End-to-End Neural Network Training for Communication Systems
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Solution Overview
Problem
Current communication systems lack an efficient method for optimizing the entire system as a single entity, leading to suboptimal performance in transmitter and receiver training, particularly in complex communication scenarios.
Innovation Solution
The system employs trainable algorithms for both transmitters and receivers, using receiver-training sequences and perturbed transmitter-training sequences to iteratively adjust weights based on loss functions, enabling simultaneous training of transmitter and receiver neural networks to optimize channel performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate design and optimisation of each part of the system is used, then device complexity is reduced, but system performance is suboptimal
Solution Approach 1:
The patent merges the separate design of transmitters and receivers into a unified end-to-end training framework. Both transmitters and receivers are trained simultaneously using a shared loss function that optimizes the entire communication system performance, rather than optimizing each component independently. This combining approach resolves the contradiction by achieving optimal system performance through integrated training while maintaining manageable complexity through modular algorithm design.
2Ease of manufacture
If traditional training methods are used for transmitters and receivers, then training process is simple, but training performance is suboptimal
Solution Approach 1:
The patent implements a feedback mechanism where the loss function computed at the receiver is transmitted back to the transmitter. This feedback loop enables gradient-based optimization where training sequences are perturbed and the resulting loss changes guide the adjustment of transmitter parameters. This feedback approach improves training accuracy while maintaining computational feasibility through efficient gradient estimation techniques.
Solution Approach 2:
The patent employs parameter changes by introducing perturbations to training sequences and adjusting transmitter/receiver weights based on loss function gradients. The training process dynamically modifies parameters such as perturbation magnitudes, learning rates, and weight values to optimize communication performance. This parameter adaptation enables high training accuracy while maintaining a systematic and manageable training process.
3Reliability
If end-to-end training is implemented, then system performance is optimized, but computational complexity increases
Solution Approach 1:
The patent segments the end-to-end training process into distinct but coordinated phases: transmitter training with perturbed sequences, receiver training with received sequences, and loss computation. Each segment can be implemented and optimized independently while contributing to the overall system performance. This segmentation reduces training complexity by breaking down the complex optimization problem into manageable sub-tasks that can be processed separately and then integrated.
Data Source
AI summary
A method, apparatus and computer program are described includes obtaining or generating a transmitter-training sequence of messages for a first transmitter of a first module of a transmission system, wherein the transmission system includes the first module having the first transmitter and a first receiver, a second module having a second transmitter and a second receiver, and a channel, wherein the first transmitter includes a transmitter algorithm having at least some trainable weights; transmitting a perturbed version of the transmitter-training sequence of messages from the first transmitter to the second receiver over the channel of the transmission system; receiving a first loss function at the first receiver from the second transmitter, wherein the first loss function is based on the transmitted perturbed versions of the transmitter-training sequence of messages as received at the second receiver and knowledge of the transmitter-training sequence of messages for the first transmitter of the transmission system; and training at least some weights of the transmitter algorithm of the first transmitter based on the first loss function.