Bayesian Transmission Power Control for Fast-Moving Communication Devices
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Solution Overview
Problem
Existing closed loop power control methods in communication devices fail to optimally adjust transmission power in rapidly changing environments, leading to inefficient power consumption, especially in high-speed scenarios.
Innovation Solution
Implementing Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to determine optimal transmission power control (TPC) values based on signal-to-interference-plus-noise ratio (SINR) and other variables, allowing for precise and efficient power management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If step-by-step power control adjustment is used, then power consumption is minimized in the long run, but the response time is too slow for high-speed moving scenarios
Solution Approach 1:
The patent transforms the static, fixed-step power control into a dynamic system that adapts to channel conditions. The machine learning model continuously learns from incoming signals and dynamically adjusts TPC values based on real-time channel state, enabling the system to respond rapidly to changing conditions while maintaining energy efficiency.
Solution Approach 2:
The patent replaces the mechanical step-by-step adjustment mechanism with an intelligent system using machine learning models. Instead of fixed incremental changes, the system uses trained models to predict optimal TPC values directly, substituting the gradual mechanical adjustment process with a cognitive decision-making system that achieves both speed and efficiency.
2Device complexity
If fixed SINR target value is used, then the control system is simple, but it cannot optimally control power in rapidly changing channels
Solution Approach 1:
The patent changes the fundamental parameter from a fixed SINR target value to dynamic TPC values generated by machine learning models. The system learns optimal power control parameters adaptively based on channel conditions, transforming the static parameter approach into a dynamic parameter optimization approach that maintains low complexity while achieving high adaptability.
Solution Approach 2:
The machine learning model serves itself by continuously learning from incoming signals and improving its predictions autonomously. The system uses the received signals to train and update the model, enabling self-adaptation to changing channel conditions without requiring complex external control mechanisms.
3Loss of energy
If incremental power adjustment is used, then power consumption is reduced over time, but battery power is wasted in fast-moving scenarios
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline with extensive channel condition data. The models learn optimal power control strategies in advance, enabling them to make accurate predictions during real-time operation without requiring incremental adjustments, thus preventing energy waste while maintaining communication quality.
Solution Approach 2:
The system implements feedback by using received signals to continuously update and refine the machine learning model predictions. The model learns from the actual channel conditions and signal quality measurements, adjusting future TPC values based on this feedback to maintain optimal communication quality while minimizing energy consumption.
Data Source
AI summary
A power control method, for a first communication device, includes applying Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to at least one data so as to determine a transmission power control value, and outputting the transmission power control value. The at least one data is extracted from at least one signal at least from a second communication device. The transmission power control value is configured to instruct the second communication device how to set the transmission power of the second communication device. Even if the second communication device moves fast, the second communication device is able to adjust its transmission power according to the optimized transmission power control value, thereby minimizing the power consumption of the second communication device.