Semantic Communication Systems for Networked Applications
Summary
Semantic communication represents a paradigm shift from conventional bit-level transmission towards the exchange of meaning. By integrating advances in artificial intelligence, machine learning and natural language processing, modern semantic communication systems extract and transmit only those elements of a message that are crucial for a given task or application. This approach dramatically reduces bandwidth demands, enhances robustness to noise and enables intelligent adaptation to diverse network conditions. In practice, a semantic transmitter employs a joint semantic–channel encoder to identify and represent key semantic features, while a corresponding decoder reconstructs the intended meaning at the receiver. Underlying these systems are techniques such as deep-learning-based feature extraction, variable-length coding, knowledge-graph-based representation and semantic segmentation. Networked applications span wireless extended reality, Internet of Things deployments, intelligent warehouse management, optical links over multimode fibres and speech transmission. By focusing on end-to-end performance in terms of task accuracy rather than raw bit-error rates, semantic communication systems promise more efficient resource allocation, lower latency and enhanced quality of experience in next-generation networks.
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Semantic Communication Systems for Networked Applications publication trend
The graph below shows the total number of articles in semantic communication systems for networked applications across all publications each year (not limited to Nature Index journals).
Technical terms
Semantic communication: A communication paradigm that prioritises the transmission of meaning or task-relevant information rather than raw bits.
Semantic encoder/decoder: Neural network modules that extract semantic features from source data and reconstruct the intended meaning at the receiver.
Variable-length coding: An adaptive strategy that assigns shorter or longer codewords based on the semantic importance and channel conditions.
Knowledge graph: A structured representation of entities and relationships used to improve semantic feature extraction and disambiguation.
Multimode fibre: An optical transmission medium supporting multiple propagation paths, here exploited for high-dimensional semantic symbol mapping.
References
- AI Empowered Wireless Communications: From Bits to Semantics. Proceedings of the IEEE (2024).
- Semantic Communications With Variable-Length Coding for Extended Reality. IEEE Journal of Selected Topics in Signal Processing (2023).
- Optical semantic communication through multimode fiber: from symbol transmission to sentiment analysis. Light: Science & Applications (2025).
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