Balazs Varga | Searcharxiv

arXiv2023

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.

cs.NI↗

arXiv2022

Deep Q-learning: a robust control approach

In this paper, we place deep Q-learning into a control-oriented perspective and study its learning dynamics with well-established techniques from robust control. We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Then, we ensure convergence via robust controllers acting as dynamical rewards in the loss function. We synthesize three controllers: state-feedback gain scheduling H2, dynamic Hinf, and constant gain Hinf controllers. Setting up the learning agent with a control-oriented tuning methodology is more transparent and has well-established literature compared to the heuristics in reinforcement learning. In addition, our approach does not use a target network and randomized replay memory. The role of the target network is overtaken by the control input, which also exploits the temporal dependency of samples (opposed to a randomized memory buffer). Numerical simulations in different OpenAI Gym environments suggest that the Hinf controlled learning performs slightly better than Double deep Q-learning.

cs.LG↗