Receiver Training Adaptive Learning Rate Switching
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Solution Overview
Problem
Current receiver training methods are inefficient, requiring prolonged time to equalize channels, which is a challenge in high-speed data communication systems where quick training is necessary to maintain low bit error rates.
Innovation Solution
Implementing a high learning rate for initial training followed by a switch to a low learning rate using exponentially weighted moving average convergence, with a selection circuit determining the transition based on a predetermined stability criteria, allowing for reduced training time and adaptive channel equalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a high learning rate is used for initial training, then the training speed is improved, but the stability of channel equalization is worsened
Solution Approach 1:
The learning rate is made dynamic by switching between high and low rates based on real-time monitoring of channel equalization stability. The system transitions from a high learning rate phase to a low learning rate phase when stability criteria are met, allowing the system to adapt its behavior over time.
Solution Approach 2:
The system implements feedback by continuously monitoring channel equalization parameters and using this information to determine when to switch learning rates. The feedback mechanism ensures that the high learning rate is used only when the channel is unstable and switched to low learning rate when stability is achieved.
2Stability of the object's composition
If a low learning rate is used for fine tuning, then the stability of channel equalization is improved, but the training time is worsened
Solution Approach 1:
The training process is segmented into two distinct phases: an initial high learning rate phase for rapid convergence and a subsequent low learning rate phase for fine tuning. This segmentation allows the system to achieve both speed and stability without requiring the entire training process to use a low learning rate.
Solution Approach 2:
The system performs preliminary action by using a high learning rate during the initial training phase to quickly converge toward the optimal channel equalization settings. Once stability is achieved, the system transitions to fine tuning, avoiding the need to use a low learning rate throughout the entire training process.
3Reliability
If the receiver trains for a prolonged time to equalize channels, then the bit error rate is reduced, but the productivity is worsened
Solution Approach 1:
The system changes the learning rate parameter dynamically during training. By switching from high to low learning rate based on stability criteria, the system achieves reliable channel equalization with reduced training time, improving both bit error rate performance and productivity.
Data Source
AI summary
Apparatus and associated methods relate to using a high learning rate to speed up the training of a receiver and switching from a high learning rate to a low learning rate for fine tuning based on exponentially weighted moving average convergence. In an illustrative example, a selection circuit may switch the high learning rate to the low learning rate based on a comparison of a moving average difference en to a predetermined stability criteria T1 of the receiver. The moving average difference en may include an exponentially weighted moving average of a difference between two consecutive exponentially weighted moving averages of an operation parameter un of the signal communication channel. By using this method, the training time for the receiver may be advantageously reduced.