Joint Transceiver Neural Network Training via Gradient Feedback
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently training transmitter and receiver neural networks due to separate designs and dynamic real-world conditions, leading to model mismatches and inefficiencies.
Innovation Solution
A method for joint online training of transmitter and receiver neural networks using a feedback mechanism that conveys information for updating parameters, allowing for real-time adaptation to dynamic conditions and addressing device variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If transmitter and receiver neural networks are trained separately, then device complexity is reduced and ease of manufacture is improved, but model mismatch occurs and communication efficiency deteriorates
Solution Approach 1:
The patent merges the training processes of transmitter and receiver neural networks into a unified joint training framework. The transmitter neural network and receiver neural network are trained simultaneously using shared training data and coordinated optimization, allowing the models to learn complementary representations that improve communication efficiency while reducing model mismatch between transmitter and receiver.
2Productivity
If neural networks are trained offline with fixed models, then training time is reduced and productivity is improved, but adaptability to dynamic real-world conditions deteriorates
Solution Approach 1:
The patent implements dynamic training where the neural network models continuously adapt to changing communication conditions. The training process incorporates real-time feedback about channel characteristics, signal quality, and communication performance, allowing the models to update their parameters and adjust their behavior dynamically rather than relying on fixed offline-trained models.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor communication performance and feed this information back into the training process. The feedback loop enables the neural networks to learn from actual communication outcomes and adjust their parameters accordingly, improving adaptability to dynamic conditions while maintaining high productivity through automated continuous training.
3Device complexity
If separate training protocols are used for transmitter and receiver, then device complexity is reduced, but communication efficiency and performance deteriorate
Solution Approach 1:
The patent creates a universal training framework that serves both transmitter and receiver neural networks simultaneously. The shared training protocol handles multiple functions including transmitter model optimization, receiver model optimization, and coordination between the two networks, reducing overall device complexity while improving communication efficiency through unified optimization.
Data Source
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AI summary
A method of wireless communication performed by a receiving device includes determining a transmission reference point value and determining a transmission reference point gradient of a loss based on the transmission reference point value. The receiving device also transmits a message comprising the transmission reference point gradient to a transmitting device. A method of wireless communication by a transmitting device includes receiving a transmission reference point gradient of a loss from a receiving device. The transmitting device determines a transmission point-payload gradient of a transmission reference point value with respect to an encoded value generated by a transmitter neural network. The transmitting device also determines a payload gradient of the loss based on a product of the transmission reference point gradient and the transmission point-payload gradient. The transmitting device further updates weights of the transmitter neural network based on the payload gradient.