An uplink multisecondary user (SU) cognitive radio system having average delay constraints as well as an interference constraint to the primary user (PU) is considered. If the interference channels between the SUs and...
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An uplink multisecondary user (SU) cognitive radio system having average delay constraints as well as an interference constraint to the primary user (PU) is considered. If the interference channels between the SUs and the PU are statistically heterogeneous due to the different physical locations of the different SUs, the SUs will experience different delay performances. This is because SUs located closer to the PU transmit with lower power levels. Two dynamicscheduling-and-power-allocation policies that can provide the required average delay guarantees to all SUs irrespective of their locations are proposed. The first policy solves the problem when the interference constraint is an instantaneous one, while the second is for problems with long-term average interference constraints. We show that although the average interference problem is an extension to the instantaneous interference one, the solution is totally different. The two policies, derived using the Lyapunov optimization technique, are shown to be asymptotically delay optimal while satisfying the delay and interference constraints. Our findings are supported by extensive system simulations and shown to outperform the existing policies as well as shown to be robust to channel estimation errors.
Multidisciplinary design optimization (MDO) models are built by assembling sub-models that represent varying disciplines and design conditions. Consequently, such models are often computationally expensive, requiring ...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
Multidisciplinary design optimization (MDO) models are built by assembling sub-models that represent varying disciplines and design conditions. Consequently, such models are often computationally expensive, requiring their evaluation in a parallel computing environment to decrease solution time. MDO modeling frameworks, which are software tools to allow construction of MDO models, generally require manual user input to specify where to parallelize their code. However, this process can be tedious if the model is large or complex. In this paper, we propose a method heavily inspired by scheduling theory that aims to fully automate parallelization of MDO models. We use static task scheduling to partition mathematical operations in the model to different processors prior to model execution. Additionally, our approach supports adjoint-based derivative computation to enable the use of gradient-based optimization algorithms. Our results show a significant performance improvement when applying our method to several MDO applications. We believe our approach enables MDO modelers to solve computationally expensive problems in a more efficient manner.
In order to avoid communication conflicts in DNC systems, the scheduling tactics adopting a modified guaranteed EDF algorithm to DNC systems' numerical control (NC) programs transmission was proposed. A condition ...
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ISBN:
(纸本)9780878492220
In order to avoid communication conflicts in DNC systems, the scheduling tactics adopting a modified guaranteed EDF algorithm to DNC systems' numerical control (NC) programs transmission was proposed. A condition was given to guarantee all NC machines in DNC systems work continuously. A mathematic model based on the actual expending rate of NC program was established to decide the priority of NC program transformation of all NC machines. A constraint condition for two machines with neighbored priority was proposed to avoid the conflict in the scheduling times. A dynamicscheduling scheme was presented for DNC systems. The validity of the presented method is verified by the practical tests.
With the rapid development of logistics, customers can enjoy more convenient service. But meanwhile, the market has also put higher requirements forward the timeliness of cargo distribution. Traffic demand has increas...
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ISBN:
(纸本)9781450376532
With the rapid development of logistics, customers can enjoy more convenient service. But meanwhile, the market has also put higher requirements forward the timeliness of cargo distribution. Traffic demand has increasing continuously, which has led people to tend to buy more motor vehicles. And the increasing vehicle ownership has brought great pressure on urban transportation, causing traffic congestion to become increasingly serious. However, traffic congestion sometimes has influence on the timeliness of cargo distribution. This article is based on the theory of automata and applies the real-time communication positioning technology of vehicle networking to simulate the running status of the distribution vehicles in the road network. Based on this, it designs a dynamic scheduling algorithm for the distribution vehicles' driving path, and it can optimize distribution vehicles' driving path in real time, and improve the efficiency of distribution.
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