Dynamic Base Station Switching-On/Off Strategies for Green Cellular Networks

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.Index Terms-Energy saving, base station switching on/off, green cellular networks.

Predictive modelling of cellular load

“…For example, a common approach to energy conservation in green networks is to dynamically switch on and off cells depending on the load [10,22]. Another cell either already covers the shut off cell or a neighbour boosts its transmission power to fill the coverage gap left by the switched off cell.…”

“…Understanding traffic patterns and predicting load in individual cells and groups of cells is becoming ever more important with the emergence of Self-Organising Networks (SON). For example, if it can be predicted that traffic in a particular cell or group of cells falls below a certain threshold at certain times then SON algorithms can use this information to alter the network to save energy [10][11][12]. Also, if low demand by primary users of valuable licensed spectrum can be predicted in certain cells/areas at for example off-peak times this can provide opportunities for secondary usage in these bands [13,14].…”

Energy-efficient infrastructure sharing in multi-operator mobile networks

“…More specifically, different criteria (e.g., traffic load [11], user spatial distribution [12] or networkimpact [13]) are considered in order to identify the optimal BS switching off strategy, guaranteeing a certain level of user satisfaction in the network. In the same context, a very recent interesting approach is the application of reinforcement learning schemes that cope with the dynamic nature of the traffic load in current cellular networks [14] in order to overcome an important limitation of the existing works, which rely on past predefined traffic patterns based on the network history.…”

Life is Short: The Impact of Power States on Base Station Lifetime

“…When a BS is in SM, the traffic is served by the neighboring BSs that remain powered on [13]. The problem can be centrally solved [14], i.e., one central node computing the power state for BSs, or in a distributed way [15], i.e., each BS decides when and how to go in SM. Moreover, each BS that remains powered on may increase its power in order to cover the users previously served by BSs that are currently in SM [16], or adopting smart technologies, like coordinated multipoint, to guarantee user coverage and capacity constraints [17].…”