Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall power consumption of a large number of SCBSs is phenomenal. As the energy harvesting technology advances, base stations (BSs) can be powered by green energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies. In this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-defined radio access network architecture, the proposed scheme is implemented as a virtually distributed algorithm, which significantly reduces the communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework enables an adjustable trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30 , at a cost of only a small increase, e.g., 8 , of the average traffic delivery latency.
The above solutions, though effectively balance the traffic loads to maximize the network utilities, do not consider the green energy utilization as a performance metric in balancing traffic loads. As green energy technologies advance, powering BSs with green energy is a promising solution to save on-grid power and reduce the carbon footprints @cite_3 . It is desirable to recognize green energy as one of the performance metrics when balancing the traffic loads. Zhou @cite_19 proposed a handover parameter tuning algorithm for target cell selection, and a power control algorithm for coverage optimization to guide mobile users to access the BSs with renewable energy supply. Considering a mobile network powered by multiple energy sources, Han and Ansari @cite_11 proposed to optimize the utilization of green energy for cellular networks by optimizing BSs' transmit powers. The proposed algorithm achieves significant on-grid power savings by scheduling the green energy consumption along the time domain for individual BSs, and balancing the green energy consumption among BSs. The authors have also proposed a user association algorithm that jointly optimizes the average traffic delivery latency and the green energy utilization @cite_10 .
@cite_19 @cite_10 @cite_3 @cite_11
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Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to greening cellular networks. With the development of green energy technologies, BSs can be powered by green energy in order to reduce the on-grid power consumption, and subsequently reduce the carbon footprints. In this paper, we have proposed a distributed scheme to enable Green-energy Aware and Latency Aware (GALA) user-BS associations in heterogeneous cellular networks whose BSs are powered by both on-grid power and green energy. The GALA algorithm considers both the traffic delivery latency and the green energy generation rate in BSs for determining the user-BS association. We have proved that the GALA algorithm converges to the optimal solution which minimizes the summation of the weighted latency ratios of BSs. The simulation results show that the GALA algorithm enables the trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a significant amount of on-grid power in heterogeneous cellular networks that are powered by both on-grid power and green energy. Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings. The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme. The spread of mobile connectivity is generating major social and economic benefits around the world, while along with the rapid growth of new telecommunication technologies like mobile broadband communication and M2M (Machine-to-Machine) networks, larger number of various base stations will be employed into the network, which will greatly increase the power expense and CO2 emission. In order to degrade the system power expense, variety of researches on new energy and novel transmission technology are put in agenda. In this paper, instead of reducing the absolute power expense, the research focuses on guiding more power consumption into green source energy, which implying that the UEs (User Equipment), especially the cell edge UEs, will have preferential access to the BSs (Base Station) with natural energy supply. To realize the tendentious connection, two detailed approaches are proposed, the HO (Hand Over) parameter tuning for target cell selection and power control for coverage optimization. The system evaluation shows that, by proper setting of parameters in HO and power control, both of the two approaches can achieve good balance between energy saving effect and system throughput impact.