AI RF Circuit Compensation for Nonlinearity and Spectral Regrowth
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Solution Overview
Problem
Modern wireless communication systems face significant signal impairments due to nonlinearity and memory effects in RF circuits, particularly in 5G edge devices with high transmission power and limited supply voltage, leading to degraded signal quality and spectral regrowth.
Innovation Solution
An AI-based method utilizing neural networks to compensate for impairments in RF communication circuits by training compensator neural networks to emulate and correct signal impairments in both transmitter and receiver circuits, including components like filters and amplifiers, through open-loop and closed-loop compensation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high transmission power and high-order modulation are used in 5G edge devices, then system throughput is improved, but signal impairments such as nonlinearity and spectral regrowth worsen
Solution Approach 1:
The patent applies preliminary action by implementing pre-compensation techniques where neural networks predict and correct for RF circuit impairments before the signal is transmitted. The system uses training data to pre-train compensator neural networks that can anticipate and counteract nonlinearity and memory effects before they degrade the signal, thereby maintaining high throughput while reducing harmful distortions.
Solution Approach 2:
The patent implements feedback mechanisms where the system measures actual signal impairments and uses this information to adjust compensation parameters. The neural networks are trained using feedback from measured signal quality metrics, and the compensation process continuously adapts based on real-time performance data, creating a closed-loop system that maintains optimal performance under varying transmission conditions.
2Use of energy by moving object
If limited supply voltage is used in edge devices, then device portability is improved, but RF circuit nonlinearity worsens
Solution Approach 1:
The patent applies parameter changes by using neural networks to dynamically adjust compensation parameters based on the actual operating conditions of the RF circuit. The system measures key parameters such as supply voltage, temperature, and power level, and uses these measurements to adapt the compensation strategy, allowing the system to maintain linear operation even under limited voltage conditions by changing the compensation parameters in real-time.
Solution Approach 2:
The patent replaces traditional mechanical or analog compensation mechanisms with neural network-based digital compensation. Instead of using complex analog circuits to compensate for nonlinearity, the system uses software-based neural networks that can be trained and adjusted without changing physical hardware, enabling effective compensation with limited supply voltage while maintaining device portability.
3Productivity
If wide bandwidths and high frequencies are used, then communication capacity is improved, but spectral regrowth and out-of-band power worsen
Solution Approach 1:
The patent applies segmentation by dividing the compensation process into multiple stages and components. The neural network compensator is divided into separate modules that address different aspects of spectral regrowth independently, such as memory effect compensation and nonlinearity compensation. This segmented approach allows the system to effectively manage spectral regrowth across wide bandwidths by treating different frequency ranges and impairment types separately.
Data Source
AI summary
A radio frequency (RF) communication assembly includes an RF communication circuit and a compensator apparatus. The compensator apparatus receives an input including an I-component of a pre-compensated signal, a Q-component of the pre-compensated signal, and encoded operating conditions of the RF communication circuit. The RF communication circuit includes RF circuit components causing signal impairments. The compensator apparatus perform neural network computing on the input, and the RF communication assembly generates a compensated output signal that compensates for at least a portion of the signal impairments.