Finite Memory Network Coding Buffer Segmentation
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Solution Overview
Problem
Network coding techniques, such as RLNC, require significant memory and computational resources, especially in nodes that need to store and process all received packets, leading to high complexity and power consumption, which is not feasible in nodes with limited processing power or memory.
Innovation Solution
Implementing finite memory network coding by limiting the number of packets stored for coding purposes, using a coding buffer to store only a finite number of packets (S packets) for network coding operations, and a separate decoding buffer for decoding, reducing memory and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random linear network coding (RLNC) is used to achieve robust high throughput multi-cast packet distribution, then network throughput and reliability are improved, but memory requirements and computational complexity increase significantly
Solution Approach 1:
The patent segments the network coding functionality into two distinct buffers: a coding buffer that stores only a finite number of packets (S packets) for coding operations, and a decoding buffer that stores all received packets for decoding. This segmentation allows the system to maintain high throughput while reducing the memory and computational burden on relay nodes by limiting the coding buffer size.
2Reliability
If all received packets are stored in a node to support coding operations, then coding completeness is improved, but memory requirements increase significantly
Solution Approach 1:
The patent extracts the coding function from the complete packet storage requirement by introducing a separate coding buffer that holds only a finite subset (S packets) of received packets. This extraction allows relay nodes to perform coding operations with limited memory while maintaining coding effectiveness, and separates the decoding requirement (needing all packets) from the coding requirement (needing only S packets).
Data Source
AI summary
Techniques for performing finite memory network coding in an arbitrary network limit an amount of memory that is provided within a node of the network for the performance of network coding operations during data relay operations. When a new data packet is received by a node, the data stored within the limited amount of memory may be updated by linearly combining the new packet with the stored data. In some implementations, different storage buffers may be provided within a node for the performance of network coding operations and decoding operations.