Abstract

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.

Highlights

  • The need for network operators to reduce their on-grid energy consumptions as well as carbon dioxide (CO2) emissions is currently steering research in communications toward more efficient and environmentally friendly networks

  • 1) Considering space and cost limitations of a base stations (BSs), we propose in this paper a new renewable energy harvesting (REH) enabled transmission framework, in which all remote radio units (RRUs) and associated power amplifiers (PAs) are powered by hybrid energy including on-grid energy and off-grid renewable energy, to realize a green and reliable transmission

  • 7 Conclusions In this paper, we propose a new REH enabled transmission framework powered by hybrid energy sources including on-grid energy source and off-grid renewable energy

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Summary

Introduction

The need for network operators to reduce their on-grid energy consumptions as well as carbon dioxide (CO2) emissions is currently steering research in communications toward more efficient and environmentally friendly networks. We focus on joint transmission and harvested energy scheduling for optimizing the utility efficiency of harvested energy in hybrid energy (including on-grid energy and off-grid renewable energy) powered cellular transmission system where the off-grid REH system, mainly consisting of the energy harvester, the battery module, and the controller, is embedded into BSs. Energy efficiency improvement is a hot topic in the research area of green wireless communication. In [22], the throughput maximization problem is investigated for only renewable energy powered relay nodes, and yet the time-varying characteristics of wireless channel are not considered. We formulate an optimal offline transmission scheduling problem with a priori knowledge about CSI, under constraint of available amount of harvested energy and stored energy at each transmission time interval.

System model
Formulation of transmission scheduling problems
Formulation of offline transmission scheduling problem
Formulation of online transmission scheduling problem
Offline solution and joint transmission and energy scheduling algorithm
Online solution and joint transmission and energy scheduling algorithm
8: Update q with q
Conclusions
Full Text
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