Multi-Label Wireless Interference Identification with Convolutional Neural Networks
This work addresses reliable coexistence management for wireless networks, but it is incremental as it applies an existing CNN method to a new multi-label dataset for interference identification.
The paper tackles the problem of identifying multiple wireless interference signals in license-free frequency bands by proposing a deep convolutional neural network approach, achieving approximately 100% accuracy for same-technology interference and at least 90% for cross-technology interference.
The steadily growing use of license-free frequency bands require reliable coexistence management and therefore proper wireless interference identification (WII). In this work, we propose a WII approach based upon a deep convolutional neural network (CNN) which classifies multiple IEEE 802.15.1, IEEE 802.11 b/g and IEEE 802.15.4 interfering signals in the presence of a utilized signal. The generated multi-label dataset contains frequency- and time-limited sensing snapshots with the bandwidth of 10 MHz and duration of 12.8 $μ$s, respectively. Each snapshot combines one utilized signal with up to multiple interfering signals. The approach shows promising results for same-technology interference with a classification accuracy of approximately 100 % for IEEE 802.15.1 and IEEE 802.15.4 signals. For IEEE 802.11 b/g signals the accuracy increases for cross-technology interference with at least 90 %.