EMBP: Towards an Efficient and Computing-Aware Base Station Placement Strategies for 5G

Shu Yang, Yuanpeng Cao, Laizhong Cui

Abstract: 5G communication performance is highly correlated with the locations of cellular base stations (BSs). Many previous works have studied the placement of BSs, however, millimeter-wave-based transmission and MEC (Mobile Edge Computing) technology brought by 5G makes the BS placement problem more complex in the 5G scenario. 5G communication enhances transmission rates, but it limits transmission distance. Besides, user experience are sensitive to MEC locations, which is also highly related to BS placement. In this paper, we design a 5G BS placement model and propose an EMBP (Efficient MEC BS placement) algorithm to compute the 5G BS locations and MEC locations. Using the historical location and computational task information of users, EMBP can choose BS and MEC deployment locations which will optimize QoE (Quality of Experience) performance in the future. The simulation results show that EMBP greatly outperforms traditional algorithms in terms of QoE and coverage. We also conducted a case study with real-world data collected, and the results validated our conclusions.