DOI:
https://doi.org/10.64539/msts.v2i2.2026.551Keywords:
Artificial Intelligence, Compliance-Aware Networking, Regulatory Compliance, Telecommunication Networks, Reinforcement Learning, Adaptive Routing, Spectrum Management, Interference MitigationAbstract
The rapid evolution of heterogeneous telecommunication networks has increased the difficulty of maintaining communication performance while operating within spectrum and transmission constraints. Existing network optimization approaches primarily focus on throughput, routing, and interference mitigation, whereas regulatory compliance is commonly treated as a separate post-processing activity. This separation limits the ability of network-control mechanisms to respond proactively to compliance risks. This study develops a Compliance-Aware AI Framework (CA-AIF) that integrates Coalition Formation Games (CFG), variance-aware DUCBQ adaptive routing, and Proximal Policy Optimization (PPO) with a policy-constrained compliance monitoring layer. The framework represents compliance risk through telemetry-derived violation indicators associated with spectrum occupancy, transmission power, and interference conditions and incorporates these indicators into network decision-making. A controlled simulation environment is developed using synthetically generated mobility, interference, channel, and spectrum-occupancy data. The proposed framework is evaluated against Q-learning, Dyna-Q, UCBQ, and DUCBQ using throughput, packet acceptance, routing stability, convergence, compliance violation rate, and computational cost. Across repeated simulations, the proposed framework achieves higher throughput and packet acceptance while reducing compliance violations relative to the evaluated baselines. The results demonstrate that incorporating compliance risk into network optimization can improve the stability of communication decisions without treating compliance as an independent post-processing task. The study contributes a reproducible simulation-based framework for compliance-aware intelligent networking and provides a basis for future validation using operational spectrum-monitoring and regulatory datasets.
References
[1] T. Kim, J. Kwak, and J. P. Choi, “Satellite Edge Computing Architecture and Network Slice Scheduling for IoT Support,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14938–14951, 2022. https://doi.org/10.1109/JIOT.2021.3132171.
[2] W. Xu and X. Zuo, “Confronting Cross-Border Data Risks: A Multidimensional Firm-Level Scale for an Era of Digital Fragmentation,” Journal of Global Information Management, vol. 34, no. 1, 2026. https://doi.org/10.4018/JGIM.399056.
[3] M. Papaioannou, M. G. Valcheva, M. Gelgi, L. Islami, and L. Sommer, “Cybersecurity and regulatory compliance in smart cities: A comprehensive review,” Smart Cities, vol. 9, no. 5, Art. no. 76, 2026. https://doi.org/10.3390/smartcities9050076.
[4] A. D. Mitchell and N. Mishra, “Cross-border data regulatory frameworks: Opportunities, challenges, and a future-forward agenda,” Fordham Intellectual Property, Media & Entertainment Law Journal, vol. 34, no. 3, pp. 842–893, 2024. https://ir.lawnet.fordham.edu/iplj/vol34/iss4/2/.
[5] International Telecommunication Union, “Technical specifications for the upgrade of the spectrum monitoring system in Albania,” International Telecommunication Union, 2025. [Online] Available: www.itu.int.
[6] O. Albu, C. Chinie, and D.-C. Berbece, “Mapping the recent landscape of anomaly detection beyond cybersecurity and finance: A Web of Science bibliometric and topic-modelling review,” Management & Marketing, vol. 21, Art. no. 6, 2026. https://doi.org/10.1007/s44491-026-00006-9.
[7] L. M. Y. Cifuentes, E. C. Muñoz, and R. C. Sánchez, “Development of a model for detecting spectrum sensing data falsification attack in mobile cognitive radio networks integrating artificial intelligence techniques,” Algorithms, vol. 18, no. 10, Art. no. 596, 2025. https://doi.org/10.3390/a18100596.
[8] D. Ünal, M. Hammoudeh, and S. Kiraz, “Policy specification and verification for blockchain and smart contracts in 5G networks,” ICT Express, vol. 6, no. 1, pp. 43-47, 2019. https://doi.org/10.1016/j.icte.2019.07.002.
[9] S. Jeddou, L. Diez, A. Baina, N. Abdellah, and R. Agüero, “A review of the Internet of Things (IoT) landscape: Technologies, applications, and open challenges,” Journal of Electrical and Computer Engineering, 2026. https://doi.org/10.1155/jece/6657578.
[10] P. E. Atamewan, “Digital compliance monitoring: Leveraging AI and IoT for real-time environmental regulation enforcement,” International Journal of Advanced Multidisciplinary Research and Studies, vol. 4, no. 4, pp. 1337–1345, 2024. https://doi.org/10.62225/2583049X.2024.4.4.4816.
[11] R. Arianti, D. D. Ariananda, and S. B. Wibowo, “Machine learning-based spectrum sensing in cognitive radio networks: A systematic review,” in Proc. 2025 Int. Conf. Information Technology and Computing, 2025. https://doi.org/10.1109/ICITCOM66635.2025.11265012.
[12] A. Aluwala, “AI-driven anomaly detection in network monitoring techniques and tools,” Journal of Artificial Intelligence & Cloud Computing, vol. 3, no. 3, p. 1, 2024. https://doi.org/10.47363/JAICC/2024(3)310.
[13] D. Q. Yagual, D. Fernández Iglesias, and F. J. Nóvoa, “A hybrid deep learning-based architecture for network traffic anomaly detection via EFMS-enhanced KMeans clustering and CNN-GRU models,” Applied Sciences, vol. 15, no. 20, Art. no. 10889, 2025. https://doi.org/10.3390/app152010889.
[14] U. C. Ukpong, O. Idowu-Bismark, E. Adetiba, J. R. Kala, E. Owolabi, O. Oshin, A. Abayomi, and O. E. Dare, “Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks,” Scientific African, vol. 27, Art. no. e02523, 2025. https://doi.org/10.1016/j.sciaf.2024.e02523.
[15] S. Saidane, F. Telch, K. Shahin, P. Gkonis, and F. Granelli, “ULTIMATE: A multi-agent deep reinforcement learning framework for false-positive optimized enterprise intrusion detection,” Array, vol. 30, Art. no. 100896, 2026. https://doi.org/10.1016/j.array.2026.100896.
[16] M. Nabil, M. Hnida, A. Haqiq, and I. Hilal, “Advanced anomaly detection in mobile networks: A hybrid approach based on statistical and machine learning techniques,” International Journal of Interactive Mobile Technologies, vol. 19, no. 13, pp. 162–182, 2025. https://doi.org/10.3991/ijim.v19i13.54539.
[17] J. Muringani, J. Noll, C. Kimambo, W. Mansour, C. Blaser Mapitsa, J. Roberson, J. Karanja, and R. Orbach, “Evidence of mobile internet usage in Sub-Saharan Africa and South Africa: D4 executive summary & D5 final report,” University of Oslo, 2024. https://doi.org/10.13140/RG.2.2.25182.75844.
[18] H. Ha and C. K. P. Chuah, “Digital economy in Southeast Asia: Challenges, opportunities and future development,” Southeast Asia: A Multidisciplinary Journal, vol. 23, no. 3, 2023. https://doi.org/10.1108/SEAMJ-02-2023-0023.
[19] D. O. Cajueiro and V. R. R. Celestino, “A comprehensive review of artificial intelligence regulation: Weighing ethical principles and innovation,” Journal of Economy and Technology, vol. 4, pp. 77–91, 2026. https://doi.org/10.1016/j.ject.2025.07.001.
[20] R. Saruthirathanaworakun, N. T. Le, T. T. Le, W. Srisiri, S. Chaitusaney, P. Kaewplung, and W. Benjapolakul, “The application of artificial intelligence in spectrum management and the analytics of frequency data using big data technology,” IEEE Access, vol. 12, pp. 144122–144149, 2024. https://doi.org/10.1109/ACCESS.2024.3471787.
[21] C. Brown and A. Ghasemi, “Evolution toward data-driven spectrum sharing: Opportunities and challenges,” IEEE Access, vol. 11, pp. 99680–99692, 2023. https://doi.org/10.1109/ACCESS.2023.3315246.
[22] E. Dritsas and M. Trigka, “Machine learning in information and communications technology: A survey,” Information, vol. 16, no. 1, Art. no. 8, 2025. https://doi.org/10.3390/info16010008.
[23] I. Laktionov, G. Diachenko, D. Moroz, and I. Getman, “A comprehensive review of cybersecurity threats to wireless infocommunications in the quantum-age cryptography,” IoT, vol. 6, no. 4, Art. no. 61, 2025. https://doi.org/10.3390/iot6040061.
[24] J. Chen, L. Lu, M. Chen, T. Qu, and Z. Li, “Optimization of Artificial Intelligence Algorithms for Intelligent Manufacturing: Enhancing Production Efficiency and Supply Chain Stability,” Journal of Organizational and End User Computing, vol. 37, no. 1, 2025. https://doi.org/10.4018/JOEUC.392071.
[25] Y. Liu, T. Shu, X. Yin, and J. Xia, “Synergistic evolutionary optimization with reinforcement learning for multi-objective energy-efficient hybrid flow shop scheduling,” Axioms, vol. 15, no. 3, Art. no. 193, 2026. https://doi.org/10.3390/axioms15030193.
[26] P. Xiao, Y. Wang, and I. Montgomery, “Deep reinforcement learning for route optimization in e-commerce return management,” Journal of Computing Innovations and Applications, vol. 2, no. 2, pp. 100–110, 2024. https://doi.org/10.63575/CIA.2024.20210.
[27] L. R. Valencia et al., “A systematic review of artificial intelligence applied to compliance: Fraud detection in cryptocurrency transactions,” Journal of Risk and Financial Management, vol. 18, no. 11, Art. no. 612, 2025. https://doi.org/10.3390/jrfm18110612.
[28] V. Freschi and E. Lattanzi, “A study on the impact of packet length on communication in low power wireless sensor networks under interference,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3820–3830, 2019. https://doi.org/10.1109/JIOT.2019.2891841.
[29] G. F. Peter, O. B. Edirin, O. O. Victor, O. E. Ewere, J. Alele, and O. Ezekiel, “Evaluating latency and packet loss of direct and hotspot internet connections for quality of service and network selection,” International Journal of Recent Engineering Science, vol. 12, no. 4, pp. 34–49, 2025. https://doi.org/10.14445/23497157/IJRES-V12I4P104.
[30] X. Wu, C. Wu, and P. Deng, “Prediction of packet loss rate in non-stationary networks based on time-varying autoregressive sequences,” Electronics, vol. 12, no. 5, Art. no. 1103, 2023. https://doi.org/10.3390/electronics12051103.
