Volume 22, Issue 1 (5-2025)
JSDP 2025, 22(1): 13-24 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Nazari A, Fallah M, Taheri M J, Diyanat A. Drive Test Route Optimization in Mobile Networks. JSDP 2025; 22 (1) :13-24
URL: http://jsdp.rcisp.ac.ir/article-1-1428-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1428-en.html
M.Sc. in Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (243 Views)
The design and maintenance of the radio access network (RAN) in mobile telecommunications requires meticulous monitoring of network performance to ensure optimal quality and coverage. Drive testing is a prevalent methodology for collecting data on network status across delineated geographical areas. This process involves systematically traversing various routes, including streets and pathways, to assess network performance metrics such as Reference Signal Received Power (RSRP). Although drive testing provides essential insights for identifying regions with inadequate signal quality, it is inherently resource-intensive, involving considerable time and financial expenditures. This paper proposes an innovative optimization methodology aimed at enhancing the efficiency of data collection during drive tests. The proposed approach is organized into four fundamental steps: (1) partitioning the map into smaller sections, (2) selecting critical points within each section, (3) employing map-matching techniques to accurately align these points with actual streets and pathways, and (4) determining the optimal route for traversing the critical points. A rectangular area of interest is selected and divided into K smaller sub-regions, within which M critical points are identified according to a predefined criterion. These points, which may not initially correspond with the existing street network, are corrected through map-matching techniques to ensure feasible traversal paths. Lastly, an optimization algorithm is utilized to compute the shortest route that encompasses all identified critical points. The efficacy of the proposed method is assessed through experimental studies that manipulate key parameters K (denoting the number of sub-regions) and M (indicating the maximum critical points per zone). The evaluation emphasizes two critical metrics: the total distance traveled and the success rate in detecting areas with RSRP values below -100 dBm. Results indicate that the proposed approach significantly decreases the distance required for drive testing while achieving substantial coverage of areas with weak signals. For example, in an experiment where the optimized route covered only 18.54 kilometers—equivalent to 34% of the distance of an entire drive test—it successfully identified 70% of regions with poor RSRP. Furthermore, this paper introduces a criterion for assessing the effectiveness of drive test routes, highlighting the balance between route length and coverage of low-signal areas. The findings substantiate that adequate data for network performance analysis can be secured with considerable savings in both cost and time compared to conventional exhaustive drive testing. While this study concentrates on RSRP measurements within 4G networks, the proposed methodology is adaptable for other metrics, including Reference Signal Received Quality (RSRQ). Future research may also examine the integration of alternative data sources, such as satellite imagery, to further refine map partitioning and critical point selection, thereby enhancing the overall efficacy of the proposed method.
Type of Study: Research |
Subject:
Paper
Received: 2024/06/28 | Accepted: 2024/12/4 | Published: 2025/06/21 | ePublished: 2025/06/21
Received: 2024/06/28 | Accepted: 2024/12/4 | Published: 2025/06/21 | ePublished: 2025/06/21
References
1. A. Taufique, M. Jaber, A. Imran, Z. Dawy, and E. Yacoub, "Planning wireless cellular networks of future: Outlook, challenges and opportunities," IEEE Access, vol. 5, pp. 4821-4845, 2017. [DOI:10.1109/ACCESS.2017.2680318]
2. T. R. A. Network, "Study on minimization of drive-tests in next generation networks;(release 9)," 3GPP TR 36.805.
3. L. M. Silalahi, S. Budiyanto, F. A. Silaban, I. U. V. Simanjuntak, and A. D. Rochendi, "Improvement of Quality and Signal Coverage LTE in Bali Province Using Drive Test Method," in 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2021, pp. 376-380. [DOI:10.1109/ISITIA52817.2021.9502227]
4. M. Skocaj et al., "Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning," Comput Commun, vol. 195, pp. 403-415, 2022. [DOI:10.1016/j.comcom.2022.09.005]
5. J. Thrane, M. Artuso, D. Zibar, and H. L. Christiansen, "Drive test minimization using deep learning with Bayesian approximation," in 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), 2018, pp. 1-5. [DOI:10.1109/VTCFall.2018.8690911] [PMID]
6. C. Sun, K. Xu, M. K. Marina, and H. Benn, "Gendt: mobile network drive testing made efficient with generative modeling," in Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies, 2022, pp. 43-58. [DOI:10.1145/3555050.3569124]
7. M. F. A. Fauzi, R. Nordin, N. F. Abdullah, H. A. H. Alobaidy, and M. Behjati, "Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization," IEEE Access, vol. 11, pp. 3096-3109, 2023. [DOI:10.1109/ACCESS.2023.3234566]
8. P. Chao, Y. Xu, W. Hua, and X. Zhou, "A survey on map-matching algorithms," in Databases Theory and Applications: 31st Australasian Database Conference, ADC 2020, Melbourne, VIC, Australia, February 3-7, 2020, Proceedings 31, 2020, pp. 121-133. [DOI:10.1007/978-3-030-39469-1_10]
9. W. Li, Y. Chen, S. Wang, H. Li, and Q. Fan, "A Novel Map Matching Method Based on Improved Hidden Markov and Conditional Random Fields Model," Int J Digit Earth, vol. 17, no. 1, p. 2328366, 2024. [DOI:10.1080/17538947.2024.2328366]
10. S. Gupta, A. Rana, and V. Kansal, "Comparison of Heuristic techniques: A case of TSP," in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2020, pp. 172-177. [DOI:10.1109/Confluence47617.2020.9058211]
11. N. Christofides, "The vehicle routing problem," Revue française d'automatique, informatique, recherche opérationnelle. Recherche opérationnelle, vol. 10, no. V1, pp. 55-70, 1976. [DOI:10.1051/ro/197610V100551]
12. ابراهیمی مود سپهر، جاویدی محمد مسعود، خسروی محمدرضا. ارائه الگوریتم جستوجوی گرانشی مقید و حل مسأله مسیریابی وسایل نقلیه. پردازش علائم و دادهها. ۱۴۰۰; ۱۸ (۴) :۲۳-۳۶
12. S. Ebrahimi Mood, M. M. Javidi, and M. R. Khosravi, "Proposing a Constrained-GSA for the Vehicle Routing Problem," Signal and Data Processing, vol. 18, no. 4, pp. 23-36, Mar. 2022, doi: 10.52547/jsdp.18.4.23. [DOI:10.52547/jsdp.18.4.23]
13. E. B. Tirkolaee, A. Goli, S. Gütmen, G.-W. Weber, and K. Szwedzka, "A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms," Ann Oper Res, vol. 324, no. 1, pp. 189-214, 2023. [DOI:10.1007/s10479-021-04486-2] [PMID] []
14. M. E. Johnson, L. M. Moore, and D. Ylvisaker, "Minimax and maximin distance designs," J Stat Plan Inference, vol. 26, no. 2, pp. 131-148, 1990. [DOI:10.1016/0378-3758(90)90122-B]
15. Neshan, "Map Matching API," 2023.
16. Neshan, "Neshan Direction API Documentation."
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
