TY  - JOUR
T1  - Study and Application of RSSI-based Wi-Fi Channel Detection Using CNN and Frequency Band Characteristics
AU  - Park, Junhyun 
AU  - Byun, Hyungho 
AU  - Kim, Chong-Kwon 
JO  - Journal of KIISE, JOK
PY  - 2020
DA  - 2020/1/14
DO  - 10.5626/JOK.2020.47.3.335
KW  - Wi-Fi scanning
KW  - ISM band
KW  - low power antenna
KW  - deep learning
KW  - convolution neural network
AB  - For mobile devices, Wi-Fi channel scanning is essential to initiating an internet connection, which enables access to a variety of services, and maintaining a stable link quality by periodic monitoring. However, inefficient Wi-Fi operation, where all channels are scanned regardless of whether or not an access point (AP) exists, wastes resources and leads to performance degradation. In this paper, we present a fast and accurate Wi-Fi channel detection method that learns the dynamic frequency band characteristics of signal strengths collected via a low power antenna using a convolution neural network (CNN). Experiments were conducted to demonstrate the channel detection accuracy for different AP combination scenarios. Furthermore, we analyzed the expected performance gain if the suggested method were to assist the scanning operation of the legacy Wi-Fi.