RL Wireless Transmission Control for CPS Reliability and Energy
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Solution Overview
Problem
Existing wireless communication networks for cyber-physical systems face challenges in efficiently managing transmission parameters to ensure reliability and availability while minimizing resource and energy consumption, particularly due to unrealistic model assumptions, lack of intent-based orchestration, and inadequate consideration of real-world blockage models.
Innovation Solution
Implementing a Reinforcement Learning (RL)-powered orchestrator and planner to manage transmit power and diversity settings dynamically, taking into account application-layer reliability and availability, and translating high-level intents into low-level orchestration objectives to optimize resource allocation and minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diversity techniques and full power transmission are enabled for all devices, then reliability is improved, but resource usage and energy consumption increase excessively
Solution Approach 1:
The patent implements dynamic control of transmission parameters including transmit power, diversity technique configuration, and resource allocation that adapt in real-time to changing channel conditions, device mobility, and network load, replacing static full-power transmission with adaptive parameter adjustment to minimize energy consumption while maintaining reliability
Solution Approach 2:
The patent applies different transmission strategies to different devices based on their specific channel conditions, location, mobility state, and service requirements, rather than uniform full-power transmission for all devices, allowing optimized energy efficiency for each device's local context
2Reliability
If diversity techniques are enabled for all devices at full power, then reliability is improved, but network load increases causing longer scheduling delays
Solution Approach 1:
The patent implements dynamic scheduling and resource allocation that adjusts diversity technique application and resource grant timing based on real-time network load, channel conditions, and device priorities, reducing scheduling delays by optimizing resource allocation dynamically rather than using static full-power transmission for all devices
Solution Approach 2:
The patent employs feedback mechanisms where devices report channel quality indicators, buffer status, and transmission outcomes to the network, enabling the network to adjust scheduling decisions and diversity technique configuration in real-time to minimize delays while maintaining reliability
3Reliability
If full power transmission is used, then reliability is improved, but interference to other users in neighboring cells increases
Solution Approach 1:
The patent dynamically changes transmission parameters including power level, frequency selection, and time resource allocation based on interference conditions detected through channel quality feedback, allowing reliable communication at reduced power levels when possible and minimizing interference to neighboring cells
4Use of energy by moving object
If transmission power is reduced, then energy consumption and interference are decreased, but coding rates must be reduced increasing resource block usage
Solution Approach 1:
The patent implements dynamic joint optimization of power control and resource allocation that adapts coding rates, modulation schemes, and resource block allocation in real-time based on channel conditions, allowing efficient use of reduced power while minimizing resource block consumption through adaptive parameter adjustment
Data Source
AI summary
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.