AI Power Management for 5G Frequency and Voltage Switching
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Solution Overview
Problem
The 5G communication system faces challenges in achieving a high data rate while minimizing power consumption and heat generation, as existing low noise techniques result in high power consumption and area usage, necessitating a method to optimize frequency and power supply based on user patterns.
Innovation Solution
A power management apparatus and method utilizing an AI controller to monitor user patterns, predict workload patterns, and adjust supply voltage and frequency through a DC-DC converter, switching between DPWM and DPSM modes to minimize noise and power consumption, optimizing power delivery based on user demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If low noise techniques are applied for high data rate in 5G communication system, then noise performance is improved, but power consumption and area increase
Solution Approach 1:
The patent applies dynamics by making the frequency and power supply adjustable based on user patterns. The system dynamically switches between different frequency bands (e.g., sub-6GHz and mmWave) and power levels according to predicted user activity, transforming a static low noise design into an adaptive system that achieves low noise performance only when needed, thereby reducing overall power consumption.
Solution Approach 2:
The patent changes physical parameters (frequency band and power supply voltage) based on user pattern predictions. The AI controller predicts user activity patterns and adjusts the operating frequency and power supply accordingly, allowing the system to operate at low power during periods of low user activity while maintaining low noise performance when user activity is high.
2Object-affected harmful factors
If low noise techniques are applied for high data rate in 5G communication system, then noise performance is improved, but area increases
Solution Approach 1:
The system dynamically activates low noise circuit components only when user pattern prediction indicates high activity periods. During low activity periods, these components can be deactivated or placed in low-power modes, reducing the effective area utilization and allowing for more compact overall design while maintaining noise performance when needed.
Solution Approach 2:
The patent implements periodic switching between different operating modes based on predicted user patterns. The system alternates between high-performance low-noise modes and low-power modes according to periodic user activity patterns, allowing the low noise circuitry to be active only during necessary periods, thereby reducing the required circuit area.
3Use of energy by moving object
If optimal frequency and power supply are applied based on user pattern, then power consumption is reduced, but system complexity increases
Solution Approach 1:
The system employs an AI controller that automatically monitors user patterns, predicts future activity, and adjusts frequency and power supply without manual intervention. This self-service capability allows the system to optimize power consumption dynamically while keeping the control logic integrated within the existing processor architecture, minimizing additional complexity.
Solution Approach 2:
The AI controller serves multiple functions: it monitors user patterns, predicts activity levels, determines optimal frequency bands, and controls power supply adjustments. By consolidating these functions into a single multi-functional controller rather than separate dedicated circuits, the patent reduces overall system complexity while achieving power optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces meaningless heat generation and power wastage by dynamically adjusting power and frequency, ensuring optimal low noise performance and efficient resource utilization in 5G communication systems, thereby enhancing system reliability and reducing performance degradation due to heat.
Implementation Method 1
a DC-DC converter configured to output a supply voltage based on the predicted user pattern
Implementation Method 2
detect a load of a low-dropout (LDO) to control a core size, a soft-start time, a discontinuous Pulse-width modulation/digital power system management (DPWM/DPSM) mode, a gate driver size, and a switching frequency to track a minimum supply voltage satisfying a predetermined minimum noise performance
Data Source
AI summary
A power management apparatus, includes an artificial intelligence (AI) controller configured to monitor a user pattern, based on frequency band selection information of all users using a base station, to predict the user pattern, and a DC-DC converter configured to output a supply voltage based on the predicted user pattern.