5G Network Deployment Planning Using Metaheuristic Approaches
<p>Block diagram of System Architecture for BS Location Optimization and Deployment.</p> "> Figure 2
<p>Block Diagram for Metaheuristic Optimization.</p> "> Figure 3
<p>Population density of Thapathali. (<b>a</b>) P.D. in Map. (<b>b</b>) P.D. in Coordinate System.</p> "> Figure 4
<p>Initial arrangement of BSs (<b>a</b>) for 28 GHz and (<b>b</b>) for 3.6 GHz.</p> "> Figure 5
<p>Using PSO for 28 GHz. (<b>a</b>) BSs before elimination. (<b>b</b>) Voronoi Plot for BSs before elimination.</p> "> Figure 6
<p>Base station after Elimination using PSO 28 GHz. (<b>a</b>) Base station after elimination. (<b>b</b>) Voronoi Plot.</p> "> Figure 7
<p>UMa and RMa PSO. (<b>a</b>) UMa at 28 GHz. (<b>b</b>) RMa at 2.6 GHz.</p> "> Figure 8
<p>After elimination GA 28 GHz. (<b>a</b>) Base stations after elimination GA 28 GHz. (<b>b</b>) Voronoi plot of BSs after elimination GA 28 GHz.</p> "> Figure 9
<p>GWO 28 GHz. (<b>a</b>) BSs after elimination. (<b>b</b>) Voronoi Plot of BSs after elimination.</p> "> Figure 10
<p>Using SA 28GHz. (<b>a</b>) After elimination of redundant BS. (<b>b</b>) Voronoi plot after elimination of BS.</p> ">
Abstract
:1. Introduction
- Conduct a detailed comparative study of various optimization algorithms, including GA, PSO, Simulated Annealing (SA), and Grey Wolf Optimizer (GWO), in the context of 5G Radio Access Network (RAN) planning.
- Optimize the positioning of base stations and evaluate the performance and feasibility of the proposed metaheuristic algorithms.
2. Related Works
3. Methodology
3.1. System Architecture and Block Diagram
3.2. Modeling 5G Network Parameters
3.3. Dimensioning
3.3.1. Coverage Dimensioning
3.3.2. Capacity Dimensioning
3.4. Metaheuristic Algorithms
Steps in Metaheuristic Optimization
3.5. Visualizing Geographic Data for 5G Network Deployment
4. Experimental Setup
4.1. Link Budget Calculation and Simulation Setup
4.2. Experimental Area
4.3. Algorithm Configurations and Assumptions Made in Simulation
5. Results and Analysis
5.1. Result for Different Carrier Frequencies Using PSO
5.2. Results for different Carrier Frequencies Using GA
5.3. Results for Different Carrier Frequencies Using GWO
5.4. Result for Different Carrier Frequencies Using SA
5.5. Final Results for Different Carrier Frequencies Using Metaheuristic Algorithms
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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Notations | Descriptions |
---|---|
UMa | Urban macro cells |
RMa | Rural macro cells |
RRU | Remote radio units |
S | The total surface of served area |
The number of RRUs, having type i, needed to cover the area of interest | |
The number of RRUs, having type i, needed to satisfy the capacity constraints | |
Maximum number of users that can be served by one RRU | |
Estimated initial number of RRUs | |
MAPL | Maximum allowed path loss for cell |
Path loss for UMa cells | |
Path loss for RMa cells | |
Surface of cells with type i | |
Channel Capacity Of Cell i per sector antenna type i | |
Number of Sector of antenna | |
BW | Bandwidth |
(DL) | Target data rate in Downlink |
SE | Spectral Efficiency |
EIRP | Effective Isotropic Radiated Power |
RRU Specifications | |||
---|---|---|---|
Specifications | Midband | Midband | mmWave |
Frequency Range | 2.6 GHz | 3.6 GHz | 28 GHz |
Band | n7 | n78 | n257 |
Duplex Mode | FDD | TDD | TDD |
Channel Bandwidth | 5, 10, 15, …50 | 10, 15, …100 | 20, 100, 200, 400 |
Selected Bandwidth | 20 MHz | 40 MHz | 100 Mhz |
Algorithm | Configuration |
---|---|
PSO | Initial number of base stations is 115 and 147. Control parameters: Cognitive coefficients (0.5, 0.09), social coefficients (0.01, 0.0007), and inertial weight (0.9, 0.99). Fitness is evaluated by measuring the distance to the nearest neighbor (BS) and the capacity by the ratio of covered area to points within the radius. Termination at 1000 iterations. |
GA | Begins with mutation rate of 0.2, 3000 generations, and a selection size of 250. These parameters guide the behavior across iterations. |
GWO | Uses random values for alpha, beta, and delta components within a range [0, 1] corresponding to the number of particles and dimensions in the optimization space. |
SA | Starts with an initial temperature of 1000, a cooling rate of 0.95, and a scaling factor of 0.01. Maximum iterations set at 2000. |
Assumption | Description |
---|---|
Bandwidth Utilization | Each population within the study area is assumed to utilize an identical amount of bandwidth or data rate, ensuring a uniform baseline for network resource allocation. |
Device Capability | It is assumed that all individuals, regardless of age group, possess devices capable of running cellular services, standardizing the potential user base for the network. |
Signal Loss | Equal signal loss is assumed across the entire specified area, providing a simplified framework for evaluating network performance without the influence of varying geographical or environmental factors. |
Interference | Interference is not explicitly considered in the simulation, simplifying the modeling process by excluding the effects of interference on network performance. |
Other Factors | Other potential factors not explicitly modeled or anticipated in the simulation may also affect the results. These could include unforeseen technological changes, regulatory updates, or unexpected user behavior patterns. |
Using 5G Link Budget Calculator | ||
---|---|---|
Carrier freq | 28 GHz | 3.6 GHz |
Bandwidth | 100 MHz | 40 MHz |
DL(m) | 220 | 795 |
UL(m) | 75 | 170 |
Capacity | 416 | 166 |
Coverage Area (sq. km) | 0.0176 | 0.0907 |
Using PSO for Optimization | |||
---|---|---|---|
Carrier Freq (Table 5) | 28 GHz | 3.6 GHz | |
Initial Arrangement | Initial BS No. | 115 | 147 |
Initial Cov | 0.8993 | 1 | |
Initial Cap | 0.868 | 0.927 | |
Before Elimination of base station | Final BS | 115 | 147 |
Final Cov | 0.9895 | 1 | |
Final Cap | 0.9756 | 0.7916 | |
After Elimination of base station | Final BS | 63 | 110 |
Final Cov | 0.9618 | 1 | |
Final Cap | 0.9166 | 0.7395 |
Using GA for Optimization | |||
---|---|---|---|
Carrier Freq | 28 GHz | 3.6 GHz | |
Initial Arrangement | Initial BS No. | 115 | 147 |
Initial Cov | 0.8993 | 1 | |
Initial Cap | 0.868 | 0.927 | |
Before Elimination of base station | Final BS | 115 | 147 |
Final Cov | 0.9618 | 1 | |
Final Cap | 0.9305 | 0.9166 | |
After Elimination of base station | Final BS | 60 | 131 |
Final Cov | 0.9618 | 1 | |
Final Cap | 0.8784 | 0.8819 |
Using GWO for Optimization | |||
---|---|---|---|
Carrier Freq | 28 GHz | 3.6 GHz | |
Initial BS No. | 115 | 147 | |
Initial Arrangement | Initial Cov | 0.8993 | 1 |
Initial Cap | 0.868 | 0.927 | |
Before Elimination of base station | Final BS | 115 | 147 |
Final Cov | 0.9444 | 1 | |
Final Cap | 0.9375 | 0.8194 | |
After Elimination of base station | Final BS | 65 | 115 |
Final Cov | 0.9201 | 0.993 | |
Final Cap | 0.8576 | 0.7777 |
Using SA for Optimization | |||
---|---|---|---|
Carrier Freq | 28 GHz | 3.6 GHz | |
Initial Arrangement | Initial BS No. | 115 | 147 |
Initial Cov | 0.8993 | 1 | |
Initial Cap | 0.868 | 0.927 | |
Before Elimination of base station | Final BS | 115 | 147 |
Final Cov | 0.9131 | 1 | |
Final Cap | 0.87152 | 0.9201 | |
After Elimination of base station | Final BS | 55 | 90 |
Final Cov | 0.833 | 1 | |
Final Cap | 0.7187 | 0.6006 |
Carrier Freq [Table 5] | 28 GHz | 3.6 GHz | |
---|---|---|---|
Using PSO for Optimization | Final BS | 63 | 110 |
Final Cov | 0.9618 | 1 | |
Final Cap | 0.9166 | 0.7395 | |
Using GA for Optimization | Final BS | 60 | 131 |
Final Cov | 0.9618 | 1 | |
Final Cap | 0.8784 | 0.8819 | |
Using GWO for Optimization | Final BS | 65 | 115 |
Final Cov | 0.9201 | 0.993 | |
Final Cap | 0.8576 | 0.7777 | |
Using SA for Optimization | Final BS | 55 | 90 |
Final Cov | 0.833 | 1 | |
Final Cap | 0.7187 | 0.606 |
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Sapkota, B.; Ghimire, R.; Pujara, P.; Ghimire, S.; Shrestha, U.; Ghimire, R.; Dawadi, B.R.; Joshi, S.R. 5G Network Deployment Planning Using Metaheuristic Approaches. Telecom 2024, 5, 588-608. https://doi.org/10.3390/telecom5030030
Sapkota B, Ghimire R, Pujara P, Ghimire S, Shrestha U, Ghimire R, Dawadi BR, Joshi SR. 5G Network Deployment Planning Using Metaheuristic Approaches. Telecom. 2024; 5(3):588-608. https://doi.org/10.3390/telecom5030030
Chicago/Turabian StyleSapkota, Binod, Rijan Ghimire, Paras Pujara, Shashank Ghimire, Ujjwal Shrestha, Roshani Ghimire, Babu R. Dawadi, and Shashidhar R. Joshi. 2024. "5G Network Deployment Planning Using Metaheuristic Approaches" Telecom 5, no. 3: 588-608. https://doi.org/10.3390/telecom5030030