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Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges
Review Article  ·  Published: 10 September 2026
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ICCK Transactions on Sensing, Communication, and Control
Volume 3, Issue 3, 2026: 139-159
Review Article Free to Read

Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges

by
Sushil Kumar Singh Sushil Kumar Singh 1 ORCID
Danish Ali Danish Ali 2 ORCID
Muhammad Talha Usman Muhammad Talha Usman 3 ORCID
Habib Khan Habib Khan 3 * ORCID
Muhammad Faizan Omer Muhammad Faizan Omer 4 ORCID
Babar Zeb Babar Zeb 5 ORCID
Theodoros Vavouras Theodoros Vavouras 6 ORCID
Alexandros Gazis Alexandros Gazis 7 * ORCID
1 Department of Computer Engineering, Marwadi University, Rajkot 360003, Gujarat, India
2 Department of Electrical and Computer Engineering, Villanova University, Villanova, PA 19085, United States
3 School of Computing, Gachon University, Seongnam-si 13120, Republic of Korea
4 Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
5 Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Rawalpindi 43701, Pakistan
6 School of Italian Language and Literature, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
7 Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece
* Corresponding Authors: Habib Khan, [email protected]; Alexandros Gazis, [email protected]
Volume 3, Issue 3
Volume 3, Issue 3 2026
Received: 03 March 2026 Revised: 20 April 2026 Accepted: 01 June 2026 Published: 10 September 2026 CrossMark
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DOI: 10.62762/TSCC.2026.286812
ARK: ark:/57805/tscc.2026.286812
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Article Information

Published in ICCK Transactions on Sensing, Communication, and Control
Volume/Issue Volume 3, Issue 3, 2026
Pages 139-159

Abstract

Cognitive-semantic communication encodes task-relevant meaning rather than raw bits, improving 6G spectral efficiency and reducing latency, yet evidence remains fragmented across methods, evaluations, and deployments. This PRISMA-based review screens 782 records (2019-2025) from seven databases, retaining 73 studies organized into five method families: neural semantic codecs, KG-based pipelines, LLM-assisted codecs, cross-modal encoders, and semantic relays. We examine how goal-oriented semantics, knowledge graphs, and LLMs shape representation, compression, and task quality under rate and latency constraints across UAVs, NTNs, ISAC, Metaverse, and IoT. Reported gains include bandwidth reductions of 20-60% over conventional baselines, task accuracy improvements of 8-23 percentage points over bit-level systems, and cross-modal alignment scores above 0.80; however, these gains derive predominantly from AWGN simulations, and robustness under realistic fading, mobility, and adversarial conditions remains the most critical open problem. Gaps include narrow datasets, inconsistent metric definitions, and unaddressed security, privacy, and standardization concerns. Recommendations include shared multimodal datasets with channel traces, standardized robustness protocols, unified metric cards pairing task outcomes with resource costs, edge-friendly split designs, and integrity checks with privacy audits.

Graphical Abstract

Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges

Keywords

cognitive semantics semantic communication knowledge graphs integrated sensing neural codecs semantic relays goal-oriented communication

Data Availability Statement

Not applicable.

Funding

This work was supported without any funding.

Conflicts of Interest

Sushil Kumar Singh and Habib Khan served as Associate Editors of the ICCK Transactions on Sensing, Communication, and Control at the time of manuscript submission. To ensure the integrity of the peer-review process, neither Sushil Kumar Singh nor Habib Khan was involved in the editorial handling, peer review, or decision-making process for this manuscript, which was handled independently by another editor. The remaining authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Cite This Article

APA Style
Singh, S. K., Ali, D., Usman, M. T., Khan, H., Omer, M. F., Zeb, B., Vavouras, T., & Gazis, A. (2026). Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges. ICCK Transactions on Sensing, Communication, and Control, 3(4), 139-159. https://doi.org/10.62762/TSCC.2026.286812
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TY  - JOUR
AU  - Singh, Sushil Kumar
AU  - Ali, Danish
AU  - Usman, Muhammad Talha
AU  - Khan, Habib
AU  - Omer, Muhammad Faizan
AU  - Zeb, Babar
AU  - Vavouras, Theodoros
AU  - Gazis, Alexandros
PY  - 2026
DA  - 2026/09/10
TI  - Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 3
SP  - 139
EP  - 159
DO  - 10.62762/TSCC.2026.286812
UR  - https://www.icck.org/article/abs/TSCC.2026.286812
KW  - cognitive semantics
KW  - semantic communication
KW  - knowledge graphs
KW  - integrated sensing
KW  - neural codecs
KW  - semantic relays
KW  - goal-oriented communication
AB  - Cognitive-semantic communication encodes task-relevant meaning rather than raw bits, improving 6G spectral efficiency and reducing latency, yet evidence remains fragmented across methods, evaluations, and deployments. This PRISMA-based review screens 782 records (2019-2025) from seven databases, retaining 73 studies organized into five method families: neural semantic codecs, KG-based pipelines, LLM-assisted codecs, cross-modal encoders, and semantic relays. We examine how goal-oriented semantics, knowledge graphs, and LLMs shape representation, compression, and task quality under rate and latency constraints across UAVs, NTNs, ISAC, Metaverse, and IoT. Reported gains include bandwidth reductions of 20-60% over conventional baselines, task accuracy improvements of 8-23 percentage points over bit-level systems, and cross-modal alignment scores above 0.80; however, these gains derive predominantly from AWGN simulations, and robustness under realistic fading, mobility, and adversarial conditions remains the most critical open problem. Gaps include narrow datasets, inconsistent metric definitions, and unaddressed security, privacy, and standardization concerns. Recommendations include shared multimodal datasets with channel traces, standardized robustness protocols, unified metric cards pairing task outcomes with resource costs, edge-friendly split designs, and integrity checks with privacy audits.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Singh2026Semantic,
  author = {Sushil Kumar Singh and Danish Ali and Muhammad Talha Usman and Habib Khan and Muhammad Faizan Omer and Babar Zeb and Theodoros Vavouras and Alexandros Gazis},
  title = {Semantic Fidelity for Intelligent 6G Communication: A Taxonomic Deep Dive into Knowledge-Driven Architectures, Benchmarks, and Challenges},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {3},
  pages = {139-159},
  doi = {10.62762/TSCC.2026.286812},
  url = {https://www.icck.org/article/abs/TSCC.2026.286812},
  abstract = {Cognitive-semantic communication encodes task-relevant meaning rather than raw bits, improving 6G spectral efficiency and reducing latency, yet evidence remains fragmented across methods, evaluations, and deployments. This PRISMA-based review screens 782 records (2019-2025) from seven databases, retaining 73 studies organized into five method families: neural semantic codecs, KG-based pipelines, LLM-assisted codecs, cross-modal encoders, and semantic relays. We examine how goal-oriented semantics, knowledge graphs, and LLMs shape representation, compression, and task quality under rate and latency constraints across UAVs, NTNs, ISAC, Metaverse, and IoT. Reported gains include bandwidth reductions of 20-60\% over conventional baselines, task accuracy improvements of 8-23 percentage points over bit-level systems, and cross-modal alignment scores above 0.80; however, these gains derive predominantly from AWGN simulations, and robustness under realistic fading, mobility, and adversarial conditions remains the most critical open problem. Gaps include narrow datasets, inconsistent metric definitions, and unaddressed security, privacy, and standardization concerns. Recommendations include shared multimodal datasets with channel traces, standardized robustness protocols, unified metric cards pairing task outcomes with resource costs, edge-friendly split designs, and integrity checks with privacy audits.},
  keywords = {cognitive semantics, semantic communication, knowledge graphs, integrated sensing, neural codecs, semantic relays, goal-oriented communication},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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ICCK Transactions on Sensing, Communication, and Control
ICCK Transactions on Sensing, Communication, and Control
ISSN: 3068-9287 (Online) | ISSN: 3068-9279 (Print)
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