DisplayPort Link Training with Noise Injection for Stable Sync
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Solution Overview
Problem
Existing link training methods in DisplayPort technology often result in signal quality that is near optimal but not fully optimized, leading to potential signal loss due to noise and synchronization issues, and the firmware-based final adjustments are not always performed or updatable.
Innovation Solution
Intentionally adding noise to the signal during the link training process using a noise source to simulate a noisy environment, allowing the link controller to find a more robust link configuration that can tolerate regular noise levels, thereby improving signal quality without altering existing firmware or devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If link training is performed without added noise, then the training process is simpler and faster, but the signal link quality is not optimized and the receiver may lose synchronization due to noise
Solution Approach 1:
The patent applies preliminary action by intentionally adding noise to the training signal before link training begins. This pre-conditioning of the signal with noise allows the receiver to learn and adapt to noisy environments during the training phase, improving reliability without complicating the overall device architecture. The noise injection is a preparatory step that enhances robustness before actual operation.
Solution Approach 2:
The patent converts the harmful effect of noise into a beneficial training mechanism. By deliberately injecting noise during link training, the system transforms what would normally be a detrimental factor (signal degradation) into a useful tool for optimizing link robustness. This allows the receiver to develop tolerance to noise, improving signal link quality while maintaining the existing link training framework.
2Reliability
If firmware-based final adjustments are performed, then signal quality optimization is attempted, but the firmware is not always performed and cannot be updated post-production
Solution Approach 1:
The patent implements self-service by enabling the source device to autonomously inject noise into the training signal without requiring firmware modifications or updates in the receiver. The source device independently performs the optimization function that would otherwise require complex firmware-based adjustments, eliminating the need for post-production firmware updates while achieving signal quality optimization.
Solution Approach 2:
The patent introduces noise injection as an intermediary mechanism between the source and receiver during link training. This intermediary approach allows optimization to be performed at the source side through signal manipulation, avoiding the need for firmware-based adjustments in the receiver. The noise injection acts as a mediator that enables optimization without requiring changes to the receiver's firmware or reducing its adaptability.
3Reliability
If noise is intentionally added to the training signal, then the link configuration becomes more robust to noise, but the training process becomes more complex
Solution Approach 1:
The patent applies parameter changes by modifying the training signal's characteristics through intentional noise injection. By changing the signal parameters (adding controlled noise components), the system enables the receiver to learn optimal detection thresholds and timing that are robust to noise. This parameter modification approach achieves noise tolerance while using existing link training infrastructure, minimizing the increase in device complexity.
Data Source
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AI summary
A method and device of over training a connection is provided. Noise is intentionally supplied and added to a signal that is subjected to a link training operation. The link training operation is used to obtain a link between a source device and a receiving device. The device includes a noise source from which noise is obtained and added to a signal to aid in link over-training.