1,766 research outputs found
Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges
Heterogeneous Vehicular NETworks (HetVNETs) can meet various
quality-of-service (QoS) requirements for intelligent transport system (ITS)
services by integrating different access networks coherently. However, the
current network architecture for HetVNET cannot efficiently deal with the
increasing demands of rapidly changing network landscape. Thanks to the
centralization and flexibility of the cloud radio access network (Cloud-RAN),
soft-defined networking (SDN) can conveniently be applied to support the
dynamic nature of future HetVNET functions and various applications while
reducing the operating costs. In this paper, we first propose the multi-layer
Cloud RAN architecture for implementing the new network, where the multi-domain
resources can be exploited as needed for vehicle users. Then, the high-level
design of soft-defined HetVNET is presented in detail. Finally, we briefly
discuss key challenges and solutions for this new network, corroborating its
feasibility in the emerging fifth-generation (5G) era
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled
either by a centralized scheduler residing in the network (e.g., a base station
in case of cellular systems) or a distributed scheduler, where the resources
are autonomously selected by the vehicles. The former approach yields a
considerably higher resource utilization in case the network coverage is
uninterrupted. However, in case of intermittent or out-of-coverage, due to not
having input from centralized scheduler, vehicles need to revert to distributed
scheduling. Motivated by recent advances in reinforcement learning (RL), we
investigate whether a centralized learning scheduler can be taught to
efficiently pre-assign the resources to vehicles for out-of-coverage V2V
communication. Specifically, we use the actor-critic RL algorithm to train the
centralized scheduler to provide non-interfering resources to vehicles before
they enter the out-of-coverage area. Our initial results show that a RL-based
scheduler can achieve performance as good as or better than the state-of-art
distributed scheduler, often outperforming it. Furthermore, the learning
process completes within a reasonable time (ranging from a few hundred to a few
thousand epochs), thus making the RL-based scheduler a promising solution for
V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201
- …