Axel Jorswieck. Foundations and Trends in Communications and Information Theory, 2015

637 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/49ee435da1991fe0af6b7efc9c87baeeaac51d03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Lajos Hanzo
2. Alessio Zappone
3. Merouane Debbah
4. Raviraj S. Adve
5. H. Vincent Poor
6. Mohamed-Slim Alouini
7. Shahram ShahbazPanahi
8. Gary Boudreau

In Electrical and Electronic Engineering

1. Rui Zhang
2. Merouane Debbah
3. Chau Yuen
4. Lajos Hanzo
5. Arumgam Nallanathan
6. H. Vincent Poor
7. Mohamed-Slim Alouini
8. Yuanwei Liu

Citations in the corpus

• A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead (2016, background): “However, only the WMEE is able to describe the complete Pareto boundary, by sweeping the weights [13].”
• Fractional Programming for Communication Systems—Part I: Power Control and Beamforming (2018, methodology): “6 compares the Dinkelbach’s transform approach [9]– [12] and the proposed quadratic transform in maximizing energy efficient for the single-link case.”
• Energy Efficient Multi-User MISO Communication Using Low Resolution Large Intelligent Surfaces (2018, background): “A realistic expression for the consumed power for this k-th wireless communication link reads as [7]”
• User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, methodology): “Problem Type Approach Reference”
• Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces (2019, methodology): “Then, this method has been widely used in resource allocation for wireless communication networks [41]–[43].”
• Intelligent Reflecting Surface-Assisted Multi-User MISO Communication: Channel Estimation and Beamforming Design (2020, positive): “The semi-definite relaxed problem is given as Problem (P5) is efficiently solved using fractional programming, which provides tools to maximize the minimum of ratios in which the numerator is a concave function, the denominator is a convex function, and the constraint set is convex [43], [44] .”
• Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication (2018, methodology): “As a consequence, problem (33) is a single-ratio maximization problem that can be globally solved with limited complexity using Dinkelbach’s algorithm [10].”