Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach
Zoubeir Mlika,
Soumaya Cherkaoui
Abstract:This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different pa… Show more
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