Reduced-State Trellis Equalizer Bounded State Enumeration
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Solution Overview
Problem
High baud rate communication systems face significant challenges with inter-symbol interference (ISI) and phase noise, which introduce errors in signal decoding, especially in next-generation networks with data rates exceeding 100 Gbps over long distances, due to channel impairments like multipath propagation and chromatic dispersion.
Innovation Solution
The implementation of reduced-state trellis equalization techniques using bounded state enumeration, which computes accumulated path metrics for a subset of candidate states selected based on a neighborhood map, reducing the computational complexity and memory requirements by excluding unnecessary states, thereby mitigating ISI and phase noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full-state trellis equalization is used to mitigate ISI and phase noise, then decoding accuracy is improved, but computational complexity and processing latency increase significantly
Solution Approach 1:
The patent segments the set of candidate states into two subsets: a first subset of candidate states and a second subset of candidate states. By dividing the full state space, the system computes accumulated path metrics only for the first subset, reducing computational complexity while maintaining decoding accuracy through selective state evaluation.
Solution Approach 2:
The patent extracts and excludes the second subset of candidate states from the computation process. By identifying and removing unnecessary states from the trellis diagram, the system eliminates redundant calculations while preserving the essential states needed for accurate decoding, thereby reducing processing latency and computational burden.
2Reliability
If full-state trellis equalization is used to reduce Bit Error Rate, then decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the candidate states into first and second subsets, computing accumulated path metrics only for the first subset. This segmentation reduces the number of computations required per symbol period, thereby reducing processing latency while maintaining adequate decoding accuracy through selective state evaluation.
Solution Approach 2:
The patent applies partial action by computing accumulated path metrics for only a portion (the first subset) of the candidate states rather than all states. This partial computation approach reduces processing time while still achieving sufficient decoding accuracy by focusing computational resources on the most relevant states.
3Productivity
If reduced-state trellis equalization is used to reduce computational complexity, then processing efficiency is improved, but decoding accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by treating different candidate states differently through selective computation. The first subset of candidate states receives full computational treatment with accumulated path metrics computed, while the second subset is excluded. This differentiated approach maintains decoding accuracy for critical states while improving processing efficiency by skipping less relevant states.
Solution Approach 2:
The patent changes the parameter of state selection by using a neighborhood map to identify and select only the first subset of candidate states based on their proximity to the current state. This parameter-based selection (spatial proximity in the trellis) ensures that computationally efficient reduced-state equalization maintains decoding accuracy by focusing on locally relevant states.
Data Source
AI summary
Embodiment reduced-state trellis equalization techniques compute accumulated path metrics (APMs) for a subset of candidate states for at least some stages in the trellis based on a neighborhood map of an ML state. This reduces the number of APMs that are computed and stored during trellis equalization. Other embodiments select a subset of candidate states for which APMs are transported to the next stage of the trellis based on the neighborhood map. This eliminates the need to sort the remaining APMs during reduced state trellis equalization. The neighborhood map identifies a subset of the highest probability neighbors for an ML state. The subset of candidate states identified as highest probability neighbors may be saved in a look-up table. The look-up table may be generated offline and/or generated/updated dynamically during run-time operation.