Residual BiLSTM Pre-Distortion for RF Power Amplifier Linearity
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Solution Overview
Problem
Existing radio frequency power amplifiers (PAs) exhibit strong nonlinearity, especially near saturation, leading to signal distortion and reduced power efficiency, particularly in high-frequency and wideband wireless communications, which conventional pre-distortion methods struggle to effectively compensate.
Innovation Solution
Implementing reduced complexity bidirectional residual neural network (BiRNN) models, specifically using residual learning and LSTM projection, to pre-distort input signals and compensate for PA nonlinearity, reducing computational complexity and memory requirements while improving linearization performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pre-distortion methods are used to compensate for PA nonlinearity, then implementation is simpler, but linearization performance is insufficient especially at high frequencies and wide bandwidths
Solution Approach 1:
The patent segments the complex nonlinearity compensation task into multiple LSTM layers with different time horizon focuses. Each layer handles specific temporal dependencies, dividing the overall complexity into manageable segments that collectively achieve superior linearization performance without requiring a single overly complex model
Solution Approach 2:
The patent extends the conventional single-directional approach by implementing bidirectional LSTM that processes signals in both forward and backward time directions. This adds a dimensional aspect to the temporal processing, enabling the model to capture past and future context simultaneously, thereby improving linearization performance without proportionally increasing complexity
2Productivity
If standard LSTM models are used for digital pre-distortion, then training convergence is slower and memory usage is higher
Solution Approach 1:
The patent extracts and removes redundant computational elements from the standard LSTM architecture by implementing selective gating mechanisms that deactivate unnecessary memory cells and connections during training. This extraction of essential functions reduces memory footprint and accelerates convergence by eliminating computationally expensive operations that do not contribute significantly to learning
Solution Approach 2:
The patent dynamically adjusts LSTM hyperparameters including learning rate, hidden layer dimensions, and memory cell counts based on training progress and available resources. This adaptive parameter changing enables faster convergence in early training stages while reducing memory consumption in later stages, optimizing both productivity and energy efficiency throughout the training process
Data Source
AI summary
One or more aspects of the techniques and models described herein provide for bidirectional recurrent neural network (BiRNN)-based digital pre-distortion techniques for radio frequency (RF) power amplifiers (PAs). As an example, a digital pre-distorter (DPD) system may implement residual learning and long short-term memory (LSTM) projection layer features to reduce computational complexity and memory requirements. Implementing the described unconventional techniques of applying residual learning in RNN (e.g., in BiLSTM), using LSTM projection to develop a DPD structure, or both, may provide several advantages over preexisting techniques. For instance, the complexity in training and pre-distortion may be reduced and significantly less memory may be required to store the DPD neural network coefficients (e.g., while achieving similar or better linearization performance compared to other LSTM models). Further, faster training convergence speed may be achieved (e.g., compared to other LSTM models).