Foroutan Fami
ABSTRACT
The increasing number of differentiated IoT services introduced to wireless networks are coming with diverse and sometimes conflicting requirements. Network slicing in 5G is the key feature to address these requirements. However, slicing is mainly intended for cellular networks, and adopting it for WiFi networks is challenging due to the lack of wireless virtualization supports in hardware. This paper proposes a new slicing solution for WiFi enterprise networks that require no virtualization support. Our solution relies on a dynamic user association mechanism that takes into account different IoT requirements. We formulate an optimization problem that maximizes the total throughput of the network with respect to different IoT requirements. To solve this high-complexity problem, a stable matching mechanism algorithm has been proposed to obtain the optimized solution in near real-time. We also advocate a Reinforcement Learning algorithm that enables practical implementations and employs a learning framework to learn different network dynamics. Simulation results show that the proposed solutions approximate the optimal results and outperform the traditional RSSI approaches while guaranteeing the requirements of different IoT slices.
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Index Terms
- Optimization of IoT slices in wifi enterprise networks
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