OPNET Simulation of Signaling System No. 7 (SS7) Network Interfaces

Abstract

This thesis presents an OPNET model and simulation of the Signaling System No.7 (SS7) network, which is dubbed the world's largest data communications network. The main focus of the study is to model one of its levels, the Message Transfer Part Level 3, in accordance with the ITU.T Recommendation Q.704. An overview of SS7 that includes the evolution and basics of SS7 architecture is provided to familarize the reader with the topic. This includes the protocol stack, signaling points, signaling links and a typical SS7 network structure. This is followed by a more detailed discussion about the functions of the various parts of the protocol, in particular, the functionality of the Message Transfer Parts. The OPNET modeling of the signaling message handling aspect of the Message Transfer Part level 3 is presented. The simulation model presented uses a hierarchical approach, with each level corresponding to the SS7 level it is modeling. Simulation results of different scenarios using varying parameters, such as packet transmission time, packet length, and load sharing, for a typical SS7 network are also presented.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2000
Accession Number
ADA378058

Entities

People

  • Kong C. Ow

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • C Programming Language
  • Cellular Networks
  • Communication Systems
  • Communications Protocols
  • Computer Networks
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Links
  • Department Of Defense
  • Digital Communications
  • Digital Information
  • Network Architecture
  • Network Protocols
  • Simulations
  • Telephone Systems
  • Voice Over Internet Protocol

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Neural Network Machine Learning.
  • Organizational Psychology.