Optical Transmitter Adaptive PAM Encoding for Channel Capacity
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Solution Overview
Problem
In optical fiber access networks, the lack of adaptive preprocessing methods at the optical transmitter side limits the utilization of channel capacity due to non-linear channel responses, leading to inter-symbol interference and reduced transmission accuracy.
Innovation Solution
An optical transmitter performs a training process to select and update Pulse Amplitude Modulation (PAM) values based on feedback from the receiver, using neural network learning or table lookup to optimize encoding strategies and maximize channel capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed or experience-based preprocessing is used in the optical transmitter, then the implementation is simple, but the flexibility and channel capacity utilization are very limited
Solution Approach 1:
The patent implements dynamic preprocessing by training a neural network model (e.g., LSTM) at the optical transmitter to adaptively predict optimal PAM values based on previously transmitted symbols. This transforms the static, fixed preprocessing into a dynamic, adaptive system that can adjust to varying channel conditions, thereby improving flexibility and channel capacity utilization while maintaining reasonable implementation complexity through software-based learning.
2Reliability
If intelligent equalization algorithms are used on the optical receiver side, then transmission errors are compensated, but the channel capacity cannot be fully utilized due to lack of transmitter-side preprocessing
Solution Approach 1:
The patent applies preliminary action by implementing transmitter-side preprocessing using a trained neural network model that predicts optimal PAM values before transmission. This pre-compensation of channel effects at the transmitter reduces inter-symbol interference and improves signal quality at the receiver, thereby enhancing both transmission reliability and channel capacity utilization without requiring complex receiver-side equalization.
3Productivity
If adaptive preprocessing is implemented at the optical transmitter, then channel capacity utilization is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent implements self-service by training the neural network model (e.g., LSTM) at the optical transmitter to automatically learn and adapt to channel characteristics through feedback from transmitted symbols. The system performs self-optimization by adjusting its preprocessing strategy based on observed transmission performance, thereby improving channel capacity utilization while managing complexity through autonomous adaptation rather than requiring external configuration or complex hardware modifications.
Data Source
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AI summary
Embodiments are disclosed of methods performed at an optical transmitter, devices and computer-readable storage media. For example, the method comprises performing a process to select a PAM value from a set of candidate PAM values based on currently transmitted training data bits, transmitted Pulse Amplitude Modulation (PAM) values corresponding to previously transmitted training data bits, and training data bits to be transmitted subsequently, the selected PAM value corresponding to the currently transmitted training data bits. The selected PAM value is transmitted to an optical receiver, and an indication as to whether the optical receiver correctly detects the currently transmitted training data bits is received from the optical receiver. Then, the process is updated at least in part based on the indication. The manner of performing neural network learning at an optical transmitter side to obtain an encoding strategy can maximize the channel allowable capacity.