Turbo Decoder Recursion for Unknown DBCRSC Boundary States
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Solution Overview
Problem
Conventional Turbo decoding techniques are inadequate for decoding double-binary circular recursive systematic convolutional (DBCRSC) encoded signals in OFDMA systems, particularly under the mobile WiMAX standard, where initial and final states are unknown.
Innovation Solution
A Turbo decoder and method that execute iterations of forward and backward recursion on a payload block and a padded block using estimated forward and backward state metrics to produce updated state metrics, reducing decoding delay, calculation complexity, and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional Turbo decoding techniques are used, then decoding can be performed when initial and final states are known, but the technique is not applicable when initial and final states are unknown as in mobile WiMAX standard
Solution Approach 1:
The patent applies preliminary action by performing forward and backward recursions to estimate the initial and final states before actual decoding. The decoder executes iterations of forward recursion to generate forward state metrics and backward recursion to generate backward state metrics, using these estimates as starting points for subsequent decoding operations. This preliminary estimation enables the decoder to handle unknown boundary conditions effectively.
Solution Approach 2:
The patent implements feedback by using the estimated forward and backward state metrics to update and refine the decoding process. The forward state metrics from one iteration are used as input for the next iteration, and similarly for backward state metrics. This iterative feedback mechanism allows the decoder to converge to accurate decoding results even when initial states are unknown.
2Productivity
If iterations of forward and backward recursion are executed with updated estimated metrics, then decoding efficiency is improved and computational burden is reduced, but the complexity of the decoding process increases
Solution Approach 1:
The patent applies segmentation by dividing the decoding process into distinct forward recursion and backward recursion phases. The forward recursion processes the received signal from beginning to end, generating forward state metrics, while the backward recursion processes from end to beginning, generating backward state metrics. This segmentation allows each phase to be optimized independently and simplifies the overall control structure.
Solution Approach 2:
The patent implements dynamics by making the decoding process adaptive through iterative updates. The estimated forward and backward state metrics are dynamically updated across multiple iterations, with each iteration refining the estimates based on previous results. This dynamic adaptation allows the decoder to improve performance while managing computational complexity through controlled iteration.
Data Source
AI summary
A Turbo decoder and method for decoding a double-binary circular recursive systematic convolutional (DBCRSC) encoded signal with unknown initial and final states executes iterations of forward and backward recursion on a payload block of the DBCRSC encoded signal and a padded block using an estimated forward state metric (FSM) and an estimated backward state metric (BSM), which are updated at one or more iterations.