Radio Emission Control Using Token-Based RF Exposure Fairness
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing radio frequency exposure to ensure compliance with international standards and local regulations, particularly during resource shortages, where devices may be treated differently in power control mechanisms.
Innovation Solution
Implementing a control policy that utilizes historical data on radiated power transmission, combined with machine learning-based weight value updates, to optimize radiated power consumption and prevent resource shortages while ensuring compliance with exposure limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radio frequency exposure is increased to support new applications and use cases, then system functionality and service capability are improved, but compliance with international standards and local regulations deteriorates
Solution Approach 1:
The patent implements dynamic power control mechanisms that continuously adjust radiated power levels based on real-time conditions, historical data, and regulatory limits. The system transitions from static power settings to adaptive dynamic control, enabling the network to optimize service capability while continuously ensuring compliance with RF exposure regulations through real-time adjustments.
Solution Approach 2:
The system incorporates feedback loops where the network entity receives measurements from user equipment about actual RF exposure levels and power consumption. This feedback is used to train machine learning models that continuously improve power control decisions, ensuring both service quality and regulatory compliance through iterative optimization.
2Use of energy by moving object
If machine learning-based power control is implemented to optimize radiated power, then energy efficiency is improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw measurement data and power control decisions. This ML intermediary processes historical data and measurements to generate optimized power settings, simplifying the control logic while improving energy efficiency. The model acts as a smart mediator that translates complex patterns into actionable power control parameters.
Solution Approach 2:
The system performs preliminary training of machine learning models using historical data before deploying them for real-time power control. By pre-processing and learning from past patterns offline, the system prepares optimized control strategies in advance, reducing the computational burden during real-time operation while maintaining energy optimization benefits.
3Measurement precision
If historical data collection and ML training are performed continuously to improve control policies, then power optimization accuracy is improved, but network signaling overhead and processing time worsen
Solution Approach 1:
The patent implements periodic training cycles where the machine learning model is retrained at scheduled intervals rather than continuously. Historical data is collected over multiple periods, and training occurs periodically when sufficient data has accumulated. This approach maintains high power control accuracy through regular model updates while avoiding the continuous processing overhead and time consumption of constant retraining.
Data Source
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AI summary
To provide fairness between served apparatuses while keeping radio frequency exposure below a defined limit, an apparatus collects, per a sampling period, historical data on tokens at the sampling period, the historical data including at least number of tokens requested during the sampling period, wherein a token is indicative of an amount of radiated power for transmission of a data element. The collected historical data is transmitted to a wireless network. The wireless network determines hindsight based estimations for maximum token consumptions that should have been allowed, and then determines for a control policy, using at least numbers of tokens requested in the historical data received and corresponding hindsight based estimations determined, updated weight values, and transmit them to the apparatus. The apparatus then updates its control policy correspondingly, applies it, collects historical data, and transmits it to the wireless network to obtain updated weight values.