• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.9
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• pp.1492-1512
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• 2011

Due to multiple hops, mobility and time-varying channel, supporting delay sensitive real-time traffic in wireless local area network-based (WLAN) mesh networks is a challenging task. In particular for real-time traffic subject to medium access control (MAC) layer control overhead, such as preamble, carrier sense waiting time and the random backoff period, the performance of real-time flows will be degraded greatly. In order to support real-time traffic, an efficient adaptive packet scheduling (APS) scheme is proposed, which aims to improve the system performance by guaranteeing inter-class, intra-class service differentiation and adaptively adjusting the packet length. APS classifies incoming packets by the IEEE 802.11e access class and then queued into a suitable buffer queue. APS employs strict priority service discipline for resource allocation among different service classes to achieve inter-class fairness. By estimating the received signal to interference plus noise ratio (SINR) per bit and current link condition, APS is able to calculate the optimized packet length with bi-dimensional markov MAC model to improve system performance. To achieve the fairness of intra-class, APS also takes maximum tolerable packet delay, transmission requests, and average allocation transmission into consideration to allocate transmission opportunity to the corresponding traffic. Detailed simulation results and comparison with IEEE 802.11e enhanced distributed channel access (EDCA) scheme show that the proposed APS scheme is able to effectively provide inter-class and intra-class differentiate services and improve QoS for real-time traffic in terms of throughput, end-to-end delay, packet loss rate and fairness.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1099-1104
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• 2020

In order to provide various types of application services based on the Internet, it is ideal to guarantee the quality of service(QoS) for each flow. However, realizing these ideas is not an easy task.. It is effective to classify multiple flows having the same or similar service quality requirements into same group, and to provide service quality for each group. The queue management mechanism in the router plays a very important role in order to efficiently transmit data and to support differentiated quality of service for each service. In order to efficiently support various multimedia services, an intelligent and adaptive queue management mechanism is required. This paper proposes an intelligent queue management mechanism based on deep reinforcement learning that decides whether to deliver packets for each group based on the traffic information of each flow group flowing in for a certain period of time and the current network state information.

• The KIPS Transactions:PartC
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• v.11C no.2
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• pp.263-268
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• 2004

The end-to-end characteristic Is an important factor for QoS support. Since network users and required bandwidths for applications increase, the efficient usage of networks has been intensively investigated for the better utilization of network resources. The distributed adaptive routing is the typical routing algorithm that is used in the current Internet. The DCLC(Delay Constrained 1.east Cost) path problem has been shown to be NP-hard problem. The path cost of LD path is relatively more expensive than that of LC path, and the path delay of LC path is relatively higher than that of LD path in DCLC problem. In this paper, we investigate the performance of heuristic algorithm for the DCLC problem with new factor which is probabilistic combination of cost and delay. Recently Dr. Salama proposed a polynomial time algorithm called DCUR. The algorithm always computes a path, where the cost of the path is always within 10% from the optimal CBF. Our evaluation showed that heuristic we propose is more than 38% better than DCUR with cost when number of nodes is more than 200. The new factor takes in account both cost and delay at the same time.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.696-706
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• 2008

In this paper we show the performance of the network-adaptive high-definition scalable video streaming using QWLAN board with IEEE 802.11e MAC monitoring and control. Realtime collected MAC parameters are used to determine which video data is extracted for the predicted available bandwidth. To achieve performance, extraction through R-D is proposed instead of the standard video packet extraction. It is shown through experiments that streaming video quality can be enhanced by fast adaptation to network conditions by using the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9A
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• pp.1048-1057
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• 2004

WCDMA system is 3rd generation wireless mobile system specified by 3GPP. In WCDMA downlink, two power control schemes are operated. One is inner loop power control operated m every slot Another is outer loop power control based on one frame time. Base staion (BS) can estimate proper transmission power by these two power control schemes. However, because each MS's transmission power makes a severe effect on BS's performance, BS cannot give excessive transmission power to the speclfic user 3GPP defined Power Control Dynamic Range (PCDR) to guarantee proper BS's performance. In this paper, we propose Adaptive PCDR algorithm. By APCDR algorithm, Radio Network Controller (RNC) can estimate each MS's current state using received signal to interference ratio (SIR) APCDR algorithm changes MS's maximum code channel power based on frame. By proposed scheme, each MS can reduce wireless channel effect and endure outages in cell edge. Therefore, each MS can obtain better QoS. Simulation result indicate that APCDR algorithm show more attractive output than fixed PCDR algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2389-2413
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• 2015

High data rates in long-term evolution (LTE) networks can affect the mobility of networks and their performance. The speed and motion of user equipment (UE) can compromise seamless connectivity. However, a proper handover (HO) decision can maintain quality of service (QoS) and increase system throughput. While this may lead to an increase in complexity and operational costs, self-optimization can enhance network performance by improving resource utilization and user experience and by reducing operational and capital expenditure. In this study, we propose the self-optimization of HO parameters based on fuzzy logic control (FLC) and multiple preparation (MP), which we name FuzAMP. Fuzzy logic control can be used to control self-optimized HO parameters, such as the HO margin and time-to-trigger (TTT) based on multiple criteria, viz HO ping pong (HOPP), HO failure (HOF) and UE speeds. A MP approach is adopted to overcome the hard HO (HHO) drawbacks, such as the large delay and unreliable procedures caused by the break-before-make process. The results of this study show that the proposed method significantly reduces HOF, HOPP, and packet loss ratio (PLR) at various UE speeds compared to the HHO and the enhanced weighted performance HO parameter optimization (EWPHPO) algorithms.