Impact of Fading on Association Probability in UAV-Enabled IoT Networks

In an unmanned aerial vehicle (UAV)-enabled Inter-net of Thing (IoT) network, the transmission rate of an IoT node from the aerial base station (ABS) is determined by the signal-to-noise ratio (SNR). In general, the characterization of the ABS-to-node (A2N) channel is determined by its instantaneous SNR. The previous works reported in this direction have considered the average SNR. This study fills the gap in understanding how fading affects the network’s performance. In this paper, we consider a UAV-enabled IoT network, where multiple ABSs are deployed to serve the IoT nodes. We model the A2N channel based on the instantaneous channel; we consider a generalized fading model, the Nakagami-m model for the A2N link, to provide a more generic analysis. Through analytical insights, we obtain the exact and closed-form approximate expression of the association probability of IoT node with the ABS and infer the capacity enhancement obtained by considering fading compared to the average SNR scenario. The average improvement in ergodic capacity is around 10% compared to the average fading scenario. Furthermore, the results show that considering fading is beneficial for low-altitude scenarios, as it provides a significant increase in the node association probability compared to the average fading scenario.