Dynamic base station planning with power adaptation for green wireless cellular networks

Along with recent increases in the energy prices, telecommunication operators started to become highly interested in energy-efficient operation. By adopting energy-aware green methods, service providers aim to decrease their operational expenses while maintaining an acceptable level of subscriber service quality. Another important motivation of green solutions is environmental awareness. Information and communication technology (ICT) industry produces 2% of the overall CO₂ emission throughout the world by consuming 3% of the worldwide energy [1, 2]. When we consider the exponential growth in data exchange [3], it is clear that the ICT sector will become one of the major CO₂ emission sources within the next few decades. High energy expenditures combined with its adverse effects on environment reveal the need for novel energy-saving methods more clearly.

Since wireless cellular access networks constitute a significant portion of the ICT industry [4], it would not be wrong to think that measures to be taken in this field can significantly contribute to make the overall communication industry greener. Although wireless cellular access networks consist of two parts, which are wired and wireless, vast majority of the energy is consumed by the wireless segment [5]. Therefore, it is considered that base stations (BSs) which are the integral part of the wireless segment are the right place to start saving energy.

Cellular access network service providers are obliged to provide a certain level of service quality regardless of time and space due to the increasing needs of subscribers. Hence, the number of deployed BSs increases rapidly parallel to the ubiquitous coverage demand of users. Figure 1 shows the base station location data of a single operator from Sydney Central Business District (CBD), Australia. Depicted BS information on the map is extracted from a website [6] which makes use of the Australian Communications and Media Authority’s [7] RadCom registry. The area covered in the map is 1.5×1.5km² and has a total of 139 BSs since Sydney CBD is one of the world’s most crowded business centers and densely concentrated with skyscrapers. The reason of high BS density is that cellular network operators usually place BSs according to the peak traffic conditions. However, the peak traffic mostly occurs in only a small portion of the time. A real traffic profile collected from a central BS and four neighboring BSs during 1 week can be seen in Figure 2[8]. As expected, the traffic load decreases dramatically during the late night hours. Yet, low traffic can also be observed all day long during weekends or holidays in particular places such as business or trade centers. Therefore, infrastructures of the cellular access networks are underutilized during the non-peak traffic periods. Hence, adoption of a green dynamic BS planning scheme can save significant amount of energy by reducing redundancy and prevent significant amount of CO₂ emissions. Moreover, the reduction of active BSs also helps to mitigate the electromagnetic pollution.

The rest of the paper is organized as follows: In Section 2, existing green network planning methods in the literature are surveyed. Section 3 elaborates the proper application areas of the GDBP, assumptions, and problem formulation. The proposed GDBP algorithm is explained in Section 4. An example application scenario, details of the system parameters, and comparative performance analysis of the proposed methods are presented in Section 5. Finally, Section 6 draws the conclusions and provides possible directions for future research.

Optimal energy-saving expressions as a function of the daily traffic profile are given in [9]. In order to apply the derived expressions in real-life scenarios, Marsan et al. propose a set of realistic regular cell topologies where each of these configurations has a specific energy-saving ratio by turning three out of four or eight out of nine BSs off. They compared the proposed topology configurations in terms of energy saving by using a realistic traffic pattern collected from a wired broadband service provider.

In [10], Son et al. propose a theoretical framework for two problems which are dynamic BS operation and user association. Rather than solving those two problems jointly, they decompose it into two subproblems and try to handle them separately. For the user association problem, they offer a distributed algorithm composed of a mobile terminal part and a base station part. For the dynamic BS operation problem, they derive two greedy BS turn on/off algorithms. They also provide three more heuristics which consider the inter-BS distance and BS utilization as decision metrics.

Zhou et al. [11] propose a centralized and a decentralized BS energy-saving algorithms. In the centralized one, it is assumed that a single entity has all channel and traffic information required to decide turning BSs on or off. In the decentralized algorithm, a BS association rule is derived for users to concentrate the traffic load on some particular BSs so that the remaining redundant ones can be turned off easily. Another distributed BS switch on/off algorithm was proposed in [8]. The analysis provided in this work shows that the mean and variance of the traffic profile along with the BS density are the dominant factors that determine the amount of energy saving.

Recently, Oh et al. [12] propose an algorithm called SWES along with three other versions of it for BS switching on/off. They introduce the notion of network-impact which considers the effect of BS transitions on the neighboring BSs in terms of traffic load and try to find solutions which have minimum effect on the network. It is shown that, according to the test case results conducted with real-life topology and traffic data, their algorithms can achieve energy savings up to 80%.

In [13], Saker et al. assume all BSs have a set of resources which can be activated and deactivated according to the traffic load where the term resource refers to available transmitters for GSM and carriers for 3G. Their method is based on use-as-required principle where communication resources of the BSs are activated as the cell load increases and deactivated as the load decreases. They bring two practical implementation issues into attention which are guard period and ping-pong effect along with their impact on energy consumption of the network. Similarly, novel network dimming methods are proposed in [14] rather than completely turning the BS off. Three basic network dimming methods are derived for coverage, frequency, and service dimming. A joint optimization problem is formulated for both frequency and service dimming, and it is shown that reducing resource usage is a promising technique for energy saving.

Since it is very common that more than one cellular access network operator offers service over the same area, cooperative management of the two separate networks was proposed in [15] to save energy by switching off one of the two during low traffic. Although this technique seems to be promising, cellular network operators should closely cooperate with each other and act as a one from the subscriber’s point of view.

In the literature, there are quite few examples which consider the dynamic cell size adjustment in order to reduce energy consumption. Among them, Niu et al. [16] introduce the cell zooming concept for energy saving to adaptively adjust the size of the cells according to the current traffic load. In their work, a cell zooming server which is a virtual entity in the network controls the procedure of cell zooming. The cell zooming server collects the information such as the traffic load, channel conditions, and user requirements and then analyzes whether there are opportunities for cell zooming or not. They also proposed centralized and distributed versions of user association algorithms for cell zooming.

Another work considering variable cell sizes for energy saving is [17]. In this work, Bhaumik et al. consider two types of BSs which are subsidiary BSs with low transmit power and umbrella BSs with high transmit power. They propose a self-operating network by adaptively turning subsidiary and umbrella BSs on and off according to the current traffic demands. Similarly, Kokkinogenis et al. [18] assume a cellular network consisting of micro and macro BSs where micro BSs have the ability of being switched on/off while macro BSs can iteratively adjust their transmission power until the required QoS is achieved. They propose a static centralized, a dynamic distributed, and a hybrid topology management schemes to reduce the overall energy consumption of the network while satisfying certain QoS requirements. Chiaraviglio et al. [19] propose a novel approach to save energy in UMTS networks by reducing the number of active access devices when they are underutilized. Authors derive two models for both circuit-switched and packet-switched services separately for quantification of possible energy savings. For further information, readers may refer to the green cellular network survey in [20].

Although we are utilizing the dynamic cell size adjustment strategy in our work, we assume a set of predefined cell sizes unlike other related works. Hence, we are able to formulate a mathematical model for the GDBP problem and solve it with an optimization tool. We also take the overhead of BS on/off transitions into account and try to minimize the total number of transitions. Moreover, we defined a fast and effective heuristic to solve the larger realistic size problems and justified our results with real-life-scale scenarios rather than small test cases.

Before going into detail, possible application areas of the GDBP along with their advantages and disadvantages are investigated from the green networking perspective.

In this section, we derive a heuristic called FastWISE for the GDBP problem which consists of three phases.

Additional variables used in FastWISE

FastWISE starts with the initialization phase. In this phase, FastWISE visits all BSs and activates the ones which have smaller overlapping coverage than a predefined threshold with the maximum possible transmission power level. By doing this, FastWISE tries to use BSs with higher transmission power levels without violating the capacity constraints in order to give energy-saving opportunities to neighboring BSs. Therefore, a preliminary coverage is provided at the end of this phase. FastWISE continues with the iteration phase. The aim of this phase is to make incremental improvements at each step on top of the preliminary coverage produced by the initialization phase until a target coverage ratio throughout the network is achieved. Initially, a covered user per energy (CUE) ratio is calculated for each inactive BS for each power level. This ratio implies the number of additional covered users per unit energy if that particular BS is turned on. As long as the desired coverage ratio is not achieved, the BS having the highest CUE ratio is simply turned on. Unlike the initialization, the iteration phase tries to maximize the energy utilization without making any capacity constraint checks. However, this may yield to overloaded BSs which in turn cause higher call blocking probabilities. Therefore, the third and the last step are required to validate that the traffic capacity constraints are met for all serving BSs, which is the validation phase. In this last part of the heuristic, all serving BSs are visited and a list of neighboring BSs is created for all overloaded ones. In order to share the load of the overloaded BSs, starting from the closest one, neighboring BSs in the list are simply activated with the minimum transmission power level until the offered traffic load drops below its capacity. At the end of this phase, FastWISE ensures that all serving BSs are operating well below their capacities.

In this work, we focus on saving energy by both turning BSs on/off and adaptively adjusting their transmission power according to the current traffic conditions. To achieve that goal, we formulated a novel nonlinear programming model for the GDBP problem to find the best possible BS topology which minimizes the energy consumption of the network while satisfying a certain level of GoS. Optimization tools can produce optimum results for the small instances of the problem. We derived a greedy heuristic called FastWISE to solve the large realistic size instances of the formulated problem and compared our results with the results of a non-commercial optimization tool and numerous MC experiments. It is shown that our green dynamic BS planning scheme adaptively adjusts to the current traffic load and saves significant amount of energy without violating the GoS constraints such as the probability of blocking and the coverage ratio.

As a future work, we are planning to improve our work to be applied in data-oriented cellular networks. Therefore, the effects of transmission power adjustment on the neighboring cell sites are required to be explored in detail. Another aspect that needs to be taken into consideration is the handoff overhead stemming from frequent topology changes. It would be very useful to implement user association rules integrated with our GDBP solution in order to reduce the handoff rates. Another interesting issue as a future work can be the installation of additional BSs to achieve greener topologies. By this way, a small amount of investment can save significant amount of energy and become very profitable in the long term. Lastly, we are planning to improve our GDBP scheme to be applied in heterogeneous access networks having different types of BSs in terms of coverage, capacity, and power consumption.

^a Incremental users covered by that particular BS when it is turned on.

^b Recall that the BS core power consumption is assumed to be fixed in Section 3.2.