End-to-End Learning for Communication System Optimization
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Solution Overview
Problem
Current communication systems are not optimally designed as a unified system, leading to suboptimal performance in transmitter, relay, and receiver components, with existing end-to-end learning methods not fully addressing the need for comprehensive optimization across differentiable performance metrics.
Innovation Solution
A transmission system comprising a transmitter, multiple channels, and a receiver, where all components have trainable weights and are optimized using a loss function based on a training sequence, allowing for iterative updates of parameters through stochastic gradient descent to improve communication performance across different channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate design and optimisation of each system component is used, then device complexity is reduced and ease of manufacture is improved, but communication performance and reliability deteriorate
Solution Approach 1:
The patent merges the transmitter, relay, and receiver into a unified end-to-end system where all components share trainable parameters. This combining approach allows the system to optimize communication performance across the entire chain rather than treating components independently, directly resolving the contradiction between ease of manufacture and communication performance.
Solution Approach 2:
The trained neural network model serves multiple functions: it acts as the transmitter encoder, relay processor, and receiver decoder simultaneously. This multi-functionality eliminates the need for separate optimized components while achieving superior communication performance across different scenarios.
2Reliability
If end-to-end learning with deep neural networks is applied, then communication performance and reliability are improved, but device complexity and training requirements worsen
Solution Approach 1:
The patent segments the communication system into distinct functional modules (transmitter, relay, receiver) that can be independently implemented while sharing a unified training process. This segmentation allows for manageable complexity in each component while maintaining end-to-end optimization benefits.
Solution Approach 2:
The system uses trainable parameters that can be dynamically adjusted during the learning process. By changing parameters through gradient descent optimization, the system achieves high communication performance without requiring complex fixed architectures, thus managing device complexity while improving reliability.
3Reliability
If iterative updating of trainable parameters is performed, then communication performance is improved, but training time and computational resources increase
Solution Approach 1:
The patent implements a feedback mechanism through the loss function that measures communication performance and guides parameter updates. This feedback loop enables efficient iterative optimization, allowing the system to achieve high performance while managing training time through directed gradient descent updates based on performance metrics.
Data Source
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AI summary
An apparatus, method and computer program is described comprising: initialising parameters of a transmission system, wherein the transmission system comprises a transmitter, a first channel, at least one relay, a second channel and a receiver, wherein the transmitter includes a transmitter algorithm having at least some trainable weights, the at least one relay includes a relay algorithm having at least some trainable weights and the receiver includes a receiver algorithm having at least some trainable weights; receiving a first training sequence of messages at the receiver, wherein the first training sequence of messages is sent from the transmitter to the at least one relay using the first channel and is sent from the at least one relay to the receiver using the second channel; obtaining or generating a loss function; and updating trainable parameters of the transmission system based on the loss function, wherein the trainable parameters include at least some of the trainable weights of the transmitter, at least some of the trainable weights of the at least one relay, and at least some of the trainable weights of the receiver.