Interference suppression methods with adaptive threshold in Internet of Things (IoT) systems

Abstract The explosive growth in home, wearable, and wireless devices has not been matched by the growth in radio spectrum bands to accommodate them. The inter-networking of all of these devices known as “the internet of things (IoT)” is expected to have tens of billions of devices, mostly wireless, definitely incurring a coexistence or interference problem. The ubiquitous industrial, scientific and medical (ISM) radio band at 2.4GHz, in particular, one of the candidate band for IoT is heavily oversubscribed due to its unlicensed nature and could become all but unusable for priority systems in a densely populated area in the future at the present rate of growth of 2.4GHz transmitters and networks. In our study, the communications of the “last 100 meters” of an IoT network, i.e., from devices to an access point (AP) are considered. The interference suppression algorithms using the probability of false alarm PF A based methods, i.e., the Neyman-Pearson (NP) criterion and the localization algorithm based on double-thresholding (LAD) are applied to enhance the transmission bit error rate (BER) performances in various scenarios. Besides the traditional fixed threshold approach, an adaptive threshold approaches are proposed to enhance the performances in frequency selective fading channels. The simulation results show that the proposed methods excellently work even in an IoT network, which contains a large number of devices