Neural Network DVFS for Wireless Modem Power and Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing Dynamic Voltage and Frequency Scaling (DVFS) mechanisms in mobile terminals face challenges in accurately adjusting working voltages and clock frequencies of wireless modems due to hysteresis in wireless channels and low reliability of empirical formulas, leading to reduced adjustment accuracy and increased power consumption.
Innovation Solution
A method and device that utilize a pre-trained neural network to acquire real-time channel parameters and adjust the wireless modem's clock frequency and working voltage to optimal target values, improving accuracy and reducing power consumption by determining the minimum required frequencies and voltages necessary for reliable data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If DVFS mechanism uses statistical data and empirical formulas to adjust working voltages and clock frequencies, then power consumption is reduced, but adjustment accuracy deteriorates due to hysteresis in wireless channels
Solution Approach 1:
The patent replaces the traditional empirical formula-based adjustment mechanism with a neural network-based adjustment mechanism. The neural network learns optimal adjustment strategies from historical data and channel state information, substituting the mechanical/empirical approach with an intelligent adaptive approach that achieves both accuracy and energy efficiency
Solution Approach 2:
The patent implements a feedback mechanism where the neural network continuously receives channel state information and transmission performance data, then adjusts working voltages and clock frequencies accordingly. This closed-loop feedback system enables real-time adaptation to channel hysteresis, improving adjustment accuracy while maintaining power consumption benefits
2Use of energy by moving object
If DVFS mechanism uses statistical data and empirical formulas to adjust working voltages and clock frequencies, then power consumption is reduced, but reliability deteriorates
Solution Approach 1:
The patent replaces the unreliable empirical formula-based mechanism with a neural network-based mechanism that has learned optimal adjustment patterns from extensive training data. This substitution improves reliability by using intelligent decision-making that adapts to varying channel conditions while maintaining the power consumption reduction goal
Solution Approach 2:
The neural network is pre-trained offline using historical channel data and transmission performance records to learn optimal adjustment strategies before deployment. This preliminary action enables the system to make reliable adjustments in real-time without requiring complex online computation, ensuring both reliability and energy efficiency
Data Source
AI summary
A method for adjusting a wireless modem includes: a channel parameter of a wireless modem at a present moment is acquired; a target clock frequency and a target working voltage of the wireless modem are generated, according to the channel parameter, with a neural network that is pre-trained; and a working voltage and a clock frequency of the wireless modem are adjusted to the target working voltage and the target clock frequency, respectively.