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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 leverage the existing Wi-Fi component, to autonomously optimize their long-term performance over the licensed spectrum band, as a function of traffic load and users' heterogeneous requirements. The proposed traffic steering solution is validated in a Long-Term Evolution (LTE) simulator augmented with Wi-Fi hotspots.
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