Q-Dimensional Product Convolutional Codes for Low-Latency Decoding
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Solution Overview
Problem
Existing error-correcting codes for ultra-high-speed communications, such as product convolutional codes, face challenges in achieving high throughputs (>500 Gb/s) while maintaining low latency, small power consumption, and sufficient coding gain, due to high decoding complexity and limited code-rate flexibility.
Innovation Solution
The introduction of Q-Dimensional Difference-Triangle Set (QD-DTS) Product Convolutional Codes (PrCCs), which reduce decoding complexity and latency, and allow for higher throughputs by employing a systematic recursive convolutional encoder structure and specific bit-mapping rules to ensure orthogonality and efficient error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If product convolutional codes are used for ultra-high-speed communications, then error-correction capability is improved, but decoding complexity increases
Solution Approach 1:
The code is segmented into multiple dimensions (Q-dimensional structure) where each dimension can be decoded independently or iteratively. This segmentation allows the decoding process to be divided into smaller, more manageable tasks, reducing the overall decoding complexity while maintaining strong error-correction capabilities through multi-dimensional parity checks.
Solution Approach 2:
The patent introduces a Q-dimensional structure to the product convolutional code, adding spatial dimensions to the error-correction process. This dimensional expansion allows for more efficient syndrome calculation and decoding by distributing the error-correction workload across multiple dimensions, thereby reducing the complexity of decoding operations while enhancing reliability.
2Reliability
If high coding gain is achieved through traditional product codes, then error-correction performance is improved, but latency increases
Solution Approach 1:
The encoder performs preliminary organization of data into the Q-dimensional structure and pre-calculates parity bits for each dimension before transmission. This preliminary action allows the decoder to quickly process received data without performing complex real-time calculations, thereby reducing latency while maintaining high coding gain through the structured multi-dimensional error-correction mechanism.
3Productivity
If ultra-high data rates (>500 Gb/s) are targeted, then throughput is improved, but power consumption increases
Solution Approach 1:
The high-speed data stream is segmented into parallel processing channels corresponding to different dimensions of the Q-dimensional code. This segmentation enables concurrent processing of multiple data streams, achieving ultra-high aggregate throughput while distributing power consumption across multiple smaller processing units, thereby improving energy efficiency compared to a single monolithic processing channel.
4Ease of manufacture
If code-rate flexibility is limited in traditional product codes, then implementation simplicity is maintained, but adaptability to different communication requirements decreases
Solution Approach 1:
The Q-dimensional product convolutional code structure is designed to be dynamically configurable, allowing the code rate and dimensionality to be adjusted based on communication requirements. The encoder and decoder can be reconfigured to use different values of Q and different component code rates, providing adaptability to various channel conditions and application needs while maintaining the structural simplicity that enables easy implementation.
Data Source
AI summary
Methods, apparatus, systems, architectures and interfaces for encoding/decoding a QD-DTS-PrCC are provided. The decoding method includes determining a number kTS of input bits included in a transmission of a data stream and a first bit of the input bits included in the transmission in the data stream; determining a number of Encoded Bit Blocks (EBBs), each of the EBBs including any number of data blocks that are previously transmitted Transmit Segments (TS) of the data stream, each of the data blocks having a bit length of kTS bits; selecting that number of EBBs for encoding a QD-DTS-PrCC component codeword (QDCC) of the transmission according to a DTS indexing method for indexing a plurality of EBBs; generating the QDCC including a TS, Virtual Segments (VSs), and rc parity bits, a dimensionality of the QD-DTS-PrCC being at least 2; and extracting the calculated TS of the QDCC to an output EBB.