Joint Fountain and Network Coding for Multi-Hop Throughput
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Solution Overview
Problem
Existing network coding methods face challenges in achieving high data throughput and low transmission delay due to packet loss, traffic fluctuations, and buffer overflow in multi-hop networks, particularly in lossy communication channels.
Innovation Solution
The proposed solution involves a network system that uses joint Fountain and Network coding (FUN) with accurate rank distribution estimation to encode and re-encode data packets, employing rateless coding and batch-wise buffer insertion to stabilize the data link quality and improve throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random linear network coding (RLNC) is used to overcome packet loss, then end-to-end throughput is improved to 1-ε, but computational complexity and coefficient overhead increase significantly
Solution Approach 1:
The patent segments the file into non-overlapping or overlapping subsets (generations) and restricts coding within each subset. This segmentation approach reduces computational complexity at relay nodes while maintaining good throughput performance, resolving the contradiction between high throughput and low complexity.
Solution Approach 2:
The patent applies different coding strategies to different parts of the data flow. Fountain codes are used for encoding at the source, while simplified coding is applied at relay nodes. This local differentiation allows the system to achieve high throughput where needed while reducing complexity at intermediate nodes.
2Device complexity
If fountain codes are used for encoding, then encoding/decoding complexity is low, but throughput degrades to (1-ε)L in L-hop networks due to accumulated packet losses
Solution Approach 1:
The patent merges fountain codes with network coding to create a hybrid scheme. Fountain codes provide low encoding/decoding complexity, while network coding components are introduced at relay nodes to combat accumulated packet losses across multiple hops, achieving both low complexity and high throughput.
Solution Approach 2:
The patent creates a composite coding scheme that combines the advantages of fountain codes (low complexity) and network coding (high throughput in multi-hop networks). This composite approach allows the system to achieve throughput close to 1-ε while maintaining manageable encoding/decoding complexity.
3Device complexity
If packets are partitioned into subsets for coding, then computational complexity is reduced, but coding efficiency decreases due to restricted coding within each subset
Solution Approach 1:
The patent introduces overlapping generations where packets can belong to multiple subsets. This dynamic structure allows relay nodes to perform coding with limited memory while maintaining better coding efficiency compared to non-overlapping partitions, resolving the contradiction between complexity reduction and efficiency maintenance.
4Productivity
If rank distribution estimation is performed accurately, then data link quality is stabilized and throughput is improved, but measurement and estimation difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where relay nodes estimate rank distribution based on received packets and transmit this information back to the source node. This feedback enables accurate rank distribution estimation without excessive complexity, as the estimation is performed incrementally using actual received data rather than requiring complete channel knowledge.
Data Source
AI summary
A network system for increasing data throughput and decreasing transmission delay along a data link from a source node to a sink node via a relay node is provided. The network system may include a first node configured to encode a second multitude of data packets based on an estimated rank distribution expressing a quality of the data link and transmit the encoded second multitude of data packets. The network system may also include at least a second node configured to estimate the rank distribution based on a first multitude of data packets received from the first node prior to receiving at least one of the encoded second multitude of data packets, transmit the estimated rank distribution to the first node, and regenerate the second multitude of data packets if receiving a sufficient quantity of the encoded second multitude of data packets.