Performance analysis of dual-hop mixed RF-FSO systems combined with NOMA

Tran Cong Hung; Tan N. Nguyen; N. H. K. Nhan; Anh-Tu Le; Pham Ngoc Son; Thu-Ha Thi Pham; Miroslav Voznak

doi:10.1371/journal.pone.0315123

Abstract

This paper investigates the performance of hybrid radio frequency/free space optical (RF/FSO) systems combined with non-orthogonal multiple access communications technology. We examine a scenario where the source and destination are separated by a large distance, with no direct link between them. The relay, denoted R, operates using the decode-and-forward (DF) protocol. Under the DF relaying scheme, the relay employs successive interference cancellation (SIC). In this setup, the FSO link from the source to the relay follows a Gamma-Gamma distribution, while the RF link from the relay to multiple users follow a Nakagami-m distribution. Based on this system model, we analyze the outage probability (OP). Our findings indicate a direct relationship between SIC and OP performance: the higher the SIC capability, the more effective the system. In addition, the system’s performance is dependent on the parameters of the FSO channel. Finally, Monte Carlo simulations are presented to further validate our framework and findings.

Citation: Hung TC, Nguyen TN, Nhan NHK, Le A-T, Son PN, Pham T-HT, et al. (2024) Performance analysis of dual-hop mixed RF-FSO systems combined with NOMA. PLoS ONE 19(12): e0315123. https://doi.org/10.1371/journal.pone.0315123

Editor: Rahat Ullah, Nanjing University of Information Science and technology, Nanjing, China., CHINA

Received: July 15, 2024; Accepted: November 20, 2024; Published: December 20, 2024

Copyright: © 2024 Hung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This research is funded by the European Union within the REFRESH project - Research Excellence For Region Sustainability and High-tech Industries ID No. CZ.10.03.01/00/22 003/0000048 of the European Just Transition Fund and by the Ministry of Education, Youth and Sports of the Czech Republic (MEYS CZ) through the project SGS ID No. SP 061/2024 conducted by VSB - Technical University of Ostrava.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

One of the most significant breakthroughs in the history of telecommunications was the invention of wireless communication. The proliferation and prevalence of wireless devices have exceeded expectations from a few decades ago and are predicted to continue growing at an exponential rate. Radio frequency (RF) technology has dominated wireless communication systems, occupying both licensed and unlicensed spectrums, in a wide variety of contexts [1, 2]. This dominance has led to widespread adoption and the existence of a large number of RF devices. However, several issues underlie the use of the RF band, including limited capacity, high costs of licensed spectrum technologies, and interference from unlicensed spectrum technologies. With the number of consumer devices growing exponentially, researchers are exploring novel wireless communication methods. It is essential to evaluate additional frequency bands in the electromagnetic spectrum for data transfer since the demand for constant communication availability exceeds the RF spectrum’s capacity. In this context, free-space optical (FSO) communication systems have drawn much interest from both academic and industry communities for their ease of implementation, low power consumption, high data rates and bandwidth, and free spectrum licensing [3–7]. Compared to traditional RF communications, FSO communications offer higher throughput, the use of unlicensed spectrums, cost-effectiveness, and other advantages. FSO systems represent a promising solution for various communications scenarios, which include backhaul/fronthaul link for cellular systems, aerial/drone-assisted wireless emergency communications for disaster recovery, and optical communications for space exploration [6, 8, 10].

Since mixed FSO-RF networks can benefit from both the broadcasting nature and wide coverage area of the RF technique as well as the high channel capacity, license-free operation, and enhanced security of the FSO technique, they have attracted a lot of interest. In an FSO-RF system, a cooperative relay is used as an intermediary to transfer signals from the transmitter to the receiver. Relay strategies are divided into two categories: protocols for amplify-and-forward (AF) relaying and decode-and-forward (DF) relaying [11]. Various fading models are also employed in the FSO and RF channels of hybrid FSO-RF relaying systems. The FSO link can function according to Gamma-Gamma [12–16] or exponentiated Weibull distributions [17, 18], among others. In contrast, the RF link can operate under Rayleigh [12, 13], Nakagami-m [14, 17, 19], generalized K–μ [18], and K distributions [20]. For example, the hybrid FSO-RF system described in [12], applies RF and FSO link defined according to the Rayleigh and Gamma-Gamma distributions, respectively, and uses an AF relay. In another study [17], both fixed-gain and variable-gain relay schemes are examined, using an RF link modeled according to the Nakagami-m distribution and an FSO link under exponentiated Weibull fading. A dual-hop hybrid FSO-RF system characterized by RF and FSO link that follow Nakagami-m and double generalized Gamma distributions, respectively, is described in [19]. Similarly, an asymmetric dual-hop AF relaying system employing RF and FSO link under Nakagami-m and Gamma-Gamma fading, respectively, is discussed in [14].

Non-orthogonal multiple access (NOMA) also leverages non-orthogonal resource allocation to enable a high number of users access the network, in contrast to traditional orthogonal multiple access (OMA) technologies. Recently, NOMA has garnered much interest as an essential technology for 5G wireless communication networks [21–25]. To ensure fairness among users, NOMA assigns lower power levels to users with stronger channel gains and higher power levels to users with weaker channel gains [26, 27]. These signals are then superposed at the source. Academic and industrial research have also demonstrated that NOMA can effectively support vast connectivity in addition to its increased spectral efficiency. This capability is crucial for supporting the Internet of Things (IoT) features in the forthcoming 5G Next networks [28–30]. Due to NOMA’s interoperability with various communication technologies, it can be seamlessly integrated into both current and future wireless systems. For instance, it has been demonstrated that NOMA is compatible with traditional OMA methods such as orthogonal frequency division multiple access (OFDMA) and time division multiple access (TDMA) [31]. NOMA has also been incorporated into the next digital TV standard, known as layered division multiplexing [32], which enhances the spectral efficiency of TV broadcasting by superimposing numerous data streams. Recent research has also explored the use of NOMA with unmanned aerial vehicle (UAV) systems [33, 34] and in satellite architectures [35, 36]. The application of multiple intelligent reflecting surfaces (IRSs) with distinct phase shifts to support NOMA networks has also been examined [37, 38]. These aforementioned instances highlight NOMA’s enormous potential not only for 5G networks but also for various current and future wireless systems.

1.1 Related work

Exploiting the advantages of both the high data rates facilitated by FSO systems and the high spectral efficiency introduced by NOMA serves as the primary motivation for combining FSO and NOMA. Another reason is that NOMA typically operates well in situations where the power received from various transmitters varies significantly [27]. This condition is often encountered in FSO systems because adverse weather conditions, such as haze and fog, result in higher path loss for FSO link compared to RF lines. However, the combination of FSO and NOMA has only recently been explored in the literature [39–41]. In [39], the authors consider employing NOMA for the FSO backhauling system, which consists of two base stations and one central unit. Another study [40] examines a multi point-to-point system consisting of K users and a central node using NOMA over an FSO channel. In [41], the authors investigate a low-altitude platform (LAP)-aided dual-hop relaying system that combines NOMA and FSO communications technology.

In addition, the incorporation of NOMA with mixed FSO-RF networks to achieve better communication performance [42–47]. A two-hop uplink NOMA RF-FSO network was investigated in [42] in order to meet the high throughput requirements for the backhaul connection. The closed-form formulation of OP and ergodic channel capacity under various interference situations was also provided. The subject of the combined optimization of power allocation and harvesting time was further investigated in [43] when the analytical expressions of OP and throughput were taken into consideration in the context of an energy harvesting enabled mixed FSO-RF network. By including NOMA into both the FSO and RF connections, the authors of [44] suggested a down-link FSO-RF network and determined the closed-form expression of OP for every user. The analytical formulation of OP was introduced in [45], taking into account that the RF channel has a Rayleigh distribution and the FSO channel has an M-distribution. [46] provided the analytical and asymptotic formulas of secrecy OP (SOP) for an FSORF system with NOMA while accounting for the imperfect channel state information. For a NOMA-based hybrid RF-UWOC system, the asymptotic formulations of ergodic capacity and the closed-form formulas of OP were provided in [47].

1.2 Contributions

Based on the above-mentioned issues, it is highly intriguing and promising to research the FSO-RF cooperation with NOMA. In this paper, we consider a data transmission protocol where a relay is available between the source and multiple destinations to investigate the effect of OP. Table 1 shows the comparison between our work and related papers and the key notations are used in this paper, as shown in Table 2. Our main contributions are as follows:

A proposed dual-hop mixed RF/FSO system that includes both RF and FSO link. In this system, the FSO link, which connects the source to the relay, follows a doubly generalized Gamma distribution. The RF link, connecting the relay to multiple users, follows the Nakagami-m distribution. The cooperative relay, functioning as an intermediary to transmit signals from the transmitter to the receiver, employs DF relaying. We examine the signal-to-interference-plus-noise ratio (SINR) for both the FSO and RF link when relay R uses successive interference cancellation (SIC), in addition to the SINR when relay R applies imperfect SIC (ipSIC).
To ensure the secure performance of the proposed system, we derive the OP expressions for both near and far users. Notably, we investigate the outages in the high-SNR regime. Analysis and simulation results evaluate the impacts of key parameters on performance.

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Table 1. The comparison of our work and the previous works.

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Table 2. Main parameter notation.

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1.3 Overview

Following the introduction in Section 1, Section 2 presents the system model of FSO/RF cooperative NOMA and the channel characteristics of FSO and RF. In Section 3, we obtain the statistical properties of the FSO/RF link. In Section 4, we derive the analytical expressions for the OP for three cases: near users, far users, and both near and far users under a high SNR regime. Section 5 provides numerical and simulation results to verify the theoretical analyses. Section 6 concludes the paper with a summary of its main findings and contributions.

2 System model and channel characteristics

In this paper, we consier the FSO/RF Cooperative NOMA system model with a source (S), a relay (R), and two users as in Fig 1. It is assumed that R operates in half-duplex mode and that the channel state information (CSI) is perfect at each terminal to detect the signal. The communications from S to R and R to D_i, i ∈ {1, 2} therefore occupy two-time slots. In the first time slot, by applying NOMA principle, S using NOMA signaling to transmits to R through the FSO link, where P_S is the transmission power at S, x₁ and x₂ are the unit-power data symbols, a₁ and a₂, a₂ > a₁ are the corresponding power allocation coefficients. The signal received at the R is therefore expressed as (1) where n_R is the additive Gaussian noise at the relay in which .

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Fig 1. FSO/RF cooperative NOMA system model.

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2.1 FSO link

Using the NOMA protocol, R decodes the information from S in x₂ by considering x₁ as an interference signal. The received SINR to identify x₂ at R is therefore given by (2) where ρ = P_S/σ² denotes the transmit signal-to-noise ratio (SNR). Note that x₁ and x₂ are normalized unity power signals, i.e, .

After performing SIC at R to identify x₂, the received SINR at R to detect x₁ in the case of imperfect SIC is expressed as (3) where χ, 0 ≤ χ ≤ 1, representing the efficiency of SIC for x₂ at R. The cases χ = 0 and χ = 1 correspond to perfect SIC (pSIC) and imperfect SIC (ipSIC) in [53], respectively.

2.2 RF link

After receiving Y_R, the relay decodes and transmits the signal to end users via Nakagami-m fading RF channel. By similarly applying NOMA principle in FSO link, R transmit the NOMA signaling to user D_i. Thus, the signals received at D_i is therefore expressed as (4) where P_R is the normalized transmission power at R, d_u is the distance between R and D_u, and δ is the path-loss exponent.

Using the NOMA protocol, since the user with the highest received signal strength, D₁, applies SIC, the SINRs required to perform SIC for x₁ and x₂ at D₁ are given by (5) (6)

In contrast, D₂ decodes its intended signal x₂ by treating x₁ as interference. Consequently, the SINR at D₂ is (7)

3 Performance analysis

In an investigation of the system performance, we first obtain the statistical property of the FSO/RF link.

3.1 Channel model

3.1.1 Fading statistics of the FSO channel.

Let be a K_G distributed random variables (RV) with three gamma-gamma parameters that were obtained from its square. This PDF is provided by [54, Eq. (1)]: (8) where α ≥ 0 and β ≥ 0 are the factors that determine the shaping of the small-scale and large-scale eddies in the scattering environment, and Ω₀ relates to the mean as , in which denotes expectation. The two effective numbers, α and β, relate to the atmospheric conditions and are expressed as [55] (9) (10) where denote the Rytov variance, , L is the distance between S and R, λ is the operational wavelength, D is the aperture diameter of the receiver, and is the altitude-dependent index of the refractive structure parameter determining the turbulence strength. Additionally, we presume that is constant during reasonably long transmit bit intervals [56].

Since it represents a variety of models often used in communication systems for a variety of combinations of α and β, the distribution in (8) is general. Thus, as α → ∞, it approximates the well-known Gamma distribution (or alternatively squared Nakagami-m [57]), whereas for β = 1, it corresponds to the statistics of a squared K-distributed RV, with a PDF given by (11) where K_q(⋅) denotes the q^th order of the modified Bessel function of the second kind.

It can then be simplified to the power statistics of the double Rayleigh model, which is often employed in cascade multipath fading channels, with the PDF obtained from [58] for the particular case of α and β: (12)

Now, we obtain the p-th moment of γ₀ as given by [54]: (13) where . Furthermore, [59, Eq. (7)] and [60, Eq. (9.31.5)] can be applied to define the cumulative density function (CDF), expressed as (14) where is the Meijer G-function [20, Eq. 9.301].

3.2 Fading statistics of the RF channel

Both the R to D₁ and R to D₂ routes in the RF path are modeled with Nakagami-m distribution and fading severity parameters and , respectively. The CDF and PDF of |h_u|², u ∈ {1, 2} may therefore be expressed as [61] (15a) (15b) where , in which m_u and λ_u represent the integer fading factor and the mean, respectively.

In addition, the scenario R₂ ≥ R₁ (R₁ and R₂ are the radii of the inner and outer circles, respectively), note that the users are deployed in D₁ and D₂ based on homogeneous poisson point processes. Consequently, NOMA users are characterized as independently and identically distributed (i.i.d.) points in D₁ and D₂, indicated by d_u, u ∈ {1, 2}, which carry location information about nearby and distant users, respectively. The d₁ and d₂ probability density functions (PDFs) are provided by [62]: (16) (17)

4 Outage probability analysis

4.1 Outage probability of D₁

Signal x₁ cannot be received successfully by D₁ when the following cases occur: when R is unable to decode either x₂ or x₁ and when D₁ fails to perform SIC for x₂ and is thus unable to decode x₁.

The outage probabilities in the first and second cases are given by (18) where , and , with being the target rate at of D_u, ∀u ∈ {1, 2}.

The closed-form formulation of is provided in Proposition 1 from (18).

Proposition 1 The proposed FSO/RF dual-hop relay system’s is calculated from the Eq: (19) where , , Θ_max = max(Θ₁, Θ₂) and .

Proof 1 See S1 Appendix.

4.2 Outage probability of D₂

Similarly, in the first case, the signals x₁ and x₂ cannot be effectively received by R. Then, in the second case, D₂ fails to decode the signal x₂. The outage probability of D₂ is therefore be calculated as follows: (20) where .

Proposition 2 gives the closed-form expression of , derived from (20).

Proposition 2 The proposed FSO/RF dual-hop relay system’s is computed from the Eq: (21) where Θ₂, Θ_max, ξ and ϑ are announced previously.

Proof 2 See S2 Appendix.

4.3 High SNR regime

In this section, we examine outages in the high SNR regime, i.e., when ρ → ∞.

From [63, Eq. (18)], the Meijer G-function is given by the approximation: (22)

Then, we obtain from: (23)

When x → 0, the approximate expressions of the CDF for the channel gain |h_u|², u ∈ {1, 2} are given by [64, Eq. (16a)] (24)

Aided by (24), we obtain and : (25) and (26)

Finally, we obtain the asymptotic outage probabilities of D₁ and D₂ from (27) and (28), respectively: (27) (28)

5 Numerical results

In this section, we evaluate the performance of the derived theoretical expression and also validate it with numerical results. The fading parameters were set to . Table 3 summarizes the main parameters. Furthermore, the corresponding noise power at D₁ and D₂ was computed as σ² = N₀ + 10 log (BW) + NF [dBm] in [65]. The technological advancement of our code resides in the implementation of symbolic computations within Matlab, which facilitated the attainment of highly precise results.

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Table 3. Main parameters for the simulations [55, 62].

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Fig 2 illustrates the outage probability of a dual-hop FSO/RF system as a function of the SIC capability, denoted χ, for near users. The outage probability decreases as the value of m increases from 1 to 3 to 5. This trend can be explained by a larger value of m resulting in a larger sequence containing the sum function in Eq (19), leading to a smaller OP when 1 minus that series is considered. As the SIC capability χ changes from perfect SIC (χ = 0) to χ = 0.25, the OP also varies, reaching its lowest value at χ = 0 and gradually increasing as χ increases. Notably, when χ is approximately 0.24, the OP approaches an asymptotic value for all three cases of m considered. This observation highlights that the more effective the relay’s effective interference cancellation capability, the better the system’s performance.

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Fig 2. Outage probability of a dual-hop FSO/RF system versus SIC capability χ, with

, a₁ = 0.2, a₂ = 0.8 and P_S = −10 [dBm].

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In Fig 3, the OP of the system is evaluated as a function of P_S(dBm). It is observed that an increase in P_S(dBm) leads to a decrease in the OP. This is because higher P_S(dBm) values result in higher energy at the relay, which significantly improves the received SINR. In addition, increasing m results in better OP performance, similar to the trend observed in Fig 2. The asymptotic OP of destinations D₁ and D₂, as presented in Eqs (27) and (28), is also accurately simulated in Fig 3. Specifically, for P_S > −15 dBm, the asymptotic OP and the actual OP are almost identical.

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Fig 3. Outage probability of a dual-hop FSO/RF system versus P_S.

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In addition to increasing m in order to improve OP performance, as observed in Figs 2 and 4 indicates that two users experience different outage performance as the power allocation factor varies. Specifically in this case, the lowest OP for user D₁ occurs when a₂ = 0.6, while for user D₂, it occurs when a₂ = 0.95. This behavior can be explained by considering that as a₂ is increases, a₁ decreases, leading to an increase in the received SINR at D₂, as indicated by Eq (7). Hence, the OP at the D₂ source always decreases. However, the received SINR at D₁ may increase (based on Eq (5)) or decrease (based on Eq (6)). Therefore, the OP expression in Eq (18) does not show a clear trend of increasing or decreasing, but through simulation and under the condition a₂ > a₁, when a₂ increases from 0.6 to 0.95, the OP of near users also increases and converges to an asymptotic value when a₂ = 0.95. As a result, as a₂ increases, the far user’s OP performance improves while the near user’s OP performance deteriorates, and vice versa. To ensure fairness between users, an optimal level of the power allocation factor exists; this is achieved when a₂ ≈ 0.7 (corresponding to a₁ = 0.3), resulting in nearly equal OP performance for both near and far users.

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Fig 4. Outage probability versus power allocation factors, with P_S = −10 [dBm],

and

.

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Fig 5 illustrates the outage probability of the model when compared to , with ρ = 30 [dB], a₁ = 0.05 and a₂ = 0.95. As increases, the user’s OP efficiency decreases, as explained by Eqs (18) and (20). In addition, the OP performance of distant users is better than that of proximity users. This is because, with a power allocation factor a₂ = 0.95, distant users achieve their best OP performance (as previously indicated in Fig 4). In Fig 6 presents the outage probability versus PS (in dBm) for different scenarios in a wireless communication system, showing that the NOMA scheme achieves better performance for both near and far users compared to the conventional OMA, with asymptotic and simulated results depicted by dashed and solid lines, respectively, demonstrating the superior robustness of NOMA, especially at higher transmit power levels.

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Fig 5. Outage probability versus

, with P_S = −10 [dBm], a₁ = 0.12 and a₂ = 0.88.

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Fig 6. Outage probability versus P_S in NOMA and OMA, with m = 2, a₁ = 0.1 and a₂ = 0.9.

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Fig 7 shows the outage probability of FSO and FSO-RF links at both near and far users as a function of P_S(dBm), with simulated (solid lines) and asymptotic (dashed lines) results indicating better performance at higher power levels. the figure highlights that both user distance and link type (FSO or FSO-RF) significantly impact outage probability, with the near-user FSO link providing the best reliability. Increasing transmit power P_S reduces the outage probability across all configurations, the performance of users for FSO-RF NOMA network is better than FSO NOMA network.

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Fig 7. Outage probability versus P_S in FSO link and FSO-RF link, with m = 2 and χ = 0.01.

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6 Conclusion

This paper provides a performance analysis of a dual-hop mixed RF-FSO system integrated with NOMA communications technology. Using DF relaying, the relay R employs successive interference cancellation, where the FSO link from the source to the relay follows a double Gamma distribution, and the RF link from the relay to multiple users follow the Nakagami-m distribution. Based on this system model, we analyzed the outage probability. Our results demonstrate that SIC has a clear effect on OP performance; specifically the greater the SIC capability, the better the system’s performance. We also varied the Nakagami-m channel parameters to examine their effect on system performance. The results indicate that increasing the m value enhances system performance. The power allocation factors also notably affect OP performance for both near and far users. A higher power allocation factor for far users results in improved OP efficiency for those users but reduced OP efficiency for near users, and vice versa. Consequently, there is a trade-off in OP performance between users. However, an optimal level of the power allocation factor exists that ensures fairness between users. The system’s performance also depends on other system parameters. An analysis of these parameters provides insight into optimizing the design and operation of dual-hop mixed RF-FSO systems with NOMA technology.

Supporting information

S1 Appendix. Proof of Proposition 1.

https://doi.org/10.1371/journal.pone.0315123.s001

(PDF)

S2 Appendix. Proof of Proposition 2.

https://doi.org/10.1371/journal.pone.0315123.s002

(PDF)

S1 File.

https://doi.org/10.1371/journal.pone.0315123.s003

(RAR)

References

1. Fazio P., Sottile C., Santamaria A. F., Tropea M. Vehicular networking enhancement and multi-channel routing optimization, based on multi-objective metric and minimum spanning tree. Advances in Electrical and Electronic Engineering 2013;11(5):349–356.
- View Article
- Google Scholar
2. Fazio, P., Tropea, M., Veltri, F., Marano, S. A new routing protocol for interference and path-length minimization in vehicular networks. In 2012 IEEE 75th Vehicular Technology Conference (VTC Spring); 2012 May;1-5; Yokohama, Japan: IEEE.
3. Khalighi M. A., Uysal M. Survey on free space optical communication: A communication theory perspective. IEEE communications surveys & tutorials 2013;16(4):2231–2258.
- View Article
- Google Scholar
4. Anbarasi K., Hemanth C., Sangeetha R. G. A review on channel models in free space optical communication systems. Optics & Laser Technology 2017;97:161–171.
- View Article
- Google Scholar
5. Garlinska M., Pregowska A., Gutowska I., Osial M., Szczepanski J. Experimental study of the free space optics communication system operating in the 8–12 μm spectral range. Electronics 2021;10(8):875.
- View Article
- Google Scholar
6. Wang Y., Xu , et al. Performance analysis of an adaptive optics system for free-space optics communication through atmospheric turbulence. Scientific reports 2018;8(1):1124. pmid:29348561
- View Article
- PubMed/NCBI
- Google Scholar
7. Ullah Rahat, et al. High-capacity free space optics-based passive optical network for 5g front-haul deployment. Photonics 2023;10(10):1073.
- View Article
- Google Scholar
8. Xu G., Song Z. Effects of solar scintillation on deep space communications: challenges and prediction techniques. IEEE Wireless Communications 2019;26(2):10–16.
- View Article
- Google Scholar
9. Kaushal H., Kaddoum G Optical communication in space: Challenges and mitigation techniques. IEEE communications surveys & tutorials 2016;19(1):57–96.
- View Article
- Google Scholar
10. Ullah Rahat, et al. Optimization and analysis of Spectral/Spatial optical code division multiple access passive optical network. AEU-International Journal of Electronics and Communications 2024;175:155084.
- View Article
- Google Scholar
11. Nguyen T. N., Duy T. T., Luu G. T., Tran P. T., Vozňák M. Energy harvesting-based spectrum access with incremental cooperation, relay selection and hardware noises. Radioengineering 2017;26(1):240–250.
- View Article
- Google Scholar
12. Liang H., Gao C., Li Y., Miao M., Li X. Performance analysis of mixed MISO RF/SIMO FSO relaying systems. Optics Communications 2021;478:126344.
- View Article
- Google Scholar
13. Petkovic M. I., Ansari I. S., Djordjevic G. T., Qaraqe K. A. Error rate and ergodic capacity of RF-FSO system with partial relay selection in the presence of pointing errors. Optics Communications 2019;438:118–125.
- View Article
- Google Scholar
14. Zedini E., Ansari I. S., Alouini M. S. Performance analysis of mixed Nakagami-m and Gamma–Gamma dual-hop FSO transmission systems. IEEE Photonics Journal 2014;7(1):1–20.
- View Article
- Google Scholar
15. Jamali M. V., Mahdavifar H. Uplink non-orthogonal multiple access over mixed RF-FSO systems. IEEE Transactions on Wireless Communications 2020;19(5):3558–3574.
- View Article
- Google Scholar
16. Yang L., Liu T., Chen J., Alouini M. S. Physical-Layer Security for Mixed η–μ and -Distribution Dual-Hop RF/FSO Systems. IEEE transactions on vehicular technology 2018;67(12):12427–12431.
- View Article
- Google Scholar
17. Wang Z., Shi W., Liu W. Performance analysis of mixed RF/FSO system with spatial diversity. Optics Communications 2019;443:230–237.
- View Article
- Google Scholar
18. Yi X., Shen C., Yue P., Wang Y., Ao Q. Performance of decode-and-forward mixed RF/FSO system over κ—μ shadowed and exponentiated Weibull fading. Optics Communications 2019;439:103–111.
- View Article
- Google Scholar
19. Balti E., Guizani M. Mixed RF/FSO cooperative relaying systems with co-channel interference. IEEE Transactions on Communications 2018;66(9):4014–4027.
- View Article
- Google Scholar
20. Upadhya A., Gupta J., Dwivedi V. K., Alouini M. S. Impact of RF I/Q imbalance on interference-limited mixed RF/FSO TWR systems with non-zero boresight error. IEEE Wireless Communications Letters 2020;10(2):416–420.
- View Article
- Google Scholar
21. Pham X. N., et al. Enhancing data rate and energy efficiency of NOMA systems using reconfigurable intelligent surfaces for millimeter-wave communications. Digital Signal Processing 2024;151:104553.
- View Article
- Google Scholar
22. Vo D. T., Nguyen T. N., Le A. T., Phan V. D., Voznak M. Holographic Reconfigurable Intelligent Surface-Aided Downlink NOMA IoT Networks in Short-Packet Communication. IEEE Access 2024;12:65266–65277.
- View Article
- Google Scholar
23. Le AT, Hieu TD, Nguyen TN, Le TL, Nguyen SQ, Voznak M. Physical layer security analysis for RIS-aided NOMA systems with non-colluding eavesdroppers. Computer Communications. 2024;219:194–203.
- View Article
- Google Scholar
24. Le AT, Tran DH, Le CB, Tin PT, Nguyen TN, Ding Z, et al. Power Beacon and NOMA-Assisted Cooperative IoT Networks With Co-Channel Interference: Performance Analysis and Deep Learning Evaluation. IEEE Transactions on Mobile Computing. 2024;23(6):7270–7283.
- View Article
- Google Scholar
25. Ma Yiming, et al. NOMA security scheme based on constellation camouflage and selective mapping. Optics Letters. 2023;48(15):4101–4104. pmid:37527128
- View Article
- PubMed/NCBI
- Google Scholar
26. Timotheou S, Krikidis I. Fairness for Non-Orthogonal Multiple Access in 5G Systems. IEEE Signal Processing Letters. 2015;22(10):1647–1651
- View Article
- Google Scholar
27. Ding Z, Lei X, Karagiannidis GK, Schober R, Yuan J, Bhargava VK. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends. IEEE Journal on Selected Areas in Communications. 2017;35(10):2181–2195.
- View Article
- Google Scholar
28. Rose K, Eldridge S, Chapin L. The internet of things: An overview. The internet society (ISOC). 2015;80(15):1–53.
- View Article
- Google Scholar
29. Li S, Xu LD, Zhao S. The internet of things: a survey. Information systems frontiers. 2015;17:243–259.
- View Article
- Google Scholar
30. Hu L, et al. Cooperative Jamming for Physical Layer Security Enhancement in Internet of Things. IEEE Internet of Things Journal. 2018;5(1):219–228.
- View Article
- Google Scholar
31. Rappaport TS. Wireless Communications: Principles and Practice. Prentice Hall; 2002.
32. Zhang L, et al. Layered-Division- Multiplexing: Theory and Practice. IEEE Transactions on Broadcasting. 2016;62(1):216–232.
- View Article
- Google Scholar
33. Qi T, Feng W, Wang Y. Outage performance of non-orthogonal multiple access based unmanned aerial vehicles satellite networks. China Communications. 2018;15(5):1–8.
- View Article
- Google Scholar
34. Sohail MF, Leow CY, Won S. Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Assisted Communication. IEEE Access. 2018;6:22716–22727.
- View Article
- Google Scholar
35. Yan X, et al. The Application of Power-Domain Non-Orthogonal Multiple Access in Satellite Communication Networks. IEEE Access. 2019;7:63531–63539.
- View Article
- Google Scholar
36. Nguyen NT, Nguyen HN, Nguyen NL, Le AT, Nguyen TN, Voznak M. Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology. IEEE Access. 2023;11:10696–10707.
- View Article
- Google Scholar
37. Cheng Y, Li KH, Liu Y, Teh KC, Karagiannidis GK. Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces. IEEE Transactions on Wireless Communications. 2021;20(11):7184–7195.
- View Article
- Google Scholar
38. Phu LS, et al. Improving the Capacity of NOMA Network Using Multiple Aerial Intelligent Reflecting Surfaces. IEEE Access. 2023;11:107958–107971.
- View Article
- Google Scholar
39. Najafi M, Jamali V, Diamantoulakis PD, Karagiannidis GK, Schober R. Non- orthogonal multiple access for FSO backhauling. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC); 2018. p. 1–6.
40. Li R, Dang A. Performance analysis of non-orthogonal multiple access in free space optical communication system. arXiv preprint arXiv:170706571. 2017;
41. Lei X, Yang L, Zhang J, Li G, Chen J. LAP-Based FSO-RF Cooperative NOMA Systems. In: 2020 IEEE 92nd Vehicular Technology Conference; 2020. p. 1–5.
42. Jamali MV, Mahdavifar H. Uplink Non-Orthogonal Multiple Access Over Mixed RF-FSO Systems. IEEE Transactions on Wireless Communications. 2020;19(5):3558–3574.
- View Article
- Google Scholar
43. Ben Halima N, Boujemaa H. Optimal power allocation and harvesting duration for mixed RF/FSO using non orthogonal multiple access. Optical and Quantum Electronics. 2020;52(10):442.
- View Article
- Google Scholar
44. Li R, Chen T, Fan L, Dang A. Performance analysis of a multiuser dual-hop amplify- and-forward relay system with FSO/RF links. Journal of Optical Communications and Networking. 2019;11(7):362–370.
- View Article
- Google Scholar
45. Nguyen ND, Le AT. Employing Non-Orthogonal Multiple Access for A Dual-Hop Relaying System With FSO/RF Links. In: 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS); 2021. p. 346–351.
46. Zhuang Y, Zhang J. Secrecy performance analysis for a NOMA based FSO-RF system with imperfect CSI. Journal of Optical Communications and Networking. 2022;14(7):500–510.
- View Article
- Google Scholar
47. Samir A, Elsayed M, El-Banna AAA, Shafique Ansari I, Rabie K, ElHalawany BM. Performance analysis of dual-hop hybrid RF-UOWC NOMA systems. Sensors. 2022;22(12):4521. pmid:35746302
- View Article
- PubMed/NCBI
- Google Scholar
48. Zhang J, Zhang L, Pan G. Outage Performance for NOMA-Based FSO-RF Systems With a Dual Energy Harvesting Mode. IEEE Internet of Things Journal. 2023;10(18):16076–16086.
- View Article
- Google Scholar
49. Zhang L, Zhang J, Hu N, Li X, Pan G. Outage Performance for NOMA- Based FSO-RF Systems With Transmit Antenna Selection and Nonlinear Energy Harvesting. IEEE Internet of Things Journal. 2023;10(7):6491–6506.
- View Article
- Google Scholar
50. Kumar R, Shukla MK, Kumar V, Tripathi R. Dual-Hop Mixed FSO-RF Wireless Powered Relaying System: Performance Analysis With Nonlinear Energy Harvest- ing. IEEE Transactions on Green Communications and Networking. 2024; p. 1–1.
- View Article
- Google Scholar
51. Zhou J, Wang R, Shihada B, Alouini MS. End-to-End Uplink Perfor- mance Analysis of Satellite-Based IoT Networks: A Stochastic Geometry Ap- proach. IEEE Open Journal of the Communications Society. 2024;5:4036–4045.
- View Article
- Google Scholar
52. Sun Q, Hu Q, Wu Y, Chen X, Zhang J, L´opez-Ben´ıtez M. Performance Analysis of Mixed FSO/RF System for Satellite-Terrestrial Relay Network. IEEE Transactions on Vehicular Technology. 2024;73(8):11378–11393.
- View Article
- Google Scholar
53. Hoang TM, Nguyen BC, Dung LT, Kim T. Outage Performance of Multi-Antenna Mobile UAV-Assisted NOMA Relay Systems Over Nakagami-m Fading Channels. IEEE Access. 2020;8:215033–215043.
- View Article
- Google Scholar
54. Chatzidiamantis ND, Karagiannidis GK. On the Distribution of the Sum of Gamma-Gamma Variates and Applications in RF and Optical Wireless Com- munications. IEEE Transactions on Communications. 2011;59(5):1298–1308.
- View Article
- Google Scholar
55. Zhang J, Dai L, Zhang Y, Wang Z. Unified Performance Analysis of Mixed Radio Frequency/Free-Space Optical Dual-Hop Transmission Systems. Journal of Lightwave Technology. 2015;33(11):2286–2293.
- View Article
- Google Scholar
56. Navidpour SM, Uysal M, Kavehrad M. BER Performance of Free-Space Optical Transmission with Spatial Diversity. IEEE Transactions on Wireless Communications. 2007;6(8):2813–2819.
- View Article
- Google Scholar
57. NAKAGAMI M. The m-Distribution—A General Formula of Intensity Distribution of Rapid Fading. In: HOFFMAN WC, editor. Statistical Methods in Radio Wave Propagation. Pergamon; 1960. p. 3–36.
58. Uysal M. Diversity analysis of space-time coding in cascaded Rayleigh fading channels. IEEE Communications Letters. 2006;10(3):165–167.
- View Article
- Google Scholar
59. Tsiftsis T. Performance of heterodyne wireless optical communication systems over gamma-gamma atmospheric turbulence channels. Electronics Letters. 2008;44(5):1.
- View Article
- Google Scholar
60. Gradshteyn IS, Ryzhik IM. Table of integrals, series, and products. Academic press; 2014.
61. Do DT, Le CB, Vahid A, Mumtaz S. Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems. IEEE Internet of Things Journal. 2023;10(9):8014–8030.
- View Article
- Google Scholar
62. Zhao B, Zhang C, Yi W, Liu Y. Ergodic Rate Analysis of STAR-RIS Aided NOMA Systems. IEEE Communications Letters. 2022;26(10):2297–2301.
- View Article
- Google Scholar
63. Lei X, Yang L, Zhang J, Li G, Chen J. LAP-Based FSO-RF Cooperative NOMA Systems. In: 2020 IEEE 92nd Vehicular Technology Conference; 2020. p. 1–5.
64. Dao TTT, Nguyen SQ, Nhung-Nguyen H, Nguyen PX, Kim YH. Performance Evaluation of Downlink Multiple Users NOMA-Enable UAV-Aided Communication Systems Over Nakagami-m Fading Environments. IEEE Access. 2021;9:151641–151653.
- View Article
- Google Scholar
65. Do TN, Kaddoum G, Nguyen TL, da Costa DB, Haas ZJ. Multi- RIS-Aided Wireless Systems: Statistical Characterization and Performance Analysis. IEEE Transactions on Communications. 2021;69(12):8641–8658.
- View Article
- Google Scholar

[ref1] 1. Fazio P., Sottile C., Santamaria A. F., Tropea M. Vehicular networking enhancement and multi-channel routing optimization, based on multi-objective metric and minimum spanning tree. Advances in Electrical and Electronic Engineering 2013;11(5):349–356.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Fazio, P., Tropea, M., Veltri, F., Marano, S. A new routing protocol for interference and path-length minimization in vehicular networks. In 2012 IEEE 75th Vehicular Technology Conference (VTC Spring); 2012 May;1-5; Yokohama, Japan: IEEE.

[ref3] 3. Khalighi M. A., Uysal M. Survey on free space optical communication: A communication theory perspective. IEEE communications surveys & tutorials 2013;16(4):2231–2258.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref4] 4. Anbarasi K., Hemanth C., Sangeetha R. G. A review on channel models in free space optical communication systems. Optics & Laser Technology 2017;97:161–171.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref5] 5. Garlinska M., Pregowska A., Gutowska I., Osial M., Szczepanski J. Experimental study of the free space optics communication system operating in the 8–12 μm spectral range. Electronics 2021;10(8):875.
View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref6] 6. Wang Y., Xu , et al. Performance analysis of an adaptive optics system for free-space optics communication through atmospheric turbulence. Scientific reports 2018;8(1):1124. pmid:29348561
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref7] 7. Ullah Rahat, et al. High-capacity free space optics-based passive optical network for 5g front-haul deployment. Photonics 2023;10(10):1073.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref8] 8. Xu G., Song Z. Effects of solar scintillation on deep space communications: challenges and prediction techniques. IEEE Wireless Communications 2019;26(2):10–16.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref9] 9. Kaushal H., Kaddoum G Optical communication in space: Challenges and mitigation techniques. IEEE communications surveys & tutorials 2016;19(1):57–96.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref10] 10. Ullah Rahat, et al. Optimization and analysis of Spectral/Spatial optical code division multiple access passive optical network. AEU-International Journal of Electronics and Communications 2024;175:155084.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref11] 11. Nguyen T. N., Duy T. T., Luu G. T., Tran P. T., Vozňák M. Energy harvesting-based spectrum access with incremental cooperation, relay selection and hardware noises. Radioengineering 2017;26(1):240–250.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref12] 12. Liang H., Gao C., Li Y., Miao M., Li X. Performance analysis of mixed MISO RF/SIMO FSO relaying systems. Optics Communications 2021;478:126344.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref13] 13. Petkovic M. I., Ansari I. S., Djordjevic G. T., Qaraqe K. A. Error rate and ergodic capacity of RF-FSO system with partial relay selection in the presence of pointing errors. Optics Communications 2019;438:118–125.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref14] 14. Zedini E., Ansari I. S., Alouini M. S. Performance analysis of mixed Nakagami-m and Gamma–Gamma dual-hop FSO transmission systems. IEEE Photonics Journal 2014;7(1):1–20.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref15] 15. Jamali M. V., Mahdavifar H. Uplink non-orthogonal multiple access over mixed RF-FSO systems. IEEE Transactions on Wireless Communications 2020;19(5):3558–3574.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref16] 16. Yang L., Liu T., Chen J., Alouini M. S. Physical-Layer Security for Mixed η–μ and -Distribution Dual-Hop RF/FSO Systems. IEEE transactions on vehicular technology 2018;67(12):12427–12431.
View Article
Google Scholar

[46] View Article

[47] Google Scholar

[ref17] 17. Wang Z., Shi W., Liu W. Performance analysis of mixed RF/FSO system with spatial diversity. Optics Communications 2019;443:230–237.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref18] 18. Yi X., Shen C., Yue P., Wang Y., Ao Q. Performance of decode-and-forward mixed RF/FSO system over κ—μ shadowed and exponentiated Weibull fading. Optics Communications 2019;439:103–111.
View Article
Google Scholar

[52] View Article

[53] Google Scholar

[ref19] 19. Balti E., Guizani M. Mixed RF/FSO cooperative relaying systems with co-channel interference. IEEE Transactions on Communications 2018;66(9):4014–4027.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref20] 20. Upadhya A., Gupta J., Dwivedi V. K., Alouini M. S. Impact of RF I/Q imbalance on interference-limited mixed RF/FSO TWR systems with non-zero boresight error. IEEE Wireless Communications Letters 2020;10(2):416–420.
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref21] 21. Pham X. N., et al. Enhancing data rate and energy efficiency of NOMA systems using reconfigurable intelligent surfaces for millimeter-wave communications. Digital Signal Processing 2024;151:104553.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref22] 22. Vo D. T., Nguyen T. N., Le A. T., Phan V. D., Voznak M. Holographic Reconfigurable Intelligent Surface-Aided Downlink NOMA IoT Networks in Short-Packet Communication. IEEE Access 2024;12:65266–65277.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref23] 23. Le AT, Hieu TD, Nguyen TN, Le TL, Nguyen SQ, Voznak M. Physical layer security analysis for RIS-aided NOMA systems with non-colluding eavesdroppers. Computer Communications. 2024;219:194–203.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref24] 24. Le AT, Tran DH, Le CB, Tin PT, Nguyen TN, Ding Z, et al. Power Beacon and NOMA-Assisted Cooperative IoT Networks With Co-Channel Interference: Performance Analysis and Deep Learning Evaluation. IEEE Transactions on Mobile Computing. 2024;23(6):7270–7283.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref25] 25. Ma Yiming, et al. NOMA security scheme based on constellation camouflage and selective mapping. Optics Letters. 2023;48(15):4101–4104. pmid:37527128
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref26] 26. Timotheou S, Krikidis I. Fairness for Non-Orthogonal Multiple Access in 5G Systems. IEEE Signal Processing Letters. 2015;22(10):1647–1651
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref27] 27. Ding Z, Lei X, Karagiannidis GK, Schober R, Yuan J, Bhargava VK. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends. IEEE Journal on Selected Areas in Communications. 2017;35(10):2181–2195.
View Article
Google Scholar

[80] View Article

[81] Google Scholar

[ref28] 28. Rose K, Eldridge S, Chapin L. The internet of things: An overview. The internet society (ISOC). 2015;80(15):1–53.
View Article
Google Scholar

[83] View Article

[84] Google Scholar

[ref29] 29. Li S, Xu LD, Zhao S. The internet of things: a survey. Information systems frontiers. 2015;17:243–259.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref30] 30. Hu L, et al. Cooperative Jamming for Physical Layer Security Enhancement in Internet of Things. IEEE Internet of Things Journal. 2018;5(1):219–228.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref31] 31. Rappaport TS. Wireless Communications: Principles and Practice. Prentice Hall; 2002.

[ref32] 32. Zhang L, et al. Layered-Division- Multiplexing: Theory and Practice. IEEE Transactions on Broadcasting. 2016;62(1):216–232.
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref33] 33. Qi T, Feng W, Wang Y. Outage performance of non-orthogonal multiple access based unmanned aerial vehicles satellite networks. China Communications. 2018;15(5):1–8.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref34] 34. Sohail MF, Leow CY, Won S. Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Assisted Communication. IEEE Access. 2018;6:22716–22727.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref35] 35. Yan X, et al. The Application of Power-Domain Non-Orthogonal Multiple Access in Satellite Communication Networks. IEEE Access. 2019;7:63531–63539.
View Article
Google Scholar

[102] View Article

[103] Google Scholar

[ref36] 36. Nguyen NT, Nguyen HN, Nguyen NL, Le AT, Nguyen TN, Voznak M. Performance Analysis of NOMA-Based Hybrid Satellite-Terrestrial Relay System Using mmWave Technology. IEEE Access. 2023;11:10696–10707.
View Article
Google Scholar

[105] View Article

[106] Google Scholar

[ref37] 37. Cheng Y, Li KH, Liu Y, Teh KC, Karagiannidis GK. Non-Orthogonal Multiple Access (NOMA) With Multiple Intelligent Reflecting Surfaces. IEEE Transactions on Wireless Communications. 2021;20(11):7184–7195.
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref38] 38. Phu LS, et al. Improving the Capacity of NOMA Network Using Multiple Aerial Intelligent Reflecting Surfaces. IEEE Access. 2023;11:107958–107971.
View Article
Google Scholar

[111] View Article

[112] Google Scholar

[ref39] 39. Najafi M, Jamali V, Diamantoulakis PD, Karagiannidis GK, Schober R. Non- orthogonal multiple access for FSO backhauling. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC); 2018. p. 1–6.

[ref40] 40. Li R, Dang A. Performance analysis of non-orthogonal multiple access in free space optical communication system. arXiv preprint arXiv:170706571. 2017;

[ref41] 41. Lei X, Yang L, Zhang J, Li G, Chen J. LAP-Based FSO-RF Cooperative NOMA Systems. In: 2020 IEEE 92nd Vehicular Technology Conference; 2020. p. 1–5.

[ref42] 42. Jamali MV, Mahdavifar H. Uplink Non-Orthogonal Multiple Access Over Mixed RF-FSO Systems. IEEE Transactions on Wireless Communications. 2020;19(5):3558–3574.
View Article
Google Scholar

[117] View Article

[118] Google Scholar

[ref43] 43. Ben Halima N, Boujemaa H. Optimal power allocation and harvesting duration for mixed RF/FSO using non orthogonal multiple access. Optical and Quantum Electronics. 2020;52(10):442.
View Article
Google Scholar

[120] View Article

[121] Google Scholar

[ref44] 44. Li R, Chen T, Fan L, Dang A. Performance analysis of a multiuser dual-hop amplify- and-forward relay system with FSO/RF links. Journal of Optical Communications and Networking. 2019;11(7):362–370.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref45] 45. Nguyen ND, Le AT. Employing Non-Orthogonal Multiple Access for A Dual-Hop Relaying System With FSO/RF Links. In: 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS); 2021. p. 346–351.

[ref46] 46. Zhuang Y, Zhang J. Secrecy performance analysis for a NOMA based FSO-RF system with imperfect CSI. Journal of Optical Communications and Networking. 2022;14(7):500–510.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref47] 47. Samir A, Elsayed M, El-Banna AAA, Shafique Ansari I, Rabie K, ElHalawany BM. Performance analysis of dual-hop hybrid RF-UOWC NOMA systems. Sensors. 2022;22(12):4521. pmid:35746302
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref48] 48. Zhang J, Zhang L, Pan G. Outage Performance for NOMA-Based FSO-RF Systems With a Dual Energy Harvesting Mode. IEEE Internet of Things Journal. 2023;10(18):16076–16086.
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref49] 49. Zhang L, Zhang J, Hu N, Li X, Pan G. Outage Performance for NOMA- Based FSO-RF Systems With Transmit Antenna Selection and Nonlinear Energy Harvesting. IEEE Internet of Things Journal. 2023;10(7):6491–6506.
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref50] 50. Kumar R, Shukla MK, Kumar V, Tripathi R. Dual-Hop Mixed FSO-RF Wireless Powered Relaying System: Performance Analysis With Nonlinear Energy Harvest- ing. IEEE Transactions on Green Communications and Networking. 2024; p. 1–1.
View Article
Google Scholar

[140] View Article

[141] Google Scholar

[ref51] 51. Zhou J, Wang R, Shihada B, Alouini MS. End-to-End Uplink Perfor- mance Analysis of Satellite-Based IoT Networks: A Stochastic Geometry Ap- proach. IEEE Open Journal of the Communications Society. 2024;5:4036–4045.
View Article
Google Scholar

[143] View Article

[144] Google Scholar

[ref52] 52. Sun Q, Hu Q, Wu Y, Chen X, Zhang J, L´opez-Ben´ıtez M. Performance Analysis of Mixed FSO/RF System for Satellite-Terrestrial Relay Network. IEEE Transactions on Vehicular Technology. 2024;73(8):11378–11393.
View Article
Google Scholar

[146] View Article

[147] Google Scholar

[ref53] 53. Hoang TM, Nguyen BC, Dung LT, Kim T. Outage Performance of Multi-Antenna Mobile UAV-Assisted NOMA Relay Systems Over Nakagami-m Fading Channels. IEEE Access. 2020;8:215033–215043.
View Article
Google Scholar

[149] View Article

[150] Google Scholar

[ref54] 54. Chatzidiamantis ND, Karagiannidis GK. On the Distribution of the Sum of Gamma-Gamma Variates and Applications in RF and Optical Wireless Com- munications. IEEE Transactions on Communications. 2011;59(5):1298–1308.
View Article
Google Scholar

[152] View Article

[153] Google Scholar

[ref55] 55. Zhang J, Dai L, Zhang Y, Wang Z. Unified Performance Analysis of Mixed Radio Frequency/Free-Space Optical Dual-Hop Transmission Systems. Journal of Lightwave Technology. 2015;33(11):2286–2293.
View Article
Google Scholar

[155] View Article

[156] Google Scholar

[ref56] 56. Navidpour SM, Uysal M, Kavehrad M. BER Performance of Free-Space Optical Transmission with Spatial Diversity. IEEE Transactions on Wireless Communications. 2007;6(8):2813–2819.
View Article
Google Scholar

[158] View Article

[159] Google Scholar

[ref57] 57. NAKAGAMI M. The m-Distribution—A General Formula of Intensity Distribution of Rapid Fading. In: HOFFMAN WC, editor. Statistical Methods in Radio Wave Propagation. Pergamon; 1960. p. 3–36.

[ref58] 58. Uysal M. Diversity analysis of space-time coding in cascaded Rayleigh fading channels. IEEE Communications Letters. 2006;10(3):165–167.
View Article
Google Scholar

[162] View Article

[163] Google Scholar

[ref59] 59. Tsiftsis T. Performance of heterodyne wireless optical communication systems over gamma-gamma atmospheric turbulence channels. Electronics Letters. 2008;44(5):1.
View Article
Google Scholar

[165] View Article

[166] Google Scholar

[ref60] 60. Gradshteyn IS, Ryzhik IM. Table of integrals, series, and products. Academic press; 2014.

[ref61] 61. Do DT, Le CB, Vahid A, Mumtaz S. Antenna Selection and Device Grouping for Spectrum-Efficient UAV-Assisted IoT Systems. IEEE Internet of Things Journal. 2023;10(9):8014–8030.
View Article
Google Scholar

[169] View Article

[170] Google Scholar

[ref62] 62. Zhao B, Zhang C, Yi W, Liu Y. Ergodic Rate Analysis of STAR-RIS Aided NOMA Systems. IEEE Communications Letters. 2022;26(10):2297–2301.
View Article
Google Scholar

[172] View Article

[173] Google Scholar

[ref63] 63. Lei X, Yang L, Zhang J, Li G, Chen J. LAP-Based FSO-RF Cooperative NOMA Systems. In: 2020 IEEE 92nd Vehicular Technology Conference; 2020. p. 1–5.

[ref64] 64. Dao TTT, Nguyen SQ, Nhung-Nguyen H, Nguyen PX, Kim YH. Performance Evaluation of Downlink Multiple Users NOMA-Enable UAV-Aided Communication Systems Over Nakagami-m Fading Environments. IEEE Access. 2021;9:151641–151653.
View Article
Google Scholar

[176] View Article

[177] Google Scholar

[ref65] 65. Do TN, Kaddoum G, Nguyen TL, da Costa DB, Haas ZJ. Multi- RIS-Aided Wireless Systems: Statistical Characterization and Performance Analysis. IEEE Transactions on Communications. 2021;69(12):8641–8658.
View Article
Google Scholar

[179] View Article

[180] Google Scholar

Figures

Abstract

1 Introduction

1.1 Related work

1.2 Contributions

1.3 Overview

2 System model and channel characteristics

2.1 FSO link

2.2 RF link

3 Performance analysis

3.1 Channel model

3.1.1 Fading statistics of the FSO channel.

3.2 Fading statistics of the RF channel

4 Outage probability analysis

4.1 Outage probability of D1

4.2 Outage probability of D2

4.3 High SNR regime

5 Numerical results

6 Conclusion

Supporting information

S1 Appendix. Proof of Proposition 1.

S2 Appendix. Proof of Proposition 2.

S1 File.

References

4.1 Outage probability of D₁

4.2 Outage probability of D₂