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

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

Adaptive Radio Resource Management for GSM

Large Scale WLAN Design and Optimisation

Adaptive Radio Resource Management for GSM Objective 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. GSM networks employ fixed channel allocation model ( FCA) to assign frequencies to base stations Increase network capacity in GSM using an adaptive radio resource management system.

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

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.

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

Large Scale WLAN Design and Optimisation Motivation 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, …).

Outline Site Description Signal Coverage and Channel Throughput Prediction AP Placement Pre-processing & Optimisation Current Implementation Result & Scalability Future Research 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

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

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

+ Very Fast

Less Accurate

+ Accurate

Computation Demanding

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

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

Fitness Function Large Scale WLAN Design and Optimisation D … User Demand Satisfaction A … Number of Access Points R … Restricted Area B … Solution Balance w i … Waiting Factors Elements of the Fitness Function: 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.

Optimisation Technique Survival of the fittest Self-adaptation Objective Function Evolutionary Operators Site-Specific Knowledge FF Population Offspring (λ) Parents(µ) Terminate Mutation(s) Selection Initialise Large Scale WLAN Design and Optimisation Evolution Strategies Evolution Strategy Modes Children only considered in Selection ( µ , λ ) Selection includes Parents ( µ + λ ) Two – Membered Strategy (1+1)

Implementation Large Scale WLAN Design and Optimisation Evaluation Test-bed and Optimisation Kernel were implemented using Borland C++ providing both speed and stability during optimisation Difference Measurement 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.

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

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

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

Segmentation

Voronoi Graph

Crystals of Variable Size

Backtracking Algorithm

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

Overcome the problem of scalability using Segmentation & Backtracking Algorithm

3D Implementation

Large scale measurement and analysis of a deployed solution

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. Large Scale WLAN Design and Optimisation

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.

Thank you for your attention!

Thank you for your attention!