Search

This paper studies the synchronization problem for mobile cellular device-to-device (D2D) networks. Depending on the number of devices that are in coverage of the base station, the D2D environment can be divided into three categories where the partial-coverage and out-of-coverage are challenging scenarios and thus are the focus of this paper. First, we discuss five main challenges imposed on the synchronization problem in mobile D2D networks. More specifically, there are different challenges in the two coverage scenarios, since they do not have exactly the same synchronization objectives. Second, we propose a low-complexity Adaptive distRibuted nEtwork Synchronization (ARES) algorithm to address the five challenges. The design principles and the theories behind the ARES scheme are also analyzed in detail. Finally, we provide comprehensive simulations to evaluate different synchronization schemes, where the proposed ARES mechanism shows very promising performance. [ABSTRACT FROM AUTHOR]

Aiming to the disadvantages of short-term load forecasting with empirical mode decomposition (EMD) such as mode mixing and many high-frequency random components, a new short-term load forecasting model based on ensemble empirical mode decomposition (EEMD) and sub-section particle swarm optimization (SS-PSO) is proposed in this paper. Firstly, the load sequence is decomposed into a limited number of intrinsic mode function (IMF) components and one remainder by EEMD, which can avoid the mode mixing problem of traditional EMD. Then, through calculating and observing the spectrum of decomposed series, some low-frequency IMFs are extracted and reconstructed. Other IMFs can be forecasted with appropriate forecasting models. Since IMF1 is main random component of the load sequence, the linear combination model is adopted to forecast IMF1. Because the weights of the linear combination model are very important to obtain high forecasting accuracy, SS-PSO is proposed and used to optimize the linear combination weights. In addition, the factors such as temperature and weekday are taken into consideration for short-term load forecasting. Simulation results show that accuracy of the load forecasting model proposed in the paper is higher than that of BP neural network, RBF neural network, support vector machine, EMD and their combinations. [ABSTRACT FROM AUTHOR]