Wireless End-to-End Learning Without Backpropagation Gradients
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Solution Overview
Problem
Existing wireless communication systems face challenges with backpropagation-based end-to-end learning due to channel layer issues, vanishing gradients, exploding gradients, and high computational and resource requirements, which hinder efficient training and deployment on memory-limited devices.
Innovation Solution
Implementing Forward-Forward learning, which involves two forward passes with positive and negative datasets to train each layer independently, reducing the need for channel gradient modeling and feedback, and enabling layer-wise training without gradient storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If backpropagation-based end-to-end learning is used for training wireless communication systems, then the system can achieve end-to-end optimization, but it suffers from vanishing gradients, exploding gradients, and high computational resource requirements
Solution Approach 1:
The patent divides the end-to-end training process into separate forward passes for different layers, training each layer independently rather than requiring full backpropagation through the entire system. This segmentation eliminates the need for gradient computation and storage across multiple layers, reducing memory requirements and computational complexity while maintaining end-to-end optimization capability.
Solution Approach 2:
The patent extracts and removes the backpropagation mechanism from the training process, replacing it with forward-forward learning that only requires forward passes. By taking out the gradient computation and feedback mechanisms, the system eliminates vanishing and exploding gradient problems while significantly reducing computational resource requirements.
2Measurement precision
If backpropagation is used for training deep neural networks in wireless systems, then the system can learn complex patterns, but it requires storing gradients and has high memory requirements
Solution Approach 1:
The patent segments the training process into independent forward passes for each layer, eliminating the need to store gradients across layers. Each layer is trained separately using only forward propagation, which requires minimal memory storage while still enabling the network to learn complex patterns through iterative forward-forward learning.
3Adaptability or versatility
If channel gradient modeling is performed in wireless communication systems, then the system can adapt to channel variations, but it increases computational complexity and training time
Solution Approach 1:
The patent extracts and removes the channel gradient modeling step from the training process. Instead of computing and storing channel gradients, the system uses forward-forward learning that adapts to channel variations through forward passes alone, significantly reducing computational complexity and training time while maintaining adaptability.
4Reliability
If feedback mechanisms are implemented for end-to-end learning, then the system can optimize performance, but it consumes additional channel resources and increases system complexity
Solution Approach 1:
The patent removes the feedback mechanism from the training process, replacing it with forward-forward learning that requires no feedback transmissions. This eliminates additional channel resource consumption for feedback while maintaining performance optimization through independent layer training based on forward pass results.
Data Source
AI summary
A wireless communication system may use forward-forward learning to for end-to-end learning. A transmitter may pass a positive dataset and a negative dataset through each of its layers for model training. Each layer may correspond to a goodness function. The transmitter may send the positive dataset to a receiver. The receiver may generate a second positive dataset and a second negative dataset based on the positive dataset sent from the receiver. The receiver may train each of its layers using the second positive dataset and the second negative dataset.