Spatial Traffic Shaping in Heterogeneous Cellular Networks with Energy Harvesting

TL;DR

This paper proposes an energy-optimal traffic shaping scheme for heterogeneous cellular networks with energy harvesting, dynamically balancing load and energy supply to reduce on-grid power consumption.

Contribution

It introduces a novel analytical framework and an energy-optimal traffic shaping scheme (EOTS) that adaptively manages off-grid small cell states based on energy availability.

Findings

EOTS significantly reduces energy consumption compared to greedy offloading.

The scheme effectively balances power demand and renewable energy supply.

Numerical results validate the efficiency of EOTS with daily traffic and solar energy profiles.

Abstract

Energy harvesting (EH), which explores renewable energy as a supplementary power source, is a promising 5G technology to support the huge energy demand of heterogeneous cellular networks (HCN). However, the random arrival of renewable energy brings great challenges to network management. By adjusting the distribution of traffic load in spatial domain, traffic shaping helps to balance the cell-level power demand and supply, and thus improves the utilization of renewable energy. In this paper, we investigate the power saving performance of traffic shaping in an analytical way, based on the statistic information of energy arrival and traffic load. Specifically, an energy-optimal traffic shaping scheme (EOTS) is devised for HCNs with EH, whereby the on-off state of the off-grid small cell and the amount of offloading traffic are adjusted dynamically with the energy variation, to minimize the on-grid power consumption. Numerical results are given to demonstrate that for the daily traffic and solar energy profiles, EOTS scheme can significantly reduce the energy consumption, compared with the greedy method where users are always offloaded to the off-grid small cell with priority.