Circular Viterbi Decoder Using Soft Correlation and Path Metrics
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Solution Overview
Problem
Existing electronic systems face challenges in signal processing due to the hardware and memory intensity of circular Viterbi algorithms, which limits their implementation in various communication systems, leading to increased costs and reduced performance.
Innovation Solution
An electronic system with a support chip and a circular Viterbi mechanism that generates path metrics, stores intermediate metrics, and calculates soft correlation metrics, utilizing Radix-4 trellis and modulo normalization to enhance throughput and reduce overflow, allowing for efficient decoding without replicating data blocks and minimizing additional hardware complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If circular Viterbi algorithm is implemented in hardware, then decoding accuracy is improved, but hardware complexity and memory requirements increase significantly
Solution Approach 1:
The patent segments the circular Viterbi decoding process into multiple iterations, where each iteration processes a portion of the repeated blocks. The decoding is divided into forward Viterbi operations on individual blocks and combination operations that integrate results across iterations, reducing the hardware complexity compared to processing all repeated blocks simultaneously.
Solution Approach 2:
The patent performs partial Viterbi operations on subsets of the repeated blocks in each iteration rather than completing full decoding of all blocks. By performing multiple partial iterations and combining results, the system achieves accurate decoding while using less hardware resources than a complete simultaneous processing approach would require.
2Reliability
If circular Viterbi algorithm processes repeated communication blocks, then signal processing performance is improved, but processing time and latency increase
Solution Approach 1:
The patent implements continuous decoding across multiple iterations by transferring soft information from previous iterations to subsequent ones. The soft correlation metrics and path metrics are accumulated and refined across iterations without interrupting the processing flow, maintaining continuous useful action that improves performance while managing latency through efficient iteration management.
Solution Approach 2:
The patent performs preliminary Viterbi operations on individual blocks before combining results in subsequent iterations. By pre-processing blocks individually and storing intermediate path metrics, the system prepares data in advance for the combination phase, reducing the computational burden during critical decoding phases and optimizing overall processing time.
3Reliability
If soft information from previous decoding attempts is transferred to next iteration, then decoding accuracy is refined, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential soft information (soft correlation metrics and path metrics) from previous decoding attempts for transfer to the next iteration, rather than storing complete decoding states. This selective extraction reduces memory requirements while preserving the critical information needed for accuracy refinement in subsequent iterations.
Solution Approach 2:
The patent applies different storage strategies to different types of data: soft correlation metrics are stored with specific precision requirements, while path metrics are stored at repetition depth with different memory allocation. This local quality approach optimizes memory usage by allocating storage resources according to the specific needs of each data type rather than using uniform storage for all information.
Data Source
AI summary
A electronic system includes: a support chip configured to receive an input code stream; a circular Viterbi mechanism, coupled to the support chip, configured to: generate a final path metric for the input code stream, store intermediate path metrics at the repetition depth, generate a repetition path metric for the input code stream, and calculate a soft correlation metric based on the final path metric, the repetition path metric, and the intermediate path metrics.