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Pilot power optimization and coverage control in WCDMA mobile networks

Author

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  • Siomina, Iana
  • Värbrand, Peter
  • Yuan, Di

Abstract

In mobile networks using wideband code division multiple access (WCDMA), common pilot channel (CPICH) signals are used by mobile terminals for channel quality estimation, cell selection, and handover. The strength of the CPICH signal determines the coverage area of the cell, impacts the network capacity, and thereby the quality of service, and is therefore a crucial parameter in network planning and optimization. Pilot power is the most important parameter that allows us to control the strength of the CPICH signal. The more power is spent for pilot signals, the better coverage is obtained. On the other hand, a higher value of the pilot power level in a cell means higher pilot pollution in the network and less power available to serve user traffic in the cell. In this paper, we consider the problem of minimizing the total amount of pilot power subject to a coverage constraint. Our modeling and solution approaches are based on mathematical programming techniques. We present a basic model for pilot power optimization subject to a full coverage constraint as well as its extended version which allows us to study various coverage levels and to consider user traffic distribution over the network. We also propose an efficient algorithm that gives near-optimal solutions to the problem within a reasonable amount of time. We report our numerical experiments for three WCDMA networks of various sizes based on realistic planning scenarios and examine the effect of different levels of the required coverage degree on the total amount of pilot power.

Suggested Citation

  • Siomina, Iana & Värbrand, Peter & Yuan, Di, 2007. "Pilot power optimization and coverage control in WCDMA mobile networks," Omega, Elsevier, vol. 35(6), pages 683-696, December.
  • Handle: RePEc:eee:jomega:v:35:y:2007:i:6:p:683-696
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    References listed on IDEAS

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    1. Vroblefski, Mark & Brown, Evelyn C., 2006. "A grouping genetic algorithm for registration area planning," Omega, Elsevier, vol. 34(3), pages 220-230, June.
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    Cited by:

    1. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    2. Samadi, Mohammadreza & Nikolaev, Alexander & Nagi, Rakesh, 2016. "A subjective evidence model for influence maximization in social networks," Omega, Elsevier, vol. 59(PB), pages 263-278.

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