Volterra-DNN Equalization for Nonlinear 5G Power Amplifiers
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Solution Overview
Problem
Modern wireless communication systems, particularly 5G systems, face challenges in enhancing power amplifier (PA) efficiency due to high peak to average power ratio (PAPR) signals, which require linear response to reduce distortion but are inefficient in nonlinear saturated response regions.
Innovation Solution
A deep neural network (DNN)-based equalizer is implemented to mitigate PA nonlinear distortions by exploiting Volterra series nonlinearity modeling. This DNN equalizer uses Volterra series components as input features, allowing it to converge rapidly to the desired nonlinear response even with limited training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power amplifiers operate in nonlinear saturated response region to achieve optimal power efficiency, then power efficiency is improved, but signal distortion increases
Solution Approach 1:
The system applies preliminary nonlinear equalization processing to the input signal before it enters the power amplifier. By pre-compensating for the expected nonlinear distortion using Volterra series-based DNN equalization, the signal is prepared in advance to counteract the distortion that will occur during amplification, allowing the PA to operate efficiently in saturation while maintaining signal integrity
Solution Approach 2:
The system employs feedback mechanisms where the output signal from the power amplifier is processed through a DNN equalizer that uses Volterra series modeling. The equalizer analyzes the distorted output and generates correction signals that are fed back to compensate for nonlinearities, creating a closed-loop system that continuously mitigates distortion while maintaining high power efficiency
2Object-generated harmful factors
If digital pre-distorters are used to linearize power amplifiers, then signal distortion is reduced, but device complexity and implementation cost increase
Solution Approach 1:
The system replaces traditional hardware-based linearization circuits with a software-defined DNN equalization approach. By using Volterra series-based neural network algorithms implemented in digital signal processing, the system achieves linearization functionality through computational methods rather than complex analog or digital circuitry, reducing hardware complexity and implementation cost
Solution Approach 2:
The system transforms the approach from fixed hardware linearization parameters to adaptive software-based parameters. The DNN equalizer dynamically adjusts equalization parameters based on operating conditions, allowing flexible optimization without requiring complex hardware reconfiguration, thereby simplifying device implementation while maintaining distortion reduction performance
3Object-generated harmful factors
If conventional equalization techniques are used for nonlinear power amplifier mitigation, then some distortion compensation is achieved, but performance is insufficient for severe nonlinearity scenarios
Solution Approach 1:
The system combines multiple equalization techniques into a composite DNN-based equalization framework. By integrating Volterra series modeling with deep neural network architecture, the system creates a hybrid approach that leverages the mathematical rigor of Volterra series for nonlinear characterization and the adaptive learning capabilities of DNNs, achieving superior distortion compensation performance for severe nonlinearity scenarios compared to conventional single-technique approaches
Data Source
AI summary
The nonlinearity of power amplifiers (PAs) has been a severe constraint in performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. Nonlinear equalizers that exploit both deep neural networks (DNNs) and Volterra series models are provided to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to effectively mitigate nonlinear PA distortions while avoiding over-fitting under limited training data. The non-linear equalizers demonstrate superior performance over conventional nonlinear equalization approaches.