Transmitter-Receiver Training Handshake for Adaptive Radio Links
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Solution Overview
Problem
Existing communication systems lack effective protocols for training communication systems using machine learning techniques, particularly in optimizing transmitters and receivers for specific radio environments and improving information rates.
Innovation Solution
A communication system protocol that includes handshaking between a transmitter and a receiver to initiate a training procedure, exchanging labeled training data, and updating trainable parameters to adapt to specific radio environments, with mechanisms for terminating the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning techniques are used to train transmitters and receivers, then adaptability to specific radio environments and information rates are improved, but device complexity and protocol complexity increase
Solution Approach 1:
The training protocol is segmented into distinct phases: handshaking phase for initiating training, data exchange phase for transferring labeled training data, and termination phase for completing training. This segmentation organizes the complex machine learning training process into manageable, standardized steps that reduce protocol complexity while maintaining adaptability.
Solution Approach 2:
The patent implements feedback mechanisms where receivers send training feedback to transmitters during the training process. This feedback loop enables iterative optimization of machine learning models, allowing the system to adapt to radio environments through structured information exchange rather than uncontrolled complexity.
2Productivity
If labeled training data is exchanged between transmitter and receiver, then training effectiveness and communication efficiency are improved, but loss of time for training procedures increases
Solution Approach 1:
The patent employs preliminary actions by pre-labeling training data before transmission and preparing training datasets in advance. This allows the actual training process to proceed more efficiently without requiring time-consuming data preparation during operation, thus reducing training time while maintaining effectiveness.
Solution Approach 2:
The training protocol enables continuous training operations where multiple training iterations can proceed without complete restarts. The structured handshaking and data exchange mechanisms allow training to continue seamlessly, maximizing productivity while minimizing idle time between training cycles.
Data Source
AI summary
A communications system and method is described comprising: handshaking between a transmitter and a receiver of the communication system to initiate a training procedure, wherein said handshaking comprises a training setup request message comprising parameters for the training procedure, wherein the transmitter comprises trainable parameters and/or the receiver comprises trainable parameters; receiving identified training data from the transmitter at the receiver, wherein the training data comprises transmitter training data and/or receiver training data; sending training information from the receiver to the transmitter, wherein the training information comprises information for controlling training at the transmitter and/or the receiver; and terminating the training procedure.