• Journal of IKEEE
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• v.15 no.2
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• pp.157-163
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• 2011

In this paper, novel traffic groomming algorithms are proposed with a primary goal for maximizing throughput in WDM Multi-ring networks. To achieve this goal, we design four network topologys that are ITG, STG, MTG, PMTG and simulate the throughput in uniform traffic environments. From this methods, we proposed novel traffic groomming algorithms to maximize throughput in WDM Multi-ring networks.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.79-84
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• 2017

In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5013-5034
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• 2019

In the Peer-to-Peer (P2P) streaming systems, peers randomly form a network overlay to share video resources with a data scheduling scheme. A data scheduling scheme can have a great impact on system performance, which should achieve two optimal objectives at the same time ideally. The two optimization objectives are to improve the perceived video quality and maximize the network throughput, respectively. Maximizing network throughput means improving the utilization of peer's upload bandwidth. However, maximizing network throughput will result in a reduction in the perceived video quality, and vice versa. Therefore, to achieve the above two objects simultaneously, we proposed a new data scheduling scheme based on multi-objective particle swarm optimization data scheduling scheme, called MOPSO-DS scheme. To design the MOPSO-DS scheme, we first formulated the data scheduling optimization problem as a multi-objective optimization problem. Then, a multi-objective particle swarm optimization algorithm is proposed by encoding the neighbors of peers as the position vector of the particles. Through extensive simulations, we demonstrated the MOPSO-DS scheme could improve the system performance effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2648-2665
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• 2022

Device-to-device (D2D) multicast has become a promising technology to provide specific services within a small geographical region with a high data rate, low delay and low energy consumption. However, D2D multicast communications are allowed to reuse the same channels with cellular uplinks and result in mutual interference in a cell. In this paper, an intelligent channel assignment algorithm is designed in D2D underlaid cellular networks with the target of maximizing network throughput. We first model the channel assignment problem to be a throughput maximizing problem which is NP-hard. To solve the problem in a feasible way, a novel channel assignment algorithm is proposed. The key idea is to find the appropriate cellular communications and D2D multicast groups to share a channel without causing critical interference, i.e., finding a channel for a D2D multicast group which generates the least interference to network based on current channel assignment status. In order to show the efficacy and effectiveness of our proposed algorithm, a novel search algorithm is proposed to find the near-optimal solution as the baseline for comparisons. Simulation results show that the proposed algorithm improves the network throughput.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.221-221
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• 1994

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.801-805
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• 1992

Due to the inherent complexity in the CIM operation, achieving the triple, meeting duedates, maximizing machine utilization, and maximizing system throughput simultaneously is practically impossible. Targeting the small-to-medium size industries of Korea, we propose an exrert system that 'provides a good and practical solution to the CIM operation problems. Heavy consideration has given to the real-time and dynamic nature of CIM in the development process of the system. The system is under testing stage at KU-FMS, model CIM plant.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.221-227
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• 2015

Spectrum sensing plays an essential role in a cognitive radio network, which enables opportunistic access to an underutilized licensed spectrum. In conventional cooperative spectrum sensing (CSS), all cognitive users (CUs) in the network spend the same amount of time on spectrum sensing and waste time in remaining silent when other CUs report their sensing results to the fusion center. This problem is solved by the superposition cooperative spectrum sensing (SPCSS) scheme, where the sensing time of a CU is extended to the reporting time of the other CUs. Subsequently, SPCSS assigns the CUs different sensing times and thus affects both the sensing performance and the throughput of the system. In this paper, we propose an algorithm to determine the optimal sensing time of each CU for SPCSS that maximizes the achieved system throughput. The simulation results prove that the proposed scheme can significantly improve the throughput of the cognitive radio network compared with the conventional CSS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.121-127
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• 2010

In this paper, we propose a computationally efficient scheduling algorithm that can arbitrarily control the throughput-fairness tradeoff in a multiuser wireless communication environment. As a new scheduling criterion, we combine linearly two well-known scheduling criteria such as one of achieving the maximum sum throughput and the other of achieving the maximum fairness, so as to control the relative proportion of the throughput and the fairness according to a control factor. For linear combining two different criteria, their optimization directivenesses and the units should be unified first. To meet these requirements, we choose an instantaneous channel capacity as a scheduling criterion for maximizing the sum throughput and the average serving throughput for maximizing the fairness. Through a unified linear combining of two optimization objectives with the control factor, it can provide various throughput-fairness tradeoffs according to the control factors. For further simplification, we exploit a high signal-to-noise ratio (SNR) approximation of the instantaneous channel capacity. Through computer simulations, we evaluate the throughput and fairness performances of the proposed algorithm according to the control factors, assuming an independent Rayleigh fading multiuser channel. We also evaluate the proposed algorithm employing the high SNR approximation. From simulation results, we could see that the proposed algorithm can control arbitrarily the throughput-fairness performance between the performance of the scheduler aiming to the maximum sum throughput and that of the scheduler aiming to the maximum fairness, finally, we see that the high SNR approximation can give a satisfactory performance in this situation.

• Journal of Communications and Networks
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• v.16 no.3
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• pp.293-300
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• 2014

As energy harvesting communication systems emerge, there is a need for transmission schemes that dynamically adapt to the energy harvesting process. In this paper, after exhibiting a finite-horizon online throughput-maximizing scheduling problem formulation and the structure of its optimal solution within a dynamic programming formulation, a low complexity online scheduling policy is proposed. The policy exploits the existence of thresholds for choosing rate and power levels as a function of stored energy, harvest state and time until the end of the horizon. The policy, which is based on computing an expected threshold, performs close to optimal on a wide range of example energy harvest patterns. Moreover, it achieves higher throughput values for a given delay, than throughput-optimal online policies developed based on infinite-horizon formulations in recent literature. The solution is extended to include ergodic time-varying (fading) channels, and a corresponding low complexity policy is proposed and evaluated for this case as well.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.402-407
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• 2007

The network throughput is an important performance criteria for the packet ring networks. Since maximizing the network throughput can lead to severe bias in bandwidth allocation among all flows, fairness should be imposed to prevent bandwidth starvation. The challenge here, therefore, is the joint optimization of the network throughput and fairness. In this paper, we present the optimal bandwidth assignment scheme to decompose this optimization problem into two tasks, one for finding fair bandwidth assignment and the other for finding the optimal routing. The network throughput is maximized under the fairness constraints when these tasks are performed iteratively.