AI/ML Power Control Service for Position-Based Interference
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Solution Overview
Problem
Current transmit power control in wireless networks, such as 5G NR, faces challenges in accurately identifying and adjusting interference sources due to limitations in power ramping step sizes, leading to sub-optimal interference management and quality of service issues.
Innovation Solution
A power control service utilizing artificial intelligence and machine learning (AI/ML) logic to identify and adjust the transmit power of end devices based on positioning and location information, applying proactive or reactive strategies to mitigate interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power control methods with fixed power ramping step sizes are used, then the control mechanism is simple, but the ability to accurately identify and adjust interference sources is limited
Solution Approach 1:
The patent introduces an AI/ML-based power control service as an intermediary layer between the network and end devices. This service uses machine learning models to predict interference patterns and determine optimal power control actions, enabling accurate interference source identification without requiring complex modifications to the basic power control mechanism. The AI/ML model processes multiple parameters including positioning information, channel conditions, and historical data to make intelligent power adjustment decisions.
Solution Approach 2:
The patent dynamically adjusts power control parameters based on real-time network conditions and predictive interference analysis. Instead of using fixed power ramping step sizes, the system adapts power control commands based on AI/ML predictions of interference patterns, allowing for more precise and flexible power management that responds to changing network dynamics.
2Loss of time
If power control adjustments are made with fixed power ramping step sizes, then the control process is straightforward, but significant power control adjustments cause notable delays
Solution Approach 1:
The AI/ML-based power control service performs preliminary analysis of interference patterns and predicts future interference scenarios before they occur. By proactively identifying potential interference sources and pre-adjusting power levels accordingly, the system eliminates delays associated with reactive power control adjustments. The model analyzes historical data and network conditions to anticipate when and where interference will occur, allowing the network to prepare appropriate power control actions in advance.
Solution Approach 2:
The system implements a feedback mechanism where the AI/ML model continuously learns from network conditions, interference patterns, and power control outcomes. This feedback loop enables the model to refine its predictions and improve the accuracy of power control adjustments over time, reducing delays by learning from past performance and adapting to changing network dynamics.
3Reliability
If traditional power control is used without AI/ML logic, then the system is simpler, but interference management is sub-optimal
Solution Approach 1:
The AI/ML-based power control service operates autonomously to manage interference, making self-service decisions about power adjustments without requiring constant manual intervention or complex centralized control. The model independently analyzes network conditions, predicts interference patterns, and determines optimal power control actions, reducing the need for complex manual management while improving interference management quality.
Data Source
AI summary
A method, an end device, and a non-transitory computer-readable storage medium are described in relation to a power control service. The power control service may include identification of an end device that contributes to interference based on positioning information of the end device. The power control service may calculate and transmit a reduced transmit power value to the end device. The power control service may calculate the reduced transmit power value based on an application service used by the end device.