Identifying Coexisting Bluetooth and Zigbee Technologies Employing Dynamic Mode Decomposition

Advancements in wireless technologies and ever-increasing growth in telecommunication systems mean that devices operating the license-free Industrial, Scientific, and Medical (ISM) band have become increasingly inexpensive and widely available. Employing Bluetooth and Zigbee-based wireless devices in the realm of the Internet of Things (IoT), smart cities, and medical applications has simultaneously intensified during recent years. Because these devices share the 2.4 GHz ISM band, achieving successful coexistence to mitigate interference and enhance spectrum sharing have become a vital concern. In this paper, we develop novel techniques based on dynamic mode decomposition (DMD) modeling to identify Bluetooth and Zigbee technologies coexisting in an experimental heterogeneous network. The proposed technique is based on identifying the time domain signature of time series raw power measurements by capturing embedded periodicity features transmitted within the signal. The advantage of the proposed technique over comparable techniques is that it does not require channel estimation, time/frequency synchronization, or long observation intervals. Our work evaluated the performance of the proposed methods to detect and differentiate between targeted signals in terms of accuracy and processing time required to identify a signal. In addition, the performance was compared with deep learning models for validation and evaluation.