Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links

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hdl:2117/119205
Document typeArticle
Defense date2018-05-26
PublisherElsevier
Rights accessOpen Access
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Abstract
Next generation, i.e., fifth generation (5G), cellular networks will provide a significant higher capacity per area to support the ever-increasing traffic demands. In order to achieve that, many small cells need to be deployed that are connected using a combination of optical fiber links and millimeter-wave (mmWave) backhaul architecture to forward heterogeneous traffic over mesh topologies. In this paper, we present a general optimization framework for the design of policies that optimally solve the problem of where to associate a user, over which links to route its traffic towards which mesh gateway, and which base stations and backhaul links to switch o¿ in order to minimize the energy cost for the network operator and still satisfy the user demands. We develop an optimal policy based on mixed integer linear programming (MILP) which considers different user distribution and traffic demands over multiple time periods. We develop also a fast iterative two-phase solution heuristic, which associates users and calculates backhaul routes to maximize energy savings. Our strategies optimize the backhaul network configuration at each timeslot based on the current demands and user locations. We discuss the application of our policies to backhaul management of mmWave cellular networks in light of current trend of network softwarization (Software-Defined Networking, SDN). Finally, we present extensive numerical simulations of our proposed policies, which show how the algorithms can efficiently trade-off energy consumption with required capacity, while satisfying flow demand requirements.
CitationMesodiakaki, A., Zola, E., Santos, R., Kassler, A.J. Optimal user association, backhaul routing and switching off in 5G heterogeneous networks with mesh millimeter wave backhaul links. "Ad hoc networks", 26 Maig 2018, vol. 78, p. 99-114.
ISSN1570-8705
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1570870518302397
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