Iterative Concatenated Decoding Using Known Correct Bits
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Solution Overview
Problem
Existing decoding methods for concatenated convolutional-encoded and block-encoded signals, particularly in higher order modulated systems, fail to effectively reduce bit error rate (BER) by not adequately utilizing known correct bits to improve soft metric calculations.
Innovation Solution
A method involving the application of encoded signals to soft metric generators and Viterbi decoders, where known correct bits are used to generate modified soft metrics, recalculate soft symbol metrics at each Viterbi decoder input, and iteratively refine the decoding process to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional soft metric generation is used in higher order modulated systems, then the decoding process is simpler, but the bit error rate is higher due to inadequate utilization of known correct bits
Solution Approach 1:
The patent implements feedback by using the output from the block decoder (which identifies known correct bits) to modify the soft metric generation process in subsequent Viterbi decoding iterations. The soft metric generator uses the known correct bit information from previous decoding passes to adjust and refine the soft metrics, creating a feedback loop that progressively improves decoding reliability and reduces bit error rate.
Solution Approach 2:
The patent applies preliminary action by performing an initial block decoding pass to identify known correct bits before the main Viterbi decoding process. This preliminary identification of reliable bits is then used to guide and constrain subsequent soft metric generation and Viterbi decoding operations, improving overall decoding performance from the outset.
2Measurement precision
If known correct bits are used to generate modified soft metrics, then decoding accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by modifying soft metrics only for those specific bit positions where known correct bit information is available, rather than uniformly processing all bits. The soft metric generator selectively adjusts metrics based on the local reliability information from block decoding, improving decoding accuracy for critical positions while minimizing unnecessary computational overhead across the entire data stream.
3Reliability
If iterative decoding with modified soft metrics is implemented, then bit error rate reduces, but processing time increases
Solution Approach 1:
The patent implements periodic action through iterative decoding passes where the block decoder and soft metric generator operate in alternating cycles. Each iteration refines the decoding based on previously identified known correct bits, progressively reducing bit error rate. The periodic nature allows the system to balance processing time and reliability by stopping after a predetermined number of iterations or when convergence is achieved.
Data Source
AI summary
Encoded symbols of a concatenated convolutional-encoded and block encoded signal are presented to a conventional first stage of a concatenated decoder, comprising in sequence a soft metric generator, a Viterbi decoder, a first de-interleaver and a first block decoder such as a Reed-Solomon decoder. The encoded symbols are also presented to a delay chain to produce progressively delayed encoded symbols. Where an output block of the conventional decoder is indicated as being a valid codeword by the first block decoder, the bytes in this block are marked as being correct. These bytes that are known to be correct are then used after interleaving and serialization as known bits input to a second stage of the decoder process operating on the delayed encoded symbols and incorporating a modified soft metric generator constrained by the known bits. This process can be extended to further iterations as required. A modified Viterbi decoder, which is also constrained by the known bits, may also be used in the second and subsequent iterative stages.