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Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
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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.
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Yang, X., Ma, S., Liang, Y. Q., & Shi, G. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3725343
Task-Aware Semantic Selection for Scene Graph-Based Semantic Communication
Zhang, C., Zhong, L., Lin, Y., Wu, J., Djahel, S., & Wu, C. (2026) · International Conference on Intelligent Multimedia, Networking, and Security (IMNS) · doi:10.1109/imns67862.2026.11655276
Hasan, S., Ratul, R. H., Mahadi, M. K., Fahim, A.-H., & Mehereen, N. (2026) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2026.3727863
An Attention-Based Variational Autoencoder for End-to-End Wireless Communication Systems
Choubey, N., & Trivedi, A. (2026) · IETE Journal of Research · doi:10.1080/03772063.2026.2719004
CMASC: Channel Model-Driven Adaptive Semantic Communication for V2X Networks
Qi, X., Ma, N., Wu, J., Liang, W., & Zhang, P. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3732500
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 ›
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