Implementation of efficient real-time industrial wireless interference identification algorithms with fuzzified neural networks

Real-time industrial wireless systems sharing a crowded spectrum band require active coexistence management measures. Identification of wireless interference is a key issue for this purpose. We propose an efficient implementation of a wireless interference identification (WII) approach called neuro-fuzzy signal classifier (NFSC). The implementation in Matlab / SIMULINK is based upon the wideband software defined radio Ettus USRP N210. The implementation is evaluated in six selected heterogeneous and harsh industrial scenarios within the license-free 2.4-GHz-ISM radio band with variously combined standard wireless technologies IEEE 802.11g-based WLAN and Bluetooth. The evaluation of the NFSC was performed with a binary classification test with the statistical measurement metrics sensitivity and specificity.