• Journal of Digital Contents Society
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• v.9 no.3
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• pp.433-439
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• 2008

A moving picture in online game is one of major ways to advertise online games, which gives a lot of help in playing game. In this case, a moving picture is compressed to variable bit rate for efficient storage use and network resource efficiency. Adaptable bandwidth allocation technique builds a transmission plan of a game moving picture. And, then some frames are discarded when transmission rate by the transmission plan is larger than available transmission rate, until transmission rate satisfies available transmission rate. Thus, performance evaluation factors in adaptable bandwidth allocation technique may be dependent on discarding order of a frame which transmission rate is much influenced. In this paper, in order to show the performance, a CBA algorithm, an MCBA algorithm, an MVBA algorithm, [6] and [7] algorithm were applied to a transmission plan in the adaptable band width allocation technique using various frame discard methods and performance evaluation factors were compared in among smoothing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9A
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• pp.888-897
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• 2007

In this paper, we propose an uplink dynamic resource allocation algorithm to increase sector throughput and fairness among users in 802.16e OFDMA TDD system. In uplink, we address the difference between uplink and downlink channel state information in 802.16e OFDMA TDD system. The simulation results show that not only an increment of 10% of sector throughput but higher level of fairness is achieved by round-robin using the FLR and the rate / margin adaptive inner closed-loop power control algorithm. The FLR algorithm determines the number of sub-channels to be allocated to the user according to the user's position. Also, we get 31.8% more sector throughput compared with the round-robin using FLR by FASA algorithm using uplink channel state information. User selection, sub-channel allocation, power allocation algorithms and simulation methodology are mentioned in Part I.

• The KIPS Transactions:PartC
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• v.11C no.4
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• pp.429-438
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• 2004

This Paper Presents the control structure for incoming traffic from arbitrary node to Provide admission control in policy-based W network management structure using fuzzy logic control approach. The proposed control structure uses scheme for deciding network resource allocation depending on requirements predefined-policies and network states. The proposed scheme enhances policy adapting methods of existing binary methods, and can use resource of network more effectively to provide adaptive admission control, according to the unpredictable network states for predefined QoS policies. Simulation results show that the proposed controller improves the ratio of packet rejection up to 26%, because it Performs the soft adaption based on the network states instead of accept/reject action in conventional CAC(Connection Admission Controller).

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.4
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• pp.56-63
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• 2009

Resource reservation scheme is effective for better QoS guarantee to handover call for the next generation cellular systems where handover rate highly increases. However, resource reservation for handover call can incur negative impact on the new user admission. In this paper, we propose a random access based adaptive group-wise reservation to effectively reduce dropping rate of handover call, while protecting new local call. In the proposed scheme, target base station divides adaptively future handover users into groups based on the expected handover time, and then does group-wise reservation, where service allocation within each group is done on random access basis. Markov approach is also provided to analyze the performance of the proposed scheme. By computer simulation, it is shown that theoretical analysis on the performance is similar to the simulation results and the proposed reservation scheme outperforms the conventional scheme with respect to dropping probability of handover call.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.687-708
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• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.

• 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.

• Journal of KIISE:Information Networking
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• v.37 no.2
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• pp.94-98
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• 2010

In this paper, we formalize a new adaptive online price control scheme based on the Stackelberg game model. By using the hierarchical interaction strategy, control decisions in each mechanism act cooperatively and collaborate with each other to satisfy conflicting performance criteria. In addition, our dynamic online approach is practical for real network implementation. With a simulation study, the proposed scheme can adaptively adjust the network price to approximate an optimized solution under widely diverse network situations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7A
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• pp.620-627
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• 2005

We develop an adaptive hot-spot operating scheme(AHOS) to mitigate the negative effects from the nonuniform distribution of user location and the variation in the mixture of QoS requirements in OFDMA downlink systems. The base station in a macrocell can control the operation of picocells within the cell, and turns on or off according to the changes in the estimated user outage probability and the AHOS gain parameter. With the computer simulation, the AHOS has been proved to maximize the system throughput while maintaining the QoS outage probability very low under various system scenarios.

• ETRI Journal
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• v.28 no.3
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• pp.282-290
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• 2006

In vertically overlaid cellular systems, a temporary traffic concentration can occur in a hot-spot area, and this adversely affects overall system capacity. In this paper, we develop an adaptive hot-spot operating scheme (AHOS) to mitigate the negative effects from the nonuniform distribution of user location and the variation in the mixture of QoS requirements in orthogonal frequency division multiple access downlink systems. Here, the base station in a macrocell can control the operation of picocells within the cell, and turns them on or off according to the system overload estimation function. In order to determine whether the set of picocells is turned on or off, we define an AHOS gain index that describes the number of subcarriers saved to the macrocell by turning a specific picocell on. For initiating the picocell OFF procedure, we utilize the changes in traffic concentration and co-channel interference to the neighboring cells. According to computer simulation, the AHOS has been proved to have maximize system throughput while maintaining a very low QoS outage probability under various system scenarios in both a single-cell and multi-cell environments.

• ETRI Journal
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• v.42 no.5
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• pp.686-699
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• 2020

In this paper, we propose a supervised-learning-based spatial performance prediction (SLPP) framework for next-generation heterogeneous communication networks (HCNs). Adaptive asset placement, dynamic resource allocation, and load balancing are critical network functions in an HCN to ensure seamless network management and enhance service quality. Although many existing systems use measurement data to react to network performance changes, it is highly beneficial to perform accurate performance prediction for different systems to support various network functions. Recent advancements in complex statistical algorithms and computational efficiency have made machine-learning ubiquitous for accurate data-based prediction. A robust network performance prediction framework for optimizing performance and resource utilization through a linear discriminant analysis-based prediction approach has been proposed in this paper. Comparison results with different machine-learning techniques on real-world data demonstrate that SLPP provides superior accuracy and computational efficiency for both stationary and mobile user conditions.