I Introduction
Unmanned aerial vehicle (UAV) communications, standing as a supplementary communication scenario in the next generation communication systems and beyond, have received considerable attention in recent years [2], such as industry standardization third Generation Partnership Project LongTerm Evolution Advanced (3GPPLTEA) standard, the fifth generation (5G) New Radio standard, and the next general digital TV standard (ATSC 3.0) [3]. UAV communication is capable of providing access services where there is a temporary need for network resources, i.e., during temporary events and after disasters in the remote areas for AirtoGround (A2G) communications and AirtoAir (A2A) communications [4, 5]. UAV communications differ significantly from conventional ground base station (BS) communications in terms of the mobility, energy constraint, user distributions, as well as the largescale and smallscale propagations [2, 6]. Compared to conventional BS communications, UAV communication is capable of offering stronger received power to users because of the existence of lineofsight (LoS) propagation between UAV and users. These advantages have stimulated interest in the design of UAV communication protocols for effectively utilizing the resources of UAV networks.
With significant advancements in multiple technologies, nonorthogonal multiple access (NOMA) has been recently recognized as a promising solution to realize the performance requirements of nextgeneration mobile networks and beyond, i.e., enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC) [7, 8, 9, 10]. More specifically, in contrast to the conventional orthogonal multiple access (OMA) techniques, NOMA is capable of exploiting the available resources more efficiently by providing enhanced spectrum efficiency and massive connectivity on the specific channel conditions of users [11], and it is capable of serving multiple users at different qualityofservice (QoS) requirements in the same resource block for both eMBB and MTC networks [12, 7]. To be more clear, NOMA technique sends the signal to multiple users simultaneously by power domain multiplexing within the same frequency, time and code block. The basic principles of NOMA techniques rely on the employment of superposition coding (SC) at the transmitter (Tx) and successive interference cancelation (SIC) techniques at the receiver (Rx) [13], and hence multiple accessed Rxs can be realized in the power domain via different power levels for Rxs in the same resource block.
Ia Motivations and Prior Work
Previous research in A2G networks [14, 15] mainly considered that multiple terrestrial Rxs were located in a disc on the ground, whereas A2A networks [5, 16] mainly considered that multiple UAVs were located in a disc in the sky with the same height. The distinctive channel propagations for both A2G and A2A networks were investigated in [6], where different types of smallscale fading channels were summarized to demonstrate the significant differences for channel propagation between UAV communications and conventional BS communications. It is demonstrated that both horizontal distance and vertical distance between UAV and users affect the smallscale fading channels. A downlink A2G network was proposed in [14], where multiple UAVs are distributed in a finite 3D network with Nakagami fading channels. An uniform binomial point process was invoked to model the finite 3D networks. It is also worth noting that Rayleigh fading channel [15], which is a wellknown model in scattering environment, can be also used to model the UAV channel characteristics in the case of large elevation angles in the mixed–urban environment. Generally speaking, Nakagami distribution and Rician distribution are used to approximate the fluctuations in fading channels with LoS propagations. It is also worth noting that the fading parameter of Nakagami fading , the distribution of Nakagami is approximately Rician fading with parameter [17, eq. (3.38)]. Recently, a new probability of LoS scenario was proposed for A2G communications [18], where the existence of LoS propagation is based on the height of the UAV, the horizontal distance between the UAV and users, the carrier frequency and type of environment. For instance, a trajectory design and a power control strategy for multiUAV networks, which were based on position data collected from Twitter, has been proposed in [19]. The simulation results demonstrated that throughput gains of about 17 percent were achieved by applying a Qlearning approach. A UAV assisted cooperative jamming for physical layer security was proposed in [20], where UAV can optimize its trajectory for jamming eavesdroppers. Two possible paradigms for UAV assisted cellular communications were proposed in [21], namely, cellularenabled UAV communication and UAVassisted cellular communication. A multipleinput multipleoutput (MIMO) assisted UAV network was proposed in [22], where the UAV serves multiple users through multibeam simultaneously. Since the number of connected devices may be practically large, which is in need of massive connectivity, new research on UAV under emerging next generation network architectures, i.e., NOMA, is needed.
Integrating NOMA into UAVtoEverything (U2X) networks is considered to be a promising technique to significantly enhance the spectrum efficiency and energy efficiency for UAV communications in the next generation wireless system and beyond, where UAVs are deployed with multiantenna to serve ground users by NOMA [23]. A general introduction of NOMA enhanced UAV communications has been proposed in [2]
. Three case studies, i.e., performance evaluation, joint trajectory design, and machine learning enhanced UAV deployment, were carried out in order to better understand NOMA enabled UAV networks. In UAVenabled wireless communications, the total UAV energy is limited, which includes propulsion energy and communication related energy
[24]. Therefore, integrating UAVs and NOMA into cellular networks is considered to be a promising technique to significantly enhance the performance of terrestrial users in the next generation wireless system and beyond, where the energy efficiency and spectrum efficiency can be greatly enhanced in downlink transmission to minimize communication related energy [25]. A cooperative UAV network was proposed in [26], where multiple UAVs, which are distributed in a 2D disc located in the sky, are used as a flying relay in NOMA assisted wireless backhaul A2A network. A NOMA enhanced multiUAV network was proposed in [27], where the imperfect SIC scenario is taken into account in the largescale cellular A2G networks in order to provide more engineering insights. All the flying UAVs and terrestrial users are located according to 2D HPPPs. A NOMA assisted uplink scenario of UAV communication was proposed in [28], where two special cases, i.e., egoistic and altruistic transmission strategies of the UAV, were considered to derive the optimized solutions. A UAV assisted millimeterwave airtoeverything networks was proposed in [29], where aerial access points provide access services to users located on the ground, air, and tower. The buildings were modeled as a Boolean linesegment process with the fixed height. The outage performance of NOMA downlink transmission in Nakagami fading channels was evaluated in [30], which indicates that NOMA sacrifices the outage performance of the user with poorer channel gain while increasing the outage performance of the user with better channel gain dramatically. The trajectory of movable UAV in both OMA and NOMA scenario was designed in [31], where new algorithms were proposed to maximize the average rate of ground users.The previous contributions [23, 26, 27, 28, 29] mainly focus on NOMA in A2G networks, where multiple Rxs are distributed in the 2D plane. Thus, the performance of NOMA enhanced U2X networks, where Rxs are distributed in a 3D sphere space, is still in its infancy. Todate, to the best of our knowledge, there has been no existing work intelligently investigating the performance of NOMA enhanced U2X frameworks, particularly with the focus of 3D distributed Rxs, which motivates us to develop this treatise. In this article, inspired by the ad hoc networks and D2D networks [32], a U2X framework for intelligently investigating the effect of NOMA enhanced U2X framework performance is desired. The motivation of proposing U2X frameworks is that the U2X framework can be deployed for multiple purposes properly in the next generation wireless systems and beyond, i.e., A2G networks, A2A networks, AirtoVehicular (A2V) networks. In this article, we will develop the first comprehensive model aimed at the downlink analysis of a finite U2X framework using tools from stochastic geometry, which is capable to provide the mathematical paradigm to model the spatial randomness of 3D sphere U2X frameworks.
IB Contributions
This paper focuses on the application of NOMA enhanced 3D sphere U2X frameworks, which is also applicable for A2G, A2A, and A2V communications. Based on the proposed framework, the primary theoretical contributions can be summarized as follows:

We propose a novel NOMA enhanced U2X framework, where stochastic geometry approaches are invoked to model the 3D sphere distributions of Rxs. By utilizing this framework, both LoS and NLoS links are considered to illustrate the general case of NOMA enhanced U2X frameworks.

We derive closedform expressions in terms of outage probability for paired NOMA Rxs in the proposed framework. Both exact results and asymptotic results are derived for obtaining engineering insights. Furthermore, diversity orders are obtained for the paired NOMA Rxs based on the developed outage probability. The obtained results confirm that the diversity order of the proposed framework is determined by the fading parameters .

We derive closedform expressions in terms of ergodic rate for paired NOMA enhanced Rxs. We obtain high SNR slopes for the paired NOMA Rxs based on the developed ergodic rate. The obtained results confirm that the high SNR slopes of NOMA enhanced U2X framework is one for both LoS and NLoS scenarios.

We also derive closedform expressions for OMA scenario in terms of outage probability and ergodic rate. We show that the NOMA enhanced U2X framework has superior performance over OMA enhanced U2X framework. Our Analytical results demonstrate that for the probability of LoS scenario, the outage performance of paired NOMA users mainly depends on the NLoS scenario.
IC Organization and Notations
The rest of the paper is organized as follows. In Section II, a model of U2X transmission framework is investigated in wireless networks, where NOMA technique is invoked. Analytical results are presented in Section III to show the performance of NOMA enhanced U2X frameworks. Our numerical results are demonstrated in Section IV for verifying our analysis, which is followed by the conclusion in Section V.
Ii System Model
Consider a NOMA enhanced U2X downlink communication scenario in which a UAV equipped with a single omni transmitting antenna is communicating with multiple Rxs equipped with a single omni transmitting antenna each. Fig. 1 illustrates the wireless communication model with a single UAV.
Iia System Description
For tractability purpose, the UAV cell coverage space is a sphere, denoted by . The radius of the sphere is , and the TxUAV is located at the center of
. It is assumed that the near Rxs and far Rxs are uniformly distributed according to homogeneous poisson point process (HPPP), which is denoted by
and associated with the density , within small sphere and large hollow sphere with radius and (), respectively. For simplicity, we only focus our attention on investigating a typical Rx pairing in this treatise, where two Rxs, th Rx and th Rx, are grouped to deploy NOMA transmission protocol.IiB Channel Model
Consider the use of a composite channel model with two parts, largescale fading and smallscale fading. denotes the largescale fading, which represents the path loss between the TxUAV and Rx. It is assumed that largescale fading and smallscale fading are independent and identically distributed (i.i.d.). Generally speaking, the largescale fading between the TxUAV and Rxs can be expressed as
(1) 
where denotes the path loss exponent, and the parameter avoids a singularity when the distance is small. For simplicity, it is assumed that the radius of small sphere is grater than , i.e., .
In order to better illustrate the LoS propagation between the TxUAV and Rxs, the probability density functions (PDFs) of smallscale fading is defined by Nakagami
m fading as(2) 
where denotes the fading parameter, and denotes Gamma function. Note that when is an integer. For notation simplicity, and denote the smallscale channel coefficients for the near Rx and the far Rx, respectively.
Thus, the received power for the th Rx from the TxUAV is given by
(3) 
where denotes the transmit power of the TxUAV. Besides, in practical wireless communication systems, obtaining the channel state information (CSI) at the transmitter or receiver is not a trivial problem, which requires the classic pilotbased training process. Therefore, in order to provide more engineering insights, it is assumed that the CSI of UAVs is partly known at the typical user, where only distance information between UAVs and typical user is required.
Iii Performance Evaluations
In this section, we discuss the performance of downlink NOMA enhanced U2X frameworks. In this paper, fixed power allocation is employed at the TxUAV. New channel statistics, outage probabilities, ergodic rates, and spectrum efficiency are illustrated in the following four subsections.
Iiia New Channel Statistics
In this subsection, we derive new channel statistics for NOMA enhanced U2X frameworks, which will be used for evaluating the outage probabilities and ergodic rates in the following subsections.
Lemma 1.
Assuming that Rxs are located according to HPPP in the space of Fig. 1. Therefore, the Rxs are independently and identically distributed in the coverage space, and the PDFs of far Rxs and near Rxs are given by
(4) 
and
(5) 
respectively.
Proof.
According to HPPP, the PDF of the far Rxs can be given by
(6) 
After some algebraic handling, Lemma 1 is proved. ∎
IiiB Outage Probabilities
In this subsection, we first focus on the outage behavior of far Rx , who is the Rx with poorer channel gain. The fixed power allocation strategy is deployed at the TxUAV, which the power allocation factors and are constant during transmission. It is assumed that the target rate of the near Rx and far Rx are and , respectively. Therefore, the outage probability of the th Rx is given by
(7) 
where denotes the additive white Gaussian noise (AWGN) power, and .
Then we turn our attention on calculating the outage probability of the far Rx, which is given in the following Theorem.
Theorem 1.
Assuming that , the closedform expression in terms of outage probability of the far Rx can be expressed as
(8)  
where , , and represents the lower incomplete Gamma function.
Proof.
Please refer to Appendix A. ∎
It is challenging to solve the integral in (8) directly due to the lower incomplete Gamma function. Thus, in order to gain further insights in the high SNR regime, the asymptotic behavior is analyzed, usually when the transmit SNR of the channels between the TxUAV and Rxs is sufficiently high, i.e., when the transmit SNR obeys .
Corollary 1.
Assuming that , and , the asymptotic outage probability of the far Rx is given by
(9)  
Proof.
Please refer to Appendix B. ∎
Remark 1.
The derived results in (9) demonstrate that the outage probability of the far Rx can be decreased in the case of higher fading parameter or decreasing the target rate of the far Rx itself.
Remark 2.
Inappropriate power allocation such as, , will lead to the outage probability always being one.
Proposition 1.
From Corollary 1, one can yield the diversity order by using the high SNR approximation, and the diversity order of the far Rx in the proposed NOMA enhanced U2X frameworks is given by
(10) 
We then attempt to derive the outage probability for a nofading environment by applying the limits . In this case, it is readily to derive that the smallscale fading coefficients of paired NOMA Rxs equal to one, i.e., . For our approach, we observe the asymptotic result of the far Rx in the following corollary.
Corollary 2.
Assuming that , and , the outage probability of the far Rx is given by
(11) 
where .
Proof.
Please refer to Appendix C. ∎
We then turn our attention on the near Rx, and recall that the near Rx needs to decode the signal for the far Rx before decoding its own message via SIC, and the SINR can be given by
(12)  
Then, the outage probability of the near Rx can be derived in the following Theorem.
Theorem 2.
Assuming that , the closedform expression in terms of outage probability of the near Rx can be expressed as
(13)  
where , , and .
Based on the results in (13), we can derive the asymptotic result of the near Rx in the following corollary.
Corollary 3.
Assuming that , and , the asymptotic outage probability of the near Rx is given by
(14)  
Proof.
Please refer to Appendix B, the asymptotic outage probability of the near Rx can be readily proved. ∎
Remark 3.
Following steps similar to the proof in Proposition 1, the diversity order of the near Rx can be obtained, which is also .
It is also worth estimating the outage probability of the near Rx in the nofading environment by applying the limits
. Thus, for our approach, we observe the asymptotic result of the near Rx in the following corollary.Corollary 4.
Assuming that , and , the outage probability of the near Rx is given by
(15) 
where , .
Proof.
Similar to Appendix C, with interchanging with , we can obtain the desired result in (20), and the proof can is complete. ∎
In order to provide more insights for U2X frameworks, the outage probability of the Rxs is also derived in the OMA case, i.e., TDMA. We propose two possible scenarios for the OMA case, where a user is uniformly located in the sphere for the first scenario. Thus, on the one hand, the outage probability of the OMA case can be given by
(16) 
where denotes the smallscale fading in the first OMA scenario, and thus the outage probability can be derived in the following Theorem.
Theorem 3.
The outage probability of the Rx in the first OMA scenario can be expressed as
(17)  
where , denotes the target rate of the OMA Rx, and .
On the other hand, another OMA scenario is also worth estimating, where two OMA Rxs, near Rx and far Rx are located in the small sphere and large hollow sphere with radius and (), respectively. The outage probability for the second OMA scenario can be derived in the following Theorem.
Theorem 4.
The outage probability of both near and far Rxs in the second OMA scenario can be expressed as
(18)  
and
(19)  
where , , and denote the target rates of the near and far OMA Rx, , and .
Since Rx can receive three groups of signals including LoS, strong reflected NLoS signals, and multiple reflected components which cause multipath fading. One common approach for modeling A2G propagation channel is to consider LoS and NLoS components along with their occurrence probabilities separately as shown in [33]. Therefore, in order to provide more engineering insights, and based on the model in [16, 33], a probability of LoS scenario is also provided in the following Proposition.
Proposition 2.
Depending on the occurrence probabilities of LoS propagations, the outage probability conditioned on the probability of LoS of paired NOMA Rxs can be given by
(20) 
and
(21) 
where denotes the LoS probability, and denote the outage probability of the LoS scenario and NLoS scenario, respectively.
IiiC Ergodic Rate
In the U2X frameworks, the ergodic rate is a critical metric, which is worth estimating for performance evaluation. Therefore, we focus on analyzing the ergodic rates of individual U2X Rxs, which are determined by their channel conditions and geometry parameters in the proposed framework. The asymptotic ergodic rate for the near Rx is shown in the following corollary.
Corollary 5.
The achievable ergodic rate of the near Rx can be expressed as follows:
(23)  
where , , , and denotes upper incomplete Gamma function.
Proof.
Please refer to Appendix D. ∎
The ergodic rate of the far Rx is also worth evaluating in the following corollary.
Corollary 6.
The achievable ergodic rate of the far Rx can be expressed as follows:
(24) 
where , and .
Proof.
Similar to Appendix Appendix D: Proof of Corollary 5, by interchanging the upper bound of the intergral, the result in (24) can be readily proved. ∎
Remark 4.
It is proved that the ergodic rates of the far Rxs is entirely dependent on the power allocation factors in the high SNR regime, which is equal to .
Remark 5.
By utilizing the exponential series expansion to the upper incomplete Gamma function, the high SNR slope of the proposed framework for any fading parameters can be readily derived, which are 1 and 0 for near Rxs and far Rxs, respectively.
In order to provide the benchmark of U2X frameworks, we also derive the asymptotic results of achievable ergodic rate in the case of OMA enhanced U2X frameworks in the following corollary.
Corollary 7.
The achievable ergodic rate of the Rx in the first OMA scenario can be expressed as follows:
(25)  
where .
Then, the ergodic rate of Rxs in the second OMA scenario can be derived in the following Corollary.
IiiD Spectrum Efficiency
Based on the analytical results of last two subsections, the spectrum efficiency of the proposed framework can be given in the following Proposition.
Proposition 3.
In the high SNR regime, the spectrum efficiency of the proposed NOMA enhanced U2X frameworks is
(30) 
where is obtained from (23).
We also want to derive the performance gap between NOMA and OMA enhanced U2X frameworks from the perspective of spectrum efficiency, which is given in the following Proposition.
Iv Numerical Studies
In this section, numerical results are provided to facilitate the performance evaluation of NOMA enhanced U2X frameworks. Monte Carlo simulations are conducted to verify analytical results. In the considered network, it is assumed that the power allocation factors are for the far Rx and for the near Rx. The power of AWGN noise is set as dBm. It is also worth noting that LoS and NLoS scenarios are indicated by the Nakagami fading parameter , where for NLoS scenarios (Rayleigh fading) and for LoS scenarios. Without loss of generality, we use to represents LoS scenario in Section IV. In order to avoid infinite received power, the minimum distance .
Iva Outage Probabilities
1) Impact of Fading: In Fig. 2, we evaluate the outage probability of paired NOMA Rxs in both NLoS and LoS scenarios. The solid curves, dashed curves and dotted curves are the exact results, analytical results and asymptotic results, respectively. We can see that, as the power of the UAV increases, the outage probability of both near and far NOMA Rxs decreases. This is due to the fact that, as higher transmit power level of the UAV is deployed, the received SINR improves. It is also confirmed the close agreement between the simulation and analytical results in the high SNR regime, which verifies our analytical results. Note that the slope of curves for both paired NOMA Rxs is , which verifies that the diversity orders of schemes are . This phenomenon validates the insights from Proposition 1 and Remark 3.
2) Impact of PathLoss Exponent: We study the impact of pathloss exponent and target rate and on outage probability in Fig. 3. It can be observed that the outage probability decreases in the case of lower pathloss exponent. Note that the free space model, where the path loss exponent , is also evaluated to provide more engineering insights. It is also worth noting that in Fig. 3, the accuracy of Corollary 1 and Corollary 3 can be confirmed similarly.
3) Outage Sum Rate: Fig. 4 plots the system outage sum rate versus transmit power with different targeted rates and fading parameters. One can observe that the case achieves the highest throughput since it has the lowest outage probability among the three selection fading parameters. The figure also demonstrates the existence of the throughput ceilings in the high SNR region. This is due to the fact that the outage probability is approaching zero and the throughput is determined only by the targeted data rate. It is also worth mentioning that for the case of and , the channel model of the proposed U2X framework can be recognized as free space model.
3) Probability of LoS: Fig. 5 plots the outage probability versus transmit power with different LoS probabilities. The outage probability for the cases of and are plotted as the benchmark schemes. One can readily observe that the outage probability of paired NOMA Rxs mainly depends on the NLoS case even in the case of . We can also see that the diversity order of the proposed probability of the probability of LoS model is one, which indicates that for the case of fixed LoS probability, the outage performance of paired NOMA Rxs mainly depends on the user with poor channel condition.
4) Performance with OMA: In Fig. 6, we evaluate the system outage probability in both NOMA and OMA scenarios versus the target rate of near Rxs and the distance of the small sphere. The outage probability of NOMA enhanced U2X frameworks is derived by . The two users scenario and one user scenario of OMA enhanced U2X frameworks in terms of outage probability are derived by and , respectively. As can be seen from Fig. 6, the outage probability of NOMA enhanced U2X framework is lower than the OMA enhanced U2X frameworks, which implies that NOMA enhanced U2X frameworks is capable of providing better access services than OMA.
IvB Ergodic Rates
5) Impact of fading: Fig. 7 compares the ergodic rates of paired NOMA Rxs versus transmit power with different fading parameters. Several observations can be drawn as follows: 1) An ergodic rate ceiling for far Rxs exists even if the transmit power goes to infinity. This is because that the power allocation factors are the dominant components of far Rxs in terms of ergodic rate. 2) The solid curves, dashed curves and triangles show the precise agreement between the exact results, asymptotic results and simulations, which verify our results. 3) As can be seen from the figure, the high SNR slope of the near Rxs is one, which also verifies Remark 5. 4) The ergodic rate of near Rxs with LoS link is higher than the NLoS case. This is because the LoS propagation increases the received power level, which increases the ergodic rate of near Rxs.
6) Spectrum Efficiency: Fig. 8 plots the spectrum efficiency of the proposed U2X frameworks with NOMA and OMA versus transmit power. The curves representing the performance of NOMA enhanced U2X frameworks are from (30). The performance of OMA enhanced U2X frameworks is illustrated as a benchmark to demonstrate the effectiveness of our proposed framework. It is can be observed that the spectrum efficiency of U2X frameworks improves as the transmit power increases. It is also worth noting that the performance of NOMA enhanced U2X frameworks outperforms the conventional OMA enhanced U2X frameworks, which in turn enhances the spectrum efficiency of the whole frameworks.
7) High SNR Slope: Fig. 9 plots the high SNR slope of paired NOMA Rxs versus transmit power. It is observed that the high SNR slope of far Rxs and near Rxs goes to zero and one, respectively. This behavior can be explained as follows. The ergodic rate of far Rxs, which changes slightly in the high SNR regime, is entirely determined by the power allocation factors. Thus, in the low SNR regime, as transmit power increases, the high SNR slope of far Rxs is increased. For near Rxs, the SNR slope increases monotonously, which shows the high SNR slope for near Rxs is one in the high SNR regime. Another insight is that the LoS link accelerates the increasing rate and the decreasing rate of the paired NOMA Rxs. As shown in TABLE I, the diversity orders and high SNR slopes of paired Rxs for both NOMA and OMA enhanced U2X framework are summarized to illustrate the comparison between them. In TABLE I, we use “D” and “S” to represent the diversity order and high SNR slope, respectively.
Access Mode  Rx  D  S 
NOMA  Near  m  1 
Far  m  0  
OMA  Near  m  0.5 
Far  m  0.5  
Only one  m  1 
DIVERSITY ORDER AND HIGH SNR SLOPE FOR U2X FRAMEWORKS
V Conclusions
In this article, the application of NOMA enhanced U2X frameworks was proposed. Specifically, stochastic geometry tools were invoked for modeling the space randomness of Rxs. Additionally, new closedform expressions in terms of outage probability and ergodic rate were derived for characterizing the performance in NOMA enhanced U2X frameworks. Diversity orders and high SNR slopes were obtained to evaluate the system performance. The performance of OMA enhanced U2X frameworks were also derived as the benchmark schemes. It was analytically demonstrated that the NOMA enhanced U2X frameworks is capable of outperforming OMA enhanced U2X frameworks. An important future direction is to add the 3D distribution of interference sources to include other interfering U2X clusters by Poisson hard core process.
Appendix A: Proof of Theorem 1
First, the outage probability of the far Rx can be written as follows:
(A.1) 
After some algebraic manipulations, the above outage probability can be rewritten to
(A.2) 
if holds, otherwise .
Recall that the far Rx is located according to a HPPP in the large hollow sphere within the radius and , and the smallscale fading follows Nakagami distribution, the outage probability can be transformed into
(A.3)  
where and denote the horizontal angle and vertical angle between the Rx and the TxUAV.
After some algebraic handling, the outage can be further transformed into
(A.4)  
Appendix B: Proof of Corollary 1
The asymptotic result of the far Rx is worth estimating. In the asymptotic outage probability, the transmit SNR between the TxUAV and Rxs obeys . Recall that , and the outage probability of the far Rx can be approximated at the high transmit SNR regime as follows:
(B.1)  
and the corollary is proved.
Appendix C: Proof of Corollary 2
Applying limits , one can know that and , and thus the outage probability is only affected on the distance of the paired NOMA Rxs. Therefore, we can have the outage probability of the far Rx conditioned on the distance as follows:
(C.1) 
where .
Recall that far Rxs are located in the hollow space between the radius from to , and applying the threshold to the distance distribution, the outage probability of the far Rx can be written to
(C.2) 
After some algebraic manipulations, the result can be transformed into
(C.3) 
and thus, the corollary is proved.
Appendix D: Proof of Corollary 5
The proof start by providing the ergodic rate of the near Rx as follows:
(D.1)  
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