RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network
Affinity propagation (AP) clustering with low complexity and high performance is suitable for radio remote head (RRH) clustering for real-time joint transmission in the cloud radio access network. The existing AP algorithms for joint transmission have the limitation of high computational complexitie...
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/2/480 |
| _version_ | 1797412818078662656 |
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| author | Seju Park Han-Shin Jo Cheol Mun Jong-Gwan Yook |
| author_facet | Seju Park Han-Shin Jo Cheol Mun Jong-Gwan Yook |
| author_sort | Seju Park |
| collection | DOAJ |
| description | Affinity propagation (AP) clustering with low complexity and high performance is suitable for radio remote head (RRH) clustering for real-time joint transmission in the cloud radio access network. The existing AP algorithms for joint transmission have the limitation of high computational complexities owing to re-sweeping preferences (diagonal components of the similarity matrix) to determine the optimal number of clusters as system parameters such as network topology. To overcome this limitation, we propose a new approach in which preferences are fixed, where the threshold changes in response to the variations in system parameters. In AP clustering, each diagonal value of a final converged matrix is mapped to the position (x,y coordinates) of a corresponding RRH to form two-dimensional image. Furthermore, an environment-adaptive threshold value is determined by adopting Otsu’s method, which uses the gray-scale histogram of the image to make a statistical decision. Additionally, a simple greedy merging algorithm is proposed to resolve the problem of inter-cluster interference owing to the adjacent RRHs selected as exemplars (cluster centers). For a realistic performance assessment, both grid and uniform network topologies are considered, including exterior interference and various transmitting power levels of an RRH. It is demonstrated that with similar normalized execution times, the proposed algorithm provides better spectral and energy efficiencies than those of the existing algorithms. |
| first_indexed | 2024-03-09T05:08:43Z |
| format | Article |
| id | doaj.art-0fb00f14001643c5a7a0753a683e5e7d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:08:43Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0fb00f14001643c5a7a0753a683e5e7d2023-12-03T12:52:06ZengMDPI AGSensors1424-82202021-01-0121248010.3390/s21020480RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access NetworkSeju Park0Han-Shin Jo1Cheol Mun2Jong-Gwan Yook3Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, KoreaDepartment of Electronics and Control Engineering, Hanbat National University, Daejeon 34158, KoreaDepartment of Electronic Engineering, Korea National University of Transportation, Chungju 27469, KoreaDepartment of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, KoreaAffinity propagation (AP) clustering with low complexity and high performance is suitable for radio remote head (RRH) clustering for real-time joint transmission in the cloud radio access network. The existing AP algorithms for joint transmission have the limitation of high computational complexities owing to re-sweeping preferences (diagonal components of the similarity matrix) to determine the optimal number of clusters as system parameters such as network topology. To overcome this limitation, we propose a new approach in which preferences are fixed, where the threshold changes in response to the variations in system parameters. In AP clustering, each diagonal value of a final converged matrix is mapped to the position (x,y coordinates) of a corresponding RRH to form two-dimensional image. Furthermore, an environment-adaptive threshold value is determined by adopting Otsu’s method, which uses the gray-scale histogram of the image to make a statistical decision. Additionally, a simple greedy merging algorithm is proposed to resolve the problem of inter-cluster interference owing to the adjacent RRHs selected as exemplars (cluster centers). For a realistic performance assessment, both grid and uniform network topologies are considered, including exterior interference and various transmitting power levels of an RRH. It is demonstrated that with similar normalized execution times, the proposed algorithm provides better spectral and energy efficiencies than those of the existing algorithms.https://www.mdpi.com/1424-8220/21/2/480machine learningclusteringaffinity propagationC-RANexterior interference |
| spellingShingle | Seju Park Han-Shin Jo Cheol Mun Jong-Gwan Yook RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network Sensors machine learning clustering affinity propagation C-RAN exterior interference |
| title | RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network |
| title_full | RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network |
| title_fullStr | RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network |
| title_full_unstemmed | RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network |
| title_short | RRH Clustering Using Affinity Propagation Algorithm with Adaptive Thresholding and Greedy Merging in Cloud Radio Access Network |
| title_sort | rrh clustering using affinity propagation algorithm with adaptive thresholding and greedy merging in cloud radio access network |
| topic | machine learning clustering affinity propagation C-RAN exterior interference |
| url | https://www.mdpi.com/1424-8220/21/2/480 |
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