Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications
<|reference_start|>Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications: Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?". Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.<|reference_end|>
@article{rong2024semantic,
    title={Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and
  Channel Security of Wireless Semantic Communications},
    author={Yankai Rong, Guoshun Nan, Minwei Zhang, Sihan Chen, Songtao Wang,
  Xuefei Zhang, Nan Ma, Shixun Gong, Zhaohui Yang, Qimei Cui, Xiaofeng Tao,
  Tony Q.S. Quek},
    journal={arXiv preprint arXiv:2402.02950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02950},
    primaryClass={cs.CR eess.SP}
}