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Application of Multistage Stochastic Programming in Strategic Telecommunication Network Planning
(2010)
Telecommunication is fundamental for the information society. In both, the
private and the professional sector, mobile communication is nowadays taken
for granted. Starting primarily as a service for speech communication, data
service and mobile Internet access are now driving the evolution of network
infrastructure. In the year 2009, 19 million users generated over 33
million GB of traffic using mobile data services. The 3rd generation
networks (3G or UMTS) in Germany comprises over 39,000 base stations with
some 120,000 cells. From 1998 to 2008, the four network operators in
Germany invested over 33 billion Euros in their infrastructure. A careful
allocation of the resources is thus crucial for the profitability for a
network operator: a network should be dimensioned to match customers
demand. As this demand evolves over time, the infrastructure has to evolve
accordingly. The demand evolution is hard to predict and thus constitutes a
strong source of uncertainty. Strategic network planning has to take this
uncertainty into account, and the planned network evolution should adapt to
changing market conditions. The application of superior planning methods
under the consideration of uncertainty can improve the profitability of the
network and creates a competitive advantage. Multistage stochastic
programming is a suitable framework to model strategic telecommunication
network planning.
We present mathematical models and effective optimization procedures for
strategic cellular network design. The demand evolution is modeled as a
continuous stochastic process which is approximated by a discrete scenario
tree. A tree-stage approach is used for the construction of non-uniform
scenario trees that serve as input of the stochastic program. The model is
calibrated by historical traffic observations. A realistic system model of
UMTS radio cells is used that determines coverage areas and cell capacities
and takes signal propagation and interferences into account. The network
design problem is formulated as a multistage stochastic mixed integer
linear program, which is solved using state-of-the-art commercial MIP
solvers. Problem specific presolving is proposed to reduce the problem
size. Computational results on realistic data is presented. Optimization
for the expected profit and the conditional value at risk are performed and
compared.