Inference Model Parameter Optimization for ISI Mitigation
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Solution Overview
Problem
Next-generation passive optical network (PON) systems face significant inter-symbol interference (ISI) due to bandwidth limitations and chromatic dispersion, which affects the reliability of data transmission and decoding.
Innovation Solution
The implementation of a near-optimal channel equalization approach using a Viterbi-type algorithm and a parameter optimization method for an inference model to optimize the performance of the channel equalizer, which reduces ISI and enhances the accuracy of data decoding by modifying parameters to maximize likelihoods of true states and minimize non-realized states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If bandwidth-limited reception is used to reduce receiver cost, then device complexity is reduced, but inter-symbol interference increases
Solution Approach 1:
The patent introduces an inference model as an intermediary component between the bandwidth-limited receiver and the Viterbi algorithm. This inference model processes the received signal to generate enhanced inputs for the Viterbi algorithm, enabling effective ISI mitigation without requiring higher bandwidth hardware. The inference model acts as a software-based mediator that compensates for the hardware limitations.
Solution Approach 2:
The patent optimizes parameters of the inference model to maximize the likelihood of true states and minimize non-realized states. By dynamically adjusting these parameters based on channel conditions, the system achieves effective equalization performance despite bandwidth limitations. The parameter optimization enables the system to adapt to varying channel characteristics and maintain robust performance.
2Length of moving object
If chromatic dispersion occurs in optical fibers, then signal transmission distance is extended, but inter-symbol interference increases
Solution Approach 1:
The patent employs a feedback mechanism where the inference model continuously processes received signals and adjusts its parameters based on optimization criteria. The system uses the received signal quality information to refine its parameter estimates, creating a closed-loop system that adapts to chromatic dispersion effects. This feedback-driven approach enables the system to compensate for dispersion-induced ISI over extended transmission distances.
Solution Approach 2:
The inference model performs preliminary processing of the received signal before it is fed into the Viterbi algorithm. By pre-processing the signal and generating optimized inputs, the system prepares the data in a form that is more resistant to the effects of chromatic dispersion. This preliminary action reduces the burden on subsequent processing stages and improves overall system performance.
3Reliability
If maximum likelihood sequence estimation equalizers are deployed to alleviate ISI, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the equalization function into two distinct components: an inference model for signal processing and a Viterbi algorithm for sequence estimation. This segmentation allows each component to be optimized independently, reducing the overall complexity compared to a monolithic MLSE equalizer. The inference model handles the computationally intensive signal processing, while the Viterbi algorithm focuses on sequence estimation.
Solution Approach 2:
The patent replaces traditional hardware-based equalization mechanisms with a software-based inference model. Instead of using complex analog or digital signal processing circuits, the system uses a trained inference model that can be implemented in software or firmware. This substitution reduces hardware complexity while maintaining or improving equalization performance.
Data Source
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AI summary
A method and apparatus for optimizing parameters of an inference model that obtains one or more data values from a signal received from a communication channel and generates an input for a Viterbi-type algorithm are disclosed. The input comprises a one or more data values where each data value is indicative of a likelihood of a sequence of transmitted data values given the obtained one or more data values from the signal. The method comprises receiving a signal via the communication channel, the signal corresponding to a transmission of a first vector of data values, obtaining a second vector of data values comprising one or more data values from the signal, evaluating the inference model on the basis of the second vector, obtaining a third vector of data values based on an output of the inference model and modifying one or more parameters of the inference model based on the first vector and the third vector.