TY - GEN
T1 - Distributed User-Centric Clustering and Base Station Mode Choose in Ultra Dense Networks
AU - Wu, Zhikun
AU - Fei, Zesong
AU - Han, Zhu
AU - Wang, Li Chun
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - To cope with the exponential growth of demand, ultra dense networks (UDNs) are a promising technology in future mobile networks. With small cells densely deployed in networks, how to allocate wireless resources in UDNs efficiently becomes a challenging research topic. In this paper, we concentrate on the distributed user-centric clustering and base station (BS) mode choose problem in UDNs. We formulate a combinatorial optimization problem, with the throughput maximization and power consumption minimization jointly considered in the optimization object. In order to reduce the complexity of the problem, we decompose the original problem into two subproblems in terms of user-centric clustering and BS mode choose, and then solve those subproblems by the max-sum algorithm in sequence. The proposed algorithm can be conducted in a distributed way, and the computational complexity grows linearly with the network size. Simulation results show that the performance of proposed algorithm approaches the performance of the exhaustive algorithm well, and outperforms the conventional algorithm significantly.
AB - To cope with the exponential growth of demand, ultra dense networks (UDNs) are a promising technology in future mobile networks. With small cells densely deployed in networks, how to allocate wireless resources in UDNs efficiently becomes a challenging research topic. In this paper, we concentrate on the distributed user-centric clustering and base station (BS) mode choose problem in UDNs. We formulate a combinatorial optimization problem, with the throughput maximization and power consumption minimization jointly considered in the optimization object. In order to reduce the complexity of the problem, we decompose the original problem into two subproblems in terms of user-centric clustering and BS mode choose, and then solve those subproblems by the max-sum algorithm in sequence. The proposed algorithm can be conducted in a distributed way, and the computational complexity grows linearly with the network size. Simulation results show that the performance of proposed algorithm approaches the performance of the exhaustive algorithm well, and outperforms the conventional algorithm significantly.
UR - http://www.scopus.com/inward/record.url?scp=85070221438&partnerID=8YFLogxK
U2 - 10.1109/ICC.2019.8761173
DO - 10.1109/ICC.2019.8761173
M3 - Conference contribution
AN - SCOPUS:85070221438
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -