TY - GEN
T1 - Ant Colony Optimization Algorithm for Network Planning in Heterogeneous Cellular Networks
AU - Tseng, Fan Hsun
AU - Kao, Fan Yi
AU - Liang, Tsung Ta
AU - Chao, Han Chieh
N1 - Funding Information:
Acknowledgements. This work was financially supported from the Young Scholar Fellowship Program by Ministry of Science and Technology (MOST) in Taiwan, under Grant MOST108-2636-E-003-001, and was partly funded by the MOST in Taiwan, under grant MOST107-2221-E-259-005-MY3.
Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2020.
PY - 2020
Y1 - 2020
N2 - In recent years, the transmission rate of mobile network becomes insufficient to serve numerous mobile users. Relay technique has been proposed to improve the data rate of mobile networks for many years. In the paper, the planning problem of heterogeneous cellular network is defined and limited in two-hop relaying. The defined problem aims to tackle with three objective functions at the same time. A meta-heuristic planning algorithm is proposed based on Ant Colony Optimization (ACO) algorithm. The proposed ACO-based algorithm optimizes the placement results of macrocells, microcells and femtocells. In the simulation-based result and analysis, the ACO-based algorithm yields the higher capacity and more covered users with the lowest construction cost compared to the two heuristic algorithms, i.e., Top-Down and Bottom-Up algorithms.
AB - In recent years, the transmission rate of mobile network becomes insufficient to serve numerous mobile users. Relay technique has been proposed to improve the data rate of mobile networks for many years. In the paper, the planning problem of heterogeneous cellular network is defined and limited in two-hop relaying. The defined problem aims to tackle with three objective functions at the same time. A meta-heuristic planning algorithm is proposed based on Ant Colony Optimization (ACO) algorithm. The proposed ACO-based algorithm optimizes the placement results of macrocells, microcells and femtocells. In the simulation-based result and analysis, the ACO-based algorithm yields the higher capacity and more covered users with the lowest construction cost compared to the two heuristic algorithms, i.e., Top-Down and Bottom-Up algorithms.
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U2 - 10.1007/978-981-15-3308-2_2
DO - 10.1007/978-981-15-3308-2_2
M3 - Conference contribution
AN - SCOPUS:85082021218
SN - 9789811533075
T3 - Advances in Intelligent Systems and Computing
SP - 11
EP - 19
BT - Genetic and Evolutionary Computing - Proceedings of the 13th International Conference on Genetic and Evolutionary Computing, 2019
A2 - Pan, Jeng-Shyang
A2 - Liang, Yongquan
A2 - Lin, Jerry Chun-Wei
A2 - Chu, Shu-Chuan
PB - Springer
T2 - 13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019
Y2 - 1 November 2019 through 3 November 2019
ER -