  Applying of network slicing in vehicular networks becomes a promising
paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse
quality of service (QoS) requirements. However, achieving effective network
slicing in dynamic vehicular communications still faces many challenges,
particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the
fast-changing network topology. By leveraging the widely deployed LTE
infrastructures, we propose a semi-decentralized network slicing framework in
this paper based on the C-V2X Mode-4 standard to provide customized network
slices for diverse V2V services. With only the long-term and partial
information of vehicular networks, eNodeB (eNB) can infer the underlying
network situation and then intelligently adjust the configuration for each
slice to ensure the long-term QoS performance. Under the coordination of eNB,
each vehicle can autonomously select radio resources for its V2V transmission
in a decentralized manner. Specifically, the slicing control at the eNB is
realized by a model-free deep reinforcement learning (DRL) algorithm, which is
a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared
to the existing DRL algorithms, the proposed DRL neither requires any prior
knowledge nor assumes any statistical model of vehicular networks. Furthermore,
simulation results show the effectiveness of our proposed intelligent network
slicing scheme.
