Telefonaktiebolaget Lm Ericsson (publ)

Hossein Shokri Ghadikolaei

Konstantinos Vandikas

Abdulrahman Alabbasi

Hjalmar Olsson

Klaus Raizer

Amin Azari

Karl Viktor Berggren

Christian Schaefer

Marina Petrova

Erik Sanders

Milad Ganjalizadeh

This document presents one or more advantageous approaches for Reinforcement Learning (RL) powered management of one or more transmission parameters, such as transmit power and diversity, for maximizing the application-layer reliability and availability of a Cyber-Physical System (CPS) with a minimized level of radio/power resource consumption. Example mathematical models are also disclosed and are useful for transforming high-level 'intents' (e.g., KPIs that are applicable to industrial automation and control systems) into low-level orchestration objectives that drive the RL-based control. These objectives are subsequently employed in the definition of an RL-powered “orchestrator,” which may comprise an appropriately configured network node or other computing platform associated with the wireless communication network used to provide inter-device communications for a CPS comprising a population of devices. Further, the disclosure details example communication—e.g., observations and corresponding control signaling—between the orchestrator and the environment being managed.

Methods and apparatus for controlling one or more transmission parameters used by a wireless communication network for a population of devices comprising a cyber-physical system

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Täby, Sweden

Hossein Shokri Ghadikolaei