• ETRI Journal
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• 제44권3호
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• pp.400-412
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• 2022

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O(M log(M)).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권10호
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• pp.2395-2410
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• 2013

Many telecommunication operators around the world have multiple networks. The networks run by each operator are always of different generations, such as 2G and 3G or even 4G systems. Each system has unique characters and specified requirements for optimal operation. It brings about resource allocation problem among these networks for the operator, because the budget of each operator is limited. However, the evaluation of resource allocation among various networks under each operator is missing for long, not to mention resource allocation optimization. The operators are dying for an algorithm to end their blind resource allocation, and the Resource Allocation Optimization Algorithm for Multi-network Operator (RAOAMO) proposed in this paper is what the operators want. RAOAMO evaluates and optimizes resource allocation in the view of overall cost for each operator. It outputs a resource distribution target and corresponding optimization suggestion. Evaluation results show that RAOAMO helps operator save overall cost in various cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• ETRI Journal
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• 제34권6호
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• pp.879-884
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• 2012

When the number of users is finite, the performance improvement of the orthogonal random beamforming (ORBF) scheme is limited in high signal-to-noise ratio regions. In this paper, to improve the performance of the ORBF scheme, the user set and transmit power allocation are jointly determined to maximize sum rate under the total transmit power constraint. First, the transmit power allocation problem is expressed as a function of a given user set. Based on this expression, the optimal user set with the maximum sum rate is determined. The suboptimal procedure is also presented to reduce the computational complexity, which separates the user set selection procedure and transmit power allocation procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.562-576
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• 2020

The power allocation optimization problem is investigated for improving the physical-layer security in two-way relaying networks, where a full-duplex relay based half-jamming protocol (HJP-FDR) is considered. Specially, by introducing a power splitter factor, HJP-FDR divides the relay's power into two parts: one for forwarding the sources' signals, the other for jamming. An optimization problem for power split factor is first developed, which is proved to be concave and closed-form solution is achieved. Moreover, we formulate a power allocation problem to determine the sources' power subject to the total power constraint. Applying the achieved closed-form solutions to the above-mentioned problems, a two-stage strategy is proposed to implement the overall power allocation. Simulation results highlight the effectiveness of our proposed algorithm and indicate the necessity of optimal power allocation.

• 산업경영시스템학회지
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• 제19권40호
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• pp.169-178
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• 1996

Geographically dispersed computing system is made of computers interconnected by a telecommunications network. To make the system operated efficiently, system designer must determine the allocation of data files to each node. In designing such distributed computing system, the most important issue is the determination of the numbers and the locations where database files are allocated. This is commonly referred to as the file allocation problem (FAP)[3]. The proposed model is a 0/l integer programming problem minimizing the sum of file storage costs and communication(query and update) costs. File allocation problem belongs to the class of NP-Complete problems. Because of the complexity, it is hard to solve. So, this paper presents an efficient heuristic algorithm to solve the file allocation problem using Tabu Search Technique. By comparing the optimal solutions with the heuristic solutions, it is believed that the proposed heuristic algorithm gives good solutions. Through the experimentation of various starting points and tabu restrictions, this paper presents fast and efficient method to solve the file allocation problem in the distributed computing system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.566-578
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• 2018

In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.201-204
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• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• 한국국방경영분석학회지
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• 제9권1호
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• pp.29-49
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• 1983

The algorithm developed in this paper utilized kettelle's [1] idea of the undominated allocation sequence and his way of tableau computation to solve the more general spares allocation problem in the system availability optimization. The algorithm is to optimally allocate resources to the independent modules which are connected to be series/parallel/mixed system configurations. It has advantages over the standard dynamic programming algorithm by eliminating the need for backtracking and by solving the allocation problem for any budget size. By careful heuristic inspection the algorithm can be made very efficient for manual calculations because large blocks of cells can be eliminated from computation. A numerical example is provided to illustrate the allocation algorithm.

• International journal of advanced smart convergence
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• 제9권3호
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• pp.49-58
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• 2020

Non-orthogonal multiple access (NOMA) has been recognized as a significant technology in the fifth generation (5G) and beyond mobile communication, which encompasses the advanced smart convergence of the artificial intelligence (AI) and the internet of things (IoT). In NOMA, since the channel resources are shared by many users, it is essential to establish the user fairness. Such fairness is achieved by the power allocation among the users, and in turn, the less power is allocated to the stronger channel users. Especially, the first and second strong channel users have to share the extremely small amount of power. In this paper, we consider the power optimization for the two users with the small power. First, the closed-form expression for the power allocation is derived and then the results are validated by the numerical results. Furthermore, with the derived analytical expression, for the various channel environments, the optimal power allocation is investigated and the impact of the channel gain difference on the power allocation is analyzed.