Kyungsup Kim

A Mobility Model and Performance Analysis in Wireless Cellular Network with General Distribution and Multi-Cell Model

Multi-cell mobility model and performance analysis for wireless cellular networks are presented. The mobility model plays an important role in characterizing different mobility-related parameters such as handoff call arrival rate, blocking or dropping probability, and channel holding time. We present a novel tractable multi-cell mobility model for wireless cellular networks under the general assumptions that the cell dwell times induced by mobiles’ mobility and call holding times are modeled by using a general distribution instead of exponential distribution. We propose a novel generalized closed-form matrix formula to support the multi-cell mobility model and call holding time with general distributions. This allows us to develop a fixed point algorithm to compute loss probabilities, and handoff call arrival rate under the given assumptions. In order to reduce computational complexity of the fixed point algorithm, the channel holding time of each cell is down-modeled into an exponentially distributed one for purposes of simplification, since the service time is insensitive in computing loss probabilities of each cell due to Erlang insensitivity. The accuracy of the multi-cell analytic mobility model is supported by the comparison of the simulation results and the analytic ones.

A construction method for positive realizations with an order bound

Abstract This paper presents an efficient construction method to address the positive realization problem with an order bound for primitive transfer functions with multiple real or complex conjugate pairs of poles using polyhedral cone concepts. Through a down-sampling step, the non-primitive transfer function is decomposed into a sum of primitive transfer functions. A construction method for the positive realization of a primitive transfer function with multiple poles is proposed by using a regular polygon in the complex plane. A sufficient condition for the existence of the positive realization with an order bound is analyzed. Numerical examples are provided to demonstrate the efficiency of the proposed method for obtaining positive realizations.

A fitting method with generalized Erlang distributions

Abstract We present a fitting technique that fits trace data into a generalized Erlang distribution class using an EM method. A generalized Erlang (GEr) distribution can be made by convolution of the third order ME distributions similar to the formulation of an Erlang distribution with exponential distributions. We give a sufficient condition for the representation to make a probability density function and we implement a fitting algorithm into a GEr distribution set by solving a nonlinear optimization problem with the EM algorithm. The effectiveness of the proposed fitting algorithm is presented by applying fitting methods to sets of synthetic data and measurement data. We present comparative numerical simulation results of our approach and other methods.

An Adaptive Clustering Algorithm with Power Control in Wireless Sensor Networks

We propose an adaptive clustering algorithm with power control, ACA-PC, that chooses adaptive clusters and controls transmission power levels in order to minimize the total energy consumption of the sensor networks and maximize the amount of transmission data. Our core concepts are zone division and dynamic power control. A sensor network is divided into n zones based on geographical information. A large number of nodes bring about collision of data transmission. According to node density, a zone can be divided into two sub-zones or merged with other zone. The close neighbor nodes are grouped by a cluster in its zone. Therefore, we control the transmission power level of the common nodes in the same cluster to avoid collision with other clusters. Using network simulator (NS-2), we verify that ACA-PC can conserve the energy of sensors and prolong the lifetime of sensor networks better than other proposed algorithms.

A Constructive Positive Realization with Sparse Matrices for a Continuous-Time Positive Linear System

This paper discusses a computational method to construct positive realizations with sparse matrices for continuous-time positive linear systems with multiple complex poles. To construct a positive realization of a continuous-time system, we use a Markov sequence similar to the impulse response sequence that is used in the discrete-time case. The existence of the proposed positive realization can be analyzed with the concept of a polyhedral convex cone. We provide a constructive algorithm to compute positive realizations with sparse matrices of some positive systems under certain conditions. A sufficient condition for the existence of a positive realization, under which the proposed constructive algorithm works well, is analyzed.

Synthesis of Tunable Long-Period Fiber Gratings with Inverted Erbium-Doped Fiber Amplifier Spectrum Using Thermal Change Parameters

We propose a new method for synthesizing piecewise-uniform long-period fiber gratings (LPFGs) using thermal change parameters. Previously proposed methods that employ the optimization algorithm based on an extended fundamental matrix model are used to design the equalizer that compensates for the nonflat gain characteristics of erbium-doped fiber amplifiers (EDFAs). We have obtained the design parameters for the thermal changes of the piecewise-uniform LPFGs that fit the inverted gain spectrum of an EDFA over the entire 1524.1 nm–1559.6 nm wavelength range. We have observed that the spectrum obtained by the proposed synthesis method is close to the spectrum measured in the wavelength band of interest.

A Computation Modification for Multi-layered Neural Network Using Extended Kalman Filter

A lot of learning algorithms for deep layered network are sincerely suffered from complex computation and slow convergence because of a very large number of free parameters. We need to develop an efficient algorithm for deep neural network. The Kalman filter concept can be applied to parameter estimation of neural network to improve computation performance. The algorithms based extended Kalman filter has a serious drawback in its computational complexity. We discuss how a fast algorithm should be developed for reduction in computation time.

On the Relation between Phase-Type Distributions and Positive Systems

The relation between phase-type representation and positive system realization in both the discrete and continuous time is discussed. Using the Perron-Frobenius theorem of nonnegative matrix theory, a transformation from positive realization to phase-type realization is derived under the excitability condition. In order to explain the connection, some useful properties and characteristics such as irreducibility, excitability, transparency, and order reduction for positive realization and phase-type representation are discussed. In addition, the connection between the phase-type renewal process and the feedback positive system is discussed in the stabilization concept.

Optical Computing for Digital Signal Process in Incoherent Fiber System Using Positive Realization

We discuss an optical digital signal process in incoherent optical fiber system. Due to the strong dependence on environmental fluctuations, stable photonic filters are difficult to implement under coherent operation. We pay attention to a stable robust technique operating in incoherent optical domain. Most of coefficients of filters working under incoherent optical domain are positive. Finding a realization of an optimal digital filter under positive constraints is known as an open and difficult problem. We propose a novel constructive method to implement optical digital filter using the sparse positive realization with possible lower order in the incoherent optical domain.