W-CDMA network design

Authors:
Qibin Cai;Joakim Kalvenes;Jeffery Kennington;Eli V. Olinick
Affiliations:
Verizon Business, Network Engineering Systems, Richardson, TX, USA.;School of Management, University of Texas at Dallas, Richardson, TX, USA.;School of Engineering, Southern Methodist University, Dallas, TX, USA.;School of Engineering, Southern Methodist University, Dallas, TX, USA
Venue:
International Journal of Mobile Network Design and Innovation
Year:
2007

Citing 7
Cited 1

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Knapsack problems: algorithms and computer implementations
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A polynomial-time approximation scheme for base station positioning in UMTS networks

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Core Problems in Knapsack Algorithms

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Base Station Location and Service Assignments in W--CDMA Networks

INFORMS Journal on Computing

Comparison of different meta-heuristics to solve the global planning problem of UMTS networks

Computer Networks: The International Journal of Computer and Telecommunications Networking

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Abstract

In this investigation, the W-CDMA network design problem ismodelled as a discrete optimisation problem that maximises revenuenet the cost of constructing base stations, Mobile TelephoneSwitching Offices (MTSOs) and the backbone network to connect basestations through MTSOs to the Public Switched Telephone Network(PSTN). The formulation results in a very large scale integerprogramming problem with up to 18,000 integer variables and 20,000constraints. To solve this large scale integer programming problem,we develop a pair of models, one for the upper bound and one forthe lower bound. The upper bound model relaxes integrality on someof the variables while the lower bound model uses a 5% optimalitygap to achieve early termination. Additionally, we develop aheuristic procedure that can solve the largest problem instancesvery quickly with a small optimality gap. To demonstrate theefficiency of the proposed solution methods, problem instances weresolved with five candidate MTSOs servicing some 11,000 simultaneouscellular phone sessions on a network with up to 160 base stations.In all instances, solutions guaranteed to be within 5% ofoptimality were obtained in less than an hour of CPU time.