An optimized small‐cell planning procedure for Heterogeneous Network to improve network energy efficiency
Summary Cell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous net...
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| Published in: | International journal of communication systems Vol. 36; no. 7 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Chichester
Wiley Subscription Services, Inc
10-05-2023
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Summary
Cell planning in conventional networks has gained more attention as it directly affects the network performance and deployment cost. Existing cell planning methodologies are framed either with identical base stations or constructing a network without any infrastructure. But heterogeneous networks (HetNets) allow the service provider to deploy small cells over the region to enhance the network performance and signal coverage probability. Thus, a small‐cell planning procedure is presented in this research work considering the low‐powered base station and deployment cost to enhance the energy efficiency of the HetNet. An adaptive fuzzy expert system is used for cell dimensioning, and a nature‐inspired ant colony optimization model is employed for automatic base station placement. Simulation analysis demonstrates that the proposed small‐cell planning procedure attains better energy efficiency and user satisfaction ratio compared to conventional planning strategies. In the two cases of simulation analysis, the proposed model attains an average of 85% user satisfaction ratio for case 1 and 87% for case 2, which is better than existing strategies like density‐based spatial clustering of applications with noise (DBSCAN), k‐means, and number‐based spatial clustering (NBSC) algorithms.
Work includes a fuzzy expert system for cell dimensioning which adjusts the cell parameters and ant colony optimization algorithm to select optimal locations for base station placement to satisfy minimum energy consumption requirements. |
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| ISSN: | 1074-5351 1099-1131 |
| DOI: | 10.1002/dac.5456 |