REIN Tracker Erasure Decoding for Telecommunication Noise
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Solution Overview
Problem
Repetitive impulse noise (REIN) degrades communication performance by corrupting data transmitted over telecommunication channels, and existing solutions like frame blanking require modifications to transmitters and additional feedback paths, increasing complexity and cost.
Innovation Solution
A system that uses a REIN tracker to predict and mark data affected by REIN, employing forward error correction with erasure decoding to mitigate noise effects without requiring transmitter modifications or additional feedback paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frame blanking is implemented to mitigate REIN, then error correction capacity is improved, but device complexity increases due to transmitter modifications and feedback path requirements
Solution Approach 1:
The receiver independently performs REIN detection and frame blanking without requiring transmitter modifications or feedback communication. The receiver uses its own received signals to identify REIN patterns and mark affected frames for erasure decoding, making the system self-sufficient and eliminating the need for complex transmitter changes or feedback paths.
Solution Approach 2:
Instead of having the transmitter modify its operation based on receiver feedback (conventional approach), the invention inverts the approach by having the receiver independently detect REIN and control the blanking process. The receiver marks affected frames and provides erasure information to the decoder, reversing the traditional controller role.
2Reliability
If frame blanking with feedback path is implemented, then REIN mitigation is improved, but system complexity increases due to additional feedback channel requirements
Solution Approach 1:
The receiver performs REIN detection and frame blanking control independently using its own received signals. No feedback channel is needed because the receiver autonomously identifies REIN patterns and marks affected frames for erasure decoding, eliminating the complexity of additional feedback paths.
Solution Approach 2:
The invention extracts the REIN detection and frame blanking control functions from the transmitter-feedback loop and relocates them to the receiver. The receiver independently performs these functions using local signal analysis, removing the need for complex feedback infrastructure.
3Reliability
If frame blanking is used to discard corrupted frames, then payload data integrity is improved, but loss of information increases due to discarded frames
Solution Approach 1:
Instead of simply discarding frames marked as affected by REIN, the system uses erasure decoding to recover the lost information. The decoder receives erasure markers indicating which frames are corrupted and uses the FEC code structure to reconstruct the original data, thereby recovering information that would otherwise be lost.
Solution Approach 2:
The system performs preliminary erasure marking of frames expected to be corrupted by REIN before decoding. This allows the decoder to prepare and allocate resources for erasure correction in advance, improving the efficiency of information recovery and reducing the impact of discarded frames.
Data Source
AI summary
The present disclosure generally pertains to systems and methods for compensating for repetitive impulse noise (REIN) affecting signals that are communicated over a telecommunication channel. A system in accordance with one exemplary embodiment of the present disclosure includes a transmitter and receiver that communicate over a telecommunication channel. The data is encoded by the transmitter using a forward error correction (FEC) algorithm, such as Reed-Solomon coding, before being transmitted over a telecommunication channel to the receiver. The REIN tracker analyzes the data received by the receiver in order to predict when future occurrences of REIN will likely affect the data being communicated over the channel. The REIN tracker then marks erasures in a received data stream based on its REIN predictions. A decoder then decodes FEC code words in the data stream based on the marked erasures.