Positioning Error Impact Compensation through Data-Driven Optimization in User-Centric Networks
Abstract: The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network. Accurately determining the Szone radius requires the precise location of the user equipment (UE) and data base stations (DBSs). Even a slight error in reported positions of DBSs or UE will lead to an incorrect determination of Szone radius and UE-DBS pairing, leading to degradation of the UE-DBS communication link. To compensate for the positioning error impact and improve the ASE and EE of the UCUDN, this work proposes a data-driven optimization and error compensation (DD-OEC) framework. The framework comprises an additional machine learning model that assesses the impact of residual errors and regulates the erroneous datadriven optimization to output Szone radius, transmit power, and DBS density values which improve network ASE and EE. The performance of the framework is compared to a baseline scheme, which does not employ the residual, and results demonstrate that the DD-OEC framework outperforms the baseline, achieving up to a 23% improvement in performance.
- J. Zhu, M. Zhao, and S. Zhou, “An Optimization Design of Ultra Dense Networks Balancing Mobility and Densification,” IEEE Access, vol. 6, pp. 32 339–32 348, 2018.
- F. Spinelli and V. Mancuso, “Toward Enabled Industrial Verticals in 5G: A Survey on MEC-Based Approaches to Provisioning and Flexibility,” IEEE Commun. Surv. & Tutor., vol. 23, no. 1, pp. 596–630, 2021.
- J. Huang, C.-X. Wang, L. Bai, J. Sun, Y. Yang, J. Li, O. Tirkkonen, and M.-T. Zhou, “A Big Data Enabled Channel Model for 5G Wireless Communication Systems,” IEEE Trans. on Big Data, vol. 6, no. 2, pp. 211–222, 2020.
- Y. Azimi, S. Yousefi, H. Kalbkhani, and T. Kunz, “Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey,” IEEE Access, vol. 10, pp. 106 581–106 612, 2022.
- K. Chen, Q. Kong, Y. Dai, Y. Xu, F. Yin, L. Xu, and S. Cui, “Recent Advances in Data-Driven Wireless Communication Using Gaussian processes: A Comprehensive Survey,” China Commun., vol. 19, no. 1, pp. 218–237, 2022.
- U. S. Hashmi, S. A. R. Zaidi, and A. Imran, “User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency,” IEEE Access, vol. 6, pp. 19 859–19 875, 2018.
- S. K. Kasi, U. S. Hashmi, S. Ekin, A. Abu-Dayya, and A. Imran, “D-RAN: A DRL-Based Demand-Driven Elastic User-Centric RAN Optimization for 6G&Beyond,” IEEE Trans. Cogn. Commun. Netw., vol. 9, no. 1, pp. 130–145, 2023.
- L. Yu, H. Zhang, L. Zhang, L. Song, Z. Han, and P. Fan, “Hypergraph-Based SCMA Codebook Allocation in User-Centric Ultra-Dense Networks with Machine Learning,” in 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019, pp. 1–6.
- B. Shang and L. Liu, “Machine Learning Meets Point Process: Spatial Spectrum Sensing in User-Centric Networks,” IEEE Wireless Commun. Lett., vol. 9, no. 1, pp. 34–37, 2020.
- I. Akbari, O. Onireti, A. Imran, M. A. Imran, and R. Tafazolli, “How Reliable is MDT-Based Autonomous Coverage Estimation in the Presence of User and BS Positioning Error?” IEEE Wireless Commun. Lett., vol. 5, no. 2, pp. 196–199, 2016.
- O. Onireti, A. Imran, M. A. Imran, and R. Tafazolli, “Impact of positioning error on achievable spectral efficiency in database-aided networks,” in 2016 IEEE International Conference on Communications (ICC), 2016, pp. 1–6.
- H. N. Qureshi and A. Imran, “Optimal Bin Width for Autonomous Coverage Estimation Using MDT Reports in the Presence of User Positioning Error,” IEEE Commun. Lett., vol. 23, no. 4, pp. 716–719, 2019.
- W. Raza, U. S. Hasmi, A. Imran, and S. Ekin, “Towards Positioning Error Impact Characterization and Minimization in User-Centric RAN,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC), 2022, pp. 734–739.
- U. S. Hashmi, S. A. R. Zaidi, A. Imran, and A. Abu-Dayya, “Enhancing Downlink QoS and Energy Efficiency Through a User-Centric Stienen Cell Architecture for mmWave Networks,” IEEE Trans. Green Commun. Netw., vol. 4, no. 2, pp. 387–403, 2020.
- S. Sun, T. A. Thomas, T. S. Rappaport, H. Nguyen, I. Z. Kovacs, and I. Rodriguez, “Path Loss, Shadow Fading, and Line-of-Sight Probability Models for 5G Urban Macro-Cellular Scenarios,” in 2015 IEEE Globecom Workshops (GC Wkshps), 2015, pp. 1–7.
- G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson, M. A. Imran, D. Sabella, M. J. Gonzalez, O. Blume, and A. Fehske, “How much energy is needed to run a wireless network?” IEEE Wireless Commun., vol. 18, no. 5, pp. 40–49, 2011.
- B. Suman, “Simulated annealing-based multiobjective algorithms and their application for system reliability,” Engineering Optimization, vol. 35, pp. 391 – 416, 2003.
- S. M. A. Zaidi, M. Manalastas, H. Farooq, and A. Imran, “SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond,” IEEE Access, vol. 8, pp. 82 938–82 950, 2020.
- Y. Wu, U. Gustavsson, A. G. i. Amat, and H. Wymeersch, “Residual Neural Networks for Digital Predistortion,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 01–06.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.