Adaptive Communication Transmitter Configuration Using Channel Features
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Solution Overview
Problem
AI models deployed in communication systems face precision loss due to environmental changes, requiring retraining which causes delays and overheads.
Innovation Solution
A first apparatus determines a transmitter configuration based on a received signal, allowing a second apparatus to update its configuration in real time without affecting signal transmission, using channel feature codewords and awareness masks to adjust processing blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an offline trained model is deployed in the system, then the model can be used initially, but the model precision is reduced due to environmental changes over time
Solution Approach 1:
The patent implements dynamic model adaptation by enabling the AI model to continuously learn from new channel state information in real-time operating conditions. The model transitions from a static offline-trained state to a dynamic state where it continuously updates its parameters based on actual environmental changes, maintaining high precision despite environmental variations.
Solution Approach 2:
The patent establishes a feedback mechanism where channel state information is continuously fed back to the AI model for retraining. The receiver obtains channel state information and feeds it back to the transmitter, which then retrains the AI model with the new information. This closed-loop feedback system enables the model to adapt to environmental changes while maintaining precision.
2Reliability
If the model is retrained to adapt to environmental changes, then the model precision can be maintained, but a delay and training overheads are introduced
Solution Approach 1:
The patent performs preliminary actions by extracting key features from channel state information before feeding them to the AI model for retraining. The receiver obtains channel state information and extracts relevant features in advance, which are then used for model retraining. This preliminary feature extraction reduces the computational burden and time required for complete model retraining.
Solution Approach 2:
The patent extracts only the essential channel state information features needed for model retraining, rather than using the complete raw data. By taking out and extracting only the relevant features from the channel state information, the system reduces the amount of data processing required during retraining, thereby minimizing training delay and overheads while maintaining model precision.
3Adaptability or versatility
If the transmitter configuration is updated in real time, then the system can adapt to environmental changes, but the signal transmission may be affected
Solution Approach 1:
The patent performs preliminary configuration updates by exchanging configuration information between transmitter and receiver before actual signal transmission occurs. The receiver feeds back channel state information to the transmitter, which updates its configuration in advance. This preliminary configuration update ensures that when signal transmission begins, the transmitter is already optimized for the current environment, avoiding disruptions to actual transmission.
Data Source
AI summary
A communication method and a communication apparatus. The method includes: a first apparatus receives a signal from a second apparatus through a first channel, where the signal is generated by the second apparatus based on a first transmitter configuration; determines a channel feature of the first channel based on the received signal; determines a second transmitter configuration based on the channel feature, where the second transmitter configuration indicates at least a processing block configuration, and the processing block configuration indicates a processing granularity for performing signal processing; and sends the second transmitter configuration to the second apparatus. In this way, the second apparatus can update a transmitter configuration in real time without affecting signal transmission, thereby avoiding a problem that the transmitter configuration is no longer accurate due to an environment change, and avoiding an extra delay and training overheads.