Feasibility Study of Enabling V2X Communications by LTE-Uu Radio Interface

Compared with the legacy wireless networks, the next generation of wireless network targets at different services with divergent QoS requirements, ranging from bandwidth consuming video service to moderate and low date rate machine type services, and supporting as well as strict latency requirements. One emerging new service is to exploit wireless network to improve the efficiency of vehicular traffic and public safety. However, the stringent packet end-to-end (E2E) latency and ultra-low transmission failure rates pose challenging requirements on the legacy networks. In other words, the next generation wireless network needs to support ultra-reliable low latency communications (URLLC) involving new key performance indicators (KPIs) rather than the conventional metric, such as cell throughput in the legacy systems. In this paper, a feasibility study on applying today's LTE network infrastructure and LTE-Uu air interface to provide the URLLC type of services is performed, where the communication takes place between two traffic participants (e.g., vehicle-to-vehicle and vehicle-to-pedestrian). To carry out this study, an evaluation methodology of the cellular vehicle-to-anything (V2X) communication is proposed, where packet E2E latency and successful transmission rate are considered as the key performance indicators (KPIs). Then, we describe the simulation assumptions for the evaluation. Based on them, simulation results are depicted that demonstrate the performance of the LTE network in fulfilling new URLLC requirements. Moreover, sensitivity analysis is also conducted regarding how to further improve system performance, in order to enable new emerging URLLC services.Comment: Accepted by IEEE/CIC ICCC 201

Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

Emerging 5G systems will need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of URLLC superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocation decisions of the eMBB scheduler. Next we consider a more general class of (convex/threshold) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but minislot-homogeneous policies. A key observation is that unlike the linear rate loss model, for the convex and threshold rate loss models, optimal eMBB and URLLC scheduling decisions do not de-couple and joint optimization is necessary to satisfy the dual objectives. We validate the characteristics and benefits of our schedulers via simulation

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201