Efficient Mobility Management in LTE Femtocell Network

The future network architecture towards heterogeneous, it would be converged many different types of network, such as cellular network, femtocell network, ad-hoc network, MANET, VANET and wireless sensor network. With new network service type and service demanding are emerging, it would cause the huge number of mobile terminal accesses to network. Hence, it makes big trouble for managing mobility, and brings forward challenge.The handover is the most important part in the mobility management, because the handover is frequently occurred when UE is moving, hence the handover number directly affects the system performance, and network QoS. A sophisticated HO decision algorithm can improve the performance of system, although in current literature there are many HO decision algorithms proposed and every algo- rithm owns dramatical advantages, but they also have limitations or drawbacks. In this report, we will survey the HO decision algorithms, and summarise them. Based on some current algorithms propose a new HO decision algorithm

Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems

Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction errors. In this paper, we propose a "preventive learning'" framework to systematically guarantee DNN solution feasibility for problems with convex constraints and general objective functions. We first apply a predict-and-reconstruct design to not only guarantee equality constraints but also exploit them to reduce the number of variables to be predicted by DNN. Then, as a key methodological contribution, we systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible. We characterize the calibration magnitudes and the DNN size sufficient for ensuring universal feasibility. We propose a new Adversary-Sample Aware training algorithm to improve DNN's optimality performance without sacrificing feasibility guarantee. Overall, the framework provides two DNNs. The first one from characterizing the sufficient DNN size can guarantee universal feasibility while the other from the proposed training algorithm further improves optimality and maintains DNN's universal feasibility simultaneously. We apply the preventive learning framework to develop DeepOPF+ for solving the essential DC optimal power flow problem in grid operation. It improves over existing DNN-based schemes in ensuring feasibility and attaining consistent desirable speedup performance in both light-load and heavy-load regimes. Simulation results over IEEE Case-30/118/300 test cases show that DeepOPF+ generates $100\%$ feasible solutions with $<$0.5% optimality loss and up to two orders of magnitude computational speedup, as compared to a state-of-the-art iterative solver.Comment: 43pages, 9 figures. In submissio

DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility

Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system constraints. Due to the inherent approximation errors, the solutions predicted by DNNs may violate the operating constraints, e.g., the transmission line capacities, limiting their applicability in practice. To address this challenge, we develop DeepOPF+ as a DNN approach based on the so-called "preventive" framework. Specifically, we calibrate the generation and transmission line limits used in the DNN training, thereby anticipating approximation errors and ensuring that the resulting predicted solutions remain feasible. We theoretically characterize the calibration magnitude necessary for ensuring universal feasibility. Our DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes. Detailed simulation results on a range of test instances show that the proposed DeepOPF+ generates 100% feasible solutions with minor optimality loss. Meanwhile, it achieves a computational speedup of two orders of magnitude compared to state-of-the-art solvers.Comment: 7 pages, SmarGridComm202

Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

The robot arm with flexible joint has good environmental adaptability and human robot interaction ability. However, the controller for such robot mostly relies on data acquisition of multiple sensors, which is greatly disturbed by external factors, resulting in a decrease in control precision. Aiming at the control problem of the robot arm with flexible joint under the condition of incomplete state feedback, this paper proposes a control method based on closed-loop PD (Proportional-Derivative) controller and EKF (Extended Kalman Filter) state observer. Firstly, the state equation of the control system is established according to the non-linear dynamic model of the robot system. Then, a state prediction observer based on EKF is designed. The state of the motor is used to estimate the output state, and this method reduces the number of sensors and external interference. The Lyapunov method is used to analyze the stability of the system. Finally, the proposed control algorithm is applied to the trajectory control of the flexible robot according to the stability conditions, and compared with the PD control algorithm based on sensor data acquisition under the same experimental conditions, and the PD controller based on sensor data acquisition under the same test conditions. The experimental data of comparison experiments show that the proposed control algorithm is effective and has excellent trajectory tracking performance

Investigation of ospC Expression Variation among Borrelia burgdorferi Strains

Outer surface protein C (OspC) is the most studied major virulence factor of Borrelia burgdorferi, the causative agent of Lyme disease. The level of OspC varies dramatically among B. burgdorferi strains when cultured in vitro, but little is known about what causes such variation. It has been proposed that the difference in endogenous plasmid contents among strains contribute to variation in OspC phenotype, as B. burgdorferi contains more than 21 endogenous linear (lp) and circular plasmids (cp), and some of which are prone to be lost. In this study, we analyzed several clones isolated from B. burgdorferi strain 297, one of the most commonly used strains for studying ospC expression. By taking advantage of recently published plasmid sequence of strain 297, we developed a multiplex PCR method specifically for rapid plasmid profiling of B. burgdorferi strain 297. We found that some commonly used 297 clones that were thought having a complete plasmid profile, actually lacked some endogenous plasmids. Importantly, the result showed that the difference in plasmid profiles did not contribute to the ospC expression variation among the clones. Furthermore, we found that B. burgdorferi clones expressed different levels of BosR, which in turn led to different levels of RpoS and subsequently, resulted in OspC level variation among B. burgdorferi strains