A novel user-clustering (UC) based downlink hybrid-nonorthogonal multiple access (hybrid-NOMA) assisted heterogeneous network (HetNet) design strategy in the presence of an eavesdropper is explored to investigate the ...
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ISBN:
(纸本)9798350310900
A novel user-clustering (UC) based downlink hybrid-nonorthogonal multiple access (hybrid-NOMA) assisted heterogeneous network (HetNet) design strategy in the presence of an eavesdropper is explored to investigate the secure resource allocation beyond fifth-generation/sixth-generation (B5G/6G) wireless communications. We have formulated a secrecy rate (SR) maximization problem based on the proposed strategy subject to key performance indicators (KPIs) such as the admission of users in clusters, the association of users with the base station (BS), allocation of power to the users, and user's minimum rate requirement. This novel problem is solved to find an epsilon-optimal solution within epsilon = 10(-3) using the outer approximation algorithm (OAA). This paper performs an analysis of the performance of the proposed strategy and it is depicted through simulation results that the proposed strategy maximizes the SR in terms of KPIs. A performance comparison of the proposed strategy with the existing orthogonal multiple access-only (OMA-only) and nonorthogonal multiple access-only (NOMA-only) strategies is conducted, and it is identified through simulation results that the proposed strategy performs best among the existing strategies. Additionally, a comparison of the performance of the proposed strategy for HetNet and macro base station-only (MBS-only) networks is conducted, and the simulation results depicted that the proposed strategy performs better in HetNet compared to the MBS-only network.
In this paper, we address the classical liner fleet deployment and slot allocation joint optimization problem in the maritime field with uncertain container transportation demand. We relax the assumption in existing s...
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In this paper, we address the classical liner fleet deployment and slot allocation joint optimization problem in the maritime field with uncertain container transportation demand. We relax the assumption in existing studies that the demand distribution function is known because container transportation demand is deeply affected by the world's economic and political landscape. With the help of advances in distributionally robust optimization theory, we develop a two-stage datadriven robust chance-constrained model. This distribution-free model requires only limited historical demand data as input and jointly optimizes the class (i.e., capacity) and number of liners assigned on each route and the scheme for allocating containers on each leg to maximize the profit (container transportation revenue minus fleet operating costs, voyage costs, and capital costs) of the liner company. The joint chance constraint in the model requires that the transportation demand of the contract shipper be satisfied with a pre-determined probability. We then reformulate the model as a second-order cone programming and design a customized algorithm to explore the global optimal solution based on the outer approximation algorithm framework. This paper can serve as a baseline distribution-free model for solving liner fleet deployment and slot allocation joint optimization problems.
The rising number of user equipment (UE) and advanced applications in fifth-generation (5G) and beyond fifth-generation (B5G) networks need energy-efficient resource allocation. The researchers have not yet considered...
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ISBN:
(纸本)9781665413688
The rising number of user equipment (UE) and advanced applications in fifth-generation (5G) and beyond fifth-generation (B5G) networks need energy-efficient resource allocation. The researchers have not yet considered hybrid non-orthogonal multiple access (H-NOMA) scheme with UE clustering in heterogeneous networks (HetNets). This paper investigates downlink H-NOMA scheme with UE clustering in HetNets to maximize energy efficiency (EE). A mathematical optimization problem considers UE admission in clusters, UE association with base stations (BSs), power allocation, the minimum rate, and transmit power requirements. We have converted the formulated non-linear concave fractional programming (CFP) problem into a concave optimization problem with Charnes-Cooper transformation (CCT). An epsilon-optimal outer approximation algorithm (OAA) solves the formulated problem. The effectiveness of the proposed scheme regarding EE, network throughput, UE admission, and UE association is shown with the simulation results.
In this article, generalization of some mixed-integer nonlinear programming algorithms to cover convex nonsmooth problems is studied. In the extended cutting plane method, gradients are replaced by the subgradients of...
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In this article, generalization of some mixed-integer nonlinear programming algorithms to cover convex nonsmooth problems is studied. In the extended cutting plane method, gradients are replaced by the subgradients of the convex function and the resulting algorithm shall be proved to converge to a global optimum. It is shown through a counterexample that this type of generalization is insufficient with certain versions of the outer approximation algorithm. However, with some modifications to the outerapproximation method a special type of nonsmooth functions for which the subdifferential at any point is a convex combination of a finite number of subgradients at the point can be considered. Numerical results with extended cutting plane method are also reported.
In this paper, an optimal voltage control scheme designed for ungrounded distribution networks is presented. Three-phase unbalanced quantities are decomposed to positive and negative components to decrease the minimiz...
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ISBN:
(纸本)9781479913039
In this paper, an optimal voltage control scheme designed for ungrounded distribution networks is presented. Three-phase unbalanced quantities are decomposed to positive and negative components to decrease the minimization model size. outer approximation algorithm (OAA) is used to solve the created mixed-integer nonlinear programming problem. The optimization model can be easily converted to solve a distribution network power flow problem which can provide a qualified initial solution to OAA solver. The effectiveness of the new scheme has been approved by test case.
In this paper, an optimal voltage control scheme designed for ungrounded distribution networks is presented. Three-phase unbalanced quantities are decomposed to positive and negative components to decrease the minimiz...
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ISBN:
(纸本)9781479913022
In this paper, an optimal voltage control scheme designed for ungrounded distribution networks is presented. Three-phase unbalanced quantities are decomposed to positive and negative components to decrease the minimization model size. outer approximation algorithm (OAA) is used to solve the created mixed-integer nonlinear programming problem. The optimization model can be easily converted to solve a distribution network power flow problem which can provide a qualified initial solution to OAA solver. The effectiveness of the new scheme has been approved by test case.
We introduce and analyse outerapproximation schemes for solving variational inequality problems in which the constraint set is as in generalized semi-infinite programming. We call these problems generalized semi-infi...
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We introduce and analyse outerapproximation schemes for solving variational inequality problems in which the constraint set is as in generalized semi-infinite programming. We call these problems generalized semi-infinite variational inequality problems. First, we establish convergence results of our method under standard boundedness assumptions. Second, we use suitable Tikhonov-like regularizations for establishing convergence in the unbounded case.
We propose an infeasible interior proximal method for solving variational inequality problems with maximal monotone operators and linear constraints. The interior proximal method proposed by Auslender, Teboulle and Be...
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We propose an infeasible interior proximal method for solving variational inequality problems with maximal monotone operators and linear constraints. The interior proximal method proposed by Auslender, Teboulle and Ben-Tiba [3] is a proximal method using a distance-like barrier function and it has a global convergence property under mild assumptions. However, this method is applicable only to problems whose feasible region has nonempty interior. The algorithm we propose is applicable to problems whose feasible region may have empty interior. Moreover, a new kind of inexact scheme is used. We present a full convergence analysis for our algorithm.
We study two outerapproximation schemes, applied to the variational inequality problem in reflexive Banach spaces. First we propose a generic outerapproximation scheme, and its convergence analysis unifies a wide cl...
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We study two outerapproximation schemes, applied to the variational inequality problem in reflexive Banach spaces. First we propose a generic outerapproximation scheme, and its convergence analysis unifies a wide class of outerapproximation methods applied to the constrained optimization problem. As is standard in this setting, boundedness and optimality of weak limit points are proved to hold under two alternative conditions: (i) boundedness of the feasible set, or (ii) coerciveness of the operator. To develop a convergence analysis where (i) and (ii) do not hold, we consider a second scheme in which the approximated subproblems use a coercive approximation of the original operator. Under conditions alternative to both (i) and (ii), we obtain standard convergence results. Furthermore, when the space is uniformly convex, we establish full strong convergence of the second scheme to a solution.
In this work we study the variational inequality problem in finite dimensional spaces. The constraint set we consider has the structure of semi-infinite programming. Standard convergence analysis for outer approximati...
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In this work we study the variational inequality problem in finite dimensional spaces. The constraint set we consider has the structure of semi-infinite programming. Standard convergence analysis for outerapproximation methods includes boundedness of the constraint set, or, alternatively, coerciveness of the data. Using recession tools, we are able to replace these assumptions by the hypotheses of boundedness of the solution set and that the domain of the operator contains the constraint set.
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