Semi-Supervised Deep Learning Based Wireless Interference Identification for IIoT Networks

Accurate wireless interference identification (WII) is vital for wireless industrial internet of things (IIoT) network to coexist with other technologies in the crowded 2.4 GHz unlicensed band. Deep learning (DL) based methods have emerged as a promising candidate for such type of task. However, to achieve good accuracy, DL methods require large amount of labeled training data, which comes from tedious annotation work by domain expert. In contrast, unlabeled data is easier to obtain. In this paper we present a semi-supervised DL based WII algorithm which combines temporal ensembling technique with CNN network to exploit unlabeled data to improve the performance. The proposed algorithm is able to differentiate interference from multiple wireless standards accurately with reduced number of labels, such as IEEE 802.11, IEEE 802.15.4 and IEEE 802.15.1. Specifically, the proposed algorithm achieves 90% accuracy with less than 2% of labeled data with medium to high signal SNR. Extensive simulation results show that the proposed algorithm achieves a better classification accuracy than benchmark algorithms under various SNR conditions and with different number of labeled data.