On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments
Abstract
:1. Introduction
1.1. Literature Review
1.1.1. Location Covering Problems
1.1.2. Base Station Optimization and Deployment
1.2. Problem Formulation and Related Work
- I is the set of service centres ,
- J is the set of customer locations .
- 1.
- A condition necessary to solve the task is that all of the customer locations are reachable from at least one location where an operating service centre is considered.
- 2.
- Customer location j is reachable from vertex i, which is designated as an operating service centre if . If this inequality is not satisfied, vertex j is unreachable from i.
1.3. Main Contribution
2. Models Developed for Network Coverage and Capacity Problems
- deploying service centres to the new area or reconfiguration of the whole network,
- deploying additional service centres to the area, where service centres already exist, but do not provide sufficient network capacity.
- —capacity of service centre i,
- —the list of devices from customer location j that need a service centre,
- —customer from location j is assigned or is not assigned to service centre i,
- —expresses the weights of service centre i (in practice, it represents the gNodeB installation costs).
2.1. Capacitated Network Area Coverage
2.2. Wireless Interference Considerations
2.3. Capacitated Network Area Coverage with Existing Services
3. Computational Concept
3.1. Propagation Models
3.2. Employment of Developed Models
Algorithm 1 The algorithm representing the whole computational concept to get the best locations to deploy service centres (gNodeB) nodes. |
|
3.3. Model Computational Complexity Considerations
4. Numerical Simulations and Results Discussion
4.1. gNodeB Parameters Settings
4.2. Simulation of Different Deployment Scenarios
4.3. Simulations Utilizing Dataset From District in Central Europe
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Mathematical Terminology | Wireless Networks Terminology |
---|---|
Service centre | gNodeB node |
Customer location | a location to cover |
Capacity | throughput that is requested by sum of user requirements in a given location to cover |
Existing service | gNodeB that already exists in the area to cover and should remain after the reconfiguration or deployment phase |
Authors | Model | Description | Published |
---|---|---|---|
Berge [27] | SCP | The base covering model for all other models. Finding the minimal number of services to cover all the demands. | 1957 |
Toregas [13] | Location Set Covering Problem (LSCP) | Extension of SCP considering services and demands locations. | 1970 |
Church et al. [28] | Maximal Covering Location Problem (MLCP) | Describes the situation of a restricted number of services with the best coverage sought. | 1974 |
Plane and Hendrick [29] | Existing Service System Location Set Covering Problem (ESS-LSCP) | Extension of LSCP addressing the need to consider the existing services. | 1977 |
Schilling [30] | Location Set Covering Problem with Facility Types (LSCP-FT) | Extension of LSCP addressing the need of specific demand to be covered by specific services. | 1979 |
Margules [31] | Site Quality Maximal Covering Location Problem (SQ-MCLP) | Takes into account the quality of the service provided for each demand. | 1986 |
Current and Storbeck [32] | Capacitated Location Set Covering Problem (CLSCP) | The first article proposing capacities in LSCP. This model could not be used in network deployment since it does not force the entire demand at a particular node being assigned to the same facility. It assumes that the demand is split among facilities (see Equation (13)). | 1988 |
Revelle and Hogan [33] | Maximal Availability Location Problem (MALP) | This model is characterized by an intention to maximize the availability provided by -reliable coverage | 1989 |
Gerrard and Church [34] | Capacitated Location Set Covering Problem with Closest Assignment (CLSCP-CA) | Models considering services and capacities to satisfy the demands using the closest service. | 1996 |
Berman and Krass [35] | Generalized Maximal Covering Location Problem (GMCLP) | Generalized variant of MLCP distinguishing the value of benefits received over a series of coverage ranges. | 2002 |
Dembski and Marks [12] | CLSCP-EA | Models considering services and capacities to equally satisfy the demands. | 2009 |
Hong and Kuby [36] | LSCP and MLCP with Threshold | This model considers a threshold meaning that each service needs some portion of demand to be viable for each type of service. | 2016 |
Authors | Description | Published |
---|---|---|
Mattos, David Issa, et al. [46] | This paper proposes a gNodeB parameters optimization with regret minimization and a low number of iterations in the presence of uncertainties due to the stochastic response of KPI metrics | 2019 |
Teague, Kory, Mohammad J. Abdel-Rahman and Allen B. MacKenzie [62] | Authors propose a two-stage stochastic optimization model to investigate the problem of gNodeB selection. They found that the genetic algorithms may be an adequate avenue for a solution. | 2019 |
Tayal, Shikha and Garg, PK and Vijay, Sandip [63] | The paper provides a case study in Uttarakhand to develop a new model for placement of the optimal number of base stations. The paper compares different models and concludes them with their pros and cons. | 2019 |
Afuzagani, Dzakyta and Suyanto, Suyanto [64] | The paper presents an evolutionary firefly algorithm and compares it to the standard firefly algorithm and uses it to deploy gNodeB stations. The authors conclude that enhanced algorithm can provide slightly better solutions in terms of the final coverage. | 2019 |
Lingcheng and Hongtao [47] | This paper is mainly focused on the deployment of algorithms with the focus on the research of optimal machine learning (ML) model to deploy gNodeB nodes. It has found that multi-layer perceptron outperforms other ML algorithms. | 2020 |
RSRP [dBm] | Signal Strength | Description |
---|---|---|
≥−80 | Excellent | Strong signal with maximum data speeds |
−80 to−90 | Good | Strong signal with good data speeds |
−90 to−100 | Fair to poor | Reliable data speeds may be attained, but marginal data with drop-outs is possible. When this value gets close to −100, performance will drop drastically |
≤−100 | No signal | Disconnection |
Use-Case | Combined gNodeB Capacity (DL + UL) [Gbit/s] | Single Cell Radius [km] | Demanded User Throughput (DL + UL) [Mbit/s] | Number of Connections/km [106] |
---|---|---|---|---|
urban | 30 | 0.5 | 43.9 | 2500 |
suburban | 30 | 1 | 43.9 | 400 |
rural | 30 | 8 | 43.9 | 100 |
Scenarios | Theoretical Number of Candidate Locations to Deploy gNodeB | Number of Users | Total Coverage Area [km] | Resulting Number of gNodeB Nodes to Be Deployed |
---|---|---|---|---|
urban | 81 | 7500 | 3 | 19 |
suburban | 421 | 7500 | 18.75 | 22 |
rural | 484 | 7500 | 75 | 18 |
Deployment Scenario | gNodeB Capacity [Gbit/s] | Single Cell Radius [km] | Demanded User Throughput (DL+UL) [Mbit/s] | N. of Existing gNodeB Nodes | N. of Users | Area [km] |
---|---|---|---|---|---|---|
Prague 11 (suburban) | 30 | 1 | 43.9 | 75 | 68,839 | 9.8 |
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Seda, P.; Seda, M.; Hosek, J. On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments. Appl. Sci. 2020, 10, 8853. https://doi.org/10.3390/app10248853
Seda P, Seda M, Hosek J. On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments. Applied Sciences. 2020; 10(24):8853. https://doi.org/10.3390/app10248853
Chicago/Turabian StyleSeda, Pavel, Milos Seda, and Jiri Hosek. 2020. "On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments" Applied Sciences 10, no. 24: 8853. https://doi.org/10.3390/app10248853
APA StyleSeda, P., Seda, M., & Hosek, J. (2020). On Mathematical Modelling of Automated Coverage Optimization in Wireless 5G and beyond Deployments. Applied Sciences, 10(24), 8853. https://doi.org/10.3390/app10248853