1st International ICST Conference on Communications and Networking in China

Research Article

A Study on Programming Model and Heuristic Optimization Algorithms for WCDMA Radio Networks

  • @INPROCEEDINGS{10.1109/CHINACOM.2006.344922,
        author={Jie  Zhang and Jun Yang and  Mehmet E.  Aydin},
        title={A Study on Programming Model and Heuristic Optimization Algorithms for WCDMA Radio Networks},
        proceedings={1st International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2007},
        month={4},
        keywords={},
        doi={10.1109/CHINACOM.2006.344922}
    }
    
  • Jie Zhang
    Jun Yang
    Mehmet E. Aydin
    Year: 2007
    A Study on Programming Model and Heuristic Optimization Algorithms for WCDMA Radio Networks
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2006.344922
Jie Zhang1,*, Jun Yang1,*, Mehmet E. Aydin1,*
  • 1: Centre of Wireless Network Design (CWIND), Dept. of Computing and Information Systems, University of Bedfordshire, UK
*Contact email: Jie.zhang@beds.ac.uk, jun.yang@beds.ac.uk, mehmet.aydin @beds.ac.uk

Abstract

The 3rd generation (3G) cellular networks, such as WCDMA (wideband code-division multiple access) networks, require accurate network planning and optimization. However, the planning and optimization of WCDMA radio network, which is a highly dynamic and inter-dependent system, are often carried out based on the static snapshot simulation due to simplicity and time limitation. Therefore, importing the link-level performance with consideration to some essential factors, such as power control and soft handover, has been a trend. Due to the complexity and inter-dependency, these characteristics have not been considered together in the previous works. In this paper, we give a brief introduction to our programming models with consideration to these characteristics, and present optimisation strategies base on three major meta-heuristics, namely genetic algorithms (GA), simulated annealing (SA) and variable neighborhood search (VNS), are presented. Extensive experiment results are provided and the performances of different algorithms are compared.