Energy Efficient Learning-Based 60GHz Band Coverage Prediction for Multi-Band WLAN

Recently, multi-band WLAN becomes a promising solution to increase the spectral efficiency, where 60GHz band provides ultra-high speed transmission and 2.4/5GHz band is used for maintaining the connectivity. For multi-band WLAN end-users, in order to detect the service area of different bands, their RF units need to be turned on all the time, which leads to substantial energy consumption overhead. To solve this problem, this paper proposes an energy efficient learning-based 60GHz band coverage prediction approach by taking into consideration the strong reflected waves in indoor environment. The simulation results demonstrate that the proposed approach could greatly improve the reliability of the prediction compared to the existing approaches.