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Pricing Based Distributed Traffic Allocation for 5G Heterogeneous Networks

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Συγγραφέας
Passas V., Miliotis V., Makris N., Korakis T.
Ημερομηνία
2020
Γλώσσα
en
DOI
10.1109/TVT.2020.3012189
Λέξη-κλειδί
Benchmarking
Costs
Economic and social effects
Heterogeneous networks
Mobile telecommunication systems
Quality of service
Radio access networks
Rats
Cellular infrastructure
Congestion conditions
Distributed solutions
Extensive simulations
Heterogeneous Network (HetNet)
Multiple traffic class
Proportional fairness
Testbed experimentations
5G mobile communication systems
Institute of Electrical and Electronics Engineers Inc.
Εμφάνιση Μεταδεδομένων
Επιτομή
Next generation (5G and beyond) mobile networks will highly rely on distributed heterogeneous infrastructure, which is foreseen to create a versatile resource ecosystem. In such network deployments, efficient traffic allocation and management is of prominent priority. The increasing congestion conditions of cellular infrastructures have steered extensive research focus towards Heterogeneous Network (HetNet) access solutions. The deployment of dense multiple radio access technology (RAT) networks combined with the enhanced capabilities of mobile devices to maintain concurrent connections through different RATs, add up to the complexity of the traffic allocation problem. Traffic allocation becomes more challenging when using diverse technologies concurrently to serve multiple traffic classes of different Quality of Service (QoS) demands. Towards a viable solution, we have proposed a pricing-based network selection process for heterogeneous networks, running in a distributed manner, which has proved to lead to equilibrium under the developed policies. In this paper, we extend our pricing algorithm by providing a new approach that introduces personalized pricing based on the load pressure that an allocation decision will impose to a RAT. The central system decisions are confined to dynamically select the UE polling rank policy that is presenting the closest potential performance to that of the optimal centralized solution, used as the benchmark ranking. The optimal policy is based on proportional fairness and is used as a reference centralized policy to drive our distributed solutions. We assess the enhanced pricing scheme proposed in this work, and we employ extensive simulations and testbed experimentation to evaluate its performance in terms of efficient bandwidth allocation, price variation, fairness and induced cost to clients served by the HetNet. © 1967-2012 IEEE.
URI
http://hdl.handle.net/11615/77965
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