The DOI matches its Crossref record; 354 of the 388 references it cites were found in a registry or an index.
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
Open accessPublished version of a preprint
Abstract
Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national labo?ratories have been developing proposals for the sixth generation (6G). Despite the many 6G proposals, the materialization of 6G as presently envisaged is fraught with many fundamental interdisciplinary, multidisciplinary, and transdisciplinary (IMT) challenges. To alleviate some of these challenges, semantic com?munication (SemCom) has emerged as promising 6G technology enabler. SemCom is designed to transmit only semantically?relevant information and hence help to minimize power usage, bandwidth consumption, and transmission delay. Thus, SemCom embodies a paradigm shift that can change the status quo that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meaning have been ignored. On the other hand, 6G is critical for the materialization of major SemCom use cases. These paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration and marriage of 6G and SemCom. To facilitate this integration and marriage, this comprehensive survey paper first provides the fundamentals of semantics and semantic information, semantic representation, semantic information, and semantic entropy. It then builds on this understanding and details the state-of-the-art research landscape of SemCom; exposes the fundamental and major challenges of SemCom; and offers promising future research directions for SemCom theories, algorithms, and realization. Accordingly, this survey article stimulates major streams of research on SemCom theories, algorithms, and implementation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
(2014) · Cited by 33,419
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems
Saad, W., Bennis, M., & Chen, M. (2020) · IEEE Network · doi:10.1109/mnet.001.1900287 · Cited by 4,985
Self-citationBiamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
Digital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654
The information bottleneck method
Tishby, N., Pereira, F. C., & Bialek, W. (2000) · arXiv · doi:10.48550/arxiv.physics/0004057 · Cited by 2,626
Rappaport, T. S., Xing, Y., Kanhere, O., Ju, S., Madanayake, A., Mandal, S., Alkhateeb, A., & Trichopoulos, G. C. (2019) · IEEE Access · doi:10.1109/access.2019.2921522 · Cited by 2,617
6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921208 · Cited by 2,575
Win, M. Z., & Scholtz, R. A. (2000) · IEEE Transactions on Communications · doi:10.1109/26.843135 · Cited by 2,538
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455
Gisin, N., & Thew, R. (2007) · Nature Photonics · doi:10.1038/nphoton.2007.22 · Cited by 2,223
The Roadmap to 6G: AI Empowered Wireless Networks
Letaief, K. B., Chen, W., Shi, Y., Zhang, J., & Zhang, Y.-J. A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1900271 · Cited by 2,207
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
You, X., Wang, C., Huang, J., Gao, X., Zhang, Z., Wang, M., Huang, Y., Zhang, C., Jiang, Y., Wang, J., et al. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2955-6 · Cited by 2,110
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995
Guaranteeing Secrecy using Artificial Noise
Goel, S., & Negi, R. (2008) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2008.060848 · Cited by 1,941
Chowdhury, M. Z., Shahjalal, M. A., Ahmed, S., & Jang, Y. M. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.3010270 · Cited by 1,925
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020) · Nature Electronics · doi:10.1038/s41928-019-0355-6 · Cited by 1,790
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773
The Road Towards 6G: A Comprehensive Survey
Jiang, W., Han, B., Habibi, M. A., & Schotten, H. D. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3057679 · Cited by 1,757
Integrated information theory: from consciousness to its physical substrate
Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.44 · Cited by 1,727
A Continual Learning Survey: Defying Forgetting in Classification Tasks
Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., & Tuytelaars, T. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3057446 · Cited by 1,647
Approximation theory of the MLP model in neural networks
Pinkus, A. M. (1999) · Acta Numerica · doi:10.1017/s0962492900002919 · Cited by 1,638
6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Tataria, H., Shafi, M., Molisch, A. F., Döhler, M., Sjöland, H., & Tufvesson, F. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3061701 · Cited by 1,615
Deep Learning Enabled Semantic Communication Systems
Xie, H., Qin, Z., Li, G. Y., & Juang, B.-H. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2021.3071210 · Cited by 1,602
Deep Joint Source-Channel Coding for Wireless Image Transmission
Bourtsoulatze, E., Kurka, D. B., & Gündüz, D. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2919300 · Cited by 1,598
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517
Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014) · doi:10.1017/cbo9781107447615 · Cited by 1,515
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480
6G and Beyond: The Future of Wireless Communications Systems
Akyildiz, I. F., Kak, A., & Nie, S. (2020) · IEEE Access · doi:10.1109/access.2020.3010896 · Cited by 1,472
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,378
A Survey of Non-Orthogonal Multiple Access for 5G
Dai, L., Wang, B., Ding, Z., Wang, Z., Chen, S., & Hanzo, L. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2835558 · Cited by 1,330
wav2vec: Unsupervised Pre-Training for Speech Recognition
Schneider, S., Baevski, A., Collobert, R., & Auli, M. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-1873 · Cited by 1,290
Space-Air-Ground Integrated Network: A Survey
Liu, J., Shi, Y., Fadlullah, Z. M., & Kato, N. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2841996 · Cited by 1,281
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Deng, S., Zhao, H., Fang, W. J., Yin, J., Dustdar, S., & Zomaya, A. Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2984887 · Cited by 1,280
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningPreprint
Rashid, T., Samvelyan, M., de Witt, C. S., Farquhar, G., Foerster, J., & Whiteson, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.11485 · Cited by 1,248
Nilsson, N. J. (1986) · Artificial Intelligence · doi:10.1016/0004-3702(86)90031-7 · Cited by 1,238
Cepstral analysis technique for automatic speaker verification
Furui, S. (1981) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1981.1163530 · Cited by 1,177
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170
6G Wireless Communications: Vision and Potential Techniques
Yang, P., Xiao, Y., Xiao, M., & Li, S. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800418 · Cited by 1,153
Gärdenfors, P. (2000) · The MIT Press eBooks · doi:10.7551/mitpress/2076.001.0001 · Cited by 1,139
Studies on the semantics of questions and the pragmatics of answers
Groenendijk, J., & Stokhof, M. (1984) · UvA-DARE (University of Amsterdam) · Cited by 1,051
Tariq, F., Khandaker, M. R. A., Wong, K.-K., Imran, M. A., Bennis, M., & Debbah, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900488 · Cited by 1,014
Self-citationBeyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Gündüz, D., Qin, Z., Aguerri, I. E., Dhillon, H. S., Yang, Z., Yener, A., Wong, K., & Chae, C. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3223408 · Cited by 893
Massive MIMO is a reality—What is next?
Björnson, E., Sanguinetti, L., Wymeersch, H., Hoydis, J., & Marzetta, T. L. (2019) · Digital Signal Processing · doi:10.1016/j.dsp.2019.06.007 · Cited by 879
What is consciousness, and could machines have it?
Dehaene, S., Lau, H., & Kouider, S. (2017) · Science · doi:10.1126/science.aan8871 · Cited by 855
Deep Learning: A Critical Appraisal
Marcus, G. (2018) · arXiv · doi:10.48550/arxiv.1801.00631 · Cited by 853
Reconfigurable Intelligent Surface-Based Index Modulation: A New Beyond MIMO Paradigm for 6G
Başar, E. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.2971486 · Cited by 842
Edge artificial intelligence for 6g: vision, enabling technologies, and applications
Letaief, K. B., Shi, Y., Lu, J., & Lu, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126076 · Cited by 829
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
Minerva, R., Lee, G. M., & Crespi, N. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2998530 · Cited by 812
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research
de Alwis, C., Kalla, A., Pham, Q., Kumar, P., Dev, K., Hwang, W., & Liyanage, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3071496 · Cited by 809
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
Chen, S., Liang, Y.-C., Sun, S., Kang, S., Cheng, W., & Peng, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900333 · Cited by 768
Strinati, E. C., Barbarossa, S., González, J. L., Kténas, D., Cassiau, N., Maret, L., & Dehos, C. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921162 · Cited by 749
Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges
Yang, W., Du, H., Liew, Z. Q., Lim, W. Y. B., Xiong, Z., Niyato, D. T., Chi, X., Shen, X., & Miao, C. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3223224 · Cited by 749
Viswanathan, H., & Mogensen, P. (2020) · IEEE Access · doi:10.1109/access.2020.2981745 · Cited by 743
Semantic Communications: Overview, Open Issues, and Future Research Directions
Luo, X., Chen, H., & Guo, Q. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.101.2100269 · Cited by 742
6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence
Gui, G., Liu, M., Tang, F., Kato, N., & Adachi, F. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900516 · Cited by 733
6G Vision and Requirements: Is There Any Need for Beyond 5G?
David, K., & Berndt, H. (2018) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2018.2848498 · Cited by 729
Semantic Communication Systems for Speech Transmission
Weng, Z., & Qin, Z. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087240 · Cited by 709
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?
Zappone, A., Renzo, M. D., & Debbah, M. A. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2924010 · Cited by 703
ProbLog: A probabilistic Prolog and its application in link discovery
Raedt, L. D., Kimmig, A., & Toivonen, H. T. T. (2006) · Lirias · Cited by 698
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909490 · Cited by 658
Federated Learning Over Wireless Fading Channels
Amiri, M. M., & Gunduz, D. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2974748 · Cited by 625
6G Wireless Communications Networks: A Comprehensive Survey
Alsabah, M. Q., Naser, M. A., Mahmmod, B. M., Abdulhussain, S. H., Eissa, M. R., Al-Baidhani, A., Noordin, N. K., Sait, S. M., Al-Utaibi, K. A., & Hashim, F. (2021) · IEEE Access · doi:10.1109/access.2021.3124812 · Cited by 622
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches
Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620
Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D. T., Yang, Q., Shen, X., & Miao, C. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3221119 · Cited by 614
Deep learning for wireless physical layer: Opportunities and challenges
Wang, T., Wen, C.-K., Wang, H., Gao, F., Jiang, T., & Jin, S. (2017) · China Communications · doi:10.1109/cc.2017.8233654 · Cited by 606
6G networks: Beyond Shannon towards semantic and goal-oriented communications
Strinati, E. C., & Barbarossa, S. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107930 · Cited by 581
The Evolution of Quantum Key Distribution Networks: On the Road to the Qinternet
Cao, Y., Zhao, Y., Wang, Q., Zhang, J., Ng, S. X., & Hanzo, L. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3144219 · Cited by 556
Chen, Z., & Liu, B. (2018) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01581-6 · Cited by 551
Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules
Xu, J., Ai, B., Chen, W. R., Yang, A., Sun, P., & Rodrigues, M. R. D. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3082521 · Cited by 545
Zhou, X., & McKay, M. R. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2059057 · Cited by 537
DeepJSCC- f : Deep Joint Source-Channel Coding of Images With Feedback
Kurka, D. B., & Gündüz, D. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2987203 · Cited by 513
Chaccour, C., Soorki, M. N., Saad, W., Bennis, M., Popovski, P., & Debbah, M. A. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3143454 · Cited by 509
Self-citationA Lite Distributed Semantic Communication System for Internet of Things
Xie, H., & Qin, Z. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036968 · Cited by 497
Task-Oriented Multi-User Semantic Communications
Xie, H., Qin, Z., Tao, X., & Letaief, K. B. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3191326 · Cited by 496
de Lima, C. T., Belot, D., Berkvens, R., Bourdoux, A., Dardari, D., Guillaud, M., Isomursu, M., Lohan, E. S., Miao, Y., Barreto, A. N., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3053486 · Cited by 494
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions
Ammar, H. A., Adve, R., Shahbazpanahi, S., Boudreau, G., & Srinivas, K. V. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3135119 · Cited by 493
A Survey of NOMA: Current Status and Open Research Challenges
Makki, B., Chitti, K., Behravan, A., & Alouini, M. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.2969899 · Cited by 485
Approximation and Estimation Bounds for Artificial Neural Networks
Barron, A. R. (1994) · Machine Learning · doi:10.1007/bf00993164 · Cited by 470
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., & Kaiser, Ł. (2018) · arXiv · doi:10.48550/arxiv.1807.03819 · Cited by 467
Deep Learning for Joint Source-Channel Coding of Text
Farsad, N., Rao, M., & Goldsmith, A. J. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461983 · Cited by 463
Key drivers and research challenges for 6G ubiquitous wireless intelligence
Latva‐aho, M., Leppänen, K., Clazzer, F., & Munari, A. (2019) · elib (German Aerospace Center) · Cited by 454
Chomsky, N. (2002) · doi:10.1515/9783110218329 · Cited by 440
The Roadmap to 6G Security and Privacy
Porambage, P., Gür, G., Osorio, D. P. M., Liyanage, M., Gurtov, A., & Ylianttila, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3078081 · Cited by 440
Barwise, J., & Seligman, J. (1997) · Cambridge University Press eBooks · doi:10.1017/cbo9780511895968 · Cited by 436
6G Technologies: Key Drivers, Core Requirements, System Architectures, and Enabling Technologies
Zong, B., Fan, C., Wang, X., Duan, X., Wang, B., & Wang, J. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921398 · Cited by 419
Bross, B., Chen, J., Ohm, J.-R., Sullivan, G. J., & Wang, Y.-K. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3043399 · Cited by 417
Yaacoub, E. E., & Alouini, M. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2976703 · Cited by 412
Natural Language Processing Advancements By Deep Learning: A SurveyPreprint
Torfi, A., Shirvani, R. A., Keneshloo, Y., Tavaf, N., & Fox, E. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01200 · Cited by 411
Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence
Kato, N., Fadlullah, Z. M., Tang, F., Mao, B., Tani, S., Okamura, A., & Liu, J. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1800365 · Cited by 407
From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems
Shi, G., Xiao, Y., Li, Y., & Xie, X. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.001.2001239 · Cited by 407
Semantic Communications: Principles and ChallengesPreprint
Qin, Z., Tao, X., Lü, J., Tong, W., & Li, G. Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.01389 · Cited by 403
Nonlinear Transform Source-Channel Coding for Semantic Communications
Dai, J., Wang, S., Tan, K., Si, Z., Qin, X., Niu, K., & Zhang, P. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3180802 · Cited by 395
6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
Khan, L. U., Yaqoob, I., Imran, M. A., Han, Z., & Hong, C. S. (2020) · IEEE Access · doi:10.1109/access.2020.3015289 · Cited by 391
Buzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 383
Gärdenfors, P. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9629.001.0001 · Cited by 372
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Hanin, B. (2019) · Mathematics · doi:10.3390/math7100992 · Cited by 372
Niiniluoto, I. (1987) · Springer eBooks · doi:10.1007/978-94-009-3739-0 · Cited by 362
Toward Semantic Communications: Deep Learning-Based Image Semantic Coding
Huang, D., Gao, F., Tao, X., Du, Q., & Lü, J. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221999 · Cited by 360
6G: A survey on technologies, scenarios, challenges, and the related issues
Lu, Y., & Zheng, X. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100158 · Cited by 360
A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks
Bariah, L., Mohjazi, L., Muhaidat, S., Sofotasios, P. C., Kurt, G. K., Yanıkömeroğlu, H., & Dobre, O. A. (2020) · IEEE Access · doi:10.1109/access.2020.3019590 · Cited by 356
6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence
Zhang, L., Liang, Y.-C., & Niyato, D. (2019) · China Communications · doi:10.23919/jcc.2019.08.001 · Cited by 354
Resource Allocation for Text Semantic Communications
Yan, L., Qin, Z., Zhang, R., Li, Y., & Li, G. Y. (2022) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2022.3170849 · Cited by 348
AI-Assisted Network-Slicing Based Next-Generation Wireless Networks
Shen, X., Gao, J., Wu, W. R., Lyu, K., Li, M., Zhuang, W., Li, X., & Rao, J. B. (2020) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2020.2965100 · Cited by 343
Less Data, More Knowledge: Building Next-Generation Semantic Communication Networks
Chaccour, C., Saad, W., Debbah, M. A., Han, Z., & Poor, H. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3412852 · Cited by 336
Zhang, P., Xu, W., Gao, H., Niu, K., Xu, X., Qin, X., Yuan, C., Qin, Z., Zhao, H., Wei, J., et al. (2021) · Engineering · doi:10.1016/j.eng.2021.11.003 · Cited by 335
Semantics-Empowered Communication for Networked Intelligent Systems
Kountouris, M., & Παππάς, Ν. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000604 · Cited by 334
Nonlinear Approximation and (Deep) $$\mathrm {ReLU}$$ Networks
Daubechies, I. C., DeVore, R., Foucart, S., Hanin, B., & Petrova, G. (2021) · Constructive Approximation · doi:10.1007/s00365-021-09548-z · Cited by 334
Causal Machine Learning: A Survey and Open Problems
Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J., & Silva, R. (2025) · Foundations and Trends® in Optimization · doi:10.1561/2400000052 · Cited by 323
Morin, A. (2006) · Consciousness and Cognition · doi:10.1016/j.concog.2005.09.006 · Cited by 316
Robust Semantic Communications With Masked VQ-VAE Enabled Codebook
Hu, Q., Zhang, G., Qin, Z., Cai, Y., Yu, G., & Li, G. Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3265201 · Cited by 316
Deep Learning Enabled Semantic Communications With Speech Recognition and Synthesis
Weng, Z., Qin, Z., Tao, X., Pan, C., Liu, G., & Li, G. Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3240969 · Cited by 310
Meter, R. V. (2014) · Wiley eBooks · doi:10.1002/9781118648919 · Cited by 303
6G Vision, Value, Use Cases and Technologies From European 6G Flagship Project Hexa-X
Uusitalo, M. A., Rugeland, P., Boldi, M. R., Strinati, E. C., Demestichas, P., Ericson, M., Fettweis, G. P., Filippou, M. C., Gati, A., Hamon, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3130030 · Cited by 293
6G Ecosystem: Current Status and Future Perspective
Bhat, J. R., & AlQahtani, S. A. (2021) · IEEE Access · doi:10.1109/access.2021.3054833 · Cited by 293
Wireless Deep Video Semantic Transmission
Wang, S., Dai, J., Liang, Z., Niu, K., Si, Z., Dong, C., Qin, X., & Zhang, P. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221977 · Cited by 292
Wireless Semantic Communications for Video Conferencing
Jiang, P., Wen, C.-K., Jin, S., & Li, G. Y. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221968 · Cited by 290
Towards a theory of semantic communication
Bao, J., Basu, P., Dean, M., Partridge, C., Swami, A., Leland, W., & Hendler, J. (2011) · IEEE Network Science Workshop (NSW) · doi:10.1109/nsw.2011.6004632 · Cited by 287
What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Lan, Q., Wen, D., Zhang, Z., Zeng, Q., Chen, X., Popovski, P., & Huang, K. (2021) · Journal of Communications and Information Networks · doi:10.23919/jcin.2021.9663101 · Cited by 284
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Hanin, B. (n.d.) · RePEc: Research Papers in Economics · Cited by 278
Task-Oriented Multi-User Semantic Communications for VQA
Xie, H., Qin, Z., & Li, G. Y. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3136045 · Cited by 276
Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data
Zhang, H., Shao, S., Tao, M., Bi, X., & Letaief, K. B. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221991 · Cited by 271
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Wang, Z., She, Q., & Ward, T. E. (2022) · ACM Computing Surveys · doi:10.1145/3439723 · Cited by 267
Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine
Guo, W. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000050 · Cited by 264
Deep Neural Network Approximation Theory
Elbrächter, D., Perekrestenko, D., Grohs, P., & Bölcskei, H. (2021) · IEEE Transactions on Information Theory · doi:10.1109/tit.2021.3062161 · Cited by 263
A Unified Multi-Task Semantic Communication System for Multimodal Data
Zhang, G., Hu, Q., Qin, Z., Cai, Y., Yu, G., & Tao, X. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3364990 · Cited by 261
Outline of a Theory of Strongly Semantic Information
Floridi, L. (2004) · Minds and Machines · doi:10.1023/b:mind.0000021684.50925.c9 · Cited by 255
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyPreprint
Wang, Z., She, Q., & Ward, T. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01529 · Cited by 254
WITT: A Wireless Image Transmission Transformer for Semantic Communications
Yang, K., Wang, S., Dai, J., Tan, K., Niu, K., & Zhang, P. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094735 · Cited by 241
Semantic Communications in Networked Systems: A Data Significance Perspective
Uysal‐Biyikoglu, E., Kaya, O., Anthony Ephremides, B., Gross, J., Codreanu, M., Popovski, P., Assaad, M., Liva, G., Munari, A., Soret, B., et al. (2022) · IEEE Network · doi:10.1109/mnet.106.2100636 · Cited by 236
Wang, Y., Chen, M., Luo, T., Saad, W., Niyato, D. T., Poor, H. V., & Cui, S. R. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3191112 · Cited by 235
Ballé, J., Chou, P. A., Minnen, D. C., Singh, S., Johnston, N., Agustsson, E., Hwang, S. J., & Toderici, G. D. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3034501 · Cited by 233
Semantic-Preserved Communication System for Highly Efficient Speech Transmission
Han, T., Yang, Q., Shi, Z., He, S., & Zhang, Z. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221952 · Cited by 233
Gündüz, D., de Kerret, P., Sidiropoulos, N. D., Gesbert, D., Murthy, C. R., & van der Schaar, M. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2933969 · Cited by 222
Terahertz-Enabled Wireless System for Beyond-5G Ultra-Fast Networks: A Brief Survey
Huq, K. M. S., Busari, S. A., Rodrı́guez, J., Frascolla, V., Bazzi, W. M., & Sicker, D. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800430 · Cited by 217
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission
Tung, T.-Y., & Gündüz, D. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3191354 · Cited by 212
Deep Source-Channel Coding for Sentence Semantic Transmission With HARQ
Jiang, P., Wen, C.-K., Jin, S., & Li, G. Y. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3180997 · Cited by 211
Terahertz band communication systems: Challenges, novelties and standardization efforts
Tekbıyık, K., Ekti, A. R., Kurt, G. K., & Görçin, A. (2019) · Physical Communication · doi:10.1016/j.phycom.2019.04.014 · Cited by 206
Zappone, A., Renzo, M. D., Debbah, M. A., Tu, L., & Qian, X. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921627 · Cited by 204
Cariolaro, G. (2015) · Signals and communication technology · doi:10.1007/978-3-319-15600-2 · Cited by 204
A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication NetworksPreprint
Zhao, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04789 · Cited by 200
Task-Oriented Communications for 6G: Vision, Principles, and Technologies
Shi, Y., Zhou, Y. H., Wen, D., Wu, Y., Jiang, C., & Letaief, K. B. (2023) · IEEE Wireless Communications · doi:10.1109/mwc.002.2200468 · Cited by 199
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Blau, Y., & Michaeli, T. (2019) · arXiv · doi:10.48550/arxiv.1901.07821 · Cited by 198
Theoretical issues in deep networks
Poggio, T., Banburski, A., & Liao, Q. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907369117 · Cited by 195
Bandwidth-Agile Image Transmission With Deep Joint Source-Channel Coding
Kurka, D. B., & Gündüz, D. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3090048 · Cited by 194
Oddie, G. (1986) · Springer eBooks · doi:10.1007/978-94-009-4658-3 · Cited by 191
Advanced Quantum Communications
Imre, S., & Gyöngyösi, L. (2012) · Wiley eBooks · doi:10.1002/9781118337462 · Cited by 182
Detection Techniques for Diffusion-based Molecular Communication
Llátser, I., Cabellos‐Aparicio, A., Pierobon, M., & Alarcón, E. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.sup2.1213005 · Cited by 182
The Information Bottleneck Problem and its Applications in Machine Learning
Goldfeld, Z., & Polyanskiy, Y. (2020) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2020.2991561 · Cited by 180
Semantics-Empowered Communications: A Tutorial-Cum-Survey
Lu, Z., Li, R., Lu, K., Chen, X., Hossain, E., Zhao, Z., & Zhang, H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3333342 · Cited by 178
Nine Challenges in Artificial Intelligence and Wireless Communications for 6G
Tong, W., & Li, G. Y. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.006.2100543 · Cited by 177
Wang, Y., Gao, Z., Zheng, D., Chen, S., Gündüz, D., & Poor, H. V. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.008.2200157 · Cited by 174
(2021) · Cambridge University Press eBooks · doi:10.1017/9781108989817 · Cited by 168
Heterogeneous Semantic and Bit Communications: A Semi-NOMA Scheme
Mu, X., Liu, Y., Guo, L., & Al‐Dhahir, N. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3222000 · Cited by 167
Semantic information, autonomous agency and non-equilibrium statistical physics
Kolchinsky, A., & Wolpert, D. H. (2018) · Interface Focus · doi:10.1098/rsfs.2018.0041 · Cited by 163
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
Tung, T.-Y., Kurka, D. B., Jankowski, M., & Gündüz, D. (2022) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2022.3231042 · Cited by 163
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Agarwal, O., Ge, H., Shakeri, S., & Al-Rfou, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.278 · Cited by 161
A Theory of Semantic Communication
Shao, Y., Cao, Q., & Gündüz, D. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3406375 · Cited by 160
Toward Self-Learning Edge Intelligence in 6G
Xiao, Y., Shi, G., Li, Y., Saad, W., & Poor, H. V. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.2000388 · Cited by 158
6G: The Personal Tactile Internet—And Open Questions for Information Theory
Fettweis, G. P., & Boche, H. (2021) · IEEE BITS the Information Theory Magazine · doi:10.1109/mbits.2021.3118662 · Cited by 156
Shahraki, A., Abbasi, M., Piran, M. J., & Taherkordi, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.12475 · Cited by 154
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
Airplane-Aided Integrated Networking for 6G Wireless: Will It Work?
Huang, X., Zhang, J. A., Liu, R. P., Guo, Y. J., & Hanzo, L. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921244 · Cited by 153
The Semantic Communication Game
Güler, B., Yener, A., & Swami, A. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2872596 · Cited by 151
Rethinking Modern Communication from Semantic Coding to Semantic Communication
Lu, K., Zhou, Q., Li, R., Zhao, Z., Chen, X., Wu, J., & Zhang, H. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.013.2100642 · Cited by 149
White Paper on Critical and Massive Machine Type Communication Towards 6GPreprint
Mahmood, N. H., Böcker, S., Munari, A., Clazzer, F., Moerman, I., Mikhaylov, K., López, O. L. A., Park, O.-S., Mercier, E., Bartz, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14146 · Cited by 148
Deep Joint Source-Channel Coding for Semantic Communications
Xu, J., Tung, T.-Y., Ai, B., Chen, W. R., Sun, Y., & Gündüz, D. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.004.2200819 · Cited by 148
Semantic Communication With Adaptive Universal Transformer
Zhou, Q., Li, R., Zhao, Z., Peng, C., & Zhang, H. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3132067 · Cited by 147
6G Communication Technology: A Vision on Intelligent Healthcare
Nayak, S., & Patgiri, R. (2021) · Studies in Computational Intelligence · doi:10.1007/978-981-15-9735-0_1 · Cited by 143
Katz, M. D., Matinmikko‐Blue, M., & Latva‐aho, M. (2018) · IEEE Latin-American Conference on Communications · doi:10.1109/latincom.2018.8613209 · Cited by 140
A Paradigm Shift toward Semantic Communications
Niu, K., Dai, J., Yao, S., Wang, S., Si, Z., Qin, X., & Zhang, P. (2022) · IEEE Communications Magazine · doi:10.1109/mcom.001.2200099 · Cited by 138
Vector Quantized Semantic Communication System
Fu, Q., Xie, H., Qin, Z., Slabaugh, G., & Tao, X. (2023) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2023.3255221 · Cited by 137
Semantic Communication Meets Edge Intelligence
Yang, W., Liew, Z. Q., Lim, W. Y. B., Xiong, Z., Niyato, D. T., Chi, X., Cao, X., & Letaief, K. B. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.004.2200050 · Cited by 134
Lifelong learning of human actions with deep neural network self-organization
Parisi, G. I., Tani, J., Weber, C., & Wermter, S. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.09.001 · Cited by 131
Secure Semantic Communications: Fundamentals and Challenges
Yang, Z., Chen, M., Li, G., Yang, Y., & Zhang, Z. (2024) · IEEE Network · doi:10.1109/mnet.2024.3411027 · Cited by 130
Flagged first, then by citations
34 of the 388 references couldn’t be matched to a known paper.
Research works printed but not linked: 15
- Hackl, 2022, Navigating The Metaverse: A Guide To Limitless Possibilities in a Web 3.0 World
- Rajatheva, 2020, White paper on broadband connectivity in 6G, arXiv:2004.14247 · arXiv
- 10.1145/584091.584093 · DOI
- Jurafsky, 2000, Speech and Language Processing: An Introduction To Natural Language Processing, Computational Linguistics, and Speech Recognition
- Morris, 1938, Foundations of the Theory of Signs
- Bear, 2016, Neuroscience: Exploring the Brain
- Kandel, 2021, Principles of Neural Science
- 10.48550/ARXIV.1404.7828 · DOI
- Pearl, 2016, Causal Inference in Statistics: A Primer
- Peters, 2017, Elements of Causal Inference: Foundations and Learning Algorithms
- 10.1145/1374376.1374397 · DOI
- Chattopadhyay, 2021, Quantifying Task Complexity Through Generalized Information Measures
- Goodfellow, Generative adversarial nets, 2672
- Kahneman, 2011, Thinking, Fast and Slow
- 10.1109/ICSC.2007.99 · DOI
Cited by
Sorted by citations
A Survey on Goal-Oriented Semantic Communication: Techniques, Challenges, and Future Directions
Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · IEEE Access · doi:10.1109/access.2024.3381967 · Cited by 71
Self-citationYan, M. G., Guo, H., Chan, C. A., Gygax, A. F., Li, C., & Chih‐Lin, I. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.2025.3600791 · Cited by 35
Physical Layer Security for Integrated Sensing and Communication: A Survey
Matsumine, T., Ochiai, H., & Shikata, J. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3598312 · Cited by 33
Zhang, M., Abdi, M., Dasari, V. R., & Restuccia, F. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111531 · Cited by 26
Zhu, F., Chen, J., Wen, J., Yang, Y., Yi, C., Tie, Y., Zhang, P., Cai, J., Niyato, D. T., & Guizani, M. (2026) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2026.3665395 · Cited by 26
Metadata
- Authors
- Getu, T. M., Kaddoum, G., & Bennis, M.
- Affiliations
- National Institute of Standards and Technology (US)
- École de Technologie Supérieure (CA)
- University of Oulu (FI)
- Venue
- Proceedings of the IEEE
- Published
- 2024-11
- Volume
- 112
- Issue
- 11
- Pages
- 1649-1685
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W4406858359
- Cited by
- 110
- References
- 388
- Citations by year
- 2026: 74 · 2025: 32 · 2024: 4
- FWCI
- 11.38
- Type
- journal article
- Language
- en
- Open access
- green
- dx.doi.org
- Licence
- Locations
- Proceedings of the IEEE (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- Figshare (submittedVersion, cc-by-nc-sa)
- Funders
- U.S. Department of Commerce and its agency National Institute of Standards and Technology
- Natural Sciences and Engineering Research Council of Canada (NSERC) through its Canada Research Chair in Unlocking the Power of the IoT 6G-Networks
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optical Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Keywords
- semantic communication, 6G, deep learning, semantic information, semantic representation, semantic entropy, research challenges, future research directions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review