  This paper studies the multi-agent resource allocation problem in vehicular
networks using non-orthogonal multiple access (NOMA) and network slicing. To
ensure heterogeneous service requirements for different vehicles, we propose a
network slicing architecture. We focus on a non-cellular network scenario where
vehicles communicate by the broadcast approach via the direct device-to-device
interface. In such a vehicular network, resource allocation among vehicles is
very difficult, mainly due to (i) the rapid variation of wireless channels
among highly mobile vehicles and (ii) the lack of a central coordination point.
Thus, the possibility of acquiring instantaneous channel state information to
perform centralized resource allocation is precluded. The resource allocation
problem considered is therefore very complex. It includes not only the usual
spectrum and power allocation, but also coverage selection (which target
vehicles to broadcast to) and packet selection (which network slice to use).
This problem must be solved jointly since selected packets can be overlaid
using NOMA and therefore spectrum and power must be carefully allocated for
better vehicle coverage. To do so, we provide a optimization approach and study
the NP-hardness of the problem. Then, we model the problem using multi-agent
Markov decision process. Finally, we use a deep reinforcement learning (DRL)
approach to solve the problem. The proposed DRL algorithm is practical because
it can be implemented in an online and distributed manner. We show that our
approach is robust and efficient when faced with different variations of the
network parameters and compared to centralized benchmarks.
