Ao Xiong

Power consumption modeling of base stations based on dynamic factors

Power models are crucial to assess the power consumption of base stations (BSs) without quantitive description. Currently available models seldom consider the dynamic factors such as indoor and outdoor temperature. As power model will affect the energy-saving gains of different green resolutions, in this paper we provide such power models for mobile communication BSs relying on practical data collected from several BSs with focus on dynamic factors, e.g., traffic load, indoor and outdoor temperature. The quantitative power models for communication equipment and air conditioning are defined and validated combined with the mathematical method of linear regression. With application of the models we develop an energy saving method which can save at least 10% of the power consumption per year through the simulation and analysis. Still, the method does not affect the normal operation of communication BSs, which takes on strong economy and green significance.

Cooperative Self-Organized Energy-Saving Mechanism of Cellular Network Based on Hybrid Energy Supplies

At present, the energy consumption of wireless communication network accounts for the vast majority of the operators expenses. A cooperative energy saving mechanism is proposed in the framework of the self-organized network, based on the hybrid energy supplied, aiming at the energy consumption of cellular is too large and the utilization rate of green energy is low. According to the framework, the cooperative energy saving mechanism involves two parts, which is sleeping base stations to reducing the energy consumption and allocating green energy optimally to enhance the utilization rate of green energy. Comparing with other mechanisms, results show that our mechanism can save 80% grid energy consumption of the network, while the quality of service is acceptable.

Power modeling of BSs based on energy storage monitoring

Current research in base station(BS) energy consumption area is mostly devoted to the study of the static energy consumption or dynamic factor. But it lacks the research on the energy storage efficiency of the BS. In this paper, we propose a power modeling of BSs based on energy storage monitoring. Firstly, the system architecture and networking scheme of energy storage monitoring are presented, and the mathematical model of BS energy consumption is analyzed. Then, based on the current networks data, a large number of real data are collected through the energy consumption analysis system. We analyzed the effect of charge and discharge time on energy storage efficiency of power grid. Based on the monitoring data, we fit the model of energy consumption and get the model of overall power consumption. Finally, based on the use control template, the optimization scheme of reducing the power consumption of authority network is proposed, which has substantial economic and green value.

Location selection with user behavior analysis for telecom operator's service halls

In this paper, we propose a planning mechanism based on telecom user behavior to choose locations of telecom operators service halls. Telecom service hall network consists of service requirements nodes (RNs) and telecom service hall sites (TSs). Telecom service hall location selection problem mainly focuses on choosing locations of TSs from RNs. With analysis of base station data, we formulate a method based on telecom user distribution model to group users and to find RNs. Then, we propose a theoretical model to obtain telecom operators greatest economic income with constraints of service satisfaction perceived by telecom users. Finally, a mechanism combined with improved genetic algorithm is put forward to solve it. Our results, supported by extensive experiments using MATLAB, confirm the feasibility and flexibility of our proposed planning mechanism.

Fault location algorithm based on probe in Electric Power Data Network

Fault location plays a crucial role in Electric Power Data Network (EPDNet). It usually realized by analyzing the connection between failures and symptoms. However, many symptoms may not synchronize with faulty nodes and potential failures with unknown types remain undetected in passive network management. In this paper, we propose a Facing-the-Impact-Factor (FIF) algorithm using active approach based on probes. We use the complex network theory to calculate the impact factor of each node according to all services in EPDNet. And probes are applied to detect failures on initiative, thus a real time network state can be showed in the form of matrix, then apply the impact factor to Bayesian network to determine the faulty nodes. Simulation results demonstrate the high accuracy and low false positive rate of FIF algorithm.