Priority-Based Codeword Builder for Low-Latency FEC Throughput
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Solution Overview
Problem
Current communication systems face challenges in achieving high performance and throughput, particularly in servicing communication traffic with varying characteristics and priorities, as they struggle to lower the signal-to-noise ratio required for a given bit error ratio or symbol error ratio, and do not adequately address the need for efficient error correction and encoding across different service flows.
Innovation Solution
The implementation of a codeword builder system that generates information blocks with varying priority profiles, using buffers and processors to manage packet storage and encoding, allowing for adaptive error correction and forward error correction (FEC) or error checking and correction (ECC) coding, thereby optimizing signal transmission across different communication pathways and service flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional communication systems use standard error correction coding, then reliability is maintained, but throughput and performance cannot be optimized for different service flows with varying priorities
Solution Approach 1:
The communication system segments traffic into different priority queues (first priority queue for latency-sensitive traffic, second priority queue for other traffic) and applies different error correction coding strategies to each segment. This allows the system to optimize throughput for non-critical traffic while maintaining reliability for latency-sensitive traffic, resolving the contradiction between overall throughput and error correction reliability.
Solution Approach 2:
The system dynamically selects error correction coding parameters and codes based on the priority and characteristics of each service flow. For first priority traffic, the system uses coding configurations optimized for latency and reliability, while for second priority traffic, it uses configurations optimized for throughput. This dynamic adaptation allows the system to simultaneously achieve high reliability where needed and high throughput where possible.
2Productivity
If the system waits for sufficient data to fill codewords, then spectral efficiency improves, but latency increases for time-sensitive traffic
Solution Approach 1:
The system segments codeword construction into priority-based batches, where first priority queue data is processed separately from second priority queue data. This segmentation allows latency-sensitive first priority traffic to be encoded and transmitted without waiting for codeword filling, while second priority traffic can wait for optimal codeword composition. This resolves the contradiction by applying different timing strategies to different traffic segments.
Solution Approach 2:
The system performs preliminary encoding of first priority queue data before the codeword is fully filled, rather than waiting for complete data accumulation. This preliminary action reduces latency for time-sensitive traffic while maintaining spectral efficiency for non-critical traffic that can wait for optimal codeword composition.
3Adaptability or versatility
If the system uses complex priority-based queue management, then service quality for different traffic types improves, but system complexity increases
Solution Approach 1:
The system divides the communication processing into distinct priority queues (first and second priority queues) with dedicated error correction coding paths for each. This segmentation provides clear, manageable structures for handling different service flows, making the complexity organized and controllable rather than chaotic. Each queue has its own coding parameters and processing logic, simplifying the overall management compared to a single undifferentiated processing path.
Solution Approach 2:
The system implements a universal error correction coding framework that can handle multiple priority levels and service flow types through a unified architecture. The same basic coding mechanisms and processing structures are reused across different priority queues, with configurable parameters rather than completely separate systems. This multi-functionality reduces overall system complexity while maintaining the ability to differentiate and optimize for various service flows.
Data Source
AI summary
A communication device (alternatively, device) includes a processor configured to support communications with other communication device(s) and to generate and process signals for such communications. In some examples, the device includes a communication interface and a processor, among other possible circuitries, components, elements, etc. to support communications with other communication device(s) and to generate and process signals for such communications. Such a communication device includes a processor configured to perform codeword builder functionality to generate information that undergoes error checking and correction (ECC) and/or forward error correction (FEC) coding. The processor intelligently selects packets from buffers to generate information blocks that undergo ECC and/or FEC coding and transmission and to meet certain latency constraints in conjunction with a predetermined period of time (e.g., a programmable threshold). Such a communication device may be implemented in a point-to-multipoint communication system that services multiple other communication devices.