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Towards the Optimal Orchestration of Steerable mmWave Backhaul Reconfiguration
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-4961-5087
University Center of Defense, San Javier Air Force Base, MDE-UPCT, Murcia, Spain.
Stevens Institute of Technology, Hoboken, NJ, United States.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
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2022 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 205, article id 108750Article in journal (Refereed) Published
Abstract [en]

Future generations of mobile networks will require increased backhaul capacity to connect a massive amount of small cells (SCs) to the network. Since having an optical connection to each SC might be infeasible, mmWave links are an interesting alternative due to their large available bandwidth. An advantage of a wireless backhaul is that the topology can be reconfigured to adapt to changing traffic demands, new operator policies, or to rapidly overcome network failures. In this work, we investigate the problem of orchestrating the reconfiguration of mmWave wireless backhaul networks with mechanically steerable antennas assuming green backhaul operation where nodes are turned off when not in use. The orchestration involves scheduling and coordinating the powering on/off of nodes, the rotation of antennas to achieve alignment for link establishment, and setting up and tearing down links to minimize packet loss during the reconfiguration. We model the problem as a Mixed Integer Linear Program (MILP) for optimal orchestration and propose a sub-optimal reduced MILP for larger instances. Numerical results for different topologies using a realistic traffic trace indicate that optimizing reconfiguration orchestration can significantly reduce packet loss in comparison to a straightforward reconfiguration approach, enabling a smooth transition between target mmWave backhaul topologies.

Place, publisher, year, edition, pages
2022. Vol. 205, article id 108750
Keywords [en]
MILP model; mmWave; Optimization; Reconfiguration; Wireless backhaul
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-76279DOI: 10.1016/j.comnet.2021.108750ISI: 000778744900007Scopus ID: 2-s2.0-85123382209OAI: oai:DiVA.org:kau-76279DiVA, id: diva2:1384975
Note

Article part of Santos' doctoral thesis (2020) Towards Resilient and Reconfigurable Software-defined Wireless Backhaul Networks as manuscript.

Available from: 2020-01-13 Created: 2020-01-13 Last updated: 2022-04-28Bibliographically approved
In thesis
1. Towards Resilient and Reconfigurable Software-defined Wireless Backhaul Networks
Open this publication in new window or tab >>Towards Resilient and Reconfigurable Software-defined Wireless Backhaul Networks
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The increase of mobile devices and services over the last decade has led to unprecedented mobile traffic growth. To cope with the increasing demands, fifth generation (5G) network architectures have been designed to provide the required capacity using a large number of small cells (SCs). However, a dense deployment of SCs requires a robust and scalable backhaul to transport the access traffic towards the Internet. In this thesis, we explore the application of the Software-defined Networking (SDN) paradigm for the management of a wireless backhaul. With SDN, the data and control planes are separated and the network is managed by a centralized entity. To that end, we provide multiple contributions that focus on achieving resilient and reconfigurable wireless backhaul networks. Firstly, we propose an SDN-based architecture to manage the wireless backhaul. Our architecture is integrated in practical testbed environments, where we use an SDN controller to configure the forwarding plane and wireless backhaul links. Secondly, we evaluate SDN-based resiliency in the wireless backhaul. We achieve that by implementing fast-failover resiliency with OpenFlow group tables and by using the bidirectional-forwarding detection protocol (BFD) to monitor the state of the backhaul links. Finally, we develop algorithms that calculate the necessary reconfiguration operations to transition between different wireless backhaul topologies, while minimizing the impact on existing user traffic. We consider that the backhaul nodes can be powered on/off and are equipped with steerable antennas that can be aligned to form links with different neighbors. Our optimization problems are modeled as mixed integer linear programs (MILP) that are optimally solved using exact mathematical programming methods. In addition, we develop greedy-based heuristic algorithms that solve the same problems and obtain good quality solutions in short time.

Abstract [en]

The increase of mobile devices and services over the last decade has led to unprecedented mobile traffic growth. To cope with the increasing demands, fifth generation (5G) network architectures have been designed to provide the required capacity using a large number of small cells (SCs). However, a dense deployment of SCs requires a robust and scalable backhaul to transport the access traffic towards the Internet.

In this thesis, we explore the application of the Software-defined Networking (SDN) paradigm for the management of a wireless backhaul. To that end, we provide multiple contributions that focus on achieving resilient and reconfigurable wireless backhaul networks. Firstly, we propose an SDN-based architecture to manage the wireless backhaul. Our architecture is integrated in practical testbed environments, where we use an SDN controller to configure the forwarding plane and wireless backhaul links. Secondly, we evaluate SDN-based fast-failover resiliency in the wireless backhaul. Finally, we develop several algorithms that orchestrate different backhaul reconfiguration operations with minimal impact on existing user traffic.

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2020. p. 42
Series
Karlstad University Studies, ISSN 1403-8099 ; 2020:9
Keywords
5G, heterogeneous networks, mmWave, resiliency, SDN, wireless backhaul
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-76286 (URN)978-91-7867-092-5 (ISBN)978-91-7867-102-1 (ISBN)
Public defence
2020-03-06, 21A342, Karlstad, 09:15 (English)
Opponent
Supervisors
Note

Article 6 and 7 part of thesis as manuscripts, now published.

Available from: 2020-02-14 Created: 2020-01-13 Last updated: 2022-03-10Bibliographically approved

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Santos, RicardoKassler, Andreas

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