mta -gsm channel model for wireless communication
J.Lakshmi Narayanan,J.Arunachalam,S.Subashini
Published in International Journal of Advanced Research in Computer Networking,Wireless and Mobile Communications
ISSN: 2320-7248 Impact Factor:1.8 Volume:2 Issue:1 Year: 08 January,2014 Pages:30-34
Abstract
Network simulator is the technique that predicts the behaviour of network and this simulation is observed by tracing of network. These tracing also divided into two categories: Consistence and Non-consistence. Several algorithms had proposed already but almost of them have inconsistence level of traces. Consistence traces produce the more approximate result for prediction. Here we compare two effective algorithms that are GSM & MTA model based traces. We use GSM traces in GSM model and generate error containing subtraces by MTA algorithm. Then we applied these traces in burst length and in run test. On these observations we proof the consistency of MTA algorithm. Also we differentiate these terms in graphical form. Keywords: Graph, error statistics, Table Introduction For these comparison, MTA algorithm generate a statistical constant.[4] Apply these constant to identify error containing and errorless segments of transmission. By analyzing the length distributions graph of the error containing and errorless segments, we can effectively characterize the transitions between them. So that it can be applied to wireless traces which analyze different error Statistics over time. Then compare with artificial traces using the MTA and Markov GSM models. [7] In next section, we discuses background information about Discrete Time Markov Chains. Then describe MTA algorithm and proof MTA algorithm have more consistency them GSM traces. [6] Background In this section, we describe for Discrete Time Markov Chains (DTMC) and related properties. Discrete time Markov chains A Discrete Time Markov Chain (DTMC) [3] is a random process that takes values in a discrete places P. A DTMC is defined by its memory and its transition probabilities of Qsteps, where Q defines the memory. To calculate the memory of a DTMC we introduce the concept of situational entropy. The situational entropy is an indication of the randomness of the next element of a trace. We determine the amount of past history necessary by calculating the initial order entropy l range of l varies from 1 to U. We choose U to be 7 because maintaining history for 27 or 128 states produces a reasonable level of implementation and processing complexity (more states lead to calculated .
Kewords
finding errorless bursts of length equal to or greater than the change-ofstate constant S
Reference
[1] GSM Technical Specification 04.2, GSM Radio Protocol For telematic services, Version 6 . [2] GSM Technical Specification 04.2, GSM Radio Protocol For telematic services, Version 6.3. [3] M. Yajnik and D. Towsley, Packet loss correlation in the multicast environment: Experimental measurements chain models, S COMPSCI Technical report 95-105 (1993). [4] M. Mouly and M.. Pautet, The GSM System for Mobile networks (Cell and Sys, jermany, 1992). [5] ETSI GSM Technical Specification 03.2, Digital cellular mesh system (Phase 3+); [6] B. Mandelbrot, S-s error group in communication systems and the concept of situational consistency, IEEE Transactions on Communication Technology COM-13 (1965). [7] E GSM Technical Specification 4.8, Digital cellular communications system (GSM RLP) Channel coding, Version (may 2000)
