Cluster-Based Dynamic FBSs On/Off Scheme in Heterogeneous Cellular Networks
Recent years, with the explosive growth of mobile data traffic, cellular communication system is faced with enormous challenges. The ultra-dense deployment of small cells will increase the network capacity while increasing the energy consumption. In this paper, we study a cluster-based dynamic FBSs on/off scheme in heterogeneous cellular networks, where the overall objective is to maximize the network energy efficiency by optimizing jointly the cell association, the base station on/off strategies and the cluster division, taking into account the load balancing and the QoS requirement of heterogenous cellular networks. The optimization problem is divided into three processes: the base station and the user equipment (UE) association scheme, the femtocell base station (FBS) clustering, and the FBS on/off scheme according to the current traffic load. A cluster-based dynamic FBSs on/off scheme is proposed to improve EE in HCNs while ensuring the load balancing, the probability of outage, and the communication requirement of UEs in the core area. Simulation result shows that the proposed algorithm could achieve significant improvement of the network energy efficiency in all aspects than comparison algorithms in literature.
KeywordsHeterogeneous cellular networks Energy efficiency Femtocell base station Cluster
- 3.Li, Y., Niu, C., Ye, F.: Graph-based femtocell enhanced universal resource allocation strategy for LTE-A HetNets. In: 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), pp. 3073–3078. Singapore (2017)Google Scholar
- 4.Liu, Y., Wang, Y., Zhang, Y., Sun, R., Jiang, L.: Game-theoretic hierarchical resource allocation in ultra-dense networks. In: 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1–6. Valencia (2016)Google Scholar
- 7.He, P., Zhang, S., Zhao, L., Shen, X.: Multi-channel power allocation for maximizing energy efficiency in wireless networks. IEEE Trans. Veh. Technol. 99, 1–14 (2018)Google Scholar
- 9.Vazirani, V.V.: Approximation Algorithms. Springer, Berlin (2001)Google Scholar