## 1 Introduction  
Recently, green networks are considered as one of the hottest topics in Information and Communication Technology (ICT), especially in mobile communication networks [1; 2; 3]. In a green network, energy saving of network nodes such as base stations (BSs), switches, and servers should be achieved efficiently [3]. In particular, BS energy savings should be achieved at a significant level since BSs are responsible for a major part of the power consumption. To reduce the power consumption of BSs, the basic functional command is to turn BSs off when traffic load is very low [3]. Also, the traffic load of the turned off BSs should be managed by neighboring BSs. Energy flows and entropy in communication networks can be evaluated. Examples of exergy-based lifecycle analysis of ICT equipment such as Node B and smartphone of fourth generation (4G) universal mobile telecommunication systems (UMTS) and exergy-based lifecycle analysis of radio access network are presented [4]. In [5], a night zone is defined, where the traffic load of a BS is low and therefore the BS can be turned off. The quality of service (QoS) of user equipment (UE) is guaranteed by controlling the transmission power of a BS in a night zone. In [6], the number of optimal BSs which can be turned off in a night zone in various macro cell layout scenarios using traffic patterns was derived but this optimization does not react to a dynamically varying traffic load environment. In a green base station network [7], BSs are clustered based on the distance between BSs, and BSs are turned on or off based on the traffic load in each cluster. During a peak hour, all the BSs are activated first and then BSs are turned off gradually based on the status of the traffic load. In [8], the distance between a BS and a UE is measured and BSs are turned off based on the descending order of the average distance from UEs. In [9], the authors use the distance between a BS and UE in order to associate UEs with a BS and BSs with load traffic load, especially during night, are turned off to reduce the power consumption of BSs without sacrificing the quality of service. In [10], to solve the coverage hole problem due to the turned off BSs, the number of activated BSs and the cell size are determined, while minimizing the energy consumption of BSs. In [11], the authors propose switching-on/off energy saving algorithm by turning off a BS one by one with the ascending order of network-impact. Then, they propose three heuristics using network-impact and show that the proposed heuristics significantly achieve energy saving. In cell zooming [12], based on the traffic load of BSs, the cell coverage is adjusted accordingly by controlling the transmission power of BSs. If a BS has low traffic load, the cell coverage is reduced, and neighboring BSs extend the cell coverage. In [13], efficient energy saving of BSs are proposed, of which a certain ratio of BSs is turned off in a time zone with a low traffic load and the cell size of the active BSs are extended by increasing the transmission power of active BSs. In a heterogeneous network architecture, where macro cell BSs and small cell BSs overlap, the energy of BSs is saved by turning off small cell BSs when they are not in use [14]. In a heterogeneous network, a macro cell BS controls the activation and deactivation of small cell BSs based on the feedback on the traffic load of UEs and position information of UEs from small cell BSs. However, this results in signaling overhead between a macro cell BS and small cell BSs [15]. In distributed activation/deactivation schemes [16], each small cell BS decides whether it should be activated or deactivated. If there is no UE within the coverage of a small cell BS, it is turned off. Then, it periodically activates to detect the signal between a macro cell BS and UEs, and it is turned on if there is any UE which should be served by the small cell BS.