Major Trends in Cellular Networks and Corresponding Optimization Issues

  • Khiem Le
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 158)


Cellular networks are entering a new phase, driven by some major evolutionary trends. With the boom in mobile data access, driven by attractive mobile Internet applications and more capable mobile devices, global mobile data traffic is expected to grow by orders of magnitude in the next five years (projections range from a ten-fold increase to a hundreds-fold increase). The Mobile Network Operators (MNOs) will incur costs to upgrade their networks to cope with that growth, but the revenues will not scale in the same proportion. To remain profitable, the MNOs have to reduce their cost per byte while increasing their revenues. There are many possible tradeoff points, depending on such considerations as the MNO’s business strategy, and operating environment. Even for a given tradeoff point, the problem does not lend itself to a tractable formulation, due to the sheer number of contributing factors, the complexity of their interactions, and unavailability of complete data. However, it is possible to identify meaningful sub-problems that are easier to solve. This chapter focuses on some of the approaches and techniques that could be part of a toolbox to tackle these sub-problems. Another major trend is the expansion of mobile access (to voice and data, not necessarily broadband) to low income population segments. While this trend significantly overlaps with the one described above, the emphasis here is to provide affordable service rather than providing mobile broadband data. Optimization in the low income space is also intractable in general, and this chapter describes approaches for some particular cases.


Medium Access Control Cellular Network Short Message Service Mobile Cloud Wireless Access Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer New York 2011

Authors and Affiliations

  1. 1.Research In MotionIrvingUSA

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