面向6G時(shí)代，本文在全球首次設(shè)計(jì)“一對多”語義通信系統(tǒng)，具有開創(chuàng)性，所提出的“一對多”語義通信系統(tǒng)“MR DeepSC”可以為未來語義通信系統(tǒng)的發(fā)展打下基礎(chǔ)。

這項(xiàng)研究工作得到了國家自然科學(xué)基金62222107、62071223、62031012、61871446和中國科協(xié)青年精英科學(xué)家資助計(jì)劃的部分支持；部分由江蘇省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目BE2020084-1資助；部分由國家自然科學(xué)基金項(xiàng)目92067201資助。

本文參考文獻(xiàn)：

[1] Z. Qin et al., “Deep learning in physical layer communications,” IEEE Wireless Commun., vol. 26, no. 2, pp. 93–99, 2019.

[2] Q. Lan et al., “What is semantic communication? A view on conveying meaning in the era of machine intelligence,” Journal of Communications and Information Networks., vol. 6, no. 4, pp. 336–371, 2021.

[3] N. Farsad et al., “Deep learning for joint sourcechannel coding of text,” IEEE Int’l. Conf. Acoustics Speech Signal Process. (ICASSP)., algary, AB, Canada, pp. 2326–2330, 2018.

[4] H. Xie et al., “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process., vol. 69, pp. 2663–2675, 2021.

[5] F. Zhou et al., “Cognitive semantic communication systems driven by knowledge graph,” IEEE ICC., to be published, 2022.

[6] Z. Weng et al., “Semantic communication systems for speech transmission,” IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2434–2444, 2021.

[7] E. Bourtsoulatze et al., “Deep joint sourcechannel coding for wireless image transmission,” IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 3, pp. 567–579, 2019.

[8] D. B. Kurka et al., “DeepJSCC-f: Deep joint source-channel coding of images with feedback,” IEEE J. Select. Areas Inf. Theory., vol. 1, no. 1, pp. 178—193, 2020.

[9] H. Xie et al., “Task-oriented multi-user semantic communications,” in arXiv preprint arXiv:2112.10255, 2021.

[10] A. Vaswani et al., “Attention is all you need,” in Adv. neural inf.proces. syst., 2017, pp. 5999–6009.

[11] V. Sanh et al., “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” in arXiv preprint arXiv:1910.01108, 2019.

[12] K. Papineni et al., “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 311–318, 2019.

[13] P. Koehn et al., “Europarl: A parallel corpus for statistical machine translation,” MT summit., vol. 5, Citeseer, pp. 79–86, 2005.

[14] Y. Kim et al., “Convolutional Neural Networks for Sentence Classification,” in arXiv preprint arXiv:1408.5882, 2014.

[15] A. Maas et al., “Learning word vectors for sentiment analysis.,” Proceed in Association for Computational Linguistics (ACL), pp. 142–150, 2011.

[16] C. Heegard et al., “Turbo coding,” in Springer Science & Business Media, Vol. 476, 2013.

【附錄】本文作者

H. Hu、X. Zhu、H. Zhu：南京郵電大學(xué)江蘇省無線通信重點(diǎn)實(shí)驗(yàn)室，南京郵電大學(xué)泛在網(wǎng)絡(luò)健康服務(wù)系統(tǒng)教育部工程研究中心。

F. Zhou：南京航空航天大學(xué)電子與信息工程學(xué)院。

W. Wu：南京郵電大學(xué)通信與信息工程學(xué)院。

R. Q. Hu：就職于美國猶他州洛根市猶他州立大學(xué)電氣與計(jì)算機(jī)工程系。

編輯：黃飛