Semantic Communication Framework for Bandwidth-Constrained Data Transmission
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional communication techniques often transmit raw data unnecessarily, wasting bandwidth and resources, as they do not effectively utilize semantic information that can convey meaningful data efficiently.
Innovation Solution
A semantic communication framework that extracts semantic information from raw data using AI/ML models and schedules the transmission of data elements and metadata based on priority and protection levels, optimizing data transmission by prioritizing critical information and reducing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional communication techniques transmit raw data, then complete information is conveyed, but bandwidth and resources are wasted
Solution Approach 1:
The patent extracts semantic information from raw data using AI/ML models before transmission. The transmitting entity processes raw data through neural networks to identify and transmit only the essential semantic content and metadata, leaving out redundant information that would otherwise require bandwidth resources.
Solution Approach 2:
The patent transforms data from raw format to semantic representation format. By changing the parameter representation from complete raw data to extracted semantic features and metadata, the system achieves efficient transmission while preserving essential information content.
2Quantity of substance
If semantic information is extracted and transmitted, then transmission overhead is reduced, but system complexity increases due to AI/ML models
Solution Approach 1:
The patent segments the communication system into distinct functional modules: raw data processing module, AI/ML semantic extraction module, metadata generation module, and transmission scheduling module. This segmentation allows complex AI/ML operations to be isolated and managed separately from the core communication functions.
Solution Approach 2:
The patent introduces AI/ML models as intermediary components between raw data and transmission. These intermediaries process and transform data into semantic representations, acting as a bridge that reduces the complexity burden on the communication system by handling extraction tasks separately.
3Productivity
If data transmission is prioritized based on semantic importance, then critical information is conveyed efficiently, but scheduling complexity increases
Solution Approach 1:
The patent assigns different priority levels and protection levels to different data elements based on their semantic importance. Critical information elements receive higher priority and enhanced protection, while less critical elements use standard transmission parameters. This local differentiation optimizes communication efficiency without requiring complex global scheduling.
Solution Approach 2:
The patent changes transmission parameters dynamically based on semantic priority. Data elements are assigned different modulation schemes, coding rates, and resource allocation parameters according to their importance level, enabling efficient prioritization through parameter adjustment rather than complex scheduling algorithms.
Data Source
AI summary
A device may include a processor configured to extract semantic information from received data, generate one or more data elements based on the extracted semantic information for an instance of time, generate metadata associated with the generated one or more data elements, schedule a transmission of the one or more data elements and the metadata according to a scheduling configuration, and encode scheduling information indicating the scheduling configuration for the transmission.