Batch Sparse Coding for Low-Complexity Multicast Transmission
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing communication systems face challenges in efficiently transmitting large files to multiple destinations with low encoding/decoding complexity, minimal network delay, high throughput, and reduced computational and storage requirements at intermediate nodes, especially when using linear network coding.
Innovation Solution
The method employs sparse matrix codes to generate batches of coded packets, allowing for efficient transmission and decoding with belief propagation algorithms, minimizing buffer size and control messages, and enabling linear network coding operations at intermediate nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If linear network coding is used to improve network throughput and resilience to packet loss, then transmission reliability and throughput are improved, but encoding/decoding complexity and computational requirements at intermediate nodes increase quickly
Solution Approach 1:
The patent divides the data file into multiple chunks, where each chunk is independently encoded and transmitted. This segmentation allows intermediate nodes to perform simple linear coding operations on individual chunks rather than the entire file, significantly reducing computational complexity while maintaining the reliability benefits of network coding.
Solution Approach 2:
The patent changes the parameter of code structure from traditional linear network coding to a segmented chunk-based approach with specific linear combination coefficients. This parameter change enables intermediate nodes to perform simpler recoding operations while preserving the overall network coding benefits for throughput and reliability.
2Device complexity
If chunk-based network coding schemes are used to reduce encoding/decoding complexity, then complexity is reduced, but transmission delay increases due to sequential chunk transmission scheduling
Solution Approach 1:
The patent enables continuous transmission of multiple chunks in parallel rather than sequential transmission. Intermediate nodes continuously recode and forward packets from multiple chunks simultaneously, eliminating idle time between chunk transmissions and reducing overall transmission delay while maintaining low encoding complexity.
Solution Approach 2:
The patent performs preliminary chunk segmentation and prepares multiple chunks for simultaneous transmission. By pre-organizing the data into independently encodable chunks with predetermined coding schemes, the system eliminates scheduling delays and enables continuous parallel transmission, reducing overall transmission time.
3Productivity
If traditional network coding schemes are used to achieve network capacity for multicast, then throughput is maximized, but control message overhead and protocol complexity increase
Solution Approach 1:
The patent extracts and eliminates the complex control message exchange mechanism from traditional network coding schemes. By using predetermined linear combination coefficients and independent chunk encoding, the system achieves network capacity without requiring extensive control messages for coordination, significantly reducing protocol overhead.
Solution Approach 2:
The patent implements self-service encoding where each intermediate node independently performs recoding operations using predetermined coefficients without requiring complex control messages from the source or destination nodes. This self-service approach maintains maximum throughput while minimizing protocol overhead by eliminating the need for extensive coordination signaling.
Data Source
AI summary
A method for data encoding and associated decoding is based on the concept of batches that allows transmission of a large data file from a source node to multiple destination nodes through communication networks that may employ network coding wherein sparse matrix codes are employed in a network setting. A batch is a set of packets generated by a subset of the input packets using sparse matrix encoder. A sparse matrix encoder can be called repeatedly to generate multiple batches. The batches are generally independent of one another. During the transmission in a communication network, network coding can be applied to packets belonging to the same batch to improve the multicast throughput. A decoder recovers all or at least a fixed fraction of the input packets using received batches. The input packets can be pre-coded using a pre-code before applying sparse matrix codes. The data file can then be reconstructed by further decoding the pre-code.