Aggregated FEC Streaming Across Concurrent Data Streams
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Solution Overview
Problem
In communication systems, especially those with limited resources and adverse environments, existing FEC technologies face challenges in efficiently encoding and decoding data across multiple streams without a reverse channel, particularly for low-power, mobile receivers and storage applications, where error correction and data recovery are critical but computational resources are constrained.
Innovation Solution
The implementation of jumbo source blocks, where source symbols from multiple streams are logically or physically grouped into a single entity for FEC processing, generating repair symbols that can be used to recover lost or corrupted data, reducing computational expense and error probability while maintaining efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If FEC encoding is performed independently for each stream, then error correction capability is maintained, but computational overhead increases significantly
Solution Approach 1:
The patent combines multiple independent FEC streams into a single aggregated stream for joint encoding. Instead of performing separate FEC operations on each stream, the system merges the streams and applies a unified FEC code, reducing the total number of encoding operations while maintaining error correction capability across all original streams.
Solution Approach 2:
The aggregated FEC system serves multiple original streams simultaneously through a single encoding process. The universal FEC code structure can correct errors in any of the constituent streams, providing multi-functional error protection without requiring separate dedicated FEC mechanisms for each stream.
2Measurement precision
If computational resources are increased to handle multiple streams independently, then error recovery accuracy improves, but power consumption increases
Solution Approach 1:
By merging multiple streams into a single aggregated FEC process, the system reduces the total computational operations required compared to independent processing of each stream. This consolidation lowers power consumption while the joint decoding process maintains the ability to accurately recover errors in any individual stream through the unified error correction code.
3Adaptability or versatility
If separate FEC processing is used for each stream, then stream independence is maintained, but processing time increases
Solution Approach 1:
The system merges multiple streams into a single FEC processing pipeline, performing encoding and decoding operations once for the aggregated data rather than separately for each stream. This significantly reduces total processing time while the underlying code structure preserves the ability to identify and correct errors in individual streams through the unified error correction mechanism.
Data Source
AI summary
Transmitters and receivers deal with streams of data, wherein the receiver is expected to begin using received data before receiving all of the data. Concurrent streams are sent and FEC coding is used with the streams and done as an aggregate. The transmitter performs FEC operations over the plurality of streams, wherein source blocks from at least two streams logically associated into a jumbo source block and FEC processing is performed to generate one or more jumbo repair block from the jumbo source block. Each of the source blocks comprises one or more source symbols from their respective stream. The jumbo source symbols can be of constant size and are suitably aligned along size boundaries that make processing efficient. Each source symbol need not be the same size, and the number of source symbols from each stream in a jumbo source block need not be the same value across streams.