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We introduce a new semantic communication mechanism - SemanticRL, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.
C. E. Shannon, “A mathematical theory of communication,” The Bell Syst. Tech. J. , vol. 27, no. 3, pp. 379–423, Jul. 1948
1948
Earlier work this paper cites.
C. E. Shannon and W. Weaver, The Mathematical Theory of Information . University of Illinois Press, Urbana, IL, 1949
1949
Earlier work this paper cites.
R. Carnap, Y. Bar-Hillel et al. , “An outline of a theory of semantic information,” Res. Lab. Electron., Massachusetts Inst. of Technol., Cambridge, MA, RLE Tech. Rep. 247, Oct. 1952
1952
Earlier work this paper cites.
G. A. Miller and W. G. Charles, “Contextual correlates of semantic similarity,” Lang. Cognit. Processes , vol. 6, no. 1, pp. 1–28, Jan. 1991
1991
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Mach. Learn. , vol. 8, no. 3, pp. 229–256, May 1992
1992
Earlier work this paper cites.
J. J. Jiang and D. W. Conrath, “Semantic similarity based on corpus statistics and lexical taxonomy,” 1997. [Online]. Available: https://arxiv.org/abs/cmp-lg/9709008
1997
Earlier work this paper cites.
V. R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” in Proc. Adv. Neural Inf. Process. Syst. , Nov. 1999, pp. 1008–1014
1999
Earlier work this paper cites.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: a method for automatic evaluation of machine translation,” in Proc. 40th Annu. Meeting Assoc. Comput. Linguistics (ACL) , Jul. 2002, pp. 311–318
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
L. Floridi, “Outline of a theory of strongly semantic information,” Minds Mach. , vol. 14, no. 2, pp. 197–221, May 2004
2004
Earlier work this paper cites.
A. Goldsmith, Wireless Communications . Cambridge University Press, 2005
2005
Earlier work this paper cites.
2005
Earlier work this paper cites.
P. Koehn et al. , “Europarl: A parallel corpus for statistical machine translation,” in Proc. AAMT 10th Mach. Transl. Summit. , vol. 5, Sep. 2005, pp. 79–86
2005
Earlier work this paper cites.
J. Bao, P. Basu, M. Dean, C. Partridge, A. Swami, W. Leland, and J. A. Hendler, “Towards a theory of semantic communication,” in Proc. IEEE Network Science Workshop , Jun. 2011, pp. 110–117
2011
Earlier work this paper cites.
S. D’Alfonso, “On quantifying semantic information,” Information , vol. 2, no. 1, pp. 61–101, Jan. 2011
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proc. Conf. Empirical Methods Natural Language Processing (EMNLP) , Oct. 2014, pp. 1532–1543
2014
Earlier work this paper cites.
W. Johannsen, “On semantic information in nature,” Information , vol. 6, no. 3, pp. 411–431, Jul. 2015
2015
Earlier work this paper cites.
R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “CIDEr: Consensus-based image description evaluation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2015, pp. 4566–4575
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of Go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, Jan. 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Zhong, “A theory of semantic information,” China Communications , vol. 14, no. 1, pp. 1–17, Feb. 2017
2017
Earlier work this paper cites.
G. Zhu and C. A. Iglesias, “Computing semantic similarity of concepts in knowledge graphs,” IEEE Trans. Knowl. Data Eng. , vol. 29, no. 1, pp. 72–85, Jan. 2017
2017
Cited alongside, same era.
Z. Xu, Y. Wang, J. Tang, J. Wang, and M. C. Gursoy, “A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs,” in Proc. IEEE Int. Conf. Commun. (ICC) , Jul. 2017, pp. 1–6
2017
Cited alongside, same era.
M. Gadaleta, F. Chiariotti, M. Rossi, and A. Zanella, “D-DASH: A deep Q-learning framework for dash video streaming,” IEEE Trans. Cognit. Commun. Netw. , vol. 3, no. 4, pp. 703–718, Sep. 2017
2017
Cited alongside, same era.
T. O’shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Trans. Cognit. Commun. Netw. , vol. 3, no. 4, pp. 563–575, Dec. 2017
2017
Cited alongside, same era.
Q. Zhang, Z. Lei, Z. Zhang, and S. Z. Li, “Context-aware attention network for image-text retrieval,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2020, pp. 3536–3545
2020
Later among the works it cites.
D. Margaris, A. Kobusińska, D. Spiliotopoulos, and C. Vassilakis, “An adaptive social network-aware collaborative filtering algorithm for improved rating prediction accuracy,” IEEE Access , vol. 8, pp. 68 301–68 310, Mar. 2020
2020
Later among the works it cites.
D. B. Kurka and D. Gündüz, “Deep joint source-channel coding of images with feedback,” in Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP) , May 2020, pp. 5235–5239
2020
Later among the works it cites.
E. C. Strinati and S. Barbarossa, “6G networks: Beyond shannon towards semantic and goal-oriented communications,” Comput. Netw. , vol. 190, p. 107930, May 2021
2021
Closest in time.
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Z. Ren, X. Wang, N. Zhang, X. Lv, and L.-J. Li, “Deep reinforcement learning-based image captioning with embedding reward,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jul. 2017, pp. 290–298
2017
Cited alongside, same era.
L. Yu, W. Zhang, J. Wang, and Y. Yu, “SeqGAN: Sequence generative adversarial nets with policy gradient,” in Proc. AAAI Conf. Artif. Intell. , vol. 31, no. 1, Feb. 2017
2017
Cited alongside, same era.
S. J. Rennie, E. Marcheret, Y. Mroueh, J. Ross, and V. Goel, “Self-critical sequence training for image captioning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jul. 2017, pp. 7008–7024
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. Adv. Neural Inf. Process. Syst. , Dec. 2017, pp. 5998–6008
2017
Cited alongside, same era.
S. Yun, J. Choi, Y. Yoo, K. Yun, and J. Young Choi, “Action-decision networks for visual tracking with deep reinforcement learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jul. 2017, pp. 2711–2720
2017
Cited alongside, same era.
S. Liu, Z. Zhu, N. Ye, S. Guadarrama, and K. Murphy, “Improved image captioning via policy gradient optimization of spider,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV) , Oct. 2017, pp. 873–881
2017
Cited alongside, same era.
2018
Cited alongside, same era.
B. Güler, A. Yener, and A. Swami, “The semantic communication game,” IEEE Trans. Cognit. Commun. Netw. , vol. 4, no. 4, pp. 787–802, Sep. 2018
2018
Cited alongside, same era.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
D. Chandrasekaran and V. Mago, “Evolution of semantic similarity—a survey,” ACM Comput. Surv. , vol. 54, no. 2, pp. 1–37, Feb. 2021
2021
Closest in time.
T.-Y. Tung, S. Kobus, J. P. Roig, and D. Gündüz, “Effective communications: A joint learning and communication framework for multi-agent reinforcement learning over noisy channels,” IEEE J. Sel. Areas Commun. , vol. 39, no. 8, pp. 2590–2603, Jun. 2021
2021
Closest in time.
E. T. Ceran, D. Gündüz, and A. György, “A reinforcement learning approach to age of information in multi-user networks with HARQ,” IEEE J. Sel. Areas Commun. , vol. 39, no. 5, pp. 1412–1426, Mar. 2021
2021
Closest in time.
H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Processing , vol. 69, pp. 2663–2675, Apr. 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
2021
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W. J. Yun, B. Lim, S. Jung, Y.-C. Ko, J. Park, J. Kim, and M. Bennis, “Attention-based reinforcement learning for real-time UAV semantic communication,” in 2021 17th International Symposium on Wireless Communication Systems (ISWCS) , 2021, pp. 1–6
2021
Closest in time.
2021
Closest in time.
Z. Weng, Z. Qin, and G. Y. Li, “Semantic communications for speech signals,” in Proc. IEEE Int. Conf. Commun. (ICC) , Jun. 2021, pp. 1–6
2021
Closest in time.
Q. Meng, S. Zhao, Z. Huang, and F. Zhou, “MagFace: A universal representation for face recognition and quality assessment,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2021, pp. 14 225–14 234
2021
Closest in time.
2022
Closest in time.
2022
Closest in time.
Q. Zhou, R. Li, Z. Zhao, C. Peng, and H. Zhang, “Semantic communication with adaptive universal transformer,” IEEE Wireless Commun. Lett. , vol. 11, no. 3, pp. 453–457, Mar. 2022
2022
Closest in time.