Semantic Communication System for High-Throughput Data Transmission
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Solution Overview
Problem
Traditional wireless communication systems struggle with high computational demands and processing delays, especially under bad channel conditions, making them inadequate for real-time applications with heavy traffic such as VR/AR and ultra-high-resolution video-on-demand.
Innovation Solution
Implementing a semantic communication system that processes data to generate semantic representations, using a variational autoencoder with a scaling layer and noise dimension elimination, and transmits these representations with a transport block containing a soft delivery part and an exact delivery part.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error-free deterministic communication modes are used, then data transmission reliability is improved, but computational demands increase and processing delays occur
Solution Approach 1:
The patent extracts and transmits only the essential semantic meaning and key features of data rather than complete raw data. The semantic communication system identifies and transmits only the most important information elements, eliminating redundant data transmission while maintaining communication reliability.
Solution Approach 2:
Instead of transmitting raw data and ensuring error-free delivery, the patent inverts the approach by transmitting semantic representations that are inherently more robust to channel errors. The semantic layer provides natural redundancy and meaning preservation that tolerates transmission imperfections.
2Reliability
If traditional communication modes are used for real-time applications with heavy traffic, then data integrity is maintained, but processing delays increase
Solution Approach 1:
The patent extracts only the essential semantic features and meaning from data before transmission. By removing redundant information and transmitting only the core semantic content, the system significantly reduces processing time while maintaining data integrity through semantic preservation.
Solution Approach 2:
The patent performs semantic processing and feature extraction before data transmission. This preliminary action prepares compressed semantic representations in advance, reducing the computational burden during real-time transmission and minimizing processing delays.
3Reliability
If complete data transmission is used for high quality experience, then data completeness is improved, but data transmission size increases
Solution Approach 1:
The patent extracts and transmits only the essential semantic meaning, key features, and important information elements from complete data sets. This extraction process maintains data completeness in terms of semantic content while dramatically reducing the actual data transmission size.
Solution Approach 2:
Instead of transmitting complete raw data, the patent inverts the approach by transmitting compressed semantic representations that capture the essence and meaning of the data. This inversion achieves data completeness at the semantic level with minimal transmission size.
Data Source
AI summary
An apparatus configured to receive data inputs corresponding to a plurality of data objects that are to be transmitted, process the data inputs to generate a semantic representation of each of the data objects, wherein the semantic representation comprises a semantic distance for each of the data objects, wherein the semantic distance relates each of the data objects to each other and prepare, for transmission, the semantic representations of the data objects.