Probability of false alarm based interference suppression methods in Internet of Things (IoT) systems

There is going to be significant growth of connected devices, such as smart phones, tablets, and sensors. It has been predicted that by 2020, there are more than 50 billion connected devices in the world. Most of common home IoT devices require little interaction and produce minimal data. Or some healthcare or factory automation sensors need or want to measure and send data to and from the Internet regularly. These are not massive consumers of bandwidth. These can be called narrowband (NB) or ultra-narrowband (UNB) signals. Even if this saves both spectrum resources and power consumption, its drawback is that in the consequence of the IoT headway, the amount of devices and, thus, the amount of NB signaling as well as radio frequency (RF) noise are significantly growing. Thus, interference suppression (IS) is indispensable. In this paper, three probability of false alarm (PFA) based methods, namely Neyman-Pearson (NP) criterion, forward consecutive mean excision (FCME), and localization algorithm based on double-thresholding (LAD) are applied for a wireless NB-IoT network, especially, in the last 100 m region, i.e., from devices to an access point (AP). Besides the traditional fixed threshold approach, an adaptive threshold setting is also proposed to enhance the performances in frequency selective fading channels. The simulation results show that the proposed methods excellently work even in a dense NB-IoT network, where contains a large number of devices.