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Optimising Cellular Wireless Networks using Evolutionary Computing

Optimising Cellular Wireless Networks using Evolutionary Computing

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Optimising Cellular Wireless Networks using Evolutionary Computing

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  1. Optimising Cellular Wireless Networks using Evolutionary Computing Martin Klepal Centre for Adaptive Wireless Systems 22nd June 2005

  2. Adaptive Radio Resource Management for GSM • Large Scale WLAN Design and Optimisation (Ken Murray, Dirk Pesch) (Martin Klepal, Alan Mc Gibney)

  3. Adaptive Radio Resource Management for GSM Objective Increase network capacity in GSM using an adaptive radio resource management system. GSM networks employ fixed channel allocation model (FCA) to assign frequencies to base stations The traffic evolution between cells is different Busy periods occur at different times Resources at quiet cells are wasted With the introduction of 2.5G services such as GPRS and EDGE, a more flexible method of resource management is required to maximize system resources.

  4. Proposed Solution Input Layer NeuralNetwork Prediction of future resource requirements for new and handover calls at each cell using Neural Networks based on historical data. Hidden Layer Prediction of new calls Historical Data Output Layer j Frequency Assignment in 20 cells Wji Current Hour Next Hour Frequencies -> Frequencies -> i Cells -> ---1------1--------- ------1------1------ ----1------1-----1-- -1------1----------- 1------1------------ -----1------1------- --1------1---------- -1------1------1---- 1------1----------1- -----1------1------- --1------1------1--- ---1------1---1----- ------1------1------ ----1------1-------- --1------1---------- ---1------1---1----1 ------1------1------ ----1------1---1---- -1------1----------- 1------1----------1- Cells -> ---1------1--------- ------1------1------ ----1------1-----1-- -1------1-----1----- 1------1------------ -----1------1------- --1------1---------- -1------1-------1--- 1------1-------1---- -----1------1------- --1------1----1----- ---1------1----1---- ------1------1------ ----1------1-------- --1------1----1----- ---1------1--------1 ------1------1------ ----1------1----1--- -1------1----------- 1------1----------1- Wik Update the frequency assignment plan based on resource requirement predictions using a Genetic Algorithm k Adaptive Radio Resource Management for GSM Using Evolutionary computing techniques, we propose an Adaptive Radio Resource Management System

  5. Results and Conclusion Adaptive Radio Resource Management for GSM Simulation has shown resource gains of up to 21% when compared with current FCA frequency assignment schemes The proposed approach has a non-invasive implementation within Operation Maintenance Centers of existing GSM network.

  6. Large Scale WLAN Design and Optimisation Martin Klepal, Alan Mc Gibney

  7. The design of wireless local area networks is currently still carried out in an ad-hoc fashion, with access point installation based on “rules of thumb” which leads to reduced performance from the deployed network. The objective of this project is to address the issues related to WLAN design, the use of Evolution Strategies for optimisation of Access Point placement to overcome the ad-hoc nature of WLAN design (WiFi, WiMax, …). Large Scale WLAN Design and Optimisation Motivation

  8. Outline Large Scale WLAN Design and Optimisation • Site Description • Signal Coverage and Channel Throughput Prediction • AP Placement Pre-processing & Optimisation • Current Implementation • Result & Scalability • Future Research

  9. Large Scale WLAN Design and Optimisation Site Description Part of CIT Campus Multi-Storey Building

  10. The Multi-Wall Model + Very Fast • Less Accurate Ray-Tracing Model + Accurate • Computation Demanding Large Scale WLAN Design and Optimisation Signal Coverage Prediction

  11. Throughput Prediction Signal level + Site-specific Information BER Predictionfor CCK 11 Throughput Prediction Large Scale WLAN Design and Optimisation

  12. Large Scale WLAN Design and Optimisation Selection of Candidate AP Candidate Access Point positions forming an undirected graph that can be traversed during the optimisation

  13. Large Scale WLAN Design and Optimisation The objective of the optimisation is to minimise the Fitness Function that evaluates if the suggested design of the network satisfies user demands by maximizing throughput with a minimum number of APs and other constraints. Fitness Function Elements of the Fitness Function: D … User Demand Satisfaction A…Number of Access Points R…Restricted Area B…Solution Balance wi…Waiting Factors

  14. Large Scale WLAN Design and Optimisation Evolution Strategies Optimisation Technique Initialise Objective Function Evolutionary Operators Site-Specific Knowledge Selection Parents(µ) Survival of the fittest Population Self-adaptation Mutation(s) FF Offspring (λ) Terminate

  15. Implementation Evaluation Test-bed and Optimisation Kernel were implemented using Borland C++ providing both speed and stability during optimisation • Features: • Drawing Tools for Environment Specification • Load/Save SVG Format • Signal Coverage Throughput Prediction • Wireless Technology Specification • Demands Specification • Environment Preprocessing Tools • Optimization Tools • Measurement Tools Evaluation Test-bed The optimisation is controlled through a GUI that allows the user to modify parameters and visualise the optimisation progress. Difference Measurement Large Scale WLAN Design and Optimisation

  16. Results Large Scale WLAN Design and Optimisation 100% Coverage with a minimum number of AP Initial results of the optimisation technique implemented are stable because the same solution is suggested after each run on the same environment.

  17. Scalability Large Scale WLAN Design and Optimisation Segmentation Voronoi Graph Crystals of Variable Size Backtracking Algorithm

  18. Overcome the problem of scalability using Segmentation & Backtracking Algorithm 3D Implementation Large scale measurement and analysis of a deployed solution Ongoing & Future Research Large Scale WLAN Design and Optimisation

  19. Large Scale WLAN Design and Optimisation Conclusion • Adaptive Radio Resource Management System for GSM • shows resource gains of up to 21% when compared with current FCA frequency assignment schemes • Large Scale WLAN Design and Optimisation • aims to developed a computer aided automatic design tool that will provide an optimum WLAN design with minimum number of APs providing required signal coverage and network capacity.

  20. Thank you for your attention!