Neural Network Symbol Modulation for Wireless Adaptability
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Solution Overview
Problem
Current communication systems face challenges in adapting symbol modulation techniques to rapidly varying channel conditions and non-linear impairments, particularly in high-frequency bands, due to limitations in conventional PHY layer approaches and feedback methods.
Innovation Solution
Implementing a data-driven approach using neural networks for symbol modulation and demodulation, where a WTRU receives training reference signals from a base station to train autoencoder neural networks, allowing for adaptive symbol constellation diagrams and modulation mapping, enabling efficient symbol modulation and demodulation even in challenging channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional PHY layer approaches are used for symbol modulation, then device complexity is reduced, but adaptability to rapidly varying channel conditions deteriorates
Solution Approach 1:
The patent implements dynamic symbol constellation diagrams that can adapt to rapidly varying channel conditions. The system transitions from static conventional modulation schemes to dynamic constellations that are adjusted in real-time based on channel state information, allowing the modulation parameters to change dynamically to match current channel conditions.
Solution Approach 2:
The system employs self-service through automated neural network-based optimization of modulation parameters. The base station and WTRU use trained neural networks to automatically determine optimal constellation points and modulation schemes without requiring complex manual configuration or intervention, enabling the system to self-adapt to channel variations.
2Adaptability or versatility
If real-time feedback is implemented for neural network training, then adaptability improves, but loss of time increases due to feedback overhead
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks using machine learning techniques before actual communication occurs. The neural networks are trained offline using simulated channel conditions and then deployed for real-time inference, eliminating the need for extensive real-time feedback while maintaining adaptability through the pre-learned models.
Solution Approach 2:
The system implements optimized feedback mechanisms where quality indicators are calculated and fed back to the base station to trigger retraining only when necessary. This selective feedback approach reduces overhead compared to continuous feedback, maintaining adaptability while minimizing time loss through intelligent feedback scheduling.
3Reliability
If neural networks are deployed for symbol demodulation, then bit error rate performance improves, but device complexity increases
Solution Approach 1:
The patent employs copying by deploying identical or similar neural network models at both the base station and WTRU. The same trained neural network architecture and parameters are used at both ends of the communication link, simplifying implementation and reducing device complexity while maintaining consistent and reliable demodulation performance.
Solution Approach 2:
The neural network model serves multiple functions: it performs both symbol demodulation and channel adaptation tasks. This multi-functionality reduces the need for separate dedicated components, thereby reducing overall device complexity while maintaining or improving bit error rate performance through the versatile neural network.
4Adaptability or versatility
If symbol constellations are dynamically adjusted, then adaptability to channel conditions improves, but manufacturing precision requirements increase
Solution Approach 1:
The patent implements parameter changes by dynamically adjusting constellation parameters such as amplitude, phase, and spacing based on channel conditions. The neural networks optimize these parameters in real-time, allowing the system to adapt to varying channel quality while maintaining sufficient precision through learned parameter adjustments rather than requiring fixed high-precision manufacturing.
Data Source
AI summary
Procedures, methods, architectures, apparatuses, systems, devices, and computer program products are disclosed are directed to data-driven wireless transmit/receive unit specific symbol modulation. In an embodiment, a method implemented in a wireless transmit/receive unit, WTRU, includes receiving, from a base station, a first transmission comprising a first information indicating one or more parameters. A neural network (NN) is initialized based on the first information. A second transmission is received from the base station and includes a reference signal (RS). The NN is trained based on the RS. A quality indicator (QI) value is computed based on a NN loss of demodulation of the RS. On condition that the QI value satisfies a threshold, the trained NN is deployed for use in connection with demodulating at least one symbol.