  The interconnection of vehicles in the future fifth generation (5G) wireless
ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds
of applications requiring delay-sensitive, compute-intensive and
bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS)
are two of the key enabler technologies in 5G networks that can be used to
optimize the allocation of the network resources and guarantee the diverse
requirements of IoV applications.
  As traditional model-based optimization techniques generally end up with
NP-hard and strongly non-convex and non-linear mathematical programming
formulations, in this paper, we introduce a model-free approach based on deep
reinforcement learning (DRL) to solve the resource allocation problem in
MEC-enabled IoV network based on network slicing. Furthermore, the solution
uses non-orthogonal multiple access (NOMA) to enable a better exploitation of
the scarce channel resources. The considered problem addresses jointly the
channel and power allocation, the slice selection and the vehicles selection
(vehicles grouping). We model the problem as a single-agent Markov decision
process. Then, we solve it using DRL using the well-known DQL algorithm. We
show that our approach is robust and effective under different network
conditions compared to benchmark solutions.
