Towards Deterministic Communications in 6G Networks: State of the Art, Open Challenges and the Way Forward

Over the last decade, society and industries are undergoing rapid digitization that is expected to lead to the evolution of the cyber-physical continuum. End-to-end deterministic communications infrastructure is the essential glue that will bridge the digital and physical worlds of the continuum. We describe the state of the art and open challenges with respect to contemporary deterministic communications and compute technologies: 3GPP 5G, IEEE Time-Sensitive Networking, IETF DetNet, OPC UA as well as edge computing. While these technologies represent significant technological advancements towards networking Cyber-Physical Systems (CPS), we argue in this paper that they rather represent a first generation of systems which are still limited in different dimensions. In contrast, realizing future deterministic communication systems requires, firstly, seamless convergence between these technologies and, secondly, scalability to support heterogeneous (time-varying requirements) arising from diverse CPS applications. In addition, future deterministic communication networks will have to provide such characteristics end-to-end, which for CPS refers to the entire communication and computation loop, from sensors to actuators. In this paper, we discuss the state of the art regarding the main challenges towards these goals: predictability, end-to-end technology integration, end-to-end security, and scalable vertical application interfacing. We then present our vision regarding viable approaches and technological enablers to overcome these four central challenges. Key approaches to leverage in that regard are 6G system evolutions, wireless friendly integration of 6G into TSN and DetNet, novel end-to-end security approaches, efficient edge-cloud integrations, data-driven approaches for stochastic characterization and prediction, as well as leveraging digital twins towards system awareness.Comment: 22 pages, 8 figure

Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

This research work was supported in part by the Euro- pean Union’s Horizon 2020 Research and Innovation Program under the “Cloud for Holography and Augmented Reality (CHARITY)” Project under Agreement 101016509, and 5G- CLARITY Project under Agreement 871428. It is also partially supported by the Spanish national research project TRUE5G: PID2019-108713RB-C53.Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-of-Service and enabling the coexistence of critical and best-effort services. Addi- tionally, they rely on programmable and cost-effective Ethernet- based forwarding planes. This article addresses the flow alloca- tion problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed “Next Generation Transport Network Optimizer” (NEPTUNO), that combines ex- act optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Quality-of-Service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO regarding the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with a novel online baseline solution for two different optimization goals. Online methods compute the flow’s allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of data analytics for the self-optimization of the future 5G TNs.Union’s Horizon 2020, 1010165095G-CLARITY 871428TRUE5G: PID2019-108713RB-C53

Many-Sources Large Deviations for Max-Weight Scheduling

In this paper, a many-sources large deviations principle (LDP) for the transient workload of a multi-queue single-server system is established where the service rates are chosen from a compact, convex and coordinate-convex rate region and where the service discipline is the max-weight policy. Under the assumption that the arrival processes satisfy a many-sources LDP, this is accomplished by employing Garcia's extended contraction principle that is applicable to quasi-continuous mappings. For the simplex rate-region, an LDP for the stationary workload is also established under the additional requirements that the scheduling policy be work-conserving and that the arrival processes satisfy certain mixing conditions. The LDP results can be used to calculate asymptotic buffer overflow probabilities accounting for the multiplexing gain, when the arrival process is an average of \emph{i.i.d.} processes. The rate function for the stationary workload is expressed in term of the rate functions of the finite-horizon workloads when the arrival processes have \emph{i.i.d.} increments.Comment: 44 page

Deep Reinforcement Learning for Scheduling and Power Allocation in a 5G Urban Mesh

We study the problem of routing and scheduling of real-time flows over a multi-hop millimeter wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm that determines which subset of the mmWave links should be activated during each time slot and using what power level. The proposed algorithm, called Adaptive Activator RL (AARL), can handle a variety of network topologies, network loads, and interference models, as well as adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. For each topology, the results of AARL are compared to those of a greedy scheduling algorithm. AARL is shown to outperform the greedy algorithm in two aspects. First, its schedule obtains higher goodput. Second, and even more importantly, while the run time of the greedy algorithm renders it impractical for real-time scheduling, the run time of AARL is suitable for meeting the time constraints of typical 5G networks