Performance Analysis of DF/AF Cooperative MISO Wireless Sensor Networks With NOMA and SWIPT Over Nakagami-&lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;$m$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt; Fading

Tran, Duc-Dung; Ha, Dac‐Binh; Vo, Van Nhan; So-In, Chakchai; Tran, Hung; Nguyen, Tri Gia; Baig, Zubair A.; Sanguanpong, Surasak

doi:10.1109/access.2018.2872935

Cited by 30 publications

(7 citation statements)

References 47 publications

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“…For clarity, we define the notations adopted throughout the remainder of this paper in Table 1. We assume that the channel coefficients g XY are independent and identically distributed (i.i.d) and modeled as Nakagami-m fading channels [42,43] with fading parameters m XY , E{|g XY | 2 } = Ω XY , and |g XY | 2 is a random variable (RV) [19], where XY ∈ {BU n , U n I i }. In practice, it is not feasible to obtain perfect CSI for a wireless network due to channel estimation errors.…”

Section: System Modelmentioning

confidence: 99%

“…The UR has to first ensure the detection of the messages from the BS; then, it can carry out EH. Therefore, the choice of the PSR in (19) is optimal [57][58][59]. Particularly, if the PSR is set as ρ > ρ DF * , too much power is directed to the EH circuit.…”

Section: Df Schemementioning

confidence: 99%

“…To further enhance the system capacity and provide an enhanced experience for the IDs, especially for IDs at a cell edge, nonorthogonal multiple access (NOMA) has recently been proposed as a promising solution for 5G wireless networks [19][20][21]. Furthermore, the NOMA technique can also be used for performance enhancement in UAV-enabled wireless networks [22,23].…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

So-In

et al. 2021

Sensors

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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.

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Section: System Modelmentioning

confidence: 99%

Section: Df Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nguyen

So-In

et al. 2021

Sensors

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“…Under the framework of decode and forward (DF) strategy with multiple relays, based on the channel gain optimal selection scheme, a new closed expression of the outage probability under the time switch protocol of the energy harvesting relay network is derived in [9]. Authors in [10] studied the downlink cooperative multiple-input single-output wireless sensor network of non-orthogonal multiple access technology, and proposed a power split ratio optimization algorithm and an optimal antenna-relay-target selection algorithm. The authors of [11] studied the SWIPT two-way relay non-orthogonal multiple access system, and derived the analytical expressions of outage probability and ergodic capacity.…”

Section: Introductionmentioning

confidence: 99%

Outage Performance of Multi-relay System with Energy Harvesting and Storage

Pan¹,

Pan²

2022

Journal of Internet Technology

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<p>This paper analyzes the performance of a two-hop half-duplex multi-relay system based on energy harvesting. The relay has energy harvesting and storage functions, and adopts an adaptive AF/DF transmission strategy and PS protocol. Based on three relay selection schemes, namely Energy Optimal Selection (EOS), Channel Gain Optimal Selection (GOS), and Energy and Channel Gain Optimal Selection (EGOS), the outage performance is compared. First, the finite state Markov chain (FSMC) is used to model the energy arrival and use status of each relay, and the energy transfer steady-state matrix is obtained. Then we use the Gauss Chebyshev formula to derive the analytical expression of the communication outage probability (COP). Finally, an optimization model for minimizing the COP for three relay selection schemes is constructed. The EOS and GOS schemes optimize power allocation for energy harvesting and information transmission, and the EGOS scheme jointly optimizes power allocation and energy thresholds. We use a one-dimensional search algorithm based on the golden section and a two-dimensional search algorithm based on iteration to solve these optimization problems. The simulation results show that the EGOS is the best among the three relay selection schemes.</p> <p> </p>

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“…However, there are many related issues that need to be addressed before these techniques can be deployed in next-generation networks, such as the network architecture, power allocation, relaying scheme selection, the combination between NOMA and other multiple access methods, fixed or dynamic user pairing/clustering, optimal user allocation and beamforming in NOMA MIMO systems, the impact of imperfect CSI, and joint optimization of diverse aspects of NOMA (spectrum efficiency, energy efficiency, security) [11]. In recent years, a number of works investigated some related issues such as performance of energy harvesting DF/AF relaying, cooperative, cognitive, and MIMO NOMA networks [12][13][14][15]. In addition, the work of [16] studied the secrecy performance of MIMO NOMA system over Nakagami-m channels with transmit antenna selection protocol.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis for NOMA Relaying System in Next-Generation Networks with RF Energy Harvesting

Ha¹,

Agrawal²

2020

Recent Wireless Power Transfer Technologies

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In this chapter, we investigate the performance of the non-orthogonal multiple access (NOMA) relaying network with radio-frequency (RF) power transfer. Specifically, this considered system consists of one RF power supply station, one source, one energy-constrained relay, and multiple energy-constrained NOMA users. The better user and relay can help the source to forward the message to worse user by using the energy harvested from the power station. The triple-phase harvest-transmit-forward transmission protocol is proposed for this considered system. The exact closed-form expressions of outage probability and throughput for each link and whole system are derived by using the statistical characteristics of signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) of transmission links. In order to understand more detail about the behavior of this considered system, the numerical results are provided according to the system key parameters, such as the transmit power, number of users, time switching ratio, and power allocation coefficients. The simulation results are also provided to confirm the correctness of our analysis.

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Performance Analysis of DF/AF Cooperative MISO Wireless Sensor Networks With NOMA and SWIPT Over Nakagami-<inline-formula> <tex-math notation="LaTeX">$m$ </tex-math> </inline-formula> Fading

Cited by 30 publications

References 47 publications

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

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

Outage Performance of Multi-relay System with Energy Harvesting and Storage

Performance Analysis for NOMA Relaying System in Next-Generation Networks with RF Energy Harvesting

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