A Novel Way to Automatically Plan Cellular Networks Supported by Linear Programming and Cloud Computing

With the increasing number of mobile subscribers worldwide, there is a need for fast and reliable algorithms for planning/optimization of mobile networks, especially because, in order to maintain a network’s quality of service, an operator might need to deploy more equipment. This paper presents a quick and reliable way to automatically plan a set of frequencies in a cellular network, using both cloud technologies and linear programming. We evaluate our pattern in a realistic scenario of a Global System for Mobile communications protocol (GSM) network and compare the results to another already implemented commercial tool. Results show that even though network quality was similar, our algorithm was twelve times faster and used four times less memory. It was also able to frequency plan seventy cells simultaneously in less than three minutes. This mechanism was successfully integrated in the professional tool Metric, and is currently being used for cellular planning. Its extension for application to 3/4/5G networks is under study.

[1]  R. Mathar,et al.  Frequency allocation and linear programming , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[2]  Rui Dinis,et al.  Combining Drive Tests and Automatically Tuned Propagation Models in the Construction of Path Loss Grids , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[3]  John R. Gilbert,et al.  Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks , 2009, SPAA '09.

[4]  M. Avram,et al.  Advantages and Challenges of Adopting Cloud Computing from an Enterprise Perspective , 2014 .

[5]  Rahul Jain,et al.  A Parallel Approximation Algorithm for Positive Semidefinite Programming , 2011, 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science.

[6]  Anton A. Huurdeman The worldwide history of telecommunications , 2003 .

[7]  Matthias Templ,et al.  Analysis of commercial and free and open source solvers for linear optimization problems 1 , 2012 .

[8]  Rui Dinis,et al.  Combining Measurements and Propagation Models for Estimation of Coverage in Wireless Networks , 2019, 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall).

[9]  Diogo Clemente,et al.  Cloud-based Cellular Network Planning System: Proof-of-Concept Implementation for GSM in AWS , 2019, 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC).

[10]  David Eppstein,et al.  Parallel Construction of Quadtrees and Quality Triangulations , 1993, WADS.

[11]  Giuseppe Aceto,et al.  Performance-based service-level agreement in cloud computing to optimise penalties and revenue , 2020, IET Commun..

[12]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[13]  Anthony Skjellum,et al.  A framework for high‐performance matrix multiplication based on hierarchical abstractions, algorithms and optimized low‐level kernels , 2002, Concurr. Comput. Pract. Exp..

[14]  Su Chao,et al.  A new method of frequency planning for new cells in GSM , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[15]  Ajay R. Mishra,et al.  Advanced Cellular Network Planning and Optimisation: 2G/2.5G/3G...Evolution to 4G , 2006 .