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.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

Unmanned aerial vehicle (UAV) wireless image transmission faces challenges of limited bandwidth and dynamic channel conditions. To address these issues, we propose a novel semantic communication architecture employing a dual-level semantic transmission strategy that decomposes original images into coarse-grained and fine-grained semantic information. Specifically, by integrating the efficient MobileViT network into a joint source-channel coding (JSCC) framework, we design a lightweight semantic encoder-decoder dedicated to coding the image details. To ensure reliable transmission, we introduce a decoupled control-data transmission (DCDT) mechanism that transmits the dual-level semantic information over independent channels, with a fusion module at the receiver integrating them to produce the reconstructed image. Experimental results demonstrate that the proposed system significantly outperforms the traditional method and standard JSCC method in both reconstruction quality and channel robustness, while achieving performance comparable to the heavyweight model with substantially reduced model complexity, thereby validating its deployment potential on resource-constrained UAVs.

Semantic communication (SemCom) is emerging as a key technology for efficient and robust data transmission in future networks. To improve its reliability, the integration of hybrid automatic repeat request (HARQ) has garnered increasing attention. However, current semantic HARQ methods often rely on redundant retransmissions and do not effectively utilize past semantic and channel information, leading to inefficient resource usage. In this paper, we propose a semantic communication framework that incorporates cross-training feedback integration. This system dynamically fuses semantic features and channel states from previous transmissions to optimize the current encoding process. At the transmitter, a fusion module utilizes the semantic representation from the previous decoding to refine the current transmission. At the receiver, the channel buffer and the semantic combiner work together to progressively integrate features, thereby improving the reliability of semantic decoding. Extensive experiments on image transmission under various channel conditions and bandwidth settings demonstrate that the proposed method achieves significant performance gains.

Semantic and task-oriented communications have emerged as significant paradigm shifts for next-generation communication networks, which extracts and transmits task-relevant information rather than raw data for downstream tasks. However, most existing work focused on bit-level loss functions, such as mean square error (MSE) and cross-entropy (CE), rather than directly optimizing at the semantic level. These approaches often lack interpretability of semantic representation and result in higher system complexity and less efficient transmission for various tasks. To this end, we develop a novel task-oriented semantic communication system for multitask scenarios and further develop a semantic-level framework. This framework can extract structured semantic representation by compressing raw data with different labels into mutually orthogonal subspaces. Simulation results demonstrate that the proposed framework not only extracts structured semantic representation, but also outperforms existing benchmarks in terms of data recovery and AI inference performance.

The 6G wireless communication demands intelligent and versatile interaction between humans and machines that can deal with various intelligent tasks. Semantic communication that focuses on transmitting the meanings rather than the data is expected to be one of the promising technologies to achieve this goal. However, most existing semantic communication systems optimize the whole system under a single objective, lacking the scalabi-lity to dynamic scenarios. For a dynamic scenario with changing channel conditions and background knowledge, we propose a layered semantic communication system (LSCS), which takes advantage of layered coding architec-ture at the semantic and syntactic levels. In addition, a symbolic attention-based denoising network is designed at the receiver to recover transmitted meanings. Simulation results demonstrate that the proposed LSCS can adapt to dynamic scenarios and achieve superior performance over benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) region.