Semantics-Empowered Space-Air-Ground-Sea Integrated Network: New Paradigm, Frameworks, and Challenges (2402.14297v3)
Abstract: In the coming sixth generation (6G) communication era, to provide seamless and ubiquitous connections, the space-air-ground-sea integrated network (SAGSIN) is envisioned to address the challenges of communication coverage in areas with difficult conditions, such as the forest, desert, and sea. Considering the fundamental limitations of the SAGSIN including large-scale scenarios, highly dynamic channels, and limited device capabilities, traditional communications based on Shannon information theory cannot satisfy the communication demands. Moreover, bit-level reconstruction is usually redundant for many human-to-machine or machine-to-machine applications in the SAGSIN. Therefore, it is imperative to consider high-level communications towards semantics exchange, called semantic communications. In this survey, according to the interpretations of the term "semantics", including "significance", "meaning", and "effectiveness-related information", we review state-of-the-art works on semantic communications from three perspectives, which are 1) significance representation and protection, 2) meaning similarity measurement and meaning enhancement, and 3) ultimate effectiveness and effectiveness yielding. Sequentially, three types of semantic communication systems can be correspondingly introduced, namely the significance-oriented, meaning-oriented, and effectiveness/task-oriented semantic communication systems. Implementation of the above three types of systems in the SAGSIN necessitates a new perception-communication-computing-actuation-integrated paradigm (PCCAIP), where all the available perception, computing, and actuation techniques jointly facilitates significance-oriented sampling & transmission, semantic extraction & reconstruction, and task decision. Finally, we point out some future challenges on semantic communications in the SAGSIN. ...
- B. Aazhang, M. Juntti, R. Kantola, P. Kyösti, S. LaValle, C. Lima, M. Matinmikko-Blue, T. Ojala, A. Pouttu, A. Pärssinen, S. Yrjola, P. Ahokangas, H. Alves, M.-S. Alouini, J. Beek, H. Benn, M. Bennis, J. Belfiore, E. Strinati, and E. Peltonen, “Key drivers and research challenges for 6G ubiquitous wireless intelligence (white paper),” 6G Flagship University of Oulu Finland, Sep. 2019.
- X. Cheng, Z. Huang, and L. Bai, “Channel nonstationarity and consistency for beyond 5G and 6G: A survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 3, pp. 1634–1669, Jun. 2022.
- F. Guo, F. R. Yu, H. Zhang, X. Li, H. Ji, and V. C. M. Leung, “Enabling massive IoT toward 6G: A comprehensive survey,” IEEE Internet of Things Journal, vol. 8, no. 15, pp. 11 891–11 915, Mar. 2021.
- H. Guo, J. Li, J. Liu, N. Tian, and N. Kato, “A survey on space-air-ground-sea integrated network security in 6G,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 53–87, Nov. 2022.
- J. Liu, Y. Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: A survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2714–2741, May 2018.
- M. M. Azari, S. Solanki, S. Chatzinotas, O. Kodheli, H. Sallouha, A. Colpaert, J. F. Mendoza Montoya, S. Pollin, A. Haqiqatnejad, A. Mostaani, E. Lagunas, and B. Ottersten, “Evolution of non-terrestrial networks from 5G to 6G: A survey,” IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2633–2672, Aug. 2022.
- S. Gu, Q. Zhang, and W. Xiang, “Coded storage-and-computation: A new paradigm to enhancing intelligent services in space-air-ground integrated networks,” IEEE Wireless Communications, vol. 27, no. 6, pp. 44–51, Dec. 2020.
- T. Hong, M. Lv, S. Zheng, and H. Hong, “Key technologies in 6G SAGS IoT: Shape-adaptive antenna and radar-communication integration,” IEEE Network, vol. 35, no. 5, pp. 150–157, Nov. 2021.
- D. Liu, J. Zhang, J. Cui, S.-X. Ng, R. G. Maunder, and L. Hanzo, “Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data,” IEEE Wireless Communications, vol. 29, no. 2, pp. 177–184, Apr. 2022.
- L. Bai, R. Han, J. Liu, J. Choi, and W. Zhang, “Relay-aided random access in space-air-ground integrated networks,” IEEE Wireless Communications, vol. 27, no. 6, pp. 37–43, Dec. 2020.
- Y. Wang, Z. Su, J. Ni, N. Zhang, and X. Shen, “Blockchain-empowered space-air-ground integrated networks: Opportunities, challenges, and solutions,” IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 160–209, Dec. 2021.
- C. E. Shannon, “A mathematical theory of communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, Jul. 1948.
- C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1,” in Proceedings of ICC ’93 - IEEE International Conference on Communications, vol. 2, May 1993, pp. 1064–1070 vol.2.
- R. Gallager, “Low-density parity-check codes,” IRE Transactions on Information Theory, vol. 8, no. 1, pp. 21–28, Jan. 1962.
- E. Arikan, “Channel polarization: A method for constructing capacity-achieving codes,” in 2008 IEEE International Symposium on Information Theory, Jul. 2008, pp. 1173–1177.
- J. Perry, P. A. Lannucci, K. Fleming, H. Balakrishnan, and D. Shah, “Spinal codes,” ACM Sigcomm Computer Communication Review, vol. 42, no. 4, pp. 49–60, Aug. 2012.
- W. Weaver, “Recent contributions to the mathematical theory of communication,” ETC: A review of general semantics, vol. 10, no. 4, pp. 261–281, Jul. 1953.
- E. Uysal, O. Kaya, A. Ephremides, J. Gross, M. Codreanu, P. Popovski, M. Assaad, G. Liva, A. Munari, B. Soret, T. Soleymani, and K. H. Johansson, “Semantic communications in networked systems: A data significance perspective,” IEEE Network, vol. 36, no. 4, pp. 233–240, Oct. 2022.
- K. Niu, J. Dai, S. Yao, S. Wang, Z. Si, X. Qin, and P. Zhang, “A paradigm shift toward semantic communications,” IEEE Communications Magazine, vol. 60, no. 11, pp. 113–119, Aug. 2022.
- G. Shi, Y. Xiao, Y. Li, and X. Xie, “From semantic communication to semantic-aware networking: Model, architecture, and open problems,” IEEE Communications Magazine, vol. 59, no. 8, pp. 44–50, Aug. 2021.
- X. Luo, H.-H. Chen, and Q. Guo, “Semantic communications: Overview, open issues, and future research directions,” IEEE Wireless Communications, vol. 29, no. 1, pp. 210–219, Jan. 2022.
- Q. Lan, D. Wen, Z. Zhang, Q. Zeng, X. Chen, P. Popovski, and K. Huang, “What is semantic communication? a view on conveying meaning in the era of machine intelligence,” Journal of Communications and Information Networks, vol. 6, pp. 336–371, Jan. 2021.
- Z. Qin, X. Tao, J. Lu, and G. Y. Li, “Semantic communications: Principles and challenges,” 2022. [Online]. Available: http://arxiv.org/abs/2201.01389
- C. Chaccour, W. Saad, M. Debbah, Z. Han, and H. V. Poor, “Less data, more knowledge: Building next generation semantic communication networks,” 2022. [Online]. Available: http://arxiv.org/abs/2211.14343
- D. Gündüz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 5–41, Nov. 2022.
- W. Yang, H. Du, Z. Q. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. Shen, and C. Miao, “Semantic communications for future Internet: Fundamentals, applications, and challenges,” IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 213–250, Nov. 2022.
- W. Xu, Z. Yang, D. W. K. Ng, M. Levorato, Y. C. Eldar, and M. Debbah, “Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing,” IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, pp. 9–39, Jan. 2023.
- M. Chafii, L. Bariah, S. Muhaidat, and M. Debbah, “Twelve scientific challenges for 6G: Rethinking the foundations of communications theory,” IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 868–904, Feb. 2023.
- Z. Wang, Z. Zhou, H. Zhang, G. Zhang, H. Ding, and A. Farouk, “AI-based cloud-edge-device collaboration in 6G space-air-ground integrated power IoT,” IEEE Wireless Communications, vol. 29, no. 1, pp. 16–23, Feb. 2022.
- S. Yu, X. Gong, Q. Shi, X. Wang, and X. Chen, “EC-SAGINs: Edge-computing-enhanced space–air–ground-integrated networks for internet of vehicles,” IEEE Internet of Things Journal, vol. 9, no. 8, pp. 5742–5754, Apr. 2021.
- S. Gu, Y. Wang, N. Wang, and W. Wu, “Intelligent optimization of availability and communication cost in satellite-UAV mobile edge caching system with fault-tolerant codes,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1230–1241, Dec. 2020.
- Z. Zhang, W. Zhang, and F.-H. Tseng, “Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques,” IEEE network, vol. 33, no. 1, pp. 70–76, Jan. 2019.
- P. Talli, F. Pase, F. Chiariotti, A. Zanella, and M. Zorzi, “Semantic and effective communication for remote control tasks with dynamic feature compression,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2023, pp. 1–6.
- ITU-T, “Representative use cases and key network requirements for network 2030,” FG-NET2030-Sub-G1, Tech. Rep. FG-NET2030-Sub-G1, Jan. 2020.
- S. Kaul, M. Gruteser, V. Rai, and J. Kenney, “Minimizing age of information in vehicular networks,” in 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, Jun. 2011, pp. 350–358.
- S. Kaul, R. Yates, and M. Gruteser, “Real-time status: How often should one update?” in 2012 Proceedings IEEE INFOCOM, Mar. 2012, pp. 2731–2735.
- A. Kosta, N. Pappas, A. Ephremides, and V. Angelakis, “Age and value of information: Non-linear age case,” in 2017 IEEE International Symposium on Information Theory (ISIT), Jun. 2017, pp. 326–330.
- Y. Sun and B. Cyr, “Sampling for data freshness optimization: Non-linear age functions,” Journal of Communications and Networks, vol. 21, no. 3, pp. 204–219, Jun. 2019.
- J. Zhong, R. D. Yates, and E. Soljanin, “Two freshness metrics for local cache refresh,” in 2018 IEEE International Symposium on Information Theory (ISIT), Aug. 2018, pp. 1924–1928.
- A. Maatouk, S. Kriouile, M. Assaad, and A. Ephremides, “The age of incorrect information: A new performance metric for status updates,” IEEE/ACM Transactions on Networking, vol. 28, no. 5, pp. 2215–2228, Jul. 2020.
- X. Zheng, S. Zhou, and Z. Niu, “Urgency of information for context-aware timely status updates in remote control systems,” IEEE Transactions on Wireless Communications, vol. 19, no. 11, pp. 7237–7250, Jul. 2020.
- A. Li, S. Wu, and S. Sun, “Goal-oriented tensor: Beyond AoI towards semantics-empowered goal-oriented communications,” May 2023.
- F. Chiariotti, J. Holm, A. E. Kalør, B. Soret, S. K. Jensen, T. B. Pedersen, and P. Popovski, “Query age of information: Freshness in pull-based communication,” IEEE Transactions on Communications, vol. 70, no. 3, pp. 1606–1622, Jan. 2022.
- R. D. Yates, Y. Sun, D. R. Brown, S. K. Kaul, E. Modiano, and S. Ulukus, “Age of information: An introduction and survey,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 5, pp. 1183–1210, Mar. 2021.
- Y. Sun, E. Uysal-Biyikoglu, R. Yates, C. E. Koksal, and N. B. Shroff, “Update or wait: How to keep your data fresh,” in IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, Apr. 2016, pp. 1–9.
- B. Zhou and W. Saad, “Optimal sampling and updating for minimizing age of information in the Internet of things,” in 2018 IEEE Global Communications Conference (GLOBECOM), Dec. 2018, pp. 1–6.
- Y. Sun, Y. Polyanskiy, and E. Uysal, “Sampling of the Wiener process for remote estimation over a channel with random delay,” IEEE Transactions on Information Theory, vol. 66, no. 2, pp. 1118–1135, Aug. 2019.
- T. Z. Ornee and Y. Sun, “Sampling and remote estimation for the Ornstein-Uhlenbeck process through queues: Age of information and beyond,” IEEE/ACM Transactions on Networking, vol. 29, no. 5, pp. 1962–1975, May 2021.
- A. M. Bedewy, Y. Sun, S. Kompella, and N. B. Shroff, “Age-optimal sampling and transmission scheduling in multi-source systems,” in Proceedings of the 20th ACM International Symposium On Mobile Ad Hoc Networking and Computing (MOBIHOC ‘19), Jul. 2019, pp. 121–130.
- Y. Chen and A. Ephremides, “Minimizing age of incorrect information for unreliable channel with power constraint,” in 2021 IEEE Global Communications Conference (GLOBECOM), Dec. 2021, pp. 1–6.
- A. Maatouk, M. Assaad, and A. Ephremides, “Semantics-empowered communications through the age of incorrect information,” in ICC 2022 - IEEE International Conference on Communications, May 2022, pp. 3995–4000.
- Y. Chen and A. Ephremides, “Minimizing age of incorrect information in the presence of timeout,” 2022. [Online]. Available: http://arxiv.org/abs/2207.02926
- K. Bountrogiannis, A. Ephremides, P. Tsakalides, and G. Tzagkarakis, “Age of incorrect information with hybrid arq under a resource constraint for n-ary symmetric markov sources,” 2023. [Online]. Available: http://arxiv.org/abs/2303.18128
- S. C. Bobbili, P. Parag, and J.-F. Chamberland, “Real-time status updates with perfect feedback over erasure channels,” IEEE Transactions on Communications, vol. 68, no. 9, pp. 5363–5374, Jul. 2020.
- A. Arafa, K. Banawan, K. G. Seddik, and H. V. Poor, “On timely channel coding with hybrid ARQ,” in 2019 IEEE Global Communications Conference (GLOBECOM), Dec. 2019, pp. 1–6.
- M. Xie, Q. Wang, J. Gong, and X. Ma, “Age and energy analysis for LDPC coded status update with and without ARQ,” IEEE Internet of Things Journal, vol. 7, no. 10, pp. 10 388–10 400, Apr. 2020.
- J. You, S. Wu, Y. Deng, J. Jiao, and Q. Zhang, “An age optimized hybrid ARQ scheme for Polar codes via Gaussian approximation,” IEEE Wireless Communications Letters, vol. 10, no. 10, pp. 2235–2239, Jul. 2021.
- A. Li, S. Wu, J. Jiao, N. Zhang, and Q. Zhang, “Age of information with hybrid-ARQ: A unified explicit result,” IEEE Transactions on Communications, vol. 70, no. 12, pp. 7899–7914, Oct. 2022.
- Y. Wang, S. Wu, D. Li, J. Jiao, and Q. Zhang, “Age-optimal IR-HARQ design in the presence of non-trivial propagation delay,” in 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), Oct. 2019, pp. 1–6.
- D. Li, S. Wu, Y. Wang, J. Jiao, and Q. Zhang, “Age-optimal HARQ design for freshness-critical satellite-IoT systems,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 2066–2076, Dec. 2020.
- D. Li, S. Wu, J. Jiao, N. Zhang, and Q. Zhang, “Age-oriented transmission protocol design in space-air-ground integrated networks,” IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5573–5585, Jan. 2022.
- S. Meng, S. Wu, A. Li, J. Jiao, N. Zhang, and Q. Zhang, “Analysis and optimization of the HARQ-based Spinal coded timely status update system,” IEEE Transactions on Communications, vol. 70, no. 10, pp. 6425–6440, Aug. 2022.
- Y. Deng, S. Wu, J. You, J. Jiao, N. Zhang, and Q. Zhang, “Optimizing age of information in Polar coded status update system,” IEEE Internet of Things Journal, pp. 1–1, Jun. 2023.
- B. R. Sharan, S. Deshmukh, S. R. B. Pillai, and B. Beferull-Lozano, “Energy efficient AoI minimization in opportunistic NOMA/OMA broadcast wireless networks,” IEEE Transactions on Green Communications and Networking, vol. 6, no. 2, pp. 1009–1022, Dec. 2021.
- S. Wu, C. Guo, Z. Deng, J. Jiao, N. Zhang, and Q. Zhang, “Optimizing age of information in adaptive NOMA/OMA/cooperative-SWIPT-NOMA system,” IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 11 125–11 138, Jul. 2022.
- S. Wu, Z. Deng, A. Li, J. Jiao, N. Zhang, and Q. Zhang, “Minimizing age-of-information in HARQ-CC aided NOMA systems,” IEEE Transactions on Wireless Communications, vol. 22, no. 2, pp. 1072–1086, Sep. 2022.
- S. Liao, J. Jiao, S. Wu, R. Lu, and Q. Zhang, “Age-optimal power allocation scheme for NOMA-based S-IoT downlink network,” in ICC 2021-IEEE International Conference on Communications, Jun. 2021, pp. 1–6.
- J. Jiao, H. Hong, Y. Wang, S. Wu, R. Lu, and Q. Zhang, “Age-optimal downlink NOMA resource allocation for satellite-based IoT network,” IEEE Transactions on Vehicular Technology, Apr. 2023.
- Z. Shi, H. Ding, S. Ma, K.-W. Tam, and S. Pan, “Inverse moment matching based analysis of cooperative HARQ-IR over time-correlated Nakagami fading channels,” IEEE Transactions on Vehicular Technology, vol. 66, no. 5, pp. 3812–3828, Aug. 2017.
- K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “BLEU: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics, Jul. 2002, pp. 311–318.
- R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “CIDEr: Consensus-based image description evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun. 2015, pp. 4566–4575.
- Y. Wang, M. Chen, T. Luo, W. Saad, D. Niyato, H. V. Poor, and S. Cui, “Performance optimization for semantic communications: An attention-based reinforcement learning approach,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 9, pp. 2598–2613, Jul. 2022.
- T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, and Y. Artzi, “BERTSCORE: Evaluating text generation with BERT,” 2019. [Online]. Available: https://arxiv.org/abs/1904.09675
- H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663–2675, Apr. 2021.
- Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, vol. 2, Nov. 2003, pp. 1398–1402.
- R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun. 2018, pp. 586–595.
- M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local Nash equilibrium,” Advances in neural information processing systems, vol. 30, Dec. 2017.
- M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” 2018. [Online]. Available: https://arxiv.org/abs/1801.01401
- P. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Wireless semantic communications for video conferencing,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 230–244, Nov. 2022.
- E. Vincent, R. Gribonval, and C. Févotte, “Performance measurement in blind audio source separation,” IEEE transactions on audio, speech, and language processing, vol. 14, no. 4, pp. 1462–1469, Jun. 2006.
- “Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs,” ITU-T recommendation P.862, Feb. 2001.
- R. Kubichek, “Mel-cepstral distance measure for objective speech quality assessment,” in Proceedings of IEEE pacific rim conference on communications computers and signal processing, vol. 1, May 1993, pp. 125–128.
- M. Bińkowski, J. Donahue, S. Dieleman, A. Clark, E. Elsen, N. Casagrande, L. C. Cobo, and K. Simonyan, “High fidelity speech synthesis with adversarial networks,” 2019. [Online]. Available: https://arxiv.org/abs/1909.11646
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional Transformers for language understanding,” in 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2019), Vol. 1, Jan. 2019, pp. 4171–4186.
- Z. Weng and Z. Qin, “Semantic communication systems for speech transmission,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2434–2444, Jun. 2021.
- K. Lu, R. Li, X. Chen, Z. Zhao, and H. Zhang, “Reinforcement learning-powered semantic communication via semantic similarity,” 2021. [Online]. Available: https://arxiv.org/abs/2108.12121
- K. Lu, Q. Zhou, R. Li, Z. Zhao, X. Chen, J. Wu, and H. Zhang, “Rethinking modern communication from semantic coding to semantic communication,” IEEE Wireless Communications, vol. 30, no. 1, pp. 158–164, Feb. 2023.
- N. Farsad, M. Rao, and A. Goldsmith, “Deep learning for joint source-channel coding of text,” in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), Apr. 2018, pp. 2326–2330.
- D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” The Journal of physiology, vol. 160, no. 1, p. 106, Jan. 1962.
- K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological cybernetics, vol. 36, no. 4, pp. 193–202, Apr. 1980.
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
- A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, May 2017.
- M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13, Sep. 2014, pp. 818–833.
- K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2014. [Online]. Available: https://arxiv.org/abs/1409.1556
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, pp. 1–9.
- J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun. 2015, pp. 3431–3440.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jun. 2016, pp. 770–778.
- G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jul. 2017, pp. 4700–4708.
- H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jul. 2017, pp. 2881–2890.
- P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, Jul. 2017, pp. 1125–1134.
- J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the national academy of sciences, vol. 79, no. 8, pp. 2554–2558, Apr. 1982.
- M. Jordan, “Serial order: a parallel distributed processing approach. Technical report, June 1985-March 1986,” California Univ., San Diego, La Jolla (USA). Inst. for Cognitive Science, Tech. Rep., May 1986.
- J. L. Elman, “Finding structure in time,” Cognitive science, vol. 14, no. 2, pp. 179–211, Mar. 1990.
- S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
- M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, Nov. 1997.
- A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional LSTM networks,” in Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., vol. 4, Jul. 2005, pp. 2047–2052.
- A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing, May 2013, pp. 6645–6649.
- K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” 2014. [Online]. Available: https://arxiv.org/abs/1406.1078
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, Dec. 2017.
- A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” 2020. [Online]. Available: https://arxiv.org/abs/2010.11929
- N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, Aug. 2020, pp. 213–229.
- Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin Transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, Oct. 2021, pp. 10 012–10 022.
- I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27 (NIPS 2014), vol. 27, Dec. 2014, pp. 2672–2680.
- M. Mirza and S. Osindero, “Conditional generative adversarial nets,” 2014. [Online]. Available: https://arxiv.org/abs/1411.1784
- A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” 2015. [Online]. Available: https://arxiv.org/abs/1511.06434
- M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning, Jul. 2017, pp. 214–223.
- H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in International conference on machine learning, May 2019, pp. 7354–7363.
- A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” 2018. [Online]. Available: https://arxiv.org/abs/1809.11096
- T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Jun. 2019, pp. 4401–4410.
- J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,” Advances in Neural Information Processing Systems, vol. 33, pp. 17 022–17 033, Dec. 2020.
- T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 2016. [Online]. Available: https://arxiv.org/abs/1609.02907
- Z. Lu, Y. Xiao, Z. Sun, Y. Li, G. Shi, X. Chenk, M. Bennis, and H. Poor, “Adversarial learning for implicit semantic-aware communications,” 2023. [Online]. Available: http://arxiv.org/abs/2301.11589
- C. K. Thomas and W. Saad, “Neuro-symbolic artificial intelligence (AI) for intent based semantic communication,” in GLOBECOM 2022-2022 IEEE Global Communications Conference, Dec. 2022, pp. 2698–2703.
- ——, “Neuro-symbolic causal reasoning meets signaling game for emergent semantic communications,” 2022. [Online]. Available: https://arxiv.org/abs/2210.12040
- D. Huang, F. Gao, X. Tao, Q. Du, and J. Lu, “Towards semantic communications: Deep learning-based image semantic coding,” 2022. [Online]. Available: http://arxiv.org/abs/2208.04094
- J. Huang, D. Li, C. Huang, X. Qin, and W. Zhang, “Joint task and data oriented semantic communications: A deep separate source-channel coding scheme,” IEEE Internet of Things Journal, pp. 1–1, Jul. 2023.
- J.-H. Lee, D.-H. Lee, E. Sheen, T. Choi, J. Pujara, and J. Kim, “Seq2Seq-SC: End-to-end semantic communication systems with pre-trained language model,” 2022. [Online]. Available: https://arxiv.org/abs/2210.15237
- F. Liu, W. Tong, Y. Yang, Z. Sun, and C. Guo, “Task-oriented image semantic communication based on rate-distortion theory,” 2022. [Online]. Available: http://arxiv.org/abs/2201.10929
- P. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep source-channel coding for sentence semantic transmission with HARQ,” IEEE Transactions on Communications, vol. 70, no. 8, pp. 5225–5240, Jun. 2022.
- X. Luo, Z. Chen, M. Tao, and F. Yang, “Encrypted semantic communication using adversarial training for privacy preserving,” IEEE Communications Letters, Apr. 2023.
- T. Han, Q. Yang, Z. Shi, S. He, and Z. Zhang, “Semantic-preserved communication system for highly efficient speech transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 245–259, Nov. 2022.
- S. Imran, G. Charan, and A. Alkhateeb, “Environment semantic aided communication: A real world demonstration for beam prediction,” 2023. [Online]. Available: http://arxiv.org/abs/2302.06736
- Y. Yang, F. Gao, X. Tao, G. Liu, and C. Pan, “Environment semantics aided wireless communications: A case study of mmWave beam prediction and blockage prediction,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 7, pp. 2025–2040, May 2023.
- Z. Qin, F. Gao, B. Lin, X. Tao, G. Liu, and C. Pan, “A generalized semantic communication system: From sources to channels,” IEEE Wireless Communications, vol. 30, no. 3, pp. 18–26, Jun. 2023.
- X. Peng, Z. Qin, D. Huang, X. Tao, J. Lu, G. Liu, and C. Pan, “A robust deep learning enabled semantic communication system for text,” in GLOBECOM 2022-2022 IEEE Global Communications Conference, Dec. 2022, pp. 2704–2709.
- J. Dai, S. Wang, K. Yang, K. Tan, X. Qin, Z. Si, K. Niu, and P. Zhang, “Adaptive semantic communications: Overfitting the source and channel for profit,” 2022. [Online]. Available: https://arxiv.org/abs/2211.04339
- K. Yang, S. Wang, J. Dai, K. Tan, K. Niu, and P. Zhang, “WITT: A wireless image transmission transformer for semantic communications,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2023, pp. 1–5.
- J. Xu, T.-Y. Tung, B. Ai, W. Chen, Y. Sun, and D. Gunduz, “Deep joint source-channel coding for semantic communications,” 2022. [Online]. Available: https://arxiv.org/abs/2211.08747
- S. Wang, J. Dai, X. Qin, Z. Si, K. Niu, and P. Zhang, “Improved nonlinear transform source-channel coding to catalyze semantic communications,” IEEE Journal of Selected Topics in Signal Processing, pp. 1–16, Aug. 2023.
- Y. Bo, Y. Duan, S. Shao, and M. Tao, “Learning based joint coding-modulation for digital semantic communication systems,” in 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), Nov. 2022, pp. 1–6.
- H. Xie and Z. Qin, “A lite distributed semantic communication system for Internet of things,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 142–153, Nov. 2020.
- Q. Zhou, R. Li, Z. Zhao, Y. Xiao, and H. Zhang, “Adaptive bit rate control in semantic communication with incremental knowledge-based HARQ,” IEEE Open Journal of the Communications Society, vol. 3, pp. 1076–1089, Jul. 2022.
- D. B. Kurka and D. Gündüz, “DeepJSCC-f: Deep joint source-channel coding of images with feedback,” IEEE Journal on Selected Areas in Information Theory, vol. 1, no. 1, pp. 178–193, Apr. 2020.
- M. Yang and H.-S. Kim, “Deep joint source-channel coding for wireless image transmission with adaptive rate control,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022, pp. 5193–5197.
- J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2300–2316, Jun. 2022.
- S. Wang, J. Dai, Z. Liang, K. Niu, Z. Si, C. Dong, X. Qin, and P. Zhang, “Wireless deep video semantic transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 214–229, Nov. 2022.
- B. Zhang, Z. Qin, and G. Y. Li, “Semantic communications with variable-length coding for extended reality,” IEEE Journal of Selected Topics in Signal Processing, pp. 1–14, Aug. 2023.
- F. Zhou, Y. Li, X. Zhang, Q. Wu, X. Lei, and R. Q. Hu, “Cognitive semantic communication systems driven by knowledge graph,” in ICC 2022-IEEE International Conference on Communications, May 2022, pp. 4860–4865.
- B. Wang, R. Li, J. Zhu, Z. Zhao, and H. Zhang, “Knowledge enhanced semantic communication receiver,” IEEE Communications Letters, vol. 27, no. 7, pp. 1794–1798, May 2023.
- S. Seo, J. Park, S.-W. Ko, J. Choi, M. Bennis, and S.-L. Kim, “Toward semantic communication protocols: A probabilistic logic perspective,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2670–2686, Jun. 2023.
- J. Choi, S. W. Loke, and J. Park, “A unified approach to semantic information and communication based on probabilistic logic,” IEEE Access, vol. 10, pp. 129 806–129 822, Dec. 2022.
- D. Wheeler, E. E. Tripp, and B. Natarajan, “Semantic communication with conceptual spaces,” IEEE Communications Letters, Dec. 2022.
- Z. Li, X. Liu, G. Nan, J. Zhou, X. Lyu, Q. Cui, and X. Tao, “Boosting physical layer black-box attacks with semantic adversaries in semantic communications,” 2023. [Online]. Available: https://arxiv.org/abs/2303.16523
- G. Nan, Z. Li, J. Zhai, Q. Cui, G. Chen, X. Du, X. Zhang, X. Tao, Z. Han, and T. Q. S. Quek, “Physical-layer adversarial robustness for deep learning-based semantic communications,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2592–2608, Jun. 2023.
- Y. E. Sagduyu, T. Erpek, S. Ulukus, and A. Yener, “Vulnerabilities of deep learning-driven semantic communications to backdoor (Trojan) attacks,” in 2023 57th Annual Conference on Information Sciences and Systems (CISS), Apr. 2023, pp. 1–6.
- T.-Y. Tung and D. Gunduz, “Deep joint source-channel and encryption coding: Secure semantic communications,” 2022. [Online]. Available: https://arxiv.org/abs/2208.09245
- T. Han, J. Tang, Q. Yang, Y. Duan, Z. Zhang, and Z. Shi, “Generative model based highly efficient semantic communication approach for image transmission,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2023, pp. 1–5.
- H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, M. Guizani, and D. I. Kim, “Rethinking wireless communication security in semantic internet of things,” IEEE Wireless Communications, vol. 30, no. 3, pp. 36–43, Jun. 2023.
- Z. Yang, M. Chen, G. Li, Y. Yang, and Z. Zhang, “Secure semantic communications: Fundamentals and challenges,” 2023. [Online]. Available: https://arxiv.org/abs/2301.01421
- X. Mu, Y. Liu, L. Guo, and N. Al-Dhahir, “Heterogeneous semantic and bit communications: A semi-NOMA scheme,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 155–169, Nov. 2022.
- W. Li, H. Liang, C. Dong, X. Xu, P. Zhang, and K. Liu, “Non-orthogonal multiple access enhanced multi-user semantic communication,” IEEE Transactions on Cognitive Communications and Networking, pp. 1–1, Aug. 2023.
- H. Hu, X. Zhu, F. Zhou, W. Wu, R. Q. Hu, and H. Zhu, “One-to-many semantic communication systems: Design, implementation, performance evaluation,” IEEE Communications Letters, vol. 26, no. 12, pp. 2959–2963, Sep. 2022.
- E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, May 2019.
- D. B. Kurka and D. Gündüz, “Successive refinement of images with deep joint source-channel coding,” in 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Jul. 2019, pp. 1–5.
- M. Ding, J. Li, M. Ma, and X. Fan, “SNR-adaptive deep joint source-channel coding for wireless image transmission,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun. 2021, pp. 1555–1559.
- C. Dong, H. Liang, X. Xu, S. Han, B. Wang, and P. Zhang, “Semantic communication system based on semantic slice models propagation,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 202–213, Nov. 2022.
- J. Dai, P. Zhang, K. Niu, S. Wang, Z. Si, and X. Qin, “Communication beyond transmitting bits: Semantics-guided source and channel coding,” IEEE Wireless Communications, pp. 1–8, Aug. 2022.
- H. Zhang, S. Shao, M. Tao, X. Bi, and K. B. Letaief, “Deep learning-enabled semantic communication systems with task-unaware transmitter and dynamic data,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 170–185, Nov. 2022.
- W. Zhang, K. Bai, S. Zeadally, H. Zhang, H. Shao, H. Ma, and V. Leung, “DeepMA: End-to-end deep multiple access for wireless image transmission in semantic communication,” 2023. [Online]. Available: https://arxiv.org/abs/2303.11543
- S. Ma, W. Qiao, Y. Wu, H. Li, G. Shi, D. Gao, Y. Shi, S. Li, and N. Al-Dhahir, “Features disentangled semantic broadcast communication networks,” 2023. [Online]. Available: https://arxiv.org/abs/2303.01892
- S. Yang, H. Pan, T.-T. Chan, and Z. Wang, “Semantic communication-empowered physical-layer network coding,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2023, pp. 1–6.
- Q. Fu, H. Xie, Z. Qin, G. Slabaugh, and X. Tao, “Vector quantized semantic communication system,” IEEE Wireless Communications Letters, vol. 12, no. 6, pp. 982–986, Mar. 2023.
- Z. Weng, Z. Qin, and G. Y. Li, “Semantic communications for speech signals,” in ICC 2021 - IEEE International Conference on Communications, Jun. 2021, pp. 1–6.
- Y. Tang, N. Zhou, Q. Yu, D. Wu, C. Hou, G. Tao, and M. Chen, “Intelligent fabric enabled 6G semantic communication system for in-cabin scenarios,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1153–1162, Jun. 2022.
- Z. Weng, Z. Qin, X. Tao, C. Pan, G. Liu, and G. Y. Li, “Deep learning enabled semantic communications with speech recognition and synthesis,” IEEE Transactions on Wireless Communications, pp. 1–1, Feb. 2023.
- N. Chinchor, “MUC-4 evaluation metrics,” in Proceedings of the 4th Conference on Message Understanding, Jun. 1992, pp. 22–29.
- Y. Qiu, S. Wu, Y. Wang, J. Jiao, N. Zhang, and Q. Zhang, “On scheduling policy for multiprocess cyber–physical system with edge computing,” IEEE Internet of Things Journal, vol. 9, no. 19, pp. 18 559–18 572, Mar. 2022.
- K. Huang, W. Liu, Y. Li, B. Vucetic, and A. Savkin, “Optimal downlink–uplink scheduling of wireless networked control for industrial IoT,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1756–1772, Oct. 2019.
- Y. E. Sagduyu, S. Ulukus, and A. Yener, “Age of information in deep learning-driven task-oriented communications,” in IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), May 2023, pp. 1–6.
- S. Xie, S. Ma, M. Ding, Y. Shi, M. Tang, and Y. Wu, “Robust information bottleneck for task-oriented communication with digital modulation,” IEEE Journal on Selected Areas in Communications, pp. 1–1, Jun. 2023.
- C. Liu, C. Guo, Y. Yang, and N. Jiang, “Adaptable semantic compression and resource allocation for task-oriented communications,” 2022. [Online]. Available: http://arxiv.org/abs/2204.08910
- M. Wang, J. Li, M. Ma, and X. Fan, “SNN-SC: A spiking semantic communication framework for classification,” 2023. [Online]. Available: http://arxiv.org/abs/2210.06836
- Q. Hu, G. Zhang, Z. Qin, Y. Cai, G. Yu, and G. Y. Li, “Robust semantic communications against semantic noise,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), Sep. 2022, pp. 1–6.
- ——, “Robust semantic communications with masked VQ-VAE enabled codebook,” IEEE Transactions on Wireless Communications, pp. 1–1, Apr. 2023.
- J. Shao, X. Zhang, and J. Zhang, “Task-oriented communication for edge video analytics,” 2022. [Online]. Available: http://arxiv.org/abs/2211.14049
- Q. Pan, H. Tong, J. Lv, T. Luo, Z. Zhang, C. Yin, and J. Li, “Image segmentation semantic communication over Internet of vehicles,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2023, pp. 1–6.
- M. Jankowski, D. Gündüz, and K. Mikolajczyk, “Wireless image retrieval at the edge,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 1, pp. 89–100, Nov. 2020.
- W. F. Lo, N. Mital, H. Wu, and D. Gündüz, “Collaborative semantic communication for edge inference,” IEEE Wireless Communications Letters, pp. 1–1, Mar. 2023.
- S. Wan, Q. Yang, Z. Shi, Z. Yang, and Z. Zhang, “Cooperative task-oriented communication for multi-modal data with transmission control,” 2023. [Online]. Available: http://arxiv.org/abs/2302.02608
- A. Mostaani, T. X. Vu, S. Chatzinotas, and B. Ottersten, “Task-oriented data compression for multi-agent communications over bit-budgeted channels,” IEEE Open Journal of the Communications Society, vol. 3, pp. 1867–1886, Oct. 2022.
- R. Li, C. Huang, X. Qin, S. Jiang, N. Ma, and S. Cui, “Coexistence between task-and data-oriented communications: A whittle’s index guided multi-agent reinforcement learning approach,” IEEE Internet of Things Journal, pp. 1–1, Jul. 2023.
- H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, and D. I. Kim, “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” IEEE Journal on Selected Areas in Communications, pp. 1–1, Jun. 2023.
- J. Kang, H. Du, Z. Li, Z. Xiong, S. Ma, D. Niyato, and Y. Li, “Personalized saliency in task-oriented semantic communications: Image transmission and performance analysis,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 186–201, Nov. 2022.
- W. Xu, Y. Zhang, F. Wang, Z. Qin, C. Liu, and P. Zhang, “Semantic communication for Internet of vehicles: A multi-user cooperative approach,” 2022. [Online]. Available: http://arxiv.org/abs/2212.03037
- H. Xie, Z. Qin, and G. Y. Li, “Task-oriented multi-user semantic communications for VQA,” IEEE Wireless Communications Letters, vol. 11, no. 3, pp. 553–557, Dec. 2021.
- H. Xie, Z. Qin, X. Tao, and K. B. Letaief, “Task-oriented multi-user semantic communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 9, pp. 2584–2597, Jul. 2022.
- H. Xie, Z. Qin, and G. Y. Li, “Semantic communication with memory,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2658–2669, Jun. 2023.
- C. Liu, C. Guo, S. Wang, Y. Li, and D. Hu, “Task-oriented semantic communication based on semantic triplets,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC), Mar. 2023, pp. 1–6.
- W. Maass, “Network of spiking neurons: the third generation of neural network models,” Neural Networks, vol. 10, no. 9, pp. 1659–1671, Dec. 1997.
- G. Zhang, Q. Hu, Z. Qin, Y. Cai, G. Yu, X. Tao, and G. Y. Li, “A unified multi-task semantic communication system for multimodal data,” 2022. [Online]. Available: http://arxiv.org/abs/2209.07689
- G. Zhang, Q. Hu, Z. Qin, Y. Cai, and G. Yu, “A unified multi-task semantic communication system with domain adaptation,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Dec. 2022, pp. 3971–3976.
- J. Bao, P. Basu, M. Dean, C. Partridge, A. Swami, W. Leland, and J. A. Hendler, “Towards a theory of semantic communication,” in 2011 IEEE Network Science Workshop, Jun. 2011, pp. 110–117.
- Y. Bar-Hillel and R. Carnap, “Semantic information,” The British Journal for the Philosophy of Science, vol. 4, no. 14, pp. 147–157, Aug. 1953.
- A. Li, S. Wu, S. Meng, and Q. Zhang, “Towards goal-oriented semantic communications: New metrics, open challenges, and future research directions,” 2023. [Online]. Available: http://arxiv.org/abs/2304.00848
- D. Wen, P. Liu, G. Zhu, Y. Shi, J. Xu, Y. C. Eldar, and S. Cui, “Task-oriented sensing, computation, and communication integration for multi-device edge AI,” IEEE Transactions on Wireless Communications, pp. 1–1, Aug. 2023.
- P. Liu, G. Zhu, S. Wang, W. Jiang, W. Luo, H. V. Poor, and S. Cui, “Toward ambient intelligence: Federated edge learning with task-oriented sensing, computation, and communication integration,” IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 1, pp. 158–172, Dec. 2022.
- X. Mu and Y. Liu, “Exploiting semantic communication for non-orthogonal multiple access,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2563–2576, Jun. 2023.