AI-Aided Channel Quality Assessment for Bluetooth Adaptive Frequency Hopping

In this work, we propose an artificial intelligence (AI) based channel quality assessment algorithm for Bluetooth adaptive frequency hopping (AFH) to avoid interference between heterogeneous systems coexist in 2.4GHz Industrial, Scientific, and Medical (ISM) band. The proposed network takes the received signal strength indicator (RSSI) of all Bluetooth channels as input and outputs the estimated channel quality, which is further used to update AFH channel map. A gated recurrent unit (GRU) is adopted to extract the temporal information of interference on each channel. A novel loss function combining classification loss and ranking loss is designed to improve the performance of the neural network. The complexity analysis shows that the network considered is lightweight and resource-friendly. Moreover, simulation experiments under different interference models show that the proposed method outperforms several existing channel selection schemes.