A Comprehensive Review on Architecture, Channel Intelligence, and AutonomousOptimization of AI-Native Communication Systems for 6G Networks
DOI:
https://doi.org/10.71426/jmt.v3.i1.pp420-434Keywords:
6G wireless communication, AI-native networks, channel intelligence, deep learning, Transformer models, autonomous optimization, semantic communication, digital twin, QoE optimization.Abstract
The evolution of sixth-generation (6G) wireless communication systems demands a paradigm shift from conventional model-driven architectures to intelligent, data-driven, and autonomous communication frameworks. Traditional communication systems, which rely on predefined models and static optimization strategies, are inadequate for highly dynamic and heterogeneous 6G environments characterized by ultra-high data rates, sub-millisecond latency, and massive connectivity. To address these challenges, this paper presents a comprehensive review and unified framework for AI-native communication systems, where artificial intelligence is deeply embedded into the communication stack as a fundamental design principle. The proposed framework integrates three core components: (i) channel intelligence based on deep learning and Transformer models for accurate estimation and proactive prediction, (ii) distributed learning using federated architectures for scalable and privacy-preserving intelligence, and (iii) reinforcement learning-based autonomous optimization for dynamic resource allocation under multi-objective constraints. A mathematical formulation is developed to model quality of experience (QoE), energy efficiency, and constrained optimization using Markov decision processes. The paper further identifies key challenges related to scalability, model generalization, computational complexity, and security, and outlines future research directions including semantic communication and digital twin-enabled optimization. Overall, this work establishes AI-native communication as a foundational paradigm for enabling intelligent, self-optimizing, and fully autonomous 6G wireless networks.
References
[1] Saad W, Bennis M, Chen M. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Network. 2020;34(3):134-142. Available from: https://doi.org/10.1109/MNET.001.1900287
[2] Yang H, Alphones A, Xiong Z, Niyato D, Zhao J, Wu K. Artificial-Intelligence-Enabled Intelligent 6G Networks. IEEE Network. 2020;34(6):272-280. Available from: https://doi.org/10.1109/MNET.011.2000195
[3] Zhang Z, Xiao Y, Ma Z, et al. 6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies. IEEE Vehicular Technology Magazine. 2019;14(3):28-41. Available from: https://doi.org/10.1109/MVT.2019.2921208
[4] Liu Y, He Y, Lin Y, Zhang H. Toward Native Artificial Intelligence in 6G. 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). 2022. Available from: https://doi.org/10.1109/BMSB55706.2022.9828649
[5] Hoydis J, Ait Aoudia F, Viswanathan H. Toward a 6G AI-Native Air Interface. arXiv preprint. 2020. Available from: https://arxiv.org/abs/2012.08285
[6] Chen X, Guo Z, Wang X, Feng C, Yang HH, Han S, Wang X, Quek TQ. Toward 6G Native-AI Network: Foundation Model-Based Cloud-Edge-End Collaboration Framework. IEEE Communications Magazine. 2025;63(8):23-30. Available from: https://doi.org/10.1109/MCOM.001.2400582
[7] Strinati EC, Di Lorenzo P, Sciancalepore V, et al. Semantic communication for 6G: Fundamentals, challenges, and future directions. arXiv preprint. 2024. Available from: https://arxiv.org/abs/2402.07573
[8] Getu TM, Kaddoum G, Bennis M. Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions. Proceedings of the IEEE. 2025;112(11):1649-1685. Available from: https://doi.org/10.1109/JPROC.2024.3520707
[9] Ibikunle F. AI-driven Optimization Techniques in Next Generation Wireless Communication Networks and Systems. Center of Artificial Intelligence. 2026;1(1). Available from: https://doi.org/10.65591/1btq4207
[10] Ogenyi FC, Ugwu CN, Ugwu OP. A comprehensive review of AI-native 6G: integrating semantic communications, reconfigurable intelligent surfaces, and edge intelligence for next-generation connectivity. Frontiers in Communications and Networks. 2025;6:1655410. Available from: https://doi.org/10.3389/frcmn.2025.1655410
[11] Usman Y, Vitashkevich D, Muriuki-Mureithi CA, Akl R, Chataut R. AI-Enhanced Deep Neural Network Architecture for Accurate Channel Estimation in 6G Networks. IEEE World AI IoT Congress (AIIoT). 2025. p. 732-736. Available from: https://doi.org/10.1109/AIIoT65859.2025.11105312
[12] Rappaport TS, Xing Y, Kanhere O, Ju S, Madanayake A, Mandal S, Alkhateeb A, Trichopoulos GC. Wireless Communications and Applications Above 100 GHz: Opportunities and Challenges for 6G and Beyond. IEEE Access. 2019;7:78729-78757. Available from: https://doi.org/10.1109/ACCESS.2019.2921522
[13] Björnson E, Özdogan Ö, Larsson EG. Reconfigurable Intelligent Surfaces: Three Myths and Two Critical Questions. IEEE Communications Magazine. 2021;58(12):90-96. Available from: https://doi.org/10.1109/MCOM.001.2000407
[14] Amri MM, Tran NM, Choi KW. Reconfigurable Intelligent Surface-Aided Wireless Communications: Adaptive Beamforming and Experimental Validations. IEEE Access. 2021;9:147442-147457. Available from: https://doi.org/10.1109/ACCESS.2021.3124319
[15] Joo J, Park MC, Han DS, Pejovic V. Deep Learning-Based Channel Prediction in Realistic Vehicular Communications. IEEE Access. 2019;7:27846-27858. Available from: https://doi.org/10.1109/ACCESS.2019.2901710
[16] Li P, Xing Y, Li W. Distributed AI-native Architecture for 6G Networks. International Conference on Information Processing and Network Provisioning (ICIPNP). 2022. p. 57-62. Available from: https://doi.org/10.1109/ICIPNP57450.2022.00019
[17] Kairouz P, McMahan HB. Advances and open problems in federated learning. Foundations and Trends in Machine Learning. 2021;14(1-2):1-210. Available from: https://doi.org/10.1561/2200000083
[18] Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Niyato D, Dobre O, Poor HV. 6G Internet of Things: A Comprehensive Survey. IEEE Internet of Things Journal. 2022;9(1):359-383. Available from: https://doi.org/10.1109/JIOT.2021.3103320
[19] Park J, Samarakoon S, Bennis M, Debbah M. Wireless Network Intelligence at the Edge. Proceedings of the IEEE. 2019;107(11):2204-2239. Available from: https://doi.org/10.1109/JPROC.2019.2946408
[20] Zhou Z, Chen X, Li E, Zeng L, Luo K, Zhang J. Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE. 2019;107(8):1738-1762. Available from: https://doi.org/10.1109/JPROC.2019.2918951
[21] Sutton RS, Barto AG. Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks. 1998 Sep 1;9(5):1054. Available from: https://doi.org/10.1109/tnn.1998.712192
[22] Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. 2017. Available from: https://doi.org/10.48550/arXiv.1707.06347
[23] Zhang K, Yang Z, Başar T. Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms. Handbook of Reinforcement Learning and Control. 2021:321-384. Available from: https://link.springer.com/chapter/10.1007/978-3-030-60990-0_12
[24] Boyd S, Vandenberghe L. Convex Optimization. Cambridge University Press; 2004. Available from: https://stanford.edu/~boyd/cvxbook/
[25] Bertsekas D. Dynamic Programming and Optimal Control: Volume I. Athena Scientific; 2012.
[26] Goldsmith A. Wireless Communications. Cambridge University Press; 2005. Available from: https://doi.org/10.1017/CBO9780511841224
[27] Tse D, Viswanath P. Fundamentals of Wireless Communication. Cambridge University Press; 2005.
[28] Liu G, Deng J, Zhu Y, Li N, Han B, Wang S, Rui H, Wang J, Zhang J, Cui Y, Cui Y. 6G autonomous radio access network empowered by artificial intelligence and network digital twin. Frontiers of Information Technology & Electronic Engineering. 2025;26(2):161-213. Available from: https://doi.org/10.1631/FITEE.2400569
[29] Lu Y, Maharjan S, Zhang Y. Adaptive Edge Association for Wireless Digital Twin Networks in 6G. IEEE Internet of Things Journal. 2021;8(22):16219-16230. Available from: https://doi.org/10.1109/JIOT.2021.3098508
[30] Goodfellow IJ, Shlens J, Szegedy C. Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572. 2014. Available from: https://doi.org/10.48550/arXiv.1412.6572
[31] McMahan HB, Moore E, Ramage D, Hampson S, Arcas BAY. Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv (Cornell University). 2016. Available from: http://arxiv.org/abs/1602.05629
[32] Ziegler V, Schneider P, Viswanathan H, Montag M, Kanugovi S, Rezaki A. Security and Trust in the 6G Era. IEEE Access. 2021;9:142314-142327. Available from: https://doi.org/10.1109/ACCESS.2021.3120143
[33] Evgenidis NG, Mitsiou NA, Tegos SA, Diamantoulakis PD, Sarigiannidis P, Krikidis I, Karagiannidis GK. Hybrid Semantic-Shannon Communications. IEEE Transactions on Wireless Communications. 2024;23(9):10926-10940. Available from: https://doi.org/10.1109/TWC.2024.3376998
[34] Popovski P, Nielsen JJ, Stefanovic C, De Carvalho E, Strom E, Trillingsgaard KF, Bana AS, Kim DM, Kotaba R, Park J, Sorensen RB. Wireless Access for Ultra-Reliable Low-Latency Communication: Principles and Building Blocks. IEEE Network. 2018;32(2):16-23. Available from: https://doi.org/10.1109/MNET.2018.1700258
[35] Wang CX, You X, Gao X, Zhu X, Li Z, Zhang C, Wang H, Huang Y, Chen Y, Haas H, Thompson JS. On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds. IEEE Communications Surveys & Tutorials. 2023;25(2):905-974. Available from: https://doi.org/10.1109/COMST.2023.3249835
[36] Alhammadi A, Shayea I, El-Saleh AA, Azmi MH, Ismail ZH, Kouhalvandi L, Saad SA. Artificial intelligence in 6G wireless networks: Opportunities, applications, and challenges. International Journal of Intelligent Systems. 2024;2024(1):8845070. Available from: https://doi.org/10.1155/2024/8845070
[37] Ye H, Liang L, Li GY, Juang BH. Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels. IEEE Transactions on Wireless Communications. 2020;19(5):3133-3143. Available from: https://doi.org/10.1109/TWC.2020.2970707
[38] Popovski P, Stefanović Č, Nielsen JJ, De Carvalho E, Angjelichinoski M, Trillingsgaard KF, Bana AS. Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC). IEEE Transactions on Communications. 2019;67(8):5783-5801. Available from: https://doi.org/10.1109/TCOMM.2019.2914652
[39] Wang CX, Di Renzo M, Stanczak S, Wang S, Larsson EG. Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. IEEE Wireless Communications. 2020;27(1):16-23. Available from: https://doi.org/10.1109/MWC.001.1900292
[40] Chen W, Lin X, Lee J, Toskala A, Sun S, Chiasserini CF, Liu L. 5G-Advanced Toward 6G: Past, Present, and Future. IEEE Journal on Selected Areas in Communications. 2023;41(6):1592-1619. Available from: https://doi.org/10.1109/JSAC.2023.3274037
[41] Kazmi SA, Khan LU, Tran NH, Hong CS. Network Slicing for 5G and Beyond Networks. Berlin: Springer; 2019. Available from: https://link.springer.com/book/10.1007/978-3-030-16170-5
[42] Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge Computing: Vision and Challenges. IEEE Internet of Things Journal. 2016;3(5):637-646. Available from: https://doi.org/10.1109/JIOT.2016.2579198
[43] Mao Y, You C, Zhang J, Huang K, Letaief KB. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Communications Surveys & Tutorials. 2017;19(4):2322-2358. Available from: https://doi.org/10.1109/COMST.2017.2745201
[44] Kamel M, Hamouda W, Youssef A. Ultra-Dense Networks: A Survey. IEEE Communications Surveys & Tutorials. 2016;18(4):2522-2545. Available from: https://doi.org/10.1109/COMST.2016.2571730
[45] Pan G, Gao Y, Gao Y, Yu W, Zhong Z, Yang X, Guo X, Xu S. AI-Driven Wireless Positioning: Fundamentals, Standards, State-of-the-Art, and Challenges. IEEE Communications Surveys & Tutorials. 2025. Available from: https://doi.org/10.1109/COMST.2025.3648577
[46] Dai L, Jiao R, Adachi F, Poor HV, Hanzo L. Deep learning for wireless communications: An emerging interdisciplinary paradigm. IEEE Wireless Communications. 2020;27(4):133-139. Available from: https://doi.org/10.1109/MWC.001.1900491
[47] Qamar F, Siddiqui MU, Hindia MN, Hassan R, Nguyen QN. Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6G networks. Electronics. 2020;9(9):1416. Available from: https://www.mdpi.com/2079-9292/9/9/1416
[48] Chowdhury MZ, Shahjalal M, Ahmed S, Jang YM. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions. IEEE Open Journal of the Communications Society. 2020;1:957-975. Available from: https://doi.org/10.1109/OJCOMS.2020.3010270
[49] Dang S, Amin O, Shihada B, Alouini MS. What should 6G be? Nature Electronics. 2020;3(1):20-29. Available from: https://www.nature.com/articles/s41928-019-0355-6
[50] Letaief KB, Chen W, Shi Y, Zhang J, Zhang YJ. The Roadmap to 6G: AI Empowered Wireless Networks. IEEE Communications Magazine. 2019;57(8):84-90. Available from: https://doi.org/10.1109/MCOM.2019.1900271
[51] Dintakurthy Y, Innmuri RK, Vanteru A, Thotakuri A. Emerging Applications of Artificial Intelligence in Edge Computing: A Comprehensive Review. Journal of Modern Technology. 2024:175-185. Available from: https://doi.org/10.71426/jmt.v1.i2.pp175-185
[52] Pothireddy SR. Cloud-Native AI-Driven Enterprise Automation for Scalable Digital Process Transformation in Multi-Industry Ecosystems. Journal of Applied Sciences and Modelling. 2025:60-74. Available from: https://doi.org/10.71426/jasm.v1.i1.pp60-74
[53] Alzubi JA, Dhandapani MG, Aiyappan A, Damodaran S, Shreyas J, Priyanka TP. Adaptive Residual Recurrent Neural Network with Heuristic Optimization for Spectral Energy Balancing in 6G Massive MIMO Systems. International Journal of Computational Intelligence Systems. 2025. Available from: https://doi.org/10.1007/s44196-025-01106-w
[54] Geagea C, Tlais AA, Abou Chakra S, Kurt GK. An Electrical Model for Software Defined Radio Sub-THz Systems. 2026 6th Middle East and North Africa Communications Conference (MENACOMM). 2026. p. 1-7. Available from: https://doi.org/10.1109/MENACOMM69507.2026.11532915
[55] Natarajan Y, Srihari K, Dhiman G, Chandragandhi S, Gheisari M, Liu Y, Lee CC, Singh KK, Yadav K, Alharbi HF. An IoT and machine learning-based routing protocol for reconfigurable engineering application. IET Communications. 2022;16(5):464-475. Available from: https://doi.org/10.1049/cmu2.12266
[56] Gheisari M, Wang G, Chen S, Seyfollahi A. A Method for Privacy-Preserving in IoT-SDN Integration Environment. 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). 2018. p. 895-902. Available from: https://doi.org/10.1109/BDCloud.2018.00132
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Mehdi Gheisari, Jafar A. Alzubi, Zahra Shirmohammadi, Seyed Danial Naghavi Sadat, Sahar Valizadeh, Roghayeh Rezaei, Sara Abou Chakra (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Journal of Modern Technology (JMT) Journal operates under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation. All authors publishing in JMT Journal accept these as the terms of publication.
