• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.556-559
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• 2007

The notion of linearity is fundamental in science and engineering. Much of system and control theory is based on the analysis of linear system, which does not care whether it is nonlinear and complex. The dynamic programming is one of concerned technology when users are interested in choosing best choice from system operation for nonlinear or dynamic system‘s performance and control problem. In this paper, we will introduce the dynamic programming which is based on discrete system. When the discrete system is constructed with discrete state, transfer between states, and the event to induct transfer, the discrete system can describe the system operation as dynamic situation or symbolically at the logical point of view. We will introduce technologies which are related with controllable of Controlled Markov Chain as shown example of simple game. The dynamic programming will be able to apply to optimal control part which has adaptable performance in the discrete system.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1219-1230
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• 2003

We analyze an integrated voice/data CDMA system, where the whole channels are divided into voice prioritized channels and voice non-prioritized channels. For real-time voice service, a preemptivc priority is granted in the voice prioritized channels. And, for delay-tolerant data service, the employment of buffer is considered. On the other hand, the transmission permission probability in best-effort packet-data service is controlled by estimating the residual capacity available for users. We build a 2-dimensional markov chain about prioritized-voice and stream-data services and accomplish numerical analysis in combination with packet-data traffic based on residual capacity equation.

• Journal of the Korean Statistical Society
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• v.35 no.3
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• pp.281-303
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• 2006

In an earlier work, we investigated the problem of using linear programming to bound performance measures in a discrete time Jackson network. There it was assumed that the system evolution is controlled by the early arrival scheme. This assumption implies that the system can't be modelled by a Markov chain. This problem was resolved and performance bounds were calculated. In the present work, we use a modification of the early arrival scheme (without corrupting it) in order to make the system evolves as a Markov chain. This modification enables us to obtain explicit expressions for certain moments that could not be calculated explicitly in the pure early arrival scheme setting. Moreover, this feature implies a reduction in the linear program size as well as the computation time. In addition, we obtained tighter bounds than those appeared before due to the new setting.

• Bulletin of the Korean Mathematical Society
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• v.43 no.3
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• pp.657-669
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• 2006

In this paper, we analyze a leaky bucket (LB) scheme with queue length dependent arrival rates. In other words, if the queue length exceeds an appropriate threshold value on buffer, the arrivals need to be controlled. In ATM networks, if the congestion occurs, the input traffics must be controlled (reduced) for congestion resolution. By the bursty and correlated properties of traffics, the arrivals are assumed to follow a Markov-modulated Poisson process (MMPP). We derive the loss probability and the waiting time distribution for arbitrary cell. The analysis is done by using the embedded Markov chain and supplementary variable method. We also present some numerical examples to show the effects of our proposed LB scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1333-1343
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• 2016

Spectrum sharing in centrally controlled cognitive radio (CR) networks has been widely studied, however, research on channel access for distributively controlled individual cognitive users has not been fully characterized. This paper conducts an analysis of random channel access of cognitive users controlled in a distributed manner in a CR network. Based on the proposed estimation method, each cognitive user can estimate the current channel condition by using its own Markov-chain model and can compute its own blocking probability, collision probability, and forced termination probability. Using the proposed scheme, CR with distributed control (CR-DC), CR devices can make self-controlled decisions based on the status estimations to adaptively control its system parameters to communicate better.

• Speech Sciences
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• v.11 no.4
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• pp.53-66
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• 2004

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech. we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results [6] with slightly increased complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3446-3464
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• 2017

Backoff mechanism serves as one of the key technologies in the MAC-layer of wireless mobile networks. The traditional Binary Exponential Backoff (BEB) mechanism in IEEE 802.11 Distributed Coordination Function (DCF) and other existing backoff mechanisms poses several performance issues. For instance, the Contention Window (CW) oscillations occur frequently; a low delay QoS guarantee cannot be provided for real-time transmission, and services with different priorities are not differentiated. For these problems, we present a novel Multi-Priority service differentiated and Adaptive Backoff (MPAB) algorithm over IEEE 802.11 DCF for wireless mobile networks in this paper. In this algorithm, the backoff stage is chosen adaptively according to the channel status and traffic priority, and the forwarding and receding transition probability between the adjacent backoff stages for different priority traffic can be controlled and adjusted for demands at any time. We further employ the 2-dimensional Markov chain model to analyze the algorithm, and derive the analytical expressions of the saturation throughput and average medium access delay. Both the accuracy of the expressions and the algorithm performance are verified through simulations. The results show that the performance of the MPAB algorithm can offer a higher throughput and lower delay than the BEB algorithm.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.540-550
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• 2016

Since energy efficiency has become a significant concern for network infrastructure, next-generation network devices are expected to have embedded advanced power management capabilities. However, how to effectively exploit the green capabilities is still a big challenge, especially given the high heterogeneity of devices and their internal architectures. In this paper, we introduce a hierarchical power management architecture (HPMA) which represents physical components whose power can be monitored and controlled at various levels of a device as entities. We use energy aware state (EAS) as the power management setting mode of each device entity. The power policy controller is capable of getting information on how many EASes of the entity are manageable inside a device, and setting a certain EAS configuration for the entity. We propose the optimal local control policy which aims to minimize the router power consumption while meeting the performance constraints. A first-order Markov chain is used to model the statistical features of the network traffic load. The dynamic EAS configuration problem is formulated as a Markov decision process and solved using a dynamic programming algorithm. In addition, we demonstrate a reference implementation of the HPMA and EAS concept in a NetFPGA frequency scaled router which has the ability of toggling among five operating frequency options and/or turning off unused Ethernet ports.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1855-1869
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• 1993

For the traditional ALOHA system without capture, the Markov chain obtained using the number of backlogged users at each slot if shown to be non-ergodic. So the infinite population ALOHA with fixed retransmission probabilities is unstable for any choice of the arrival rates and retransmission probabilities. The capture ALOHA system of also shown to be unstable for any arrival rate unless it has perfect. In this paper, we study a stabilization policy for capture ALOHA system that controls the retransmission probabilities and prove the stability of its multidimensional Markovian model by empolying a continuous Lyapunov function, and thus identify the stability region. We also study a delay performance through computer simulation th show the stability for any input rate below the maximum achievable channel throughput.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.895-900
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• 2010

As the diverse telecommunication services have been developed, network designers need to prevent congestion which may be caused by properties of timecorrelation and burstiness, and unpredictable statistical fluctuation of traffic streams. This paper considers the leaky bucket scheme with combined control of arrival and token rates, in which the arrival rate and the token generation interval are controlled according to the queue length. By using the embedded Markov chain and the supplementary variable methods, we obtain the queue length distribution as well as the loss probability and the mean waiting time.