In this paper, we investigate the performance of conventional cooperative sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing in an energy harvesting (EH)-enabled heterogeneous cognitive rad...
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In this paper, we investigate the performance of conventional cooperative sensing (CCS) and superior selective reporting (SSR)-based cooperative sensing in an energy harvesting (EH)-enabled heterogeneous cognitive radio network (HCRN). In particular, we derive expressions for the achievable throughput of both schemes and formulate nonlinear integer programming problems, in order to find the throughput-optimal set of spectrum sensors scheduled to sense a particular channel, given primary user (PU) interference and EH constraints. Furthermore, we present novel solutions for the underlying optimization problems based on the cross-entropy (CE) method, and compare the performance with exhaustive search and greedy algorithms. Finally, we discuss the tradeoff between the average achievable throughput of the SSR and CCS schemes, and highlight the regime where the SSR scheme outperforms the CCS scheme. Notably, we show that there is an inherent tradeoff between the channel available time and the detection accuracy. Our numerical results show that, as the number of spectrum sensors increases, the channel available time gains a higher priority in an HCRN, as opposed to detection accuracy.
It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is cal...
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It is very often the case that at some moment a time series process abruptly changes its underlying structure and, therefore, it is very important to accurately detect such change-points. In this problem, which is called a change-point (or break-point) detection problem, we need to find a method that divides the original nonstationary time series into a piecewise stationary segments. In this paper, we develop a flexible method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function, which is based on the Minimum Description Length principle, we develop a cross-entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. We also apply the proposed method to real data of daily AUD/CNY exchange rate series from 2 January 2018 to 24 March 2020.
In view of the complexity of the unit commitment in the power system unit, a method was put forward based on cross-entropy algorithm to optimize unit commitment. By establishing a system unit portfolio optimization mo...
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ISBN:
(纸本)9781479913909
In view of the complexity of the unit commitment in the power system unit, a method was put forward based on cross-entropy algorithm to optimize unit commitment. By establishing a system unit portfolio optimization model, the cross-entropy algorithm has been utilized to optimize the unit commitment. The calculation and simulation results indicated the quickness and the effectiveness by using the cross-entropy algorithm in unit combinatorial optimization.
This paper develops a graph-theoretic framework for large scale bi-dimensional transport networks and provides new insight into the dynamic traffic assignment. Reactive dynamic assignment are deployed to handle the tr...
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ISBN:
(纸本)9783319489445;9783319489438
This paper develops a graph-theoretic framework for large scale bi-dimensional transport networks and provides new insight into the dynamic traffic assignment. Reactive dynamic assignment are deployed to handle the traffic contingencies, traffic uncertainties and traffic congestion. New shortest paths problem in large networks is defined and routes cost calculation is provided. Since mathematical modelling of traffic flow is a keystone in the theory of traffic flow management, and then in the traffic assignment, it is convenient to elaborate a good model of assignment for large scale networks relying on an appropriate model of flow related to very large networks. That is the zone-based optimization of traffic flow model on networks developed by [8], completed and improved by [9].
In this paper, the parameters and reliability characteristics of the mixture of the failure time distribution are estimated based on a complete sample using both Markov chain Monte Carlo (MCMC) method and maximum like...
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ISBN:
(纸本)9781509056897
In this paper, the parameters and reliability characteristics of the mixture of the failure time distribution are estimated based on a complete sample using both Markov chain Monte Carlo (MCMC) method and maximum likelihood estimation via cross-entropy (CE) algorithm. While maximum likelihood estimation is the most frequently used method for parameter estimation, MCMC has recently emerged as a good alternative. The most popular MCMC method, called the Metropolis-Hastings algorithm, is used to provide a complete analysis of the concerned posterior distribution. A simulation study is provided to compare MCMC with CE, and differences between the estimates obtained by the two approaches are evaluated.
P2P-like applications are quickly gaining popularity in the Internet. Such applications are commonly modeled as graphs with nodes and edges. Usually nodes represent running processes that exchange information with eac...
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P2P-like applications are quickly gaining popularity in the Internet. Such applications are commonly modeled as graphs with nodes and edges. Usually nodes represent running processes that exchange information with each other through communication channels as represented by the edges. They often need to autonomously determine their suitable working mode or local status for the purpose of improving performance, reducing operation cost, or achieving system-level design goals. In order to achieve this objective, the concept of status configuration is introduced in this article and a mathematical correspondence is further established between status configuration and an optimization index (OI), which serves as a unified abstraction of any system design goals. Guided by this correspondence and inspired by the cross-entropy algorithm, a cross-entropy-driven self-organization mechanism (CESM) is proposed in this article. CESM exhibits the self-organization property since desirable status configurations that lead to high OI values will quickly emerge from purely localized interactions. Both theoretical and experimental analysis have been performed. The results strongly indicate that CESM is a simple yet effective technique which is potentially suitable for many P2P-like applications.
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