The imminent transition from 5G to the seventhgeneration (7G) mobile ecosystem promises to redefine connectivity through unprecedented data rates, pervasive ultralow latency, and holistic integration of terrestrial, aerial, and spaceborne networks. Central to this evolution is Artificial Intelligence (AI), which is poised to become the nervous system that orchestrates, optimizes, and secures the hyperdynamic 7G fabric. This paper surveys the emerging AIdriven paradigms that will underpin 7G, focusing on three interlocking pillars: (i) Cognitive RadioMesh Intelligence, where deep reinforcement learning autonomously negotiates spectrum, beamforming, and topology reconfiguration across heterogeneous nodes; (ii) SemanticLevel Communication, in which generative AI compresses and reconstructs intentrich content, slashing payload volume while preserving meaning; and (iii) SelfHealing Autonomous Management, leveraging federated learning and continual online adaptation to detect, diagnose, and remediate anomalies in real time. By coupling these capabilities with emerging hardware accelerators neuromorphic chips, photonic processors, and quantumenhanced inference engines 7G can achieve a closedloop intelligence that not only reacts to network conditions but anticipates user needs and environmental constraints. The analysis draws on recent experimental testbeds, crosslayer simulation results, and earlystage standardization drafts to illustrate how AI can transform raw connectivity into an intelligent, contextaware service continuum. The findings highlight a paradigm shift: from “networkasinfrastructure” to “networkascognition,” laying a blueprint for researchers, industry stakeholders, and policy makers to navigate the AIinfused 7G frontier.

Keywords - Artificial Intelligence, Mobile communication, 7G, AI-driven, Cognitive radio, Semantic Communication

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1.  kumar Kondeti VS, Kulkarni R, Pillai BS, Rajendran S. Slice-Based 6G Network with Enhanced Manta Ray Deep Reinforcement Learning-Driven Proactive and Robust Resource Management. Computers, Materials & Continua. 2025 Jul 30;84(3):4973-95.
2. Xu Y, Xu G, Ma C, An Z. An advancing temporal convolutional network for 5G latency services via automatic modulation recognition. IEEE Transactions on Circuits and Systems II: Express Briefs. 2022 Feb 18;69(6):3002-6.
3. Mukherjee M, Guo M, Lloret J, Zhang Q. Leveraging intelligent computation offloading with fog/edge computing for tactile internet: Advantages and limitations. IEEE Network. 2020 Aug 12;34(5):322-9.
4. Tsinos CG, Boulogeorgos AA, Tsiftsis TA. Neuroris: Neuromorphic-inspired metasurfaces. IEEE Wireless Communications Letters. 2024 May 8;13(7):1878-82.
5. Nakao M, Sakai M, Hanada Y, Murai H, Sato M. Graph optimization algorithm for low- latency interconnection networks. Parallel Computing. 2021 Sep 1;106:102805.
6. Li B, Battiti R, Fang Y. Achieving optimal performance by using the IEEE 802.11 MAC protocol with service differentiation enhancements. IEEE Transactions on Vehicular Technology. 2007 May 15;56(3):1374-87.
7. Tesfaw BA, Juang RT, Lin HP, Tarekegn GB, Kabore WN. Multi-agent deep reinforcement learning based optimizing joint 3D trajectories and phase shifts in RIS- assisted UAV-enabled wireless communications. IEEE Open Journal of Vehicular Technology. 2024 Oct 24;5:1712-26.
8. Tian Y, Zhao Q, Boukhalfa F, Wu K, Bader F. Multimodal transformers for wireless communications: A case study in beam prediction. arXiv preprint arXiv:2309.11811. 2023 Sep 21.
9. Alqasi MA, Alkelanie YA, Alnagrat AJ. Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: research of artificial intelligence. 2024 Nov 11;4(2):625-37.
10. Vaigandla KK. A systematic survey on artificial intelligence in 6G wireless networks: Security, opportunities, applications, advantages, future research directions and challenges. Babylonian Journal of Artificial Intelligence. 2025 Jul 12;2025:99-106.
11. Kutubuddin K. Detection of Malicious Nodes in IoT Networks based on packet loss using ML. Journal of Mobile Computing, Communication & mobile Networks. 2022;9(3):9-16.
12. Azman F, Suraya Q, Rahim FA, Mohd MS, Ariffin NA. My guardian: A personal safety mobile application. In2018 IEEE Conference on Open Systems (ICOS) 2018 Nov 21 (pp. 37-41). IEEE.
13. Sharma PK. AI-Driven Network Management for IoT Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST). 2025 Jan 1;8(1):11521-5.

The imminent transition from 5G to the seventhgeneration (7G) mobile ecosystem promises to redefine connectivity through unprecedented data rates, pervasive ultralow latency, and holistic integration of terrestrial, aerial, and spaceborne networks. Central to this evolution is Artificial Intelligence (AI), which is poised to become the nervous system that orchestrates, optimizes, and secures the hyperdynamic 7G fabric. This paper surveys the emerging AIdriven paradigms that will underpin 7G, focusing on three interlocking pillars: (i) Cognitive RadioMesh Intelligence, where deep reinforcement learning autonomously negotiates spectrum, beamforming, and topology reconfiguration across heterogeneous nodes; (ii) SemanticLevel Communication, in which generative AI compresses and reconstructs intentrich content, slashing payload volume while preserving meaning; and (iii) SelfHealing Autonomous Management, leveraging federated learning and continual online adaptation to detect, diagnose, and remediate anomalies in real time. By coupling these capabilities with emerging hardware accelerators neuromorphic chips, photonic processors, and quantumenhanced inference engines 7G can achieve a closedloop intelligence that not only reacts to network conditions but anticipates user needs and environmental constraints. The analysis draws on recent experimental testbeds, crosslayer simulation results, and earlystage standardization drafts to illustrate how AI can transform raw connectivity into an intelligent, contextaware service continuum. The findings highlight a paradigm shift: from “networkasinfrastructure” to “networkascognition,” laying a blueprint for researchers, industry stakeholders, and policy makers to navigate the AIinfused 7G frontier.

Keywords - Artificial Intelligence, Mobile communication, 7G, AI-driven, Cognitive radio, Semantic Communication

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1.  kumar Kondeti VS, Kulkarni R, Pillai BS, Rajendran S. Slice-Based 6G Network with Enhanced Manta Ray Deep Reinforcement Learning-Driven Proactive and Robust Resource Management. Computers, Materials & Continua. 2025 Jul 30;84(3):4973-95.
2. Xu Y, Xu G, Ma C, An Z. An advancing temporal convolutional network for 5G latency services via automatic modulation recognition. IEEE Transactions on Circuits and Systems II: Express Briefs. 2022 Feb 18;69(6):3002-6.
3. Mukherjee M, Guo M, Lloret J, Zhang Q. Leveraging intelligent computation offloading with fog/edge computing for tactile internet: Advantages and limitations. IEEE Network. 2020 Aug 12;34(5):322-9.
4. Tsinos CG, Boulogeorgos AA, Tsiftsis TA. Neuroris: Neuromorphic-inspired metasurfaces. IEEE Wireless Communications Letters. 2024 May 8;13(7):1878-82.
5. Nakao M, Sakai M, Hanada Y, Murai H, Sato M. Graph optimization algorithm for low- latency interconnection networks. Parallel Computing. 2021 Sep 1;106:102805.
6. Li B, Battiti R, Fang Y. Achieving optimal performance by using the IEEE 802.11 MAC protocol with service differentiation enhancements. IEEE Transactions on Vehicular Technology. 2007 May 15;56(3):1374-87.
7. Tesfaw BA, Juang RT, Lin HP, Tarekegn GB, Kabore WN. Multi-agent deep reinforcement learning based optimizing joint 3D trajectories and phase shifts in RIS- assisted UAV-enabled wireless communications. IEEE Open Journal of Vehicular Technology. 2024 Oct 24;5:1712-26.
8. Tian Y, Zhao Q, Boukhalfa F, Wu K, Bader F. Multimodal transformers for wireless communications: A case study in beam prediction. arXiv preprint arXiv:2309.11811. 2023 Sep 21.
9. Alqasi MA, Alkelanie YA, Alnagrat AJ. Intelligent infrastructure for urban transportation: The role of artificial intelligence in predictive maintenance. Brilliance: research of artificial intelligence. 2024 Nov 11;4(2):625-37.
10. Vaigandla KK. A systematic survey on artificial intelligence in 6G wireless networks: Security, opportunities, applications, advantages, future research directions and challenges. Babylonian Journal of Artificial Intelligence. 2025 Jul 12;2025:99-106.
11. Kutubuddin K. Detection of Malicious Nodes in IoT Networks based on packet loss using ML. Journal of Mobile Computing, Communication & mobile Networks. 2022;9(3):9-16.
12. Azman F, Suraya Q, Rahim FA, Mohd MS, Ariffin NA. My guardian: A personal safety mobile application. In2018 IEEE Conference on Open Systems (ICOS) 2018 Nov 21 (pp. 37-41). IEEE.
13. Sharma PK. AI-Driven Network Management for IoT Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST). 2025 Jan 1;8(1):11521-5.