Air-space-ground Internet of Things performance analysis method and system based on SWIPT and NOMA

Abstract

The invention discloses an air-space-ground Internet of Things performance analysis method and system based on SWIPT and NOMA, and belongs to the technical field of air-space-ground integrated Internet of Things. Comprising the following steps: constructing an air-space-ground Internet of Things system model comprising an LEO satellite, an unmanned aerial vehicle relay and a ground user, and considering heterogeneous channel characteristics and node dynamics; deducing a closed-form solution of the outage probability of the system through joint transformation of a moment generation function and a characteristic function of a random variable; a high-order Taylor expansion and moment matching method is adopted to carry out approximation on the closed-form solution, a first-order approximate solution and a second-order approximate solution are obtained, and an upper bound of an approximate error is deduced. According to the method, the problem of accurate modeling of the outage probability under heterogeneous link coupling in the air-space-ground Internet of Things is solved, theoretical preciseness and engineering practicability are both achieved, and an effective tool is provided for network reliability analysis and optimization.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of space-air-ground Internet of Things, and particularly relates to a space-air-ground Internet of Things performance analysis method and system based on SWIPT and NOMA. BACKGROUND

[0002] To realize the vision of the sixth generation of mobile communication ubiquitous connection, the construction of low earth orbit (LEO) satellite communication systems has attracted widespread attention. However, due to the high-speed movement and ultra-long distance transmission of LEO satellites, the path loss is as high as 170 dB or more, which is difficult to meet the demand of low latency and high reliability communication. The International Telecommunication Union report shows that about 30% of remote areas in the world still cannot access the network, and the deployment cost of traditional ground base stations is 6 times that of satellite systems. To address this challenge, unmanned aerial vehicles (UAVs) as a dynamic relay solution have attracted attention. With the flexible characteristics of aerial platforms, UAVs can provide near-distance high-speed access services for ground users as aerial base stations. Therefore, the space-air-ground Internet of Things integrating LEO satellites, UAVs and ground equipment has become an effective way to solve the coverage blind area of ground network and improve the communication service quality in remote areas.

[0003] However, due to the limitation of onboard energy of UAV relay, it is difficult to frequently replace or supplement energy, which significantly affects the communication persistence and network lifetime. At the same time, with the continuous increase in the number of users and the growing demand for communication, traditional orthogonal multiple access technology has been difficult to effectively meet the demand for efficient use of spectrum resources. To address the challenges of energy-limited UAVs and spectrum resource shortage, wireless power transfer (SWIPT) and non-orthogonal multiple access (NOMA) technologies are introduced into the space-air-ground Internet of Things, which can optimize energy management and spectrum allocation while improving the performance of the communication system, further enhancing the overall service quality.

[0004] As one of the core indicators for measuring network reliability, the outage probability can reflect the possibility of communication interruption of the space-air-ground Internet of Things under certain conditions (such as channel fading, energy shortage, interference enhancement, etc.). Therefore, after introducing satellite and UAV cooperation, the three-dimensional multi-layer, heterogeneous and dynamic complex space-air-ground Internet of Things accesses more terminal devices, and it is urgent to analyze the outage probability of the network.

[0005] Existing techniques derive closed-form expressions for the Rayleigh fading model using integral transforms or eigenfunction methods to effectively quantify the probability of communication link outages. Specifically, by introducing the Rayleigh fading model, assuming that the signal strength follows an exponential distribution, a probability generation function or eigenfunction is used to represent the signal distribution characteristics. Then, using integral transforms to analyze link quality, a closed-form expression for the outage probability is obtained.

[0006] Existing technology two provides an in-depth analysis of the outage probability of a SWIPT-NOMA system based on an Independent and Identically Distributed (iid) Rayleigh fading channel. Specifically, this technology establishes a signal reception model for each user under the NOMA decoding sequence and models the user's available power in conjunction with energy harvesting efficiency. Then, under the Rayleigh channel assumption, the probability density function of the user's received signal-to-noise ratio is derived using statistical properties. Based on this, and combined with the definition of outage probability, an approximate expression for the system outage probability is obtained using the Gamma function expansion.

[0007] Existing technology three proposes an air-to-ground channel modeling method based on stochastic geometry, which models the UAV position distribution as a Poisson Point Process (PPP) and derives an expression for the outage probability by combining a ray tracing model. Specifically, firstly, an air-to-ground channel model is established, and the UAV position distribution is described using PPP. Then, the propagation loss of each signal path is estimated using a ray tracing model, thereby deriving the outage probability based on the signal-to-interference-plus-noise ratio (SINR).

[0008] However, the problem with existing technologies is: While existing technology can effectively reduce computational complexity, it is still insufficient in the quantitative analysis of error sources. In particular, the error of the approximate solution is usually verified only through simulation results, lacking rigorous mathematical derivation, making it difficult to guarantee the applicability of the error bound in different scenarios (such as high signal-to-noise ratio and dense user environments).

[0009] Although SWIPT and NOMA technologies have gained widespread attention in their respective fields, research on combining them for application in the space-air-ground Internet of Things (IoT) remains relatively scarce. Existing technologies neglect the coupled impact of energy harvesting and access mechanisms on communication reliability, and lack accurate modeling and theoretical derivation of the outage probability based on SWIPT and NOMA.

[0010] The synergistic effect of heterogeneous links has not been fully considered. Space-air-ground IoT involves three types of nodes: LEO satellites, drones, and ground users. These nodes exhibit significantly different channel characteristics. Existing technology simplifies the three-level link to a two-hop model, neglecting the dynamic mobility of drones and the impact of user distribution on network performance. This leads to a discrepancy between the outage probability expression and the actual system performance.

[0011] The difficulty in solving the above technical problems mainly lies in the complex coupling of multiple dimensions and the challenges of theoretical modeling: First, to address the insufficient quantitative analysis of error sources, existing technologies only verify approximate solution errors through simulation, lacking rigorous mathematical derivation. It is necessary to establish analytical models of error sources (such as Taylor expansion truncation error and moment matching approximation deviation) and derive their boundaries. At the same time, it is necessary to verify the universality of these boundaries in different scenarios such as high signal-to-noise ratio and dense user environments. This involves the accurate calculation of higher-order moments of random variables and the error propagation analysis of nonlinear transformations, which is theoretically quite challenging.

[0012] Secondly, research on the collaborative application of SWIPT and NOMA in the space-air-ground IoT is lacking. It is necessary to simultaneously address the dual impacts of energy harvesting (power division and transmission time slot coupling of SWIPT) and access mechanisms (user pairing and power allocation of NOMA). Existing technologies have not established a joint optimization model for the two, resulting in a lack of theoretical support for the accurate derivation of the interruption probability.

[0013] Finally, the synergistic effect of heterogeneous links has not been fully modeled. Existing technologies simplify the three-level satellite-UAV-user link into a two-hop model, ignoring the impact of UAV dynamic mobility and user distribution on channel characteristics. It is necessary to combine the heterogeneous channel characteristics of satellite-UAV channel (Rician fading) and UAV-user channel (Rayleigh fading) to construct a unified outage probability model that includes dynamic topology and spatial distribution, while also dealing with the complex correlation of multi-node interactions, which further exacerbates the difficulty of theoretical modeling.

[0014] In summary, solving the above problems requires the comprehensive application of multidisciplinary methods such as stochastic process analysis and heterogeneous network modeling, and also requires a balance between theoretical accuracy and engineering feasibility, which places higher demands on technical depth and innovation. Summary of the Invention

[0015] The technical problem to be solved by this invention is to provide a performance analysis method and system for air-space-ground IoT based on SWIPT and NOMA, which addresses the shortcomings of the prior art. This method is used to solve the technical problem that it is difficult to accurately and efficiently model and analyze the system outage probability in air-space-ground IoT due to the dynamic coupling of the three-level heterogeneous links of satellite-UAV-user and the mutual influence of SWIPT energy harvesting and NOMA multiple access.

[0016] The present invention adopts the following technical solution: The performance analysis method for air-space-ground IoT based on SWIPT and NOMA includes the following steps: S1. Construct an air-space-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication technology. The system model includes a low Earth orbit satellite, a drone relay, and multiple users. The low Earth orbit satellite is equipped with multiple antennas and uses power domain NOAMI to transmit signals to the drone relay. The drone relay uses wireless power-carrying communication technology to collect energy from the satellite signals and forward the data to the users. The users are randomly distributed within the drone's trajectory coverage area. S2. Based on the air-space-ground IoT system model obtained in step S1, by introducing the moment generating function and characteristic function of random variables for joint transformation, the closed-form solution of the interruption probability of the air-space-ground IoT system model is derived, where the interruption probability is the probability that the user cannot correctly demodulate the signal. S3. Using a higher-order Taylor expansion and moment matching method, the closed-form solution obtained in step S2 is approximated to obtain a first-order approximation solution and a second-order approximation solution. By analyzing the influence of the higher-order moments of the disturbance term on the signal-to-interference-plus-noise ratio distribution, the upper bound of the approximation error is derived.

[0017] Preferably, a space-air-ground Internet of Things (IoT) system model based on non-orthogonal multiple access and wireless power-carrying communication technology is constructed, specifically as follows: The channel between the satellite and the UAV is a Rician fading channel, which consists of deterministic path loss and small-scale Rayleigh fading components. The channel between the UAV and the user is a Rayleigh fading channel. The drone moves at a constant speed along a pre-set circular trajectory with an angular velocity of ω and a radius of R. The location of the user follows a two-dimensional Poisson point process distribution, with the number of users being U and the user density being λ.

[0018] Preferably, the drone employs SWIPT technology to receive signals. Proportional energy is used for harvesting The remaining The proportional energy is used to demodulate information, and the energy harvesting efficiency is... The UAV selects the antenna that receives the most energy from the satellite's multiple antennas for data transmission.

[0019] Preferably, the first u Signal-to-interference-plus-noise ratio for individual users for:

[0020] in, Forwarding data from drones to the first u Transmit power of each user For drones to the first u Instantaneous channel gain for each user, Forwarding data from drones to the first k Transmit power of each user This represents the noise power spectral density.

[0021] Preferably, the closed-form solution for deriving the interruption probability specifically includes: The probability that the user cannot correctly demodulate the signal is defined as the interruption probability, based on the signal-to-interference-plus-noise ratio (SINR) threshold. The closed-form solution is obtained by calculating the cumulative distribution function of the signal-to-interference-plus-noise ratio. Based on the expected user distribution, the interruption probability is expressed as an analytical expression that includes path loss, Rice factor, and power allocation coefficient.

[0022] Preferably, the interruption probability The closed-form solution is expressed as:

[0023] in, For the number of users, For noise power spectral density, The proportion of the signal received by the drone used for energy harvesting. The total transmission power of the satellite, for Expectations for The maximum value, For the satellite's first i Instantaneous channel gain from the root antenna to the UAV. for , For drones to the first u Distance between users This is the path loss index. For small-scale fading variance, For the satellite's first i The NOMA power allocation factor of the root antenna, This represents the number of antennas.

[0024] Preferably, the method of employing higher-order Taylor expansion and moment matching specifically includes: The channel gain is decomposed into a mean term and a perturbation term, while retaining the first and second moment information; Taylor expansion of the UAV's available transmission power function yields the first-order approximate solution and the second-order approximate solution; By analyzing the influence of the higher-order moments of the disturbance term on the SINR distribution, the upper bound of the error is derived.

[0025] Preferably, the derivation of the upper bound of the error includes: The approximation error is defined as the difference between the closed-form solution and the approximate solution. Based on the disturbance terms of the satellite-UAV link and the UAV-user link, the analytical expression of the upper bound of the error is calculated.

[0026] Preferably, the first-order approximate interruption probability The calculation is as follows:

[0027] Second-order approximate interruption probability The calculation is as follows:

[0028] in, for Expectations The available transmission power for the drone, As an intermediate variable, As an intermediate variable, This is the path loss index. For small-scale fading variance, For noise power spectral density, The signal-to-interference-plus-noise ratio (SINR) threshold. for The variance.

[0029] Secondly, embodiments of the present invention provide a space-air-ground IoT performance analysis system based on SWIPT and NOMA, including: The network module constructs an air-space-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication technology. The system model includes a low Earth orbit satellite, a drone relay, and multiple users. The low Earth orbit satellite is equipped with multiple antennas and uses power domain NOAMI to transmit signals to the drone relay. The drone relay uses wireless power-carrying communication technology to collect energy from the satellite signals and forward the data to the users. The users are randomly distributed within the drone's trajectory coverage area. The derivation module, based on the obtained air-space-ground IoT system model, derives the closed-form solution of the interruption probability of the air-space-ground IoT system model by introducing the moment generating function and characteristic function of random variables for joint transformation. The interruption probability is the probability that the user cannot correctly demodulate the signal. The analysis module uses a higher-order Taylor expansion and moment matching method to approximate the obtained closed-form solution, obtaining a first-order approximate solution and a second-order approximate solution. By analyzing the influence of the higher-order moments of the perturbation term on the signal-to-interference-plus-noise ratio distribution, the upper bound of the approximation error is derived.

[0030] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described air-space-ground IoT performance analysis method based on SWIPT and NOMA.

[0031] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described performance analysis method for the space-air-ground Internet of Things based on SWIPT and NOMA.

[0032] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described air-space-ground IoT performance analysis method based on SWIPT and NOMA.

[0033] Sixthly, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described air-space-ground IoT performance analysis method based on SWIPT and NOMA.

[0034] Compared with the prior art, the present invention has at least the following beneficial effects: A performance analysis method for air-space-ground IoT based on SWIPT and NOMA is proposed. By deeply integrating the SWIPT energy harvesting mechanism with NOMA multiple access technology, a unified performance analysis model for air-space-ground IoT is established. A joint transformation technique using moment generating functions and characteristic functions of random variables effectively solves the problem of accurately deriving the interruption probability under the dynamic coupling of three-level heterogeneous links between satellites, UAVs, and users. The proposed high-order Taylor expansion and moment matching approximation method significantly reduces computational complexity while maintaining theoretical accuracy, achieving a seamless connection from theoretical analysis to engineering applications, and providing key technical support for real-time performance monitoring and dynamic optimization of air-space-ground IoT.

[0035] Furthermore, by precisely defining the Rician fading characteristics of the satellite-UAV link and the Rayleigh fading characteristics of the UAV-user link, a heterogeneous channel model conforming to the actual propagation environment of the air-space-ground IoT was established. The UAV trajectory was quantified as a circular trajectory and angular velocity parameters, overcoming the shortcomings of insufficient UAV dynamic modeling in existing technologies. A Poisson point process was used to describe the user spatial distribution, effectively characterizing the impact of user randomness on system performance, making the established system model closer to the actual deployment scenario, and laying a solid foundation for subsequent accurate performance analysis.

[0036] Furthermore, by employing an optimal antenna selection strategy (selecting the antenna that receives the most energy), the energy harvesting efficiency of the UAV is maximized, effectively solving the problem of continuous communication under conditions of limited airborne energy. This energy management mechanism, in conjunction with NOMA power allocation, forms a synergistic optimization, providing technical support for balancing energy harvesting and information transmission, and significantly improving network lifetime and communication reliability.

[0037] Furthermore, the impact mechanism of multi-user interference in the NOMA system is clearly revealed through a formulaic description. This expression considers multiple factors such as UAV forwarding power, channel gain, and noise power, providing crucial input for the accurate calculation of subsequent outage probabilities. By quantifying the interference relationships between users, a theoretical basis is provided for optimizing power allocation and improving spectral efficiency, enhancing the feasibility of the proposed solution.

[0038] Furthermore, by incorporating the expected user distribution into the analytical expression, the challenge of performance evaluation under stochastic topologies is innovatively solved. The obtained closed-form solution includes key parameters such as path loss, Rice factor, and power allocation coefficient, clearly revealing the influence of each parameter on system reliability. This provides clear theoretical guidance for system parameter optimization and overcomes the limitations of traditional simulation methods in terms of parameter sensitivity analysis.

[0039] Furthermore, by comprehensively considering multi-dimensional factors such as antenna selection, power allocation, and channel characteristics, the system achieves an accurate characterization of complex system behavior. The analytical results provide a direct theoretical tool for system design, allowing engineers to quickly evaluate system performance by adjusting relevant parameters, significantly shortening the design cycle, improving optimization efficiency, and possessing significant engineering application value.

[0040] Furthermore, by preserving the first and second-order moment information, an optimal balance is achieved between computational complexity and accuracy. The Taylor expansion of the UAV's transmitted power function effectively addresses the analytical difficulties arising from nonlinear transformations. This method, through theoretical derivation, replaces traditional Monte Carlo simulation, reducing computation time from hours to seconds, providing crucial technical support for real-time system monitoring and dynamic optimization.

[0041] Furthermore, based on perturbation term modeling of the satellite-UAV and UAV-user links, an analytical expression for the upper bound of the error is derived, theoretically guaranteeing the reliability of the approximation results for the first time. This error bound adaptively adjusts with changes in system parameters, providing a quantitative basis for selecting approximation methods in different application scenarios and ensuring the robustness and reliability of engineering applications.

[0042] Furthermore, by introducing statistics such as the mean channel gain and the expected power of the UAV, an effective approximation of complex probability distributions is achieved. The second-order approximation significantly improves the computational accuracy in the high signal-to-noise ratio region by introducing correction terms, while the first-order approximation maintains sufficient accuracy in low-complexity scenarios. This hierarchical approximation strategy provides flexible choices for application scenarios with different accuracy requirements, satisfying both the accuracy needs of theoretical research and the efficiency requirements of engineering applications.

[0043] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0044] In summary, this invention, through a closed-loop design of dynamic modeling, theoretical derivation, engineering optimization, and experimental verification, combines theoretical rigor with engineering practicality. It not only fills the theoretical gap in weighted and rate optimization of air-to-ground vehicle networks, but also provides quantifiable and adjustable key parameter optimization basis for system deployment, promoting the evolution of intelligent transportation systems towards high reliability and low latency.

[0045] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention; Figure 2 A schematic diagram of an air-space-ground Internet of Things based on SWIPT and NOMA provided for an embodiment of the present invention; Figure 3 The figure shows the curve of the interruption probability as a function of satellite transmission power, provided by the embodiments of the present invention, under different satellite transmission power conditions. Figure 4 The curve showing the absolute value of the interruption probability error as a function of satellite transmission power, provided in this embodiment of the invention, is shown under different satellite transmission power conditions. Figure 5 The present invention provides a curve showing the change in interruption probability with user density under different user density conditions. Figure 6 The curve showing the absolute value of the interruption probability error as a function of user density, provided in this embodiment of the invention, is shown below; Figure 7The curves showing the change in interruption probability as a function of the drone's angular velocity are provided in this embodiment of the invention, under different drone angular velocities. Figure 8 The curve showing the absolute value of the interruption probability error as a function of the drone's angular velocity under different drone angular velocities is provided in the embodiments of the present invention. Figure 9 A comparison of the simulation running times of the closed-form solution, the first-order approximate solution, and the second-order approximate solution provided in the embodiments of the present invention; Figure 10 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 11 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0047] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0048] This invention provides a performance analysis method for air-space-ground IoT based on SWIPT and NOMA. First, based on the least squares (LS) channel estimation method, a joint channel model is constructed, incorporating the three-dimensional motion trajectory of UAVs, vehicle dynamic distribution, co-channel interference, self-interference, and Ricean fading factor. Then, using generalized integrals and infinite series summation operations, an accurate analytical expression for the total channel capacity is derived. Based on this, to address the high computational complexity of the accurate expression, a truncated series approximation method is designed, and a closed-form mapping relationship between the number of truncated terms and the approximation error is established through theoretical analysis. Finally, combined with Monte Carlo simulation verification, the error between the accurate expression and the approximation method is given, and the difference in total capacity between FD-NOMA and FD-OMA schemes is compared and analyzed, quantifying the impact of key parameters (such as the Ricean factor) on the total channel capacity. This invention, by constructing a unified air-space-ground IoT model, innovatively derives a closed-form solution for the outage probability and proposes a controllable approximation method, solving the performance analysis problem under heterogeneous link coupling. It combines theoretical rigor with engineering practicality, providing an accurate and efficient evaluation tool for system design and significantly improving the efficiency of network reliability analysis and optimization.

[0049] Example 1 Please see Figure 1 This invention provides a performance analysis method for air-space-ground Internet of Things (IoT) based on SWIPT and NOMA, comprising the following steps: S1. Taking into account the channel characteristics of satellite-UAV links and UAV-user links, multi-antenna transmission, energy harvesting mechanisms, and multi-user NOMA power allocation, a space-air-ground IoT system model based on non-orthogonal multiple access (NOMA) and wireless power-carrying communication (SWIPT) technologies is constructed. Please see Figure 2 , Figure 2 This is a schematic diagram of an air-space-ground IoT based on SWIPT and NOMA. Specifically, the air-space-ground IoT consists of LEO satellites, drone relays, and... U A user set consists of individual users. A user set is defined as... The satellite downlink transmission is achieved using power domain NOMA technology, and power is provided for UAV relay via SWIPT technology. The LEO satellite, serving as the transmitting end, is equipped with... A Root antenna (antenna set is defined as) The system employs power-domain NOMA technology to transmit signals to multiple users (or relays) on the same time-frequency resource. The receiving end then uses Successive Interference Cancellation (SIC) technology to demodulate the target signal. Furthermore, the UAV acting as a relay is equipped with a single antenna and moves at a constant speed along a pre-set circular trajectory. The UAV's angular velocity is defined as... The radius of motion is R The center of the circular trajectory is The drone uses SWIPT technology to receive signals from satellites. A The antenna that receives the most energy is selected from the root antennas for data demodulation and forwarding. In addition, U Each user is equipped with a single antenna within the drone's trajectory coverage area. D The user's location follows a two-dimensional PPP distribution, therefore the number of users is random. ,in User density (number of users per unit area).

[0050] The channel between the satellite and the UAV is a Rician fading channel, consisting of deterministic path loss (LoS component) and small-scale fading (NLoS component). The satellite's... i root Instantaneous channel gain from antenna to UAV for: (1) in, K The Rice factor is used to characterize the power ratio of the LoS component to the NLoS component. For deterministic path loss; For small-scale Rayleigh fading components, ,in Let be the variance. Because... It follows a Rice distribution, and its probability density function (PDF) is... for: (2) in, This is a modified Bessel function of the first type.

[0051] The channel between the drone and the user is a Rayleigh fading channel, containing only small-scale fading. (Definition) For drones to the first u indivual The user's instantaneous channel gain, its PDF It follows an exponential distribution, represented as: (3) in, ,in For drones to the first u Distance between users This is the path loss index. This represents the small-scale fading variance. Furthermore, when users are uniformly distributed within the trajectory, , .

[0052] The satellite's total transmission power is The power allocation factor is , can be obtained Therefore, the satellite's first i The transmit power of the antenna is The drone uses SWIPT technology to receive signals. Proportional energy is used for harvesting The remaining The proportional energy is used to demodulate information. The energy harvesting efficiency is... (J / W), then the drone starts from the first i Energy rate collected by the root antenna for: (4) The drone selects the one that captures the most energy. Data transmission is performed using a satellite antenna, i.e., selection. ; Choose the first After the antenna, the available transmission power of the drone for: (5) After demodulation, the drone is used in power Forward data to the first uUsers, among whom For the first u NOMA power allocation factor for each user .definition For the first u SINR for the [number] user. For the [number]th user... u For an individual user, its received signal Represented as: (6) in, k For the first k individual users ; and The first u The user and the k Data symbols sent by a user ; For noise, This represents the noise power spectral density.

[0053] According to NOMA's SIC demodulation rules, the first u The user with the highest power signal is demodulated first (assuming the first user is the one whose signal is the highest power signal). u If the user is the user with the highest power, then its SINR is: (7) S2. Based on the obtained air-space-ground IoT system model, by introducing the moment generating function and characteristic function of random variables for joint transformation, the closed-form solution of the air-space-ground IoT interruption probability based on SWIPT and NOMA is derived. Interruption probability Defined as the probability that the user cannot correctly demodulate the signal, i.e. ,in Let be the target signal-to-interference-plus-noise ratio (SIN / N) threshold. Substituting into equation (7), equation (7) simplifies to: (8) when At that time, a transmission interruption will occur in the space-air-ground Internet of Things (IoT), namely: (9) make Then the interruption probability Rewritten as: (10) because ,therefore The cumulative distribution function (CDF) is: Therefore, we can conclude that: (11) in, for The maximum value.

[0054] Furthermore, since the user's location follows a Poisson distribution and the drone's trajectory is circular, the drone will reach the [missing information - likely a specific location or point]. u Distance between users The distribution is a circularly symmetric Rayleigh distribution.

[22] . Expectations for: (12) in, .

[0055] Using the law of large numbers, the impact of user distribution can be approximated by expectation, and the final closed-form solution of the interruption probability is expressed as: (13) S3. Using a combination of higher-order Taylor expansion and moment matching, this paper decomposes complex random variables into mean and disturbance terms based on closed-form solutions, retaining first- and second-order moment information, and proposes first- and second-order approximate solutions. In addition, by analyzing the influence of higher-order moments of the disturbance term on the signal-to-interference-plus-noise ratio (SINR) distribution, the upper bound of the approximation error is derived.

[0056] S301, Approximate Analysis of Interruption Probability Since the interruption probability derived by equation (13) involves complex multiple integrals and joint distribution of random variables, this section proposes an analytical method based on Taylor expansion and dominant term approximation.

[0057] Specifically, channel gain It follows Rician decay and can be further decomposed into: (14) in, The LoS component represents the distance from the satellite to the drone. Related; For NLoS components, we get: (15) also, Calculated as: (16) in, This is the distance from the satellite to the center of the orbital circle; This is the initial angle.

[0058] To simplify the calculation, let ,in For the average Loss component, Z is the maximum value of the NLoS component. .because It is an independent, identically exponentially distributed distribution. Z The CDF and PDF are as follows: (17) Then, according to Z Find the CDF and PDF. Z The mean and variance are as follows: (18) in, is the Euler-Marschroni constant.

[0059] therefore, The mean and variance are as follows: (19) For typical NOMA scenarios , can be obtained .therefore, Can be converted to: (20) in, , , Then, the interrupt condition was rewritten as follows: (twenty one) Combining equations (10) and (21), the interruption probability Approximately: (twenty two) in, According to equation (5), it can be known that It is a random variable, and we need to calculate its expectation. Therefore, in Pair function Performing a Taylor expansion, we get: (twenty three) in, .

[0060] Then, take By approximating the first order and finding its expectation, we get: (twenty four) As mentioned above, the first-order approximate interruption probability The calculation is as follows: (25) In addition, to improve calculation accuracy, consider The second moment. Using Taylor expansion, we can... Expanded to: (26) Substituting equation (26) into And retain the second-order terms, It can be further approximated as: (27) Therefore, the second-order approximate interruption probability The calculation is as follows: (28) In summary, the interruption probability can be approximated using Taylor expansions of arbitrary order, thereby improving the approximation accuracy.

[0061] S302, Error Assessment of Approximate Interruption Probability To evaluate the difference between the approximate solution and the closed-form solution, their difference is... Defined as the approximation error. To derive this error, it is necessary to analyze the disturbance terms introduced by the key random variables in the closed-form solution. Given that the space-air-ground IoT can be divided into two parts, "satellite-UAV link" and "UAV-user link", the disturbance terms in the satellite-UAV link are first modeled and derived.

[0062] Specifically, the transmission power of the UAV is decomposed. From the derived closed-form solution, we can see the available transmission power of the UAV. Therefore, its disturbance term It can be calculated as: (29) Secondly, for the drone-user link, its disturbance term The calculation is as follows: (30) Disturbance term and Substitute into equation (7). Convert to: (31) First-order approximate interruption probability In the middle, a disturbance term is set. and Therefore, first order Approximately: (32) In closed-form solutions, The precise distribution needs to be considered and The joint distribution of the two groups, whose joint PDF is: (33) As mentioned above, the first-order approximate interruption probability Upper bound of the resulting error Represented as: (34) in, For the closed-form solution, SINR is in PDF at the location; and Calculated separately as follows: (35) Furthermore, using a second-order approximation can further reduce the error of the approximate solution. Since the approximation of the interruption probability is based on a Taylor series expansion, therefore... n The error of the first-order approximate solution mainly comes from higher-order solutions. n Rank (i.e.) The neglect of higher-order moments (of order 1 and above) constitutes the main source of error.

[0063] Example 2 A space-air-ground IoT performance analysis system based on SWIPT and NOMA is provided. This system can be used to implement the aforementioned space-air-ground IoT performance analysis method based on SWIPT and NOMA. Specifically, the space-air-ground IoT performance analysis system based on SWIPT and NOMA includes a network module, a derivation module, and an analysis module.

[0064] The network module constructs an air-space-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication technology. The system model includes a low Earth orbit satellite, a drone relay, and multiple users. The low Earth orbit satellite is equipped with multiple antennas and uses power domain NOAMI to transmit signals to the drone relay. The drone relay uses wireless power-carrying communication technology to collect energy from the satellite signals and forward the data to the users. The users are randomly distributed within the drone's trajectory coverage area. The derivation module, based on the obtained air-space-ground IoT system model, derives the closed-form solution of the interruption probability of the air-space-ground IoT system model by introducing the moment generating function and characteristic function of random variables for joint transformation. The interruption probability is the probability that the user cannot correctly demodulate the signal. The analysis module uses a higher-order Taylor expansion and moment matching method to approximate the obtained closed-form solution, obtaining a first-order approximate solution and a second-order approximate solution. By analyzing the influence of the higher-order moments of the perturbation term on the signal-to-interference-plus-noise ratio distribution, the upper bound of the approximation error is derived.

[0065] Example 3 This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for the operation of a space-air-ground IoT performance analysis method based on SWIPT and NOMA, including: A space-air-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication (WPC) technologies is constructed. The system model includes a low-Earth orbit (LEO) satellite, a drone relay, and multiple users. The LEO satellite is equipped with multiple antennas and uses power-domain NOAMI to transmit signals to the drone relay. The drone relay uses WPC to collect energy from the satellite signals and forward data to the users. The users are randomly distributed within the drone's trajectory coverage area. Based on the obtained space-air-ground IoT system model, a closed-form solution for the interruption probability of the model is derived by introducing a joint transformation of the moment generating function and characteristic function of random variables. The interruption probability is the probability that a user cannot correctly demodulate the signal. A higher-order Taylor expansion and moment matching method are used to approximate the obtained closed-form solution, yielding a first-order approximate solution and a second-order approximate solution. Furthermore, by analyzing the influence of the higher-order moments of the disturbance term on the signal-to-interference-plus-noise ratio (SIR) distribution, an upper bound for the approximation error is derived.

[0066] Please see Figure 10The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for estimating the concentration of radioactive iodine species in the containment structure after an accident, as described in this embodiment. To avoid repetition, these methods are not detailed here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the air-space-ground IoT performance analysis system based on SWIPT and NOMA, as described in this embodiment. To avoid repetition, these functions are not detailed here.

[0067] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 10 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0068] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0069] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0070] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0071] Please see Figure 11 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0072] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0073] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0074] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0075] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0076] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0077] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0078] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0079] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0080] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the air-space-ground IoT performance analysis method based on SWIPT and NOMA in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: A space-air-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication (WPC) technologies is constructed. The system model includes a low-Earth orbit (LEO) satellite, a drone relay, and multiple users. The LEO satellite is equipped with multiple antennas and uses power-domain NOAMI to transmit signals to the drone relay. The drone relay uses WPC to collect energy from the satellite signals and forward data to the users. The users are randomly distributed within the drone's trajectory coverage area. Based on the obtained space-air-ground IoT system model, a closed-form solution for the interruption probability of the model is derived by introducing a joint transformation of the moment generating function and characteristic function of random variables. The interruption probability is the probability that a user cannot correctly demodulate the signal. A higher-order Taylor expansion and moment matching method are used to approximate the obtained closed-form solution, yielding a first-order approximate solution and a second-order approximate solution. Furthermore, by analyzing the influence of the higher-order moments of the disturbance term on the signal-to-interference-plus-noise ratio (SIR) distribution, an upper bound for the approximation error is derived.

[0081] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0083] The technical effects of the present invention will be described in detail below with reference to simulation.

[0084] This experiment simulates the performance analysis method of the air-space-ground IoT based on SWIPT and NOMA and an existing mechanism based on the same network parameters to verify the superiority of the method of this invention. The specific steps are as follows: The same network parameters are: , , , , , , , , , .

[0085] In addition, the satellite orbital altitude is 800 km, the UAV flight altitude is 0.3 km, the path loss index of the satellite-UAV link is 1.5, the standard deviation of shadow fading is 8 dB, the path loss index of the UAV-user link is 2.0, and the small-scale fading variance is [missing information]. (Reference distance is 1m), total number of tests: .

[0086] Please see Figure 3 and Figure 4 , Figure 3The figure shows the trend of outage probability as satellite transmission power changes. As can be seen from the figure, the simulated outage probability curve of the space-air-ground IoT matches the corresponding theoretical curve well, verifying the effectiveness of the theoretical analysis in this invention. Moreover, as the satellite transmission power increases, the outage probability gradually decreases, indicating that increasing the satellite transmission power helps improve the successful transmission probability of the space-air-ground IoT, thereby reducing network outages. Furthermore, it was observed that when the satellite transmission power is less than 46 dBm, the first-order and second-order approximation curves almost completely coincide with the theoretical curve, while when the satellite transmission power is greater than 46 dBm, the second-order approximation curve still maintains a high degree of consistency with the theoretical curve. To further verify the rationality of the approximation analysis, Figure 4 The curve showing the absolute value of the interruption probability error as a function of satellite transmission power is presented. Based on the above analysis, Figure 3 and Figure 4 This not only verifies the theoretical validity of the closed-form solution, but also confirms the applicability of the second-order approximate solution in engineering applications, and provides a basis for the applicability of the first-order approximate solution.

[0087] Please see Figure 5 and Figure 6 , Figure 5 The graph shows the trend of interruption probability as a function of user density. It can be observed from the graph that all curves monotonically increase with increasing user density. When user density increases from 70 users / km... 2 Increase to 100 users / km 2 At that time, the outage probability of the space-air-ground IoT system increased from approximately 0.4% to approximately 4.7%, indicating that the impact of multi-user interference on system reliability is particularly significant in dense user scenarios. Meanwhile, the closed-form solution shows a high degree of agreement with the simulation results, verifying its accuracy in statistical modeling of multi-user interference. This closed-form solution effectively characterizes the impact of multi-user interference on the outage probability by incorporating the mean and variance of multi-user interference into the modeling of the SINR distribution.

[0088] also, Figure 6 The curves showing the absolute value of the interruption probability error as a function of user density are presented. The results indicate that the first-order approximation solution, by neglecting the higher-order moments of multi-user interference, exhibits a decreasing accuracy as the interference variance increases at higher user densities, leading to a more discrete SINR distribution. This is because the assumptions of the first-order approximation are difficult to satisfy. In contrast, the second-order approximation solution, by introducing the second-order moments of interference to correct the interruption probability model, demonstrates stronger error control and robustness under varying user density.

[0089] Please see Figure 7 and Figure 8 , Figure 7The graph shows the variation of the outage probability with the drone's angular velocity. As can be seen, the outage probability exhibits a clear trend of first decreasing and then increasing. This is because when the drone's angular velocity is less than 0.03 rad / s, the drone's motion provides time-domain diversity gain, effectively reducing the outage probability. However, when the drone's angular velocity exceeds 0.07 rad / s, the outage probability of the air-to-ground IoT network increases significantly due to the hysteresis effect of channel estimation. Furthermore, Figure 8 The graph shows the absolute value of the interruption probability error as a function of the UAV's angular velocity. It can be seen from the graph that the accuracy of the first-order approximation solution is significantly affected by the UAV's angular velocity, while the accuracy of the second-order approximation solution is relatively stable and less affected by changes in angular velocity.

[0090] Please see Figure 9 , Figure 9 The comparison of simulation runtime for closed-form, first-order approximation, and second-order approximation solutions is presented. As can be seen from the figure, the simulation runtime of the closed-form solution is significantly longer than that of the first-order and second-order approximation solutions. This is because the closed-form solution requires rigorous mathematical derivation to obtain an explicit expression for the interruption probability and involves complex integration and matrix operations, thus maximizing computational complexity in complex scenarios such as high user density or high-speed movement. The first-order approximation solution simplifies computation by truncating higher-order terms, effectively reducing computational complexity and achieving the lowest simulation runtime. However, due to its lower accuracy, the first-order approximation solution is only suitable for low-complexity scenarios. The second-order approximation solution, while retaining the first-order terms, further introduces second-order moment corrections, improving accuracy. Although the second-order approximation solution has a higher computational cost than the first-order approximation solution, it is far less computationally intensive than the closed-form solution, exhibiting a more ideal performance balance between computational accuracy and efficiency, making it the preferred performance analysis scheme.

[0091] In summary, this invention presents a performance analysis method and system for space-air-ground IoT based on SWIPT and NOMA. Using SWIPT and NOMA technologies, it studies the downlink interruption probability of space-air-ground IoT. First, by jointly applying the moment generating function and characteristic function transformation of random variables, a closed-form solution for the interruption probability is derived. Then, to explore the influence of relevant parameters on the interruption probability, first-order and second-order approximate solutions are obtained by combining higher-order Taylor expansion and moment matching methods. Based on this, the influence of higher-order moments of the disturbance term on the SINR distribution is analyzed, and the upper bound of the approximation error is derived. This invention can provide a theoretical basis for the design and optimization of space-air-ground IoT.

[0092] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A performance analysis method for air-space-ground IoT based on SWIPT and NOMA, characterized in that, Includes the following steps: S1. Construct an air-space-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication technology. The system model includes a low Earth orbit satellite, a drone relay, and multiple users. The low Earth orbit satellite is equipped with multiple antennas and uses power domain NOAMI to transmit signals to the drone relay. The drone relay uses wireless power-carrying communication technology to collect energy from the satellite signals and forward the data to the users. The users are randomly distributed within the drone's trajectory coverage area. S2. Based on the air-space-ground IoT system model obtained in step S1, by introducing the moment generating function and characteristic function of random variables for joint transformation, the closed-form solution of the interruption probability of the air-space-ground IoT system model is derived, where the interruption probability is the probability that the user cannot correctly demodulate the signal. S3. Using a higher-order Taylor expansion and moment matching method, the closed-form solution obtained in step S2 is approximated to obtain a first-order approximation solution and a second-order approximation solution. By analyzing the influence of the higher-order moments of the perturbation term on the signal-to-interference-plus-noise ratio distribution, the upper bound of the approximation error is derived.

2. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 1, characterized in that, A model for an air-space-ground Internet of Things (IoT) system based on non-orthogonal multiple access and wireless power-carrying communication technologies is constructed, specifically as follows: The channel between the satellite and the UAV is a Rician fading channel, which consists of deterministic path loss and small-scale Rayleigh fading components. The channel between the UAV and the user is a Rayleigh fading channel. The drone moves at a constant speed along a pre-set circular trajectory, with an angular velocity of... The radius of motion is R ; The location of the user follows a two-dimensional Poisson point process distribution, with the number of users being U and the user density being λ.

3. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 2, characterized in that, The drone uses SWIPT technology to receive signals. Proportional energy is used for harvesting The remaining The proportional energy is used to demodulate information, and the energy harvesting efficiency is... The UAV selects the antenna that receives the most energy from the satellite's multiple antennas for data transmission.

4. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 3, characterized in that, No. u Signal-to-interference-plus-noise ratio for individual users for: in, Forwarding data from drones to the first u Transmit power of each user For drones to the first u Instantaneous channel gain for each user, Forwarding data from drones to the first k Transmit power of each user This represents the noise power spectral density.

5. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 1, characterized in that, The closed-form solution for deriving the interruption probability specifically includes: The probability that the user cannot correctly demodulate the signal is defined as the interruption probability, based on the signal-to-interference-plus-noise ratio (SINR) threshold. The closed-form solution is obtained by calculating the cumulative distribution function of the signal-to-interference-plus-noise ratio. Based on the expected user distribution, the interruption probability is expressed as an analytical expression that includes path loss, Rice factor, and power allocation coefficient.

6. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 5, characterized in that, Interruption probability The closed-form solution is expressed as: in, For the number of users, For noise power spectral density, The proportion of the signal received by the drone used for energy harvesting. The total transmission power of the satellite, for Expectations for The maximum value, For the satellite's first i Instantaneous channel gain from the root antenna to the UAV. for , For drones to the first u Distance between users This is the path loss index. For small-scale fading variance, For the satellite's first i The NOMA power allocation factor of the root antenna, This represents the number of antennas.

7. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 1, characterized in that, The method of employing higher-order Taylor expansion and moment matching specifically includes: The channel gain is decomposed into a mean term and a perturbation term, while retaining the first and second moment information; Taylor expansion of the UAV's available transmission power function yields the first-order approximate solution and the second-order approximate solution; By analyzing the influence of the higher-order moments of the disturbance term on the SINR distribution, the upper bound of the error is derived.

8. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 7, characterized in that, The derivation of the upper bound of the error includes: The approximation error is defined as the difference between the closed-form solution and the approximate solution. Based on the disturbance terms of the satellite-UAV link and the UAV-user link, the analytical expression of the upper bound of the error is calculated.

9. The air-space-ground IoT performance analysis method based on SWIPT and NOMA according to claim 7, characterized in that, First-order approximate interruption probability The calculation is as follows: Second-order approximate interruption probability The calculation is as follows: in, for Expectations The available transmission power for the drone, As an intermediate variable, As an intermediate variable, This is the path loss index. For small-scale fading variance, For noise power spectral density, The signal-to-interference-plus-noise ratio (SINR) threshold. for The variance.

10. A performance analysis system for air-space-ground Internet of Things (IoT) based on SWIPT and NOMA, characterized in that, include: The network module constructs an air-space-ground Internet of Things (IoT) system model based on non-orthogonal multiple access (NOAMI) and wireless power-carrying communication technology. The system model includes a low Earth orbit satellite, a drone relay, and multiple users. The low Earth orbit satellite is equipped with multiple antennas and uses power domain NOAMI to transmit signals to the drone relay. The drone relay uses wireless power-carrying communication technology to collect energy from the satellite signals and forward the data to the users. The users are randomly distributed within the drone's trajectory coverage area. The derivation module, based on the obtained air-space-ground IoT system model, derives the closed-form solution of the interruption probability of the air-space-ground IoT system model by introducing the moment generating function and characteristic function of random variables for joint transformation. The interruption probability is the probability that the user cannot correctly demodulate the signal. The analysis module uses a higher-order Taylor expansion and moment matching method to approximate the obtained closed-form solution, obtaining a first-order approximate solution and a second-order approximate solution. By analyzing the influence of the higher-order moments of the perturbation term on the signal-to-interference-plus-noise ratio distribution, the upper bound of the approximation error is derived.

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