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5G Network Coverage Hole Detection & Resolution: A Review

Tobechukwu C. Obiefuna Mathew Ehikhamenle

Subject area: Science,Engineering and Technology  ·  Area of research: Information Systems Engineering

Abstract

The evolution of network technologies such as the 5G network is inextricably linked to the rise in demands for mobile devices. The deployment of 5G systems seeks to provide high throughput and ultra-low communications latencies, to improve users? quality of experience (QoE). To meet these demands, Conventional sub-6 GHz cellular systems are incapable of delivering the rapid data speeds and low latency needed by millimeter wave (mmWave) networks. However, mmWave signals are more vulnerable to blocking than lower frequency bands, leading to a higher number of coverage holes (CH) in a radio environment. Traditionally, cellular coverage hole detection is performed through drive tests, which consist of geographically measuring different network coverage metrics with a motor vehicle equipped with mobile radio measurement facilities. The collected network measurements need to be processed by radio experts for network coverage optimization, e.g., by tuning network parameters such as transmission power, antenna orientations and tilts, etc. The use of drive tests implies large Operational Expenditure (OPEX) and delays in detecting the problems, and they cannot offer a complete and reliable picture of the network situation. When users have poor wireless performance, the Key Performance Indicators (KPIs) for those clients are reported to a central manager, who converts them into a visual client-side perspective map. In this paper, other more efficient methods of detecting and resolving coverage holes such as Topographical, Probabilistic, UMAP and ML are explored.

Keywords

5G, Coverage hole, 5G KPIs, UMAP and ML.

References

[1] Peak data rates

[2] Peak spectral efficiency

[3] Area traffic capacity

[4] Latency

[5] Connection density

[6] Energy efficiency

[7] Reliability

[8] Mobility

[9] Bandwidth

[10] Coverage Hole (CH) Repair

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How to cite this paper

Tobechukwu C. Obiefuna, Mathew Ehikhamenle "5G Network Coverage Hole Detection & Resolution: A Review" Iconic Research And Engineering Journals Volume 7 Issue 4 2023 Page 90-97
Tobechukwu C. Obiefuna, Mathew Ehikhamenle "5G Network Coverage Hole Detection & Resolution: A Review" Iconic Research And Engineering Journals, vol. 7, no. 4, Oct. 2023
Tobechukwu C. Obiefuna, Mathew Ehikhamenle (2023). 5G Network Coverage Hole Detection & Resolution: A Review. Iconic Research And Engineering Journals, 7(4).
Tobechukwu C. Obiefuna, Mathew Ehikhamenle "5G Network Coverage Hole Detection & Resolution: A Review" Iconic Research And Engineering Journals, vol. 7, no. 4, Oct. 2023.
@article{1705028,
      author = {Tobechukwu C. Obiefuna, Mathew Ehikhamenle},
      title = {5G Network Coverage Hole Detection & Resolution: A Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {4},
      pages = {90-97},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705028.pdf},
      abstract = {The evolution of network technologies such as the 5G network is inextricably linked to the rise in demands for mobile devices.  The deployment of 5G systems seeks to provide high throughput and ultra-low communications latencies, to improve users? quality of experience (QoE). To meet these demands, Conventional sub-6 GHz cellular systems are incapable of delivering the rapid data speeds and low latency needed by millimeter wave (mmWave) networks. However, mmWave signals are more vulnerable to blocking than lower frequency bands, leading to a higher number of coverage holes (CH) in a radio environment. Traditionally, cellular coverage hole detection is performed through drive tests, which consist of geographically measuring different network coverage metrics with a motor vehicle equipped with mobile radio measurement facilities. The collected network measurements need to be processed by radio experts for network coverage optimization, e.g., by tuning network parameters such as transmission power, antenna orientations and tilts, etc. The use of drive tests implies large Operational Expenditure (OPEX) and delays in detecting the problems, and they cannot offer a complete and reliable picture of the network situation. When users have poor wireless performance, the Key Performance Indicators (KPIs) for those clients are reported to a central manager, who converts them into a visual client-side perspective map. In this paper, other more efficient methods of detecting and resolving coverage holes such as Topographical, Probabilistic, UMAP and ML are explored.},
      keywords = {5G, Coverage hole, 5G KPIs, UMAP and ML.},
      month = {October},
  }