Abstract
In this paper, we investigate dynamic base station (BS) switching to reduce energy consumption in wireless cellular networks. Specifically, we formulate a general energy minimization problem pertaining to BS switching that is known to be a difficult combinatorial problem and requires high computational complexity as well as large signaling overhead. We propose a practically implementable switching-on/off based energy saving (SWES) algorithm that can be operated in a distributed manner with low computational complexity. A key design principle of the proposed algorithm is to turn off a BS one by one that will minimally affect the network by using a newly introduced notion of network-impact, which takes into account the additional load increments brought to its neighboring BSs. In order to further reduce the signaling and implementation overhead over the air and backhaul, we propose three other heuristic versions of SWES that use the approximate values of network-impact as their decision metrics. We describe how the proposed algorithms can be implemented in practice at the protocol-level and also estimate the amount of energy savings through a first-order analysis in a simple setting. Extensive simulations demonstrate that the SWES algorithms can significantly reduce the total energy consumption, e.g., we estimate up to 50-80% potential savings based on a real traffic profile from a metropolitan urban area.
I. INTRODUCTION
A. Motivation
Recently, there has been an explosion in mobile data [2], which is mainly driven by smart-phones that offer ubiquitous Internet access and diverse multimedia applications. However, this also brings ever-increasing energy consumptions and carbon footprint to the mobile communications industry. In particular, the whole information and communication technology (ICT) sector has been estimated to contribute to about 2 percent of global CO2 emissions, and about 1.5 percent of global CO2 equivalent (CO2e1) emissions in 2007 [3], [4]. A quantitative study in [5] estimated the corresponding figure for cellular networks to be 0.2 and 0.4 percent of the global CO2e emissions in 2007 and 2020, respectively. Note that while the overall ICT footprint will less than double between 2007 and 2020, the footprint of cellular networks is predicted to almost triple within the same period.
VI. CONCLUSION
In this paper, we focused on the problem of BS switching for energy savings in wireless cellular networks. In particular, we suggested a design principle based on the newly introduced concept of network-impact. Taking into account the implementation difficulty, the computational complexity and the amount of feedback information problems, we proposed several SWES algorithms. Furthermore, our proposed algorithms are designed to be online distributed algorithms that could be operated without any centralized controller. Finally, from the first-order analysis we showed the amount of energy saving is dependent upon the traffic ratio of mean and variance and the BS deployment. We empirically showed that the proposed simple algorithms can not only perform close to the optimal exhaustive algorithm but also can achieve significant energy savings up to 80%.
