Sequential Fountain Decoding With Triangular Matrix for Low Latency
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Solution Overview
Problem
Radio communication networks face latency issues due to the need for receivers to wait for all data packets to arrive before decoding, which increases processing time and power consumption, especially with traditional fountain codes that require O(n^3) operations and do not extend to larger alphabets or reduce latency.
Innovation Solution
Implementing a fountain-code based decoding method that uses a triangular decoding matrix, allowing for sequential decoding with the arrival of data packets, reducing the number of operations to O(k^2) and enabling decoding of sub-messages as they arrive, with the transmitter splitting messages into sub-messages and forming data packets through linear combinations for efficient transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fountain codes are used for decoding, then error correction capability is maintained, but decoding latency increases due to O(n^3) operations and requirement to wait for all packets
Solution Approach 1:
The patent segments the decoding process into iterative steps that can be executed progressively as packets arrive. Instead of performing complete O(n^3) matrix inversion only after all packets are received, the decoder performs sequential matrix operations on received packets, updating the solution incrementally. This allows partial decoding results to be obtained earlier while maintaining the same error correction capability through complete decoding when sufficient packets are received.
Solution Approach 2:
The patent performs preliminary decoding actions as packets arrive before all packets are received. The receiver begins matrix operations and decoding calculations with the subset of packets already received, rather than waiting for complete packet collection. This preliminary action reduces latency by producing intermediate results earlier while the complete decoding is finalized when additional packets arrive.
2Measurement precision
If all data packets are collected before decoding, then decoding accuracy is improved, but processing time and power consumption increase
Solution Approach 1:
The patent implements a dynamic decoding approach where the decoding process adapts to the arrival of packets over time. The decoder transitions from an static batch-processing model (wait for all packets then decode) to a dynamic iterative model where decoding operations are continuously updated as new packets arrive. This dynamic approach allows the system to balance between decoding accuracy and power consumption by performing calculations progressively rather than all at once.
Solution Approach 2:
The patent maintains continuous useful action by performing decoding operations continuously as packets arrive, rather than having idle time while waiting for all packets. The decoder continuously processes received packets, updating the solution matrix and recovering message bits progressively. This continuous processing utilizes computational resources efficiently and reduces total power consumption by avoiding large batch operations concentrated at the end.
3Loss of time
If sequential decoding with packet arrival is implemented, then latency is reduced, but computational complexity management becomes more challenging
Solution Approach 1:
The patent segments the computational workload into manageable iterative steps corresponding to packet arrivals. Each packet triggers a specific matrix operation (e.g., Gaussian elimination step, matrix inversion on sub-matrix) rather than requiring the complete O(n^3) operation to be managed as a single complex task. This segmentation makes computational complexity more manageable by breaking it into smaller, predictable units that can be executed sequentially.
Solution Approach 2:
The patent changes the parameter of computational approach from batch processing to iterative processing. The matrix operations are performed with changing dimensions as packets arrive (e.g., inverting smaller sub-matrices progressively rather than one large matrix at the end). This parameter change transforms the computational complexity from a single large operation to multiple smaller operations, making it more manageable while achieving the same result.
Data Source
AI summary
The present disclosure concerns radio communication. More particularly, the disclosure relates to encoding/decoding. A method is performed by a receiver (300) for decoding data from a message which is received from a transmitter (100). The receiver (300) is generally configured to use a decoding matrix in the decoding. Furthermore, the message is typically split into a pre-defined number of sub-messages. The receiver (300) receives data packets from the transmitter (100), wherein the received data packets are formed according to a certain structure. Furthermore, the receiver (300) decodes the received data packets as the data packets are received and the decoding is performed sequentially with the arrival (i.e. reception) of the data packets. Hereby it is made possible to reduce, and thus received improve, the latency.