This work targets to address the problem of identifying the resource of wireless interference by using a recent popular deep learning technique called Transfer Learning (TL).
The Wireless Interference Identification (WII) is targeting in recognizing the type of wireless channel’s resource and trying to avoid usage of already occupied spectrum channel by another technology type in order to avoid the Wide Band Interference (WBI). As Deep learning is becoming a powerful tool in classification of materials, the adoption of DL in solving WII is becoming a trend. Among available DL technical tools, TL is one of the newest and most efficient technique in enhancing the classification accuracy.
In this work, we applied TL in recognizing the interference resource type of WiFi and Bluetooth as they are the most significant ‘resident’ of the 2.4 GHz band.
