## 1 Introduction  
With the rapid increase in mobile data traffic, cellular networks are expected to provide high-capacity services to users. In order to satisfy the requirements, cellular networks have been studied and many network architectures have been developed. In traditional network architectures, one base station (BS) is deployed in one cell structure. In order to improve the original system bandwidth and performance, many architectures have been studied for future 5G cellular networks. As a larger data capacity is provided, the energy consumption has become an important issue. The data capacity and energy consumption exhibit a \"trade-off\" relationship. In high-capacity services, the energy consumption from the BS side corresponds to approximately 75% of the total energy consumption in cellular networks [1]. Considering the increasing energy consumption, studies are performed for an effective and minimized energy consumption. Heterogeneous network (HetNet), cloud radio access network (C-RAN), and heterogeneous CRAN (H-CRAN) are representative architectures. First, in HetNet, one macrocell and multi-small-cells are overlapped and deployed densely in hot spots to improve spectrum efficiency. The small cells consist of multi-picocells or -femtocells, which cover a smaller range than a macrocell. Small cells closely associate with users so that the system can provide a higher data rate to users and reduce the transmission power. Second, in order to minimize the energy consumption, the BS structure is separated in the C-RAN architecture. A traditional BS consists of two parts: base band unit (BBU), which processes signals, and remote radio head (RRH), which serves as a radio-frequency transceiver. In C-RAN, BSs are divided into the two parts to reduce energy consumption. BBUs are centralizedas a BBU pool and RRHs are distributed and connected to the BBU pool through fronthaul links. Third, the H-CRAN architecture has been studied to utilize the advantages of both HetNet and C-RAN. In H-CRAN, pico- or femto-RRHs are connected to the BBU pool through fronthaul links and a macro BS (MBS) is connected to the BBU pool through a backhaul link [2]. H-CRAN provides services by two parts: the MBS provides administration of voice service, while the RRHs mainly provide high-data-rate services. RRHs serve only as radio-frequency transceivers, while the BBU pool manages resources with a centralized architecture, so that it is possible to efficiently allocate resources in H-CRAN. With the implementation of the architectures in network systems, studies considering user mobility have been performed to provide personalized services. Users usually move in certain patterns in their daily lives (i.e., the places they visit can be regarded as certain patterns). In particular, users who use vehicles have more predictable patterns as it is difficult to suddenly change speed or direction. In addition, in urban areas, vehicles tend to move more consistently in patterns owing to temporal and spatial factors. There are various rules such as traffic lights that vehicles have to follow while moving in certain patterns. Vehicles generally move only along designated roads, which makes their mobility patterns more consistent. They can be provided with information about blocked road sections during periods of traffic congestion. Therefore, vehicular mobility patterns in urban areas can be introduced for various applications. In this study, we propose a joint power control algorithm for an efficient energy consumption consisting of three parts. By patterning mobilities of vehicle users, we predict mobility patterns based on a Markov prediction algorithm. We consider vehicular mobilities in urban environments for mobility prediction, owing to the consistent patterns in urban scenarios. We refer to vehicles as \"user equipment\" (UE). Based on the UE mobility prediction, we control the RRH switching operation and transmission power to maximize the energy efficiency. The remainder of this paper is organized as follows.