Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

The vision for sixth generation wireless communication entails an intelligent and personalized communication scheme that addresses individual needs. To accomplish this, semantic communication with the help of machine learning and natural language processing technologies can play a crucial role. However, existing semantic communication systems fail to model the interaction between semantics and emotion, which is vital for personalized communication. This letter proposes an emotion-aided semantic communication system that utilizes an external knowledge base for emotion extraction at the transmitter and adopts emotion recognition and selection at the receiver for semantic recovery. Simulation results demonstrate the effectiveness of incorporating emotional knowledge into semantic communication, especially in the low signal-to-noise ratio (SNR) region.

In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

In the low signal-to-noise ratio region, a large number of bit errors occur, and it may exceed the channel error correction capability of the receiver. Traditional communication system may use the technology of automatic repeat-request to deal with this problem, which is time consuming and a waste of resources. To enhance the reliability of the communication system, we investigate reasoning and decoding at the semantic level instead of the grammar level. In particular, we propose a semantic communication model for text transmission, assisting the communication system to be more robust in terrible channel environments. Based on the traditional communication system, the language model BERT, part of speech tagging, and prior information concerning bit-flipping are introduced to enhance the semantic reasoning ability of the transceiver. Furthermore, this paper analyzes the effects of the sub-strategies on the performances of the improved communication model, such as the existence of a candidate set and language model. The numerical results show the effectiveness of our model in terms of improving the semantic accuracy measured by BLUE, the METEOR score, and the similarity score based on BERT between transmitted messages and recovered messages.

Semantic communication (SemCom) is expected to play a key role in 6G and digital twin network (DTN) environments, where reliability depends on preserving meaning and user intent rather than reconstructing bit sequences. This paper presents a GenAI-driven, contextual-knowledge-enhanced SemCom framework that strengthens semantic recovery by integrating relational, intra-sentential, inter-sentential, and historical knowledge. At the transmitter, a context-attentive (CA) mechanism combines sentence-level semantic features with relational knowledge generated by a relation-guided knowledge base. At the receiver, a knowledge enhancement (KE) mechanism employs Transformer-XL to incorporate historical semantic context and utilizes a relational knowledge modeling module to refine semantic inference. Experimental results demonstrate that the proposed framework achieves robust semantic recovery across a wide range of signal-to-noise ratios (SNRs), with significant improvements in low-SNR conditions. Ablation studies further highlight the complementary roles of contextual knowledge at the transmitter and receiver. These results confirm the effectiveness of contextual knowledge in enhancing deep learning-based SemCom and underscore its potential for deployment within future DTN-enabled 6G architectures.