Dynamic Beam Hopping Time Slots Allocation Based on Genetic Algorithm of Satellite Communication under Time-Varying Rain Attenuation

Beam hopping technology is considered to provide a high level of flexible resource allocation to manage uneven traffic requests in multi-beam high throughput satellite systems. Conventional beam hopping resource allocation methods assume constant rainfall attenuation. Different from conventional methods, by employing genetic algorithm this paper studies dynamic beam hopping time slots allocation under the effect of time-varying rain attenuation. Firstly, a beam hopping system model as well as rain attenuation time series based on Dirac lognormal distribution are provided. On this basis, the dynamic allocation method by employing genetic algorithm is proposed to obtain both quantity and arrangement of time slots allocated for each beam. Simulation results show that, compared with conventional methods, the proposed algorithm can dynamically adjust time slots allocation to meet the non-uniform traffic requirements of each beam under the effect of time-varying rain attenuation and effectively improve system performance.

Research on Optimization of Power System Regulation Capacity Based on New Energy Installed Capacity

As the proportion of new energy continues to increase, the safty and stability of the power system faces severe challenges. Many a flexible resource should be established to ensure that the regulating ability of the power system is enough, so as to cope with the load fluctuations caused by the gird-connected new energy. This article analyses the technical characteristics of various flexible resources, and which aspect that each flexible resource can contribute to the regulating ability of the power system. On this basis, this paper proposes a quantitative analysis method of the improvement of power system regulating ability based on the consumption model of new energy, using the addition of new energy grid-connected capacity as a quantitative indicator to evaluate the improvement of system regulating ability contributed by each type of flexible resource. Combined with calculation examples, a quantitative analysis of usual flexibility resources is carried out.

Reward Expectation Differentially Modulates Global and Local Spatial Working Memory Accuracy

Although it has been suggested that reward expectation affects the performance of spatial working memory tasks, controversial results have been found in previous experiments. Hence, it is still unclear to what extent reward expectation has an effect on working memory. To clarify this question, a memory-guided saccade task was applied, in which participants were instructed to retain and reconstruct a temporospatial sequence of four locations by moving their eyes in each trial. The global- and local-level spatial working memory accuracies were calculated to determine the reward effect on the global and local level of processing in spatial working memory tasks. Although high reward expectation enhanced the encoding of spatial information, the percentage of trials in which the cued location was correctly fixated decreased with increment of reward expectation. The reconstruction of the global temporospatial sequence was enhanced by reward expectation, whereas the local reconstruction performance was not affected by reward. Furthermore, the improvements in local representations of uncued locations and local sequences were at the cost of the representation of cued locations. The results suggest that the reward effect on spatial working memory is modulated by the level of processing, which supports the flexible resource theory during maintenance.

Multi-Connectivity Enhanced Communication-Incentive Distributed Computation Offloading in Vehicular Networks

Flexible resource scheduling and network forecast are crucial functions to enhance mobile vehicular network performances. However, BaseStations (BSs) and their computing unit which undertake the functions cannot meet the delay requirement because of limited computation capability. Offloading the time-sensitive functions to User Equipment (UE) is believed to be an effective method to tackle this challenge. The disadvantage of the method is offloading occupies communication resources, which deteriorate the system capability. To better coordinate offloading and communication, a multi-connectivity enhanced joint scheduling scheme for distributed computation offloading and communication resources allocation in vehicular networks is proposed in this article. Computation tasks are divided into many slices and distributed to UEs to aggregate the computation capability. A communication-incentive mechanism is provided for involving UEs to compensate the loss of UEs, while multi-connectivity is adopted to enhance the system throughput. We also defined offloading failure ratio as a conclusive condition for offloading size by analyzing the movement of UEs. By a two-step optimization, the co-scheduling of offloading size and throughput is solved. The system-level simulation results show that the offloading size and throughput of the proposed scheme are larger than comparisons when the time constraint is tight.

Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks

To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max–min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.

A Review of Dynamic Resource Allocation Framework for Large Amount of Cloud Enterprises

Cloud computing is an on-demand service because it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner. Because of the consistently increasing demands of the clients for services or resources, it gets hard to allocate resources accurately to the client demands to satisfy their solicitations and also to take care of the Service Level Agreements (SLA) gave by the service suppliers. Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research network in the last couple of years. Many researchers around the world have thought of new ways of facing this challenge. Ad-hoc parallel data handling has arisen to be one of the executioner applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud supplier companies has started to incorporate frameworks for parallel data handling in their item which making it easy for clients to access these services and to convey their programs. The handling frameworks which are at present utilized have been intended for static and homogeneous bunch arrangements. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase preparing cost and time. Again because of opaque nature of cloud, static allocation of resources is conceivable, yet the other way around in dynamic situations. The proposed new generic data handling framework is expected to expressly misuse the dynamic resource allocation in cloud for task scheduling and execution.