Abstract
In this work, we analyze performances of the unmanned aerial vehicle‐ (UAV‐) assisted wireless powered sensor communication, where sensor transmission ability is supported by the UAV. Harvested energy from the UAV‐broadcasted signal is further used at the sensor nodes for uplink information transmission to the UAV, over assumed shadowed κ − μ fading channels. Here, we observe a general scenario in which due to the flight conditions of the UAV, the channel’s content include the LOS components affected by the shadowing effect, modeled by the general shadowed κ − μ channel model, which can be reduced to other well‐known channel models as its special cases. We derive closed‐form expressions for the outage probability (OP) of such wireless sensor network (WSN) operating in shadowed κ − μ fading environments. Further, we analyze the optimization of time allocation to minimize OP subjected to UAV’s energy constraints. The impact of channel parameters on observed performance measures is analyzed, and obtained results are numerically validated.
Highlights
The potential use of unmanned aerial vehicles (UAVs are known as drones) for data collection in wireless sensor networks (WSNs) has procured significant interest recently due to their effectiveness and deployment flexibility with changing trajectory, which can be considered major advantages of unmanned aerial vehicle- (UAV-)assisted WSNs
In emergency situations, intelligent heterogeneous architectures based on UAV-assisted WSNs could be considered a promising new solution for developing future 5G communication networks based on ultrareliable low-latency communication (URLLC) and massive machine-type communications
This is due to the fact that the increase in τ leads to reduction of transmission time, which eventually increases the overall outage probability (OP) of the system
Summary
The potential use of unmanned aerial vehicles (UAVs are known as drones) for data collection in wireless sensor networks (WSNs) has procured significant interest recently due to their effectiveness and deployment flexibility with changing trajectory, which can be considered major advantages of UAV-assisted WSNs. Presented model covers propagation scenarios with the following physical interpretation: multipath fading effects, wireless signal in a line-of-sight (LOS) environment while shadowing is considered to affect only the power of the dominant components. This general fading model, written in the function of three parameters: shadowing fluctuation parameter m; clusters parameter μ; and dominant components, power/scattered wave power ratio parameter κ, can be simplified and transformed in other well-known fading models, by setting corresponding parameter values (i.e., one-sided Gaussian model: μ = 0:5, κ → 0, m → 1; Rayleigh model μ = 1, κ → 0, m → 1; Nakagami-m model, μ = m, κ → 0, m → 1; Rician model, μ = 1, κ = K, m → 1; κ − μ model, μ = μ, κ = κ, m → 1; and Rician shadowed model, μ = 1, κ = K, m = m).
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