Optimization Methods for Feasible Deployment Planning of Future Mobile Networks

The exponential growth of mobile traffic requires mobile operators to update their network infrastructure to provide greater capacity and better connections for end-users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macrocells and small cells; however, this alt...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-09, Vol.2022, p.1-12
Main Authors: Araújo, Welton Vasconcelos, Vieira, Rafael Fogarolli, Silva da Silva, Marcelino, Cardoso, Diego Lisboa
Format: Article
Language:English
Subjects:
Bandwidths
Cellular communication
Cost control
Data transmission
Heuristic
Infrastructure
Linear programming
Optimization
Placement
Quality of service
Spectrum allocation
Traffic planning
Transportation networks
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Summary:The exponential growth of mobile traffic requires mobile operators to update their network infrastructure to provide greater capacity and better connections for end-users. A promising alternative is to deploy heterogeneous networks (HetNets) that combine macrocells and small cells; however, this alternative increases the complexity and cost of transport (connections between the small cells and the operator’s control center). Most planning strategies outlined in the literature are aimed at reducing the number of small cells without considering important aspects involving transport (access backbone). With the advent of centralized architectures, this point becomes essential, since it is necessary to consider the potential impact of the transport segment on the deployment cost of the network (with the advent of the fronthaul). In this sense, this work proposes an optimal multiobjective model of radio and transport allocation based on linear programming to minimize the total cost of the network and two efficient heuristics to obtain a near-optimal solution. Considering a real case study of the literature, we show the cost (financial and computational) of the optimal placement of radio and transport infrastructure and the limitations of the solution. We also compare the proposed function placement heuristic with the optimal solution in terms of cost efficiency and execution time and demonstrate that it can provide a good estimation of the deployment cost in a much shorter time, with an approximation of up to 10% in relation to the optimal model.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/8837970