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Modern broadband hybrid optical-wireless access networks have gained the attention of academia and industry due to their strategic advantages (cost-efficiency, huge bandwidth, flexibility, and mobility). At the same time, the proliferation of Software Defined Networking (SDN) enables the efficient reconfiguration of the underlying network components dynamically using SDN controllers. Hence, effective traffic-aware schemes are feasible in dynamically determining suitable configuration parameters for advancing the network performance. To this end, a novel machine learning mechanism is proposed for an SDN-enabled hybrid optical-wireless network. The proposed architecture consists of a 10-gigabit-capable passive optical network (XG-PON) in the network backhaul and multiple Long Term Evolution (LTE) radio access networks in the fronthaul. The proposed mechanism receives traffic-aware knowledge from the SDN controllers and applies an adjustment on the uplink-downlink configuration in the LTE radio communication. This traffic-aware mechanism is capable of determining the most suitable configuration based on the traffic dynamics in the whole hybrid network. The introduced scheme is evaluated in a realistic environment using real traffic traces such as Voice over IP (VoIP), real-time video, and streaming video. According to the obtained numerical results, the proposed mechanism offers significant improvements in the network performance in terms of latency and jitter

DIANA: A Machine Learning Mechanism for Adjusting the TDD Uplink-Downlink Configuration in XG-PON-LTE Systems