In the GSM environment, mobility prediction is concerned with envisaging of the mobile station’s next movement. By accurately employing the predicted movement, the GSM network is capable of attaining enhanced resource allocation and reservations, better assignment of cells to location areas, more efficient paging, and call admission control. Numerous studies have been carried out in mobile station prediction and as such, many mobility predictions have been developed. The goal of this paper was to statistically analyze these mobility prediction models in order to understand their strengths and weaknesses. The results of this analysis indicated that the current mobility models base their predictions on movement patterns of users throughout physical space. However, they do not directly relate to movement throughout the network based on the cell presently hosting a mobile station. In addition, several base transceiver stations may overlap over a single physical location, a complication not captured by the current mobility prediction models. Therefore, there is need for a generic mobility prediction model that can depict movement of mobile users more realistically within the GSM network coverage areas. As such, this paper proposes the development of a mobility prediction model that is capable of accurately reflecting mobile station movement in real-world cellular networks, taking into consideration the actual scenarios such as base transceiver stations overlapping. The requirements for this novel mobility prediction model are also provided, and were based on the shortcomings noted in the existing mobility prediction models. The significance of the proposed model lies on the fact that in order to pre-allocate resources for seamless connectivity during handovers, the mobility prediction model should anticipate the actual cell that the mobile station will next connect to, rather than the physical location they will move to.
Table of Contents
PART I
GSM CELLULAR COMMUNICATIONS
1.1 Preamble
1.2 Significance of Mobility Prediction
PART II
GSM MOBILITY PREDICTION MODELS
2.1 Introduction
2.2 Obstacle Mobility Model
2.3 Street Unit Model
2.4 City Area Mobility Model
2.5 Random Walk Model
2.6 Random Waypoint Model
2.7 Markovian Random Walk
2.8 Random Direction Model
2.9 Shortest Path Model
2.10 Normal Walk Models
2.11 Smooth Random Mobility Model
2.12 Microscopic Models
2.13 Mesoscopic Models
2.14 Macroscopic Models
2.15 Reference Point Group Mobility
2.16 Column Mobility Model
2.17 Pursue Mobility Model
2.18 Nomadic Community Mobility Model
2.19 Activity-Based Model
2.20 Cell-Residence-Time-Based Model
2.21 Pathway Mobility
PART III
MOBILITY TAXONOMY ARCHITECTURE
3.1 Introduction
3.2 Taxonomy Development
3.3 Taxonomy Parameters
PART IV
GSM MOBILITY MODELS PARAMETRIC ANALYSIS
4.1 Introduction
4.2 Models Based On Location and Fixed Velocity
4.3 High Probability Prediction Models
4.4 Cell to Cell Mobility Models
4.5 Models Based On Feasible Future Sequence of Cells
4.6 Models Based On Degree Of Randomness
4.6.1 Trace-Based Models
4.6.2 Constrained Topology Models
4.6.3 Statistical Models
4.7 Models Based On Level of Description
4.7.1 Fluid Flow Model
4.7.2 Gravity Mobility Models
4.8 Models Based On Individual User Behaviors
4.9 Models Based On Nodes Movement Dependency
4.9.1 Temporal Dependency of Velocity
4.9.2 Spatial Dependency of Velocity
4.10 Models Based On Real Network Movement Depiction
PART V
CRITIC OF GSM MOBILITY PREDICTION MODELS
5.1 Introduction
5.2 Models Based On Location and Fixed Velocity
5.3 Models Based On High Probability Prediction
5.4 Cell to Cell Mobility Models
5.5 Models Based On Feasible Future Sequence of Cells
5.6 Models Based On Degree of Randomness
5.7 Models Based On Level of Description
5.8 Models Based On Individual User Behaviors
5.9 Models Based On Nodes Movement Dependency
5.10 Models Based On Real Network Movement Depiction
PART VI
PROPOSED MOBILITY PREDICTION MODEL
6.1 Introduction
6.2 Requirements for the Proposed Model
Research Objectives and Themes
This paper aims to statistically analyze existing mobility prediction models in GSM networks to identify their inherent strengths, weaknesses, and limitations, ultimately proposing a more realistic mobility prediction model to enhance resource allocation and connectivity.
- Statistical assessment of current mobility prediction models.
- Development of a comprehensive taxonomy for classifying mobility models.
- Parametric analysis of model constructs and required computations.
- Identification of operational flaws in existing predictive approaches.
- Formulation of requirements for an inclusive, realistic mobility prediction model.
Excerpts from the Book
4.2 Models Based On Location and Fixed Velocity
The mobility prediction models that concentrate on the position and a fixed velocity of the mobile station are generally deterministic in nature. In these modes, the future values of the mobile station movement can be predicted from the past values. Examples of these models are Freeway, Manhattan, Feynman-Verlet and first order kinetic model. In these models, the new position Q (Xi+1, Yi+1) of mobile stations that were initially at point P(X, Y) is computed using (1):
In this relation, X and Y are the X-coordinates and Y-coordinates respectively while Vx and Vy are the velocities along the X and Y coordinates respectively. The kinetic distance between these two points P and Q is calculated as illustrated in (2):
Summary of Chapters
PART I: GSM CELLULAR COMMUNICATIONS: This section provides an overview of the role of mobility prediction in modern GSM networks and addresses security and traffic challenges arising from the rapid expansion of mobile services.
PART II: GSM MOBILITY PREDICTION MODELS: A comprehensive review of various established mobility models, ranging from random walk and waypoint models to obstacle-based and cell-residence-time models.
PART III: MOBILITY TAXONOMY ARCHITECTURE: This chapter introduces a developed taxonomy for categorizing mobile station mobility prediction models based on their constructs and computational similarities.
PART IV: GSM MOBILITY MODELS PARAMETRIC ANALYSIS: Provides a detailed parametric analysis of the mobility models, covering the mathematical computations and input parameters necessary for movement prediction.
PART V: CRITIC OF GSM MOBILITY PREDICTION MODELS: Evaluates the identified models to highlight their operational flaws and challenges when implemented in real-world GSM mobile environments.
PART VI: PROPOSED MOBILITY PREDICTION MODEL: Outlines the requirements for a novel, inclusive mobility prediction model that addresses the shortcomings observed in current approaches.
Keywords
GSM, Mobile station, Mobility prediction, Mobility models, Taxonomy, Deterministic models, Stochastic processes, Resource allocation, Handover, Network performance, Cell-residence time, Parametric analysis, Network traffic, Connectivity, Signal propagation.
Frequently Asked Questions
What is the primary focus of this paper?
The paper focuses on the statistical analysis of current GSM mobility prediction models to understand their strengths and weaknesses in predicting mobile station movement.
What are the central themes discussed?
The central themes include mobile station mobility management, the mathematical foundations of various prediction models, and the classification of these models through a new taxonomy.
What is the main research goal?
The goal is to determine why current mobility models fail to provide accurate movement depictions and to define the requirements for an improved, realistic mobility prediction model.
What scientific methodology is utilized?
The research employs a mathematical and analytical approach, performing a parametric analysis of model constructs and evaluating them against real-world GSM network scenarios.
What does the main part of the paper cover?
The main part covers the review of existing models, the development of a taxonomy for these models, and a critical analysis of their operational performance in the GSM environment.
Which keywords characterize this work?
Key terms include GSM, Mobile Station, Mobility Prediction, Taxonomy, Parametric Analysis, and Network Performance.
What specific problem do these models address regarding signal blocking?
The paper highlights that current obstacle mobility models wrongly assume total signal absorption by obstacles, whereas the proposed model advocates for the assumption that only physical movement is blocked.
How does the proposed model handle overlapping base transceiver stations?
The paper notes that existing models fail to capture overlapping regions, and proposes that a new model must be capable of accurately depicting movement patterns specifically within these overlapping areas.
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- Vincent Nyangaresi (Autor:in), Silvance Abeka (Autor:in), Solomon Ogara (Autor:in), 2017, Statistical analysis of the GSM (Global System for Mobile Communications) mobility prediction models, München, GRIN Verlag, https://www.grin.com/document/371131
