Reliability-Oriented Cellular Infrastructure Sharing for Resilient Urban Networks under Spatially Heterogeneous Demand, Coverage Failures, and Service Recovery Requirements
Keywords:
Infrastructure Sharing, Network Resilience, Coverage Failures, Spatial Heterogeneity, Service RecoveryAbstract
The proliferation of advanced urban cellular networks necessitates robust infrastructure capable of withstanding unexpected disruptions while catering to highly fluctuating user demands. This paper presents a comprehensive investigation into reliability-oriented cellular infrastructure sharing mechanisms designed to enhance the resilience of urban networks. Modern municipal environments are characterized by spatially heterogeneous traffic patterns and an increasing susceptibility to localized coverage failures stemming from extreme weather events, hardware malfunctions, or sudden power grid anomalies. Traditional redundant deployments by individual network operators are economically prohibitive and spatially inefficient. Therefore, this research proposes a dynamic, multi-operator infrastructure sharing framework that dynamically reallocates radio access network resources and backhaul capacities during degradation events. By integrating stochastic geometry with optimization theory, the study models the spatial distribution of demand and the probabilistic nature of base station failures. The proposed reliability-oriented optimization framework balances the immediate necessity of service recovery with the long-term operational costs of inter-operator sharing agreements. Algorithmic solutions are evaluated through extensive system-level simulations reflecting realistic urban topologies. The results demonstrate that cooperative resource pooling significantly reduces service downtime and improves overall user satisfaction metrics during cascaded network failures. Furthermore, the analysis highlights the critical thresholds of spatial demand heterogeneity where active infrastructure sharing becomes mathematically imperative for maintaining minimum service guarantees. Ultimately, this work provides a foundational theoretical model and actionable strategic guidelines for telecommunications operators and regulatory bodies aiming to foster resilient, shared telecommunications ecosystems in dense metropolitan areas.
References
1. Long, J., Xu, Z., Jiang, T., Yao, W., Jia, S., Ma, C., & Chen, X. (2025, April). Robust SAM: on the adversarial robustness of vision foundation models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 6, pp. 5775-5783).
2. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
3. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
4. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
5. Ou, J., Guo, J., Chen, Y., Jiang, S., Xue, R., & Tian, W. (2025, June). A Neural Network-Based Pipeline Parallel Strategy Solver for Heterogeneous Environments. In 2025 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE.
6. Zhang, J., & Ding, J. (2026). CA-PPO-AXI: Coverage-Aware Deep Reinforcement Learning for Adaptive AXI Bus Protocol Verification.Fundamental Scientific Reports in Multidisciplinary Areas,2(02), 159-169.
7. Wan, H., Cheng, J., Deng, Y., Wu, D., Chen, Y., Lin, Z., ... & Ji, X. Towards Physics Aware Embodied Control with Graph based Object-centric Learning. ACM Transactions on Cyber-Physical Systems.
8. Yao, Y., Sun, J., Miao, P., Chen, G., & Tafazolli, R. (2026). Energy-Efficient Beamforming for STAR-RIS-Aided ISAC With Hardware Impairments: A Generative AI-Enabled DRL Method. IEEE Transactions on Wireless Communications.
9. Chen, A., Li, X., Li, Y., Di, X., & Liu, X. (2021). A high-precision interface for tunneling magnetoresistance sensors with 0.15 nT/√ Hz resolution.Modern Physics Letters B,35(36), 2150527.
10. Tian, Y., & Yang, Z. (2026, May). SAEC: scene-aware enhanced edge-cloud collaborative industrial vision inspection with multimodal LLM. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 19747-19751). IEEE.
11. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
12. Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
13. Yao, Y., Zhu, Z., Miao, P., Cheng, X., Shu, F., & Wang, J. (2025). Optimizing hybrid RIS-aided ISAC systems in V2X networks: A deep reinforcement learning method for anti-eavesdropping techniques. IEEE Transactions on Vehicular Technology, 74 (6), 9224-9239.
14. Li, X., Chen, W., Yin, L., Fu, Q., & Liu, X. (2017). A closed-loop Sigma-Delta modulator for a tunneling magneto-resistance sensor.IEICE Electronics Express,14(15), 20170700-20170700.
15. Fang Z, Yang G, Lyu W, et al. Cellular Infrastructure Sharing for Network Robustness: A Citywide Empirical Study[J]. IEEE Transactions on Mobile Computing, 2025, 24(11): 11386-11400.
16. Wan, H., Zhang, Y., Wang, J., Wu, D., Li, M., Chen, X., ... & Ji, X. (2025). Toward universal embodied planning in scalable heterogeneous field robots collaboration and control. Journal of Field Robotics, 42(5), 2318-2336.
17. Yang, W., Qin, Y., Yi, Z., & Yang, Y. (2019, May). Content-based hyperbolic routing and push mechanism in named data networking. In ICC 2019-2019 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
18. Yao, Y., Lu, Z., Chen, G., Huang, C., Feng, C., & Quek, T. Q. (2026). Interference Management in ISAC-SAGINs Based on Transformer-Enabled Mean-Field Reinforcement Learning Method. IEEE Transactions on Wireless Communications.
19. Li, X., Chen, W., Liu, X., & Ren, M. (2015). A high-order sigma-delta accelerometer interface circuit.IEICE Electronics Express,12(2), 20141035-20141035.
20. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
21. Xu, C., Wang, C., & Li, Z. (2026). H2-LBM: A Hierarchical Hybrid Deep Reinforcement Learning Framework for L7 Load Balancing and Global Traffic Scheduling in Multi-Cloud LLM Serving. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 109-120.
22. DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., et al. (2007). Dynamo: Amazon’s highly available key-value store. In Proceedings of SOSP.
23. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
24. Xia, L., Yang, M., Wang, J., Yan, Z., Ren, Y., Yu, G., & Lei, K. (2025, September). Mamba4Net: Distilled Hybrid Mamba Large Language Models For Networking. In2025 IEEE 33rd International Conference on Network Protocols (ICNP)(pp. 1-10). IEEE.
25. Zhang, W., Li, M., & Chen, X. (2026). Distributed Edge-Fog-Cloud Architecture for Intelligent Fault Diagnosis of Permanent Magnet Synchronous Motors Using Temporal Fusion Transformers and Federated Learning. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 121-131.
26. Yang, W., Qin, Y., & Li, R. (2022). A network-embedding-based approach for scalable network navigability in content-centric social IoT. IEEE Internet of Things Journal, 9(17), 16418-16428.
27. Li, Q., Li, L., Liu, Z., Sun, W., Li, W., Li, J., & Zhao, W. (2025). Cloud-edge collaboration for industrial Internet of Things: Scalable neurocomputing and rolling-horizon optimization. IEEE Internet of Things Journal, 12(12), 19929–19943.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kea E. Olivier, Ethan Sithole, Bongani S. Radebe

This work is licensed under a Creative Commons Attribution 4.0 International License.
