TY - GEN
T1 - UE Clustering Based on Grid Affinity Propagation for mmWave D2D in Virtual Small Cells
AU - Danisya, Achmad Rizal
AU - Hendrantoro, Gamantyo
AU - Handayani, Puji
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, Grid Assisted Affinity Propagation Clustering (GAPC) algorithm is proposed to enhance the total spectral efficiency of Cell Head based Virtual Small Cell (CHVSC) service by increasing the number of Cell Heads (CH). The algorithm builds upon the previous method of Modified Affinity Propagation Clustering (MAPC) with addition of grid zonal division and Depth-First Search algorithm for advanced eligible-UE selection. Afterwards, both SNR and SIR are used for member selection in GAPC-SNR and GAPC-SIR respectively. From Monte Carlo simulation, MAPC still have higher average SINR compared to GAPC-SNR, but GAPC algorithm outperforms the MAPC algorithm in the number of CH appointed. With the compensation of higher accuracy of cluster finding inside MBS service zone, GAPC-SNR enhances overall bandwidth efficiency, silhouette score, and reduces computational complexity, as well as alleviating traffic burdens for each CH in comparison to MAPC.
AB - In this paper, Grid Assisted Affinity Propagation Clustering (GAPC) algorithm is proposed to enhance the total spectral efficiency of Cell Head based Virtual Small Cell (CHVSC) service by increasing the number of Cell Heads (CH). The algorithm builds upon the previous method of Modified Affinity Propagation Clustering (MAPC) with addition of grid zonal division and Depth-First Search algorithm for advanced eligible-UE selection. Afterwards, both SNR and SIR are used for member selection in GAPC-SNR and GAPC-SIR respectively. From Monte Carlo simulation, MAPC still have higher average SINR compared to GAPC-SNR, but GAPC algorithm outperforms the MAPC algorithm in the number of CH appointed. With the compensation of higher accuracy of cluster finding inside MBS service zone, GAPC-SNR enhances overall bandwidth efficiency, silhouette score, and reduces computational complexity, as well as alleviating traffic burdens for each CH in comparison to MAPC.
KW - Clustering
KW - D2D
KW - Machine Learning
KW - Virtual Small Cells
KW - mmWave
UR - http://www.scopus.com/inward/record.url?scp=85169471192&partnerID=8YFLogxK
U2 - 10.1109/IAICT59002.2023.10205374
DO - 10.1109/IAICT59002.2023.10205374
M3 - Conference contribution
AN - SCOPUS:85169471192
T3 - Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023
SP - 38
EP - 44
BT - Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023
Y2 - 13 July 2023 through 15 July 2023
ER -