System Performance Analysis for an Energy Harvesting IoT System Using a DF/AF UAV-Enabled Relay with Downlink NOMA under Nakagami-m Fading

Abstract

This paper investigates system performance in the Internet of Things (IoT) with an energy harvesting (EH) unmanned aerial vehicle (UAV)-enabled relay under Nakagami-m fading, where the time switching (TS) and adaptive power splitting (APS) protocols are applied for the UAV. Our proposed system model consists of a base station (BS), two IoT device (ID) clusters (i.e., a far cluster and a near cluster), and a multiantenna UAV-enabled relay (UR). We adopt a UR-aided TS and APS (U-TSAPS) protocol, in which the UR can dynamically optimize the respective power splitting ratio (PSR) according to the channel conditions. To improve the throughput, the nonorthogonal multiple access (NOMA) technique is applied in the transmission of both hops (i.e., from the BS to the UR and from the UR to the ID clusters). The U-TSAPS protocol is divided into two phases. In the first phase, the BS transmits a signal to the UR. The UR then splits the received signal into two streams for information processing and EH using the APS scheme. In the second phase, the selected antenna of the UR forwards the received signal to the best far ID (BFID) in the far cluster and the best near ID (BNID) in the near cluster using the decode-and-forward (DF) or amplify-and-forward (AF) NOMA scheme. We derive closed-form expressions for the outage probabilities (OPs) at the BFID and BNID with the APS ratio under imperfect channel state information (ICSI) to evaluate the system performance. Based on these derivations, the throughputs of the considered system are also evaluated. Moreover, we propose an algorithm for determining the nearly optimal EH time for the system to minimize the OP. In addition, Monte Carlo simulation results are presented to confirm the accuracy of our analysis based on simulations of the system performance under various system parameters, such as the EH time, the height and position of the UR, the number of UR antennas, and the number of IDs in each cluster.

Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

“…In order to model the channel fading behavior in air-to-ground propagation scenarios, several statistical distributions, e.g., Rician, Rayleigh, and Nakagami- m distributions, have been previously used [ 43 , 44 , 45 ]. Among these distributions, the Nakagami- m distribution was experimentally validated in UAV-based scenarios [ 46 ], whereas it also provides flexibility in several environments by including Rayleigh distribution as a special case or by approximately describing Rician fading.…”

Performance Analysis and Optimization for IoT Mobile Edge Computing Networks With RF Energy Harvesting and UAV Relaying

“…Moreover, we consider that UR and IDs can harvest RF energy from a PB [33] to improve the data offloading process. In addition, we consider UR communications via Line-of-Sight (LoS) and non-Line-of-Sight (NLoS) links [11], [14], as well as the Nakagami-m fading channel [13]. Furthermore, we investigate the performance of UR NOMA-based MEC systems in practical cases of ICSI at receiver nodes and ISIC [34].…”

“…In practice, perfect channel state information (PCSI) is difficult to achieve in wireless systems due to channel estimation errors or feedback delays. Thus, the channel coefficient is represented as [13] g ab = ĝab + ε ab ,…”

“…U estimates the signal-to-noise ratios (SNRs) of all transmission channels from two clusters and then selects BIA, denoted as I m * , and BIB, denoted as I n * , as the signals with the greatest received SNRs at the selected UAV terminal. Therefore, the indices and channel power gains of the selected ID in clusters M and N are as follows [13]:…”

Performance Enhancement for Full-Duplex Relaying with Time-Switching-Based SWIPT in Wireless Sensors Networks

“…The Internet of Things (IoT) has played a key role in fifth generation (5G) and beyond due to its ability to improve human quality of life such as health care, wearable devices, smart cities, agriculture, industrial automation, intelligent street lighting, maintenance management, etc. [ 1 , 2 , 3 , 4 ]. The number of IoT devices (IoTDs) is forecast to reach 10.07 and 25.44 in 2021 and 2030, respectively [ 5 ].…”