Optimal joint transmission and harvested energy scheduling for renewable energy harvesting enabled cellular network under coordinated multi-point transmission

Authors

Zejue Wang, Chinese Academy of Sciences
Hongjia Li, Chinese Academy of SciencesFollow
Dan Hu, Cisco Systems, Inc.
Song Ci, University of Nebraska-LincolnFollow

Date of this Version

2015

Citation

Wang et al. EURASIP Journal on Wireless Communications and Networking (2015) 2015:97 DOI 10.1186/s13638-015-0313-6.

Comments

© 2015 Wang et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.

Abstract

On-grid energy consumption of base stations (BSs) contributes up a significant fraction of the total carbon dioxide (CO₂) emissions of cellular networks, among which remote radio units (RRUs) absorb most of the energy consumption. To eliminate the on-grid energy consumption and the corresponding CO₂ emission, we propose a new transmission framework, in which all RRUs and associated power amplifiers (PAs) are powered by hybrid energy sources including on-grid energy source and off-grid renewable energy source. Based on the framework, we pursue a systematic study on the joint transmission and harvested energy scheduling algorithm for the hybrid energy powered cellular transmission system under coordinated multi-point (CoMP) transmission. Firstly, we formulate an optimal offline transmission scheduling problem with a priori knowledge about channel state information (CSI), under constraint of available amount of harvested energy and stored energy at each transmission time interval. Considering a practical constraint of limited pre-knowledge about CSI, we further transform the offline problem into an energy-aware energy efficient transmission problem. To solve the proposed problems, we undertake a convex optimization method to the optimal offline transmission scheduling problem and design corresponding optimal offline joint transmission and energy scheduling algorithm, which provides the upper bound on actual system performance. Then, we extend the non-linear fractional programming to the transmission scheduling problem with limited pre-knowledge about CSI and design corresponding joint transmission and energy scheduling algorithm, named as online algorithm. Numerical results show that the performance of the proposed online algorithm is close to that of the obtained upper bound and outperforms the existing algorithm. We also find that at each transmission time interval during the finite transmission period, the transmit power of each RRU is proportional to the weighted channel-gain-to-noise ratio (CNR) of each sub-channel.