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Applying of network slicing in vehicular networks becomes a promisingparadigm to support emerging Vehicle-to-Vehicle (V2V) applications with diversequality of service (QoS) requirements. However, achieving effective networkslicing in dynamic vehicular communications still faces many challenges,particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and thefast-changing network topology. By leveraging the widely deployed LTEinfrastructures, we propose a semi-decentralized network slicing framework inthis paper based on the C-V2X Mode-4 standard to provide customized networkslices for diverse V2V services. With only the long-term and partialinformation of vehicular networks, eNodeB (eNB) can infer the underlyingnetwork situation and then intelligently adjust the configuration for eachslice to ensure the long-term QoS performance. Under the coordination of eNB,each vehicle can autonomously select radio resources for its V2V transmissionin a decentralized manner. Specifically, the slicing control at the eNB isrealized by a model-free deep reinforcement learning (DRL) algorithm, which isa convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Comparedto the existing DRL algorithms, the proposed DRL neither requires any priorknowledge nor assumes any statistical model of vehicular networks. Furthermore,simulation results show the effectiveness of our proposed intelligent networkslicing scheme.

Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning: A Model-free Deep Reinforcement Learning Approach