In this paper, a power allocation scheme based on a stackelberg game model is proposed to coordinate complicated interference in heterogeneous network. Firstly, the power allocation problem is modeled as a stackelberg...
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ISBN:
(纸本)9781538663554
In this paper, a power allocation scheme based on a stackelberg game model is proposed to coordinate complicated interference in heterogeneous network. Firstly, the power allocation problem is modeled as a stackelberg game where base stations (BS) are treated as competitive players. Then a twostage pricing-based power allocation scheme is proposed, where macro-BS and micro-BS cooperate with each other in order to provide optimal power allocation strategy. Theoretical analysis and simulation results both validate that the proposed scheme not only makes improvement in spectral and energy efficiency, but also reduces computational complexity, especially in dense deployment scenario.
Recent widespread interest in Convex Hull pricing has not been accompanied by an equally broad understanding of the method. This paper seeks to narrow the gap between enthusiasm and comprehension. The connection betwe...
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Recent widespread interest in Convex Hull pricing has not been accompanied by an equally broad understanding of the method. This paper seeks to narrow the gap between enthusiasm and comprehension. The connection between Convex Hull pricing and basic electricity market clearing processes is clearly developed, and a new formulation of the pricing problem is presented. From this formulation, several important consequences of the method are derived and discussed. Some counterintuitive properties of Convex Hull pricing are highlighted and demonstrated with simple examples. It is hoped that this paper will spur additional research on Convex Hull pricing so that an informed judgment can be made regarding its costs and benefits.
An alternative and simple algorithm for valuating the price of discrete barrier options is presented. This algorithm computes the price just exactly the same as the Cox-Ross-Rubinstein (CRR) model. As opposed to other...
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An alternative and simple algorithm for valuating the price of discrete barrier options is presented. This algorithm computes the price just exactly the same as the Cox-Ross-Rubinstein (CRR) model. As opposed to other pricing methodologies, this recursive algorithm utilizes only the terminal nodes of the binomial tree and it captures the intrinsic property, the knock-in or knock-out feature, of barrier options. In this paper, we apply the algorithm to compute the price of an Up and Out Put (UOP) barrier option and compare the results obtained from the CRR model. We then determine the time complexity of the algorithm and show that it is Theta(n(2)).
A distributed group-pricing algorithm is presented for market-based resource scheduling in grid context, which is integrated with quick convergence of the centralized simultaneous pricing algorithm and scalability of ...
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A distributed group-pricing algorithm is presented for market-based resource scheduling in grid context, which is integrated with quick convergence of the centralized simultaneous pricing algorithm and scalability of the distributed WALRAS algorithm. Firstly, an agent-based framework for resources pricing is described: resource agent and auctioneer for groups of resources. Secondly, the distributed group-pricing algorithm is introduced: resources in the system are grouped according to the degree of interaction, and each auctioneer adjusts the price of one group of resources respectively until the excess demand of resources becomes zero when the demand and supply of resources of the system changes. Finally, the performance of the WALRAS algorithm and the presented distributed group-pricing algorithm is compared by experiments, and the experimental results indicate that the distributed group-pricing algorithm can attain better performance than the WALRAS algorithm.
In the US Air Force, a Logistic Readiness Squadron (LRS) provides material management, distribution, and oversight of contingency operations. Dispatchers in the LRS must quickly prepare schedules that meet the needs o...
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In the US Air Force, a Logistic Readiness Squadron (LRS) provides material management, distribution, and oversight of contingency operations. Dispatchers in the LRS must quickly prepare schedules that meet the needs of their customers while dealing with real-world constraints such as time windows, delivery priorities, and intermittent recurring missions. Currently, LRS vehicle operation elements are faced with a shortage of manpower and lack an efficient scheduling algorithm and tool. The purpose of this research is to enhance the dispatchers capability to handle flexible situations and produce good schedules within current manpower restrictions. In this research, a new scheduling model and algorithm are provided as an approach to crew scheduling for a base-level delivery system with a single depot. A Microsoft Excel application, the Daily Squadron Scheduler (DSS), was built to implement the algorithm. DSS combines generated duties with the concept of a set covering problem. It utilizes a Linear Programming pricing algorithm and Excel Solver as the primary engine to solve the problem. Reduced costs and shadow prices from sub problems are used to generate a set of feasible duties from which an optimal solution to the LP relaxation can be found. From these candidate duties the best IP solution is then found. The culmination of this effort was the development of both a scheduling tool and an analysis tool to guide the LRS dispatcher toward efficient current and future schedules.
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