LAUSR

This article investigates a heterogeneous network (HetNet) consisting of an overlapped coverage of radio frequency (RF) and optical wireless communication (OWC) to support human connectivity to the Internet of Things (IoT) in the fifth generation and beyond (5G+) mobile networks. Such a HetNet-based IoT benefits from the high throughput of the OWC and the high reliability of the RF communications. To ensure a reliable link with Quality-of-Service (QoS) guarantee in terms of network delay and throughput, vertical handovers are triggered within the HetNet. A cross-layer data-driven approach is adopted to reach optimal handover decisions and tackle the challenges associated with the mobility and reliability of IoT. As mobile time-varying wireless channel gains in optical links are not publicly available, we first present a realistic model for the mobile optical channel that reflects the nature of human mobility, and hence, a useful model that features the spatial–temporal patterns of indoor mobile channels is proposed. Using the created data set, we then present a data-driven algorithm that predicts abrupt outages in Line-of-Sight (LOS) optical links and evaluates the optical channel quality through deep learning. Given the resulting LOS link outage prediction in OWC, a reinforcement-learning-based approach is proposed to implement optimal vertical handover decisions with the QoS guarantee. The proposed handover decision algorithm learns to make a tradeoff between the outage risk and the cost of excessive handovers. The numerical results demonstrate considerable improvement in overall latency and handover rate under indoor mobility for bidirectional links.