Adaptive Traffic Management and Energy Cooperation in Renewable-Energy-Powered Cellular Networks

Hyun Suk Lee; Jang Won Lee

doi:10.1109/JSYST.2018.2890281

Adaptive Traffic Management and Energy Cooperation in Renewable-Energy-Powered Cellular Networks

Hyun Suk Lee, Jang Won Lee

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

In this paper, we consider a cellular system, in which base stations (BSs) are powered by both on-grid and renewable energy sources. To efficiently utilize the harvested energy of the BSs, we study adaptive traffic management (TM) and energy cooperation (EC) that aim at minimizing the on-grid energy consumption, while guaranteeing minimum average throughputs. To achieve this, we develop an adaptive TM and EC algorithm that jointly decides the energy sharing among BSs, the user association to BSs, and the sub-channel and power allocation in BSs. Within the algorithm, a network scheduling problem, which is mixed-integer non-linear programming (MINLP), should be solved in each timeslot. To efficiently solve it, we develop a network scheduling algorithm applying generalized Benders decomposition (GBD) that optimally solves the MINLP problem. In addition, we also develop a heuristic network scheduling algorithm that has a much lower computational complexity than the GBD algorithm, while providing comparable performance. Through the numerical results, we show that our algorithms always outperform the algorithms that use only one of TM or EC regardless of the system conditions.

Original language	English
Article number	8606113
Pages (from-to)	132-143
Number of pages	12
Journal	IEEE Systems Journal
Volume	14
Issue number	1
DOIs	https://doi.org/10.1109/JSYST.2018.2890281
Publication status	Published - 2020 Mar

Bibliographical note

Funding Information:
Manuscript received August 16, 2018; revised November 16, 2018; accepted December 24, 2018. Date of publication January 9, 2019; date of current version March 2, 2020. This work was supported by the National Research Foundation of Korea (NRF) through the Midcareer Researcher Program under Grant NRF-2017R1A2B4006908 funded by the Ministry of Science and ICT. A previous version of this paper was presented at the 2016 IEEE Global Communications Conference [1], in which the sub-channel allocation and inter-cell interference were not considered. (Corresponding author: Jang-Won Lee.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:, hs.lee@yonsei.ac.kr; jangwon@yonsei.ac.kr). Digital Object Identifier 10.1109/JSYST.2018.2890281

Publisher Copyright:
© 2007-2012 IEEE.

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Information Systems
Computer Science Applications
Computer Networks and Communications
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/JSYST.2018.2890281

Cite this

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abstract = "In this paper, we consider a cellular system, in which base stations (BSs) are powered by both on-grid and renewable energy sources. To efficiently utilize the harvested energy of the BSs, we study adaptive traffic management (TM) and energy cooperation (EC) that aim at minimizing the on-grid energy consumption, while guaranteeing minimum average throughputs. To achieve this, we develop an adaptive TM and EC algorithm that jointly decides the energy sharing among BSs, the user association to BSs, and the sub-channel and power allocation in BSs. Within the algorithm, a network scheduling problem, which is mixed-integer non-linear programming (MINLP), should be solved in each timeslot. To efficiently solve it, we develop a network scheduling algorithm applying generalized Benders decomposition (GBD) that optimally solves the MINLP problem. In addition, we also develop a heuristic network scheduling algorithm that has a much lower computational complexity than the GBD algorithm, while providing comparable performance. Through the numerical results, we show that our algorithms always outperform the algorithms that use only one of TM or EC regardless of the system conditions.",

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note = "Funding Information: Manuscript received August 16, 2018; revised November 16, 2018; accepted December 24, 2018. Date of publication January 9, 2019; date of current version March 2, 2020. This work was supported by the National Research Foundation of Korea (NRF) through the Midcareer Researcher Program under Grant NRF-2017R1A2B4006908 funded by the Ministry of Science and ICT. A previous version of this paper was presented at the 2016 IEEE Global Communications Conference [1], in which the sub-channel allocation and inter-cell interference were not considered. (Corresponding author: Jang-Won Lee.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:, hs.lee@yonsei.ac.kr; jangwon@yonsei.ac.kr). Digital Object Identifier 10.1109/JSYST.2018.2890281 Publisher Copyright: {\textcopyright} 2007-2012 IEEE.",

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Adaptive Traffic Management and Energy Cooperation in Renewable-Energy-Powered Cellular Networks

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

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