The massive random accesses of machine type communication (MTC) in current wireless networks (e.g. LTE-A, WiMAX) are a challenging and urgent issue. In this paper, the random access performances in LTE-A networks are ...
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ISBN:
(纸本)9781510804166
The massive random accesses of machine type communication (MTC) in current wireless networks (e.g. LTE-A, WiMAX) are a challenging and urgent issue. In this paper, the random access performances in LTE-A networks are first analyzed in terms of success probability, access delay, physical random access channel efficiency, and the number of retransmissions. Based on the analysis, two QoS-based optimization algorithms are then proposed for the random access of massive MTC devices. Simulation results show that the proposed algorithms achieve better performances than non-optimized random access, providing required QoS guarantees for each MTC application, and meanwhile optimizing the spectrum efficiency and energy efficiency.
Interactive evolutionary multi-objective optimization algorithms have received widespread research. The goal of these algorithms is to iteratively involve decision makers(DM) in the solution process, by providing pref...
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Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the problem with access to inexact first-orde...
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Given any algorithm for convex optimization that uses exact first-order information (i.e., function values and subgradients), we show how to use such an algorithm to solve the problem with access to inexact first-order information. This is done in a "black-box" manner without knowledge of the internal workings of the algorithm. This complements previous work that consider the performance of specific algorithms like (accelerated) gradient descent with inexact information. In particular, our results apply to a wider range of algorithms beyond variants of gradient descent, e.g., projection-free methods, cutting-plane methods, or any other first-order methods formulated in the future. Further, they also apply to algorithms that handle structured nonconvexities like mixed-integer decision variables. Copyright 2024 by the author(s)
This paper presents a study on the localization of debris from the Seven Rockets using nonlinear optimization algorithms and linear matching. The research first determines the time differences of sonic booms reaching ...
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Metaheuristic algorithms are essential for solving complex optimization problems in different fields. However, the difficulty in comparing and rating these algorithms remains due to the wide range of performance metri...
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One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Pr...
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Surrogate models are increasingly used to reduce the computational costs of building performance simulation (BPS) models. However, they are rarely coupled with optimization algorithms to inform retrofitting decisions....
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The flexibility in protein docking is a major challenge in bioinformatic research.. Protein docking problem is essentially an optimization problem. In this review we describe the methods of existing flexible protein-p...
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The flexibility in protein docking is a major challenge in bioinformatic research.. Protein docking problem is essentially an optimization problem. In this review we describe the methods of existing flexible protein-protein docking ,focusing on the optimization algorithms . We divide the different methods into categories for presenting clearly.
Numerical reservoir models are constructed from limited available static and dynamic data,and history matching is a process of changing model parameters to find a set of values that will yield a reservoir simulation p...
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Numerical reservoir models are constructed from limited available static and dynamic data,and history matching is a process of changing model parameters to find a set of values that will yield a reservoir simulation prediction of data that matches the observed historical production *** minimize the objective function involved in the history matching procedure,we need to apply the optimization *** paper is based on the optimization algorithms used in automatic history *** optimization algorithms will be compared in this paper.
In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2...
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