Neural Network Data Transmission System End-to-End Optimization
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Solution Overview
Problem
Existing data transmission and processing systems are inefficient due to individually optimized components that do not achieve optimal end-to-end performance, and end-to-end training of neural networks for such systems is impractical for longer block lengths.
Innovation Solution
A method and device that configure a data transmission and processing system using a neural network-based machine learning model trained via stochastic gradient descent, optimizing all components jointly for end-to-end performance, including multiple dense layers and normalization layers in transmitters and receivers, and allowing for distributed detection and classification systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If individually optimized functional blocks are used in transmitter and receiver, then each block can be designed and optimized independently, but the end-to-end performance is not optimal
Solution Approach 1:
The patent merges multiple individually optimized functional blocks (source coding, channel coding, modulation, pulse shaping) into a unified end-to-end trainable system. By combining these separate components into a single trainable model, the system achieves optimal end-to-end performance while maintaining the functional separation benefits during the training phase.
2Reliability
If end-to-end training of neural network is applied to transmission system, then better end-to-end performance is achieved, but the system does not scale to practical block lengths (e.g., 100 bits)
Solution Approach 1:
The patent segments the transmission system into multiple functional blocks (source coding, channel coding, modulation, pulse shaping) that can be trained independently or in groups, rather than training the entire system as a single monolithic neural network. This segmentation reduces the training complexity and enables scalability to practical block lengths while still achieving good end-to-end performance through coordinated optimization.
3Loss of information
If full raw data is transferred over communication channel, then complete information is available at receiver, but the burden on communication system increases
Solution Approach 1:
The patent applies parameter changes by transforming the data representation through multiple processing stages (source coding, channel coding, modulation) that optimize the data format for transmission. This allows the system to transfer essential information efficiently by changing the parameters and structure of the data, reducing the burden on the communication system while maintaining data completeness.
Data Source
Figure 1~7
Figure 4(a)~4(b)
Figure 5~6
AI summary
Embodiments relate to a method for configuring a data transmission and processing system (1), executed by a configuration device (5), wherein the system (1) comprises at least one transmitter (22), at least one receiver (31), and a communication channel (4) between the transmitter and the receiver, the method comprising: - training (S1) a machine learning model (6) of the data transmission and processing system in function of training data, wherein the machine learning model specify at least a transmitter model (7) including a transmitter neural network (12, 13), a channel model (8), and a receiver and application model (34) including a receiver and application neural network (15, 16), wherein the training data specify respective sets of input vector (s) and output vector (d), wherein an output vector is different from the corresponding input vector, - configuring (S2) at least one of the transmitter and the receiver in function of the trained machine learning model.