Semantic Communication Adaptation for Variable Symbols and RIS Channels
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Solution Overview
Problem
Existing semantic communication technologies lack flexibility and efficiency due to fixed semantic symbol transmission and assumption of error-free communication channels, limiting their effectiveness in real-world applications.
Innovation Solution
Adaptive generation and interpretation of semantic symbols, combined with reconfigurable network nodes to dynamically adjust wireless and semantic channel configurations based on observed data and channel conditions, using reconfigurable intelligent surfaces (RIS) to enhance communication quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed number of semantic symbols is transmitted, then the communication protocol is simple, but the communication efficiency and adaptability deteriorate
Solution Approach 1:
The patent applies dynamics by making the number of semantic symbols transmitted variable rather than fixed. The system dynamically adjusts the number of symbols based on the determined impact of the wireless channel on semantic communication effectiveness. When channel conditions are good, more symbols can be transmitted; when conditions are poor, fewer symbols are transmitted, optimizing communication efficiency while adapting to changing conditions.
Solution Approach 2:
The patent changes the parameter of semantic symbol count based on channel conditions. By determining the impact of the wireless channel and then adapting the number of semantic symbols accordingly, the system optimizes communication efficiency. This parameter change allows the system to balance between transmission efficiency and protocol complexity.
2Reliability
If error-free communication channel is assumed, then the semantic communication model is simplified, but the reliability and effectiveness in real-world applications deteriorate
Solution Approach 1:
The patent applies feedback by determining the impact of the wireless channel on semantic communication effectiveness and using this information to adapt the communication process. The system continuously assesses channel conditions and adjusts the number of semantic symbols transmitted accordingly, creating a closed-loop system that improves reliability while maintaining manageable complexity through intelligent adaptation rather than overly complex error correction mechanisms.
3Reliability
If reconfigurable network nodes are used to adapt channel configuration, then the communication quality improves, but the system complexity and resource requirements increase
Solution Approach 1:
The patent changes parameters of the wireless channel and semantic chain based on determined channel impact. By dynamically adjusting configuration parameters such as the number of semantic symbols and channel settings, the system improves communication quality while avoiding the need for overly complex reconfigurable hardware. The adaptation is achieved through intelligent parameter selection rather than physical reconfiguration of complex systems.
Data Source
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AI summary
The present invention concerns a method for carrying out a semantic communication between a sender and a receiver through a wireless channel comprising one or several reconfigurable network nodes, the method comprising the following steps: - generating (110) semantic symbols to be sent, the semantic symbols being generated by a semantic chain including identifying semantic concepts from observed data and mapping the semantic concepts into semantic symbols; - determining (120) an impact of the wireless channel on a semantic communication effectiveness from the sender to the receiver; - based on the determined impact, adapting (130) configuration parameters of the wireless channel and/or the semantic chain.