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Rethinking Offload: How to intelligently combine Wi-Fi and Small Cells?

Abstract : As future small cell base stations (SCBSs) are set to be multi-mode capable (i.e., transmitting on both licensed and unlicensed bands), a cost-effective integration of both technologies/systems coping with peak data demands, is crucial. Using tools from reinforcement learning (RL), a distributed cross-system traffic steering framework is proposed, whereby SCBSs autonomously optimize their long-term performance, as a function of traffic load and users' heterogeneous requirements. Leveraging the (existing) Wi-Fi component, SCBSs learn their optimal transmission strategies over both unlicensed and licensed bands. The proposed traffic steering solution is validated in a Long-Term Evolution (LTE) simulator augmented with Wi-Fi hotspots. Remarkably, it is shown that the cross-system learning-based approach outperforms several benchmark algorithms and traffic steering policies, with gains reaching up to 300% when using a traffic-aware scheduler (as compared to the classical proportional fair (PF) scheduler).
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Submitted on : Tuesday, January 14, 2014 - 10:07:06 AM
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Meryem Simsek, Mehdi Bennis, Mérouane Debbah, Andreas Czylwik. Rethinking Offload: How to intelligently combine Wi-Fi and Small Cells?. ICC 2013, Jun 2013, Budapest, Hungary. pp.5204 - 5208, ⟨10.1109/ICC.2013.6655411⟩. ⟨hal-00926000⟩



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