Balancing downlink and uplink soft-handover areas in UMTS networks

In this paper a static network simulator is used to find downlink and uplink SHO areas. By introducing a penalty-based objective function and some hard constraints, we formally define the problem of balancing SHO areas in UMTS networks. The state-of-the-art mathematical model used and the penalty scores of the objective function are set according to the configuration and layout of a real mobile network, deployed in Slovenia by Telekom Slovenije, d.d.. The balancing problem is then tackled by three optimization algorithms, each of them belonging to a different category of metaheuristics. We report and analyze the optimization results, as well as the performance of each of the optimization algorithms used.

[1]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[2]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[3]  Kai Keng Ang,et al.  A synergy of econometrics and computational methods (GARCH-RNFS) for volatility forecasting , 2010, IEEE Congress on Evolutionary Computation.

[4]  Di Yuan,et al.  Pilot power management in WCDMA networks: coverage control with respect to traffic distribution , 2004, MSWiM '04.

[5]  Di Yuan,et al.  Minimum pilot power for service coverage in WCDMA networks , 2008, Wirel. Networks.

[6]  Antti Toskala,et al.  HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications , 2006 .

[7]  Ying Sun,et al.  CPICH power settings in irregular WCDMA macro cellular networks , 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

[8]  Stuart M. Allen,et al.  Optimising CDMA Cell Planning with Soft Handover , 2013, Wirel. Pers. Commun..

[9]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[10]  Lei Chen,et al.  CPICH power planning for optimizing HSDPA and R99 SHO performance: Mathematical modelling and solution approach , 2008, 2008 1st IFIP Wireless Days.

[11]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[12]  Mario Garcia-Lozano,et al.  CPICH power optimisation by means of simulated annealing in an UTRA-FDD environment , 2003 .

[13]  Lei Chen,et al.  Coverage planning for optimizing HSDPA performance and controlling R99 soft handover , 2012, Telecommun. Syst..

[14]  B. Suman,et al.  A survey of simulated annealing as a tool for single and multiobjective optimization , 2006, J. Oper. Res. Soc..

[15]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[16]  Mauro Birattari,et al.  Dm63 Heuristics for Combinatorial Optimization Ant Colony Optimization Exercises Outline Ant Colony Optimization: the Metaheuristic Application Examples Generalized Assignment Problem (gap) Connection between Aco and Other Metaheuristics Encodings Capacited Vehicle Routing Linear Ordering Ant Colony , 2022 .

[17]  Kumbesan Sandrasegaran,et al.  Impact of soft handover and pilot pollution on video telephony in a commercial network , 2010, 2010 16th Asia-Pacific Conference on Communications (APCC).

[18]  Tomás̆ Novosad,et al.  Radio Network Planning and Optimisation for Umts , 2006 .

[19]  Hamid Aghvami,et al.  Understanding UMTS Radio Network Modelling, Planning and Automated Optimisation: Theory and Practice , 2006 .

[20]  Bogdan Filipic,et al.  The differential ant-stigmergy algorithm , 2012, Inf. Sci..

[21]  Armin Fügenschuh,et al.  Optimisation Methods for UMTS Radio Network Planning , 2004 .

[22]  Matti Manninen,et al.  UMTS radio network planning, optimization and QOS management for practical engineering tasks , 2006, IEEE Communications Magazine.

[23]  Antti Toskala,et al.  WCDMA for UMTS: Radio Access for Third Generation Mobile Communications , 2000 .