End-to-End Neural Network Chain for Wireless Data Streaming
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Solution Overview
Problem
The complexity of designing and implementing a robust and adaptable data transmission process in wireless systems, particularly in video calls, is hindered by the modular design approach, which leads to high complexity and difficulty in ensuring compatibility and optimal latency across various devices in the transmission path.
Innovation Solution
Implementing an end-to-end neural network chain that spans the nodes of the transmission path, allowing for joint training of transmitter and receiver neural networks to encode and decode data efficiently, thereby reducing the need for individual process design and testing and enhancing adaptability to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a modular design approach is used with individually handcrafted processes at each node, then each process can be designed and implemented separately, but the overall system complexity increases and adaptability to changing conditions decreases
Solution Approach 1:
The patent merges multiple individually designed processes (data encoding, channel encoding, modulation, demodulation, channel decoding, data decoding) into a unified end-to-end neural network model. This single model processes the entire data transmission chain, eliminating the need for separate design and coordination of each modular component, thereby reducing overall system complexity while maintaining implementation flexibility.
2Ease of manufacture
If individually handcrafted processes are implemented at each node, then design and testing can be done separately for each process, but ensuring compatibility and optimal latency across all devices becomes difficult
Solution Approach 1:
By combining all transmission and reception processes into a single end-to-end neural network model, the patent ensures inherent compatibility across all devices in the transmission path. The model is trained to optimize the entire data flow from source to destination, automatically achieving optimal latency and compatibility without requiring complex coordination between separately designed components.
Solution Approach 2:
The patent employs feedback mechanisms during the training phase of the neural network, where the model learns from end-to-end transmission performance. This feedback loop allows the system to automatically adjust and optimize parameters across all devices, ensuring compatibility and minimal latency without manual intervention in coordinating each individual process.
3Ease of operation
If a modular design with multiple entities overseeing different devices is used, then each entity can manage their respective devices independently, but it becomes difficult to ensure optimal end-to-end transmission performance
Solution Approach 1:
The patent merges the functionality of multiple independently managed devices into a unified neural network model that processes data end-to-end. This approach allows each entity to continue managing their respective hardware independently while the combined model ensures optimal overall transmission performance, effectively decoupling operational independence from system optimization.
Data Source
AI summary
Systems and techniques provide for the joint training and implementation of an end-to-end chain of neural networks along the nodes of an at least partially wireless transmission path used to transmit a data stream between a data source device and at least one data sink device. The source-side neural networks of the chain can implement one or both of data encoding and channel encoding of outgoing data blocks, and the sink-side neural networks of the chain conversely can implement one or both of channel decoding and data decoding to provide efficient end-to-end transmission of the data stream without necessitating individual design, test, and implementation of discrete processes for each coding and decoding stage, while also facilitating the adaptation of the end-to-end neural network chaining process to various operational parameters.