Space-air-ground integrated flexible access method based on random network calculation

Abstract

The invention relates to the field of wireless communication, in particular to a space-air-ground integrated flexible access method based on random network calculation, which comprises the following steps: dividing ground users into K clusters, and forwarding received signals from the users to a satellite; analyzing a signal to interference plus noise ratio expression of each user, and designing unmanned aerial vehicle receiving and sending beam forming based on channel angle information; the method comprises the following steps: representing time delay performance in a variable channel environment, constructing a user data queue, modeling a violation probability of queue time delay into a dynamic change function of an arrival process and a service process, deducing a closed expression of an upper bound of the violation probability of the time delay, and quantitatively describing the time delay performance; on the condition that QoS time delay guarantee and transmission steady state are met, an optimization problem is constructed by taking system total transmitting power minimization as a target and taking a queue time delay threshold and a time delay violation probability threshold as constraint conditions, and transmitting power distribution of a user is solved and dynamically adjusted by applying an alternating iterative search algorithm.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication, and specifically to a flexible access method for space-air-ground integrated networks based on stochastic network calculus. Background Art

[0002] With the rapid development of global informatization and intelligentization, traditional terrestrial communication networks have been difficult to meet the growing mobile data demands, especially in remote, disaster, and emergency communication scenarios. As an important part of the new generation of communication technologies, the space-air-ground integrated network combines the advantages of air-based, space-based, and ground-based networks, providing the possibility for global coverage, flexible access, and efficient transmission. However, the current space-air-ground integrated communication network still faces many technical challenges: First, the existing access technologies are mainly based on orthogonal multiple access technologies, with low spectrum resource utilization and difficulty in effectively meeting the concurrent access demands of a large number of users. Second, due to the diverse types and large numbers of users served by the space-air-ground integrated network, multi-user interference is complex, making it difficult to simultaneously meet the quality of service requirements of multiple users, and the long communication distance of the air-to-ground link leads to non-negligible delay problems. In addition, the space-air-ground integrated network involves multiple communication links (such as user-drone, drone-satellite, etc.), and the channel characteristics of these links have high dynamics and uncertainties, making it difficult for existing modeling and optimization methods to adapt to the complex and changing channel environment. Finally, due to the limited transmission power of user terminals, how to ensure the QoS and delay requirements of users with lower system energy consumption, so as to achieve efficient and flexible transmission, remains a difficult point in current research.

[0003] To address the above problems, the present invention proposes a novel flexible access method by introducing the Stochastic Network Calculus (SNC) method and combining Non-Orthogonal Multiple Access (NOMA) and Space Division Multiple Access (SDMA) technologies. Through accurate channel modeling and queue delay characterization, this method significantly improves the spectrum utilization efficiency and quality of service guarantee ability of the space-air-ground integrated network, providing important technical support for the development of future 6th generation mobile networks (6G). Summary of the Invention

[0004] The purpose of the present invention is to provide a flexible access method for space-air-ground integrated networks based on stochastic network calculus to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] Step 1: Ground users are divided into K clusters based on channel correlation and difference. Each cluster of users communicates with the satellite with the assistance of a UAV relay. The UAV uses NOMA technology to serve two users within the cluster to improve spectral efficiency; and uses SDMA technology to enhance system capacity and enable simultaneous service of multiple clusters of users. The UAV uses the amplify-and-forward technique to forward the signals received from users to the satellite.

[0007] Step 2: Under the condition that the satellite uses SIC technology to decode user signals, the signal-to-interference-plus-noise ratio (SINR) expressions for each user are derived. Based on the channel angle information, the UAV receive and transmit beamforming is designed to eliminate the interference between users in different clusters and reduce the computational complexity.

[0008] Step 3: Using the theory of stochastic network calculus, the delay performance in a time-varying channel environment is characterized. A user data queue is constructed, and the violation probability of the queue delay is modeled as a dynamic function of the arrival process and the service process. A closed-form expression for the upper bound of the delay violation probability is derived to quantitatively describe the delay performance.

[0009] Step 4: Under the conditions of meeting the QoS delay guarantee and transmission stability, with the goal of minimizing the total system transmit power and subject to the queue delay threshold and the delay violation probability threshold as constraints, an optimization problem is constructed, and an alternating iterative search algorithm is used to solve it to dynamically adjust the transmit power allocation of users.

[0010] Preferably, in Step 1, ground users are divided into K user clusters based on channel correlation and channel gain difference. In a large-scale user access scenario, fully adopting NOMA technology may lead to too high system complexity, and the successive decoding of SIC technology will increase the processing delay and may cause decoding errors, affecting the communication quality of weak users. To address this problem, this patent adopts a hybrid scheme combining intra-cluster NOMA and inter-cluster SDMA. Specifically, users are divided into multiple clusters according to channel conditions and requirements, and two users with large channel similarity and large channel gain gap are grouped into one cluster for uplink NOMA transmission. NOMA technology is used for the two users within the cluster, and SDMA technology is used between clusters.

[0011] The channel model is for the wireless channel from U k,i (k = 1, …, K, i = 1, 2) to the satellite, including: the channel vector h k,i (t) of the user-UAV link, and the channel vector h RS (t) of the UAV-satellite link.

[0012] After the UAV receives the superimposed signals of all users and performs beamforming, its received signal can be expressed as:

[0013]

[0014] Among them, x k,i represents the signal of user U k,i , P k,i represents the transmit power, and w k represents the receive beamforming weight vector. In addition, n UR (t) represents additive white Gaussian noise (AWGN) with a mean of 0 and a variance of σ 2 ;

[0015] The unmanned aerial vehicle (UAV) adopts the amplify-and-forward (AF) protocol and sends the received signal to the satellite after amplification, forwarding, and beamforming processing through a fixed gain factor . Therefore, the satellite received signal can be expressed as:

[0016]

[0017] Among them, P R represents the transmit power of the UAV, and n RS (t) represents AWGN with a mean of 0 and a variance of σ 2 . w RS represents the transmit beamforming weight vector in the UAV-satellite channel.

[0018] Preferably, in step 2, when the satellite decodes the users using the SIC technique, if the channel gains of user U k,1 and user U k,2 satisfy |h k,1 (t)| 2 >|h k,2 (t)| 2 , then the power satisfies P k,1 >P k,2 . At this time, the satellite first decodes the signal x k,1 of user U k,1 , and regards the signal x k,2 of user U k,2 as interference. Then, after successfully decoding the signal x k,1 of user U k,1 , the signal x k,1 is subtracted from the superimposed signal using the SIC technique. At this time, the signal x k,2 of user U k,2 is only affected by noise.

[0019] Therefore, at time slot t, the signal-to-interference-plus-noise ratios (SINRs) of user U k,1 and user U k,2 are respectively:

[0020]

[0021] To eliminate the interference between clusters, a zero-forcing beamforming scheme is adopted, and then w k can be expressed as:

[0022]

[0023] where, is the channel response matrix,

[0024] In the case where the angle information θ of the UAV-satellite channel RS is known, the transmit beamforming weight vector w RS can be expressed as:

[0025]

[0026] Therefore, the beamforming can be simplified to:

[0027]

[0028] Preferably, in step 3, the data to be transmitted by the user is constructed into a data queue, and the queue of user U k,i from time slot τ to time slot t-1 of the cumulative arrival process, service process, and departure process are respectively defined as and Assuming that the queue satisfies the first-come-first-served principle, then at time slot t, the queue delay ω of user U k,i can be defined as: the time required for the information bits arriving at time slot t to be successfully transmitted. Therefore, the queue delay can be expressed as: k,i (t) can be defined as: the time required for the information bits arriving at time slot t to be successfully transmitted. Therefore, the queue delay can be expressed as:

[0029] ω k,i (t) = inf{u≥0:A k,i (0,t) ≤ D k,i (0,t+u)};

[0030] The delay violation probability can finally be expressed as:

[0031]

[0032] where, represents the Mellin transform of the arrival process, represents the Mellin transform of the service process;

[0033] By deriving the Mellin transform expressions of the arrival process and the service process the closed-form expression of the delay violation probability can be obtained.

[0034] Preferably, in step 4, the construction of the air-ground-space integrated wireless transmission optimization problem can be expressed as:

[0035]

[0036] where P max represents the maximum transmission power of the user.

[0037] According to the performance upper bound optimization problem of the delay violation probability expressed in step 3, it can be further expressed as:

[0038]

[0039] where represents the steady-state condition that needs to be satisfied when there is an upper bound on the delay violation probability.

[0040] Next, an alternating iterative search method is used to solve the above optimization problem. First, the user power is initialized. Then, the power of user one is fixed to optimize the power of user two, and then the power of user two is fixed to optimize the power of user one. After each iteration, the power value is updated, and it is checked whether the objective function satisfies the constraint conditions and converges. Through alternating iterative search, the minimum total transmission power required under the conditions of satisfying the delay violation probability constraint and the steady-state constraint is finally obtained, thus completing the optimization design of the system power control, increasing the network capacity and improving the spectrum utilization efficiency on the premise of saving power consumption.

[0041] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0042] The present invention provides an air-ground-space integrated flexible access method based on stochastic network calculus, which has significant advantages in improving the system spectrum efficiency, meeting the QoS requirements and delay requirements of multiple services, and saving power consumption, and is suitable for the practical application of future mobile communication systems; by combining non-orthogonal multiple access technology and space division multiple access technology, the efficient utilization of spectrum resources is realized, and the queue delay performance of the network in a dynamic channel environment is accurately characterized by the SNC method. On this basis, an optimization design method that satisfies the QoS delay constraint is constructed, with the goal of minimizing the total transmission power of the system, further reducing the system energy consumption. The present invention is applicable to the large-scale user access scenario of future 6G communication networks, providing technical support for building a globally covered and flexibly accessible air-ground-space integrated network. Description of the Drawings

[0043] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0044] Figure 1 Schematic diagram of the air-space-ground integrated network model of the present invention;

[0045] Figure 2 Schematic diagram of the delay performance index based on SNC in the present invention;

[0046] Figure 3 Flow chart of the air-space-ground integrated flexible access method based on stochastic network calculus provided by the present invention. Specific implementation manners

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0048] Please refer to Figures 1 - 3 , the present invention provides a technical solution:

[0049] Embodiment 1:

[0050] This embodiment proposes an air-space-ground integrated flexible access method based on stochastic network calculus. For the scenario of multi-user concurrent random access in the air-space-ground integrated network, a flexible access model that organically combines NOMA and SDMA is constructed, where ground users communicate with the satellite S through the UAV relay R. Here, a specific example is used to describe the technical solution proposed in this embodiment in detail and completely. As Figure 1 shown, it is assumed that both the satellite and ground users are equipped with single antennas, and the UAV is equipped with a uniform linear array of N antennas. All ground users are divided into K clusters according to channel correlation and channel gain difference. Users within the same cluster use NOMA technology to communicate with the UAV simultaneously, and SDMA is used between different clusters, so as to provide services for a large number of users. It is also assumed that the user-UAV relay link follows the Nakagami-m distribution, the UAV relay-satellite link follows the shadow Rice distribution, and the UAV relay uses the AF protocol to assist users in communicating with the satellite.

[0051] This method first constructs a wireless channel model from ground users to UAVs and a satellite channel model from UAVs to satellites. To improve the spectrum utilization rate, NOMA technology is adopted for users within a cluster, and SDMA technology is adopted between different clusters. After being amplified and forwarded by the UAV, it communicates with the satellite simultaneously. Therefore, beamforming is performed on the superimposed signals received by the UAV from all users. To eliminate inter-cluster interference, a beamforming zero-forcing scheme is adopted. In addition, the UAV adopts the AF protocol, and the received signal is amplified, forwarded, and beamformed before being sent to the satellite, thereby representing the satellite received signal. Finally, the satellite uses SIC technology to decode the users, assigns higher transmit power to users with better channel quality, and obtains the signal-to-interference-plus-noise ratio of each user.

[0052] Next, for the scenario of large-scale user access, the upper bound of the delay violation probability is derived using the Mellin transform of the arrival process and the service process. Under the conditions of meeting the QoS delay guarantee of the service and the steady-state condition of service transmission, to further improve the spectrum utilization efficiency of the system, this project proposes to establish an optimization problem with the goal of minimizing the total transmit power under the conditions of meeting the queue delay threshold and the delay violation probability threshold. Since the transmission power of user one is restricted by the interference from user two, the transmit power of user two can be determined first. Moreover, the larger the transmit power of the user, the smaller the upper bound of the delay violation probability. Correspondingly, for a given transmit power, the steady-state interval can be determined under the condition of meeting the steady-state condition. As long as the steady-state condition is satisfied, the steady-state kernel is a convex function, and an alternating iterative algorithm can be designed to solve the optimization problem and dynamically adjust the transmit power allocation of users, thereby completing the design of the entire system transmission scheme. The detailed steps are as follows:

[0053] (1) For the wireless channel from user U k,i (k = 1,…, K, i = 1, 2) to the UAV, considering the fading characteristics of the wireless channel and the influence of parameters such as free space loss during the radio wave propagation process, the user-UAV link channel vector h k,i (t) can be expressed as:

[0054] h k,i (t) = L k,i g k,i (t);

[0055] Among them, L k,i is the free space path loss, expressed as L k,i = λ / (4πd k,i ), where λ and d k,i represent the operating frequency wavelength and the distance from the user to the UAV respectively. In addition, g k,i (t) is the small-scale fading and can be expressed as:

[0056] g k,i (t) = ρk,i (t)a(θ k,i );

[0057] where a(θ k,i ) represents the array steering matrix, specifically expressed as:

[0058] a(θ k,i ) = [1, exp(jκd e sinθ k,i ), …, exp(j(N - 1)κd e sinθ k,i )] T ;

[0059] where κ = 2π / λ, d e represents the element spacing of the ULA, and θ k,i represents the angle of arrival from the user to the ground relay. λ represents the operating frequency wavelength;

[0060] In addition, ρ k,i (t) is a random variable following the Nakagami - m distribution with fading parameter m k,i and average power Ω k.i , then the probability density function and cumulative distribution function of |g k,i (t)| 2 can be respectively expressed as:

[0061]

[0062] To reflect a more realistic satellite channel, in addition to considering the fading characteristics of the wireless channel and the free - space loss during the radio wave propagation, the influence of the space - borne antenna gain also needs to be considered. Therefore, the channel vector h RS (t) of the UAV relay - satellite link can be expressed as:

[0063] h RS (t) = ζ RS g RS ();

[0064] where ζ RS is the path loss of the R - S link, and its calculation formula is d RS represents the distance from the UAV to the satellite, and G S represents the space - borne antenna gain, which can be specifically expressed as:

[0065]

[0066] where, Denotes the maximum satellite antenna gain, where J1(x) and J3(x) denote the first-order and third-order Bessel functions of the first kind, respectively. In addition, u = 2.07123sinθ / sinθ 3dB , where θ represents the angle by which the UAV deviates from the center of the satellite beam, and θ 3dB represents the 3dB angle of the main lobe;

[0067] In addition, g RS (t) represents the small-scale fading of the UAV relay-satellite link, which is described by Rice fading and can be expressed mathematically as:

[0068]

[0069] where, represents the component caused by multipath, represents the spatial correlation matrix, represents an independent and identically distributed complex Gaussian random vector, and 2b is the average power of the scattered component. The direct path (Line-of-Sight, LoS) component is expressed as:

[0070]

[0071] where, Z RS is a random variable following the Nakagami-m distribution with the fading parameter m R , and the average power is Ω R . In addition, a(θ RS ) is the array response vector of the ULA, and θ RS is the angle of arrival of the LoS signal.

[0072] Then the PDF of |g RS (t)| 2 can be expressed as:

[0073]

[0074] where, 1F1(a; b; z) represents the confluent hypergeometric function, α = [2bm / 2bm + Ω] m / (2b), β = 1 / (2b), and δ = Ω / [2b(2bm + Ω)]. In addition, Ω represents the average power of the direct path component, 2b represents the average power of the scattered component, and m is the fading value of the direct component.

[0075] (2) Cluster the users. To improve the spectrum utilization, NOMA technology is adopted for users within a cluster, and SDMA technology is used between different clusters. After being amplified and relayed by the UAV, they communicate with the satellite simultaneously. Two users with strong channel similarity and large channel gain difference among the ground users covered by the UAV are placed in the same cluster, so that the channel directions between clusters are approximately orthogonal. Therefore, after the UAV receives the superimposed signals of all users and performs beamforming, the received signal can be expressed as:

[0076]

[0077] where \(x\) k,i represents the signal of user \(U\) k,i , \(P\) k,i represents the transmit power, and \(w\) k represents the receive beamforming weight vector. In addition, \(n\) UR (t) represents additive white Gaussian noise with a mean of 0 and a variance of \(\sigma\) 2 .

[0078] When the satellite uses SIC technology to decode the users, if the channel gains of user \(U\) k,1 and user \(U\) k,2 satisfy \(|h\) k,1 (t)|\) 2 \(>|h\) k,2 (t)|\) 2 , then the power satisfies \(P\) k,1 \(>P\) k,2 . At this time, the satellite first decodes the signal \(x\) k,1 of user \(U\) k,1 , and regards the signal \(x\) k,2 of user \(U\) k,2 as interference. Then, after successfully decoding the signal \(x\) k,1 of user \(U\) k,1 , the signal \(x\) k,1 is subtracted from the superimposed signal using SIC technology. At this time, the signal \(x\) k,2 of user \(U\) k,2 is only affected by noise.

[0079] Therefore, at time slot \(t\), the signal-to-interference-plus-noise ratios of user \(U\) k,1 and user \(U\) k,2 are respectively:

[0080]

[0081] To eliminate the inter-cluster interference, a zero-forcing beamforming scheme is adopted, then \(w\) k can be expressed as:

[0082]

[0083] Among them, is the channel response matrix,

[0084] Given the angle information θ of the UAV-satellite channel RS is known, the transmit beamforming weight vector w RS can be expressed as:

[0085]

[0086] Therefore, after zero-forcing beamforming, the interference between clusters is eliminated, and the signal-to-interference-plus-noise ratio of user U k,1 and user U k,2 can be simplified to:

[0087]

[0088] (3) The cumulative arrival process, service process, and departure process of user U k,i from time slot τ to time slot t-1 are respectively defined as and where a k,i (j), r k,i (j), and d k,i (j) respectively represent the instantaneous arrival process, service process, and departure process of user U k,i in time slot j (τ ≤ j ≤ t-1). Assuming that the queue satisfies the first-come-first-served principle, the queue delay ω k,i of user U k,i at time slot t can be defined as: the time required for the information bits arriving at time slot t to be successfully transmitted. Therefore, the queue delay can be expressed as:

[0089] ω k,i (t) = inf{u ≥ 0: A k,i (0, t) ≤ D k,i (0, t + u)};

[0090] It should be noted that the above A k,i (τ, t), R k,i (τ, t), and D k,i (τ, t) are all defined in the bit domain, and it is difficult to calculate a closed-form expression for the delay violation probability. Therefore, the (min,×) SNC is used to convert the bit domain to the SNR domain, and the upper bound of the delay violation probability is derived using the Mellin transforms of the arrival process and the service process. The corresponding cumulative arrival, service, and departure processes in the SNR domain are expressed as and In addition, convolution and deconvolution are two important operations in SNC, which are defined as follows:

[0091]

[0092] According to the dynamic server properties The queue delay can be further expressed as:

[0093]

[0094] The upper bound of the corresponding queue delay violation probability can be expressed as:

[0095] Pr{ω k,i (t) > ω} ≤ Pr{A k,i %R k,i (t + ω, t) > 1};

[0096] According to the Chernoff inequality The above formula can be further expressed as:

[0097]

[0098] where M X (1 + s, τ, t) = E[(X(τ, t)) s-1 represents the Mellin transform of the non - negative bivariate stochastic process X(τ, t). According to the inequality Define the steady - state kernel K k (s, - ω) as:

[0099]

[0100] The queue delay violation probability is further expressed as Pr{ω k,i (t) > ω} ≤ inf s＞0 {K k (s, - ω)};

[0101] Assume that a k,i (j), r k,i (j) are independent and identically distributed in different time slots. The instantaneous arrival and service traffic can be represented by a k,i , r k,i . Therefore, the Mellin transform of the cumulative arrival and service processes can be further expressed as:

[0102]

[0103] where K k (s, - ω) can be further expressed as:

[0104]

[0105] And only when the steady - state condition is satisfied The above equation holds only when the time delay violation probability can be finally expressed as:

[0106]

[0107] Next, the Mellin transforms of the arrival process and the service process in the SNR domain will be derived separately:

[0108] Assume that the arrival process with low rate and low burstiness follows a Poisson distribution with rate λ k,i , so the Mellin transform of the arrival process can be expressed as:

[0109]

[0110] Assume that the user communicates with the satellite through a UAV relay. The link between the user and the UAV follows a Nakagami - m distribution, and the link between the UAV and the satellite follows a shadowed Rice distribution. The Mellin transform of the service process is expressed as:

[0111]

[0112] For user one, the expression of the service process is:

[0113]

[0114] For user two, the expression of the service process is:

[0115]

[0116] Finally, substituting the Mellin transform expressions of the arrival process and the service process, the closed - form expression of the upper bound of the time delay violation probability can be obtained.

[0117] (4) Based on the previously constructed integrated space - air - ground flexible access model, it is necessary to further improve the spectral efficiency of the system under the conditions of meeting the QoS time - delay guarantee of services and the steady - state conditions of service transmission. For this purpose, the patent of this project proposes to establish a corresponding optimization problem with the goal of minimizing the total transmission power under the conditions of meeting the queue time - delay threshold and the time - delay violation probability threshold ε k,i , that is:

[0118]

[0119] Among them, P max represents the maximum transmission power of the user. Using the Mellin transforms of the arrival process and the service process in the SNR domain and methods such as SNC, the performance upper bound of the time - delay violation probability under fading channel conditions is characterized. Therefore, the above - mentioned optimization problem can be further expressed as:

[0120]

[0121] Among them, represents the steady-state condition that needs to be satisfied when there is an upper bound on the delay violation probability.

[0122] (5) Solve the optimization problem by alternating iteration to obtain the optimal user transmission power, and complete the design of the entire system scheme. The process includes:

[0123] 1) Initialize the calculation accuracy δ, the iteration number t = 0, the power of user one, and the power of user two

[0124] 2) Use the steady-state condition to determine that the steady-state interval is (0, s m );

[0125] 3) Traverse the steady-state interval to find the optimal value of s;

[0126] 4) Substitute s into it and judge the result. If it is less than 0, increase If it is greater than 0, decrease Output the power of user two

[0127] 5) Then make Repeat steps 2) and 3);

[0128] 6) Substitute s into it and judge the result. If it is less than 0, increase If it is greater than 0, decrease Output the power of user one

[0129] 7) Update the iteration number t = t + 1;

[0130] 8) Judge whether the convergence condition is satisfied. If it is satisfied, output the optimal power; otherwise, return to step 2).

[0131] Example 2:

[0132] The computer-readable storage medium of this example stores a computer program, and when the program is executed by a processor, it implements the steps in a space-air-ground integrated flexible access method based on stochastic network calculus in Example 1.

[0133] The computer-readable storage medium of this embodiment can be an internal storage unit of the terminal, such as the hard disk or memory of the terminal; the computer-readable storage medium of this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash card, etc. equipped on the terminal; further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the terminal.

[0134] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0135] Embodiment 3:

[0136] The computer device of this embodiment includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in a method for space-air-ground integrated flexible access based on stochastic network calculus in Embodiment 1.

[0137] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.; the memory can include a read-only memory and a random access memory, and provides instructions and data to the processor. A part of the memory can also include a non-volatile random access memory. For example, the memory can also store information about the device type.

[0138] Those skilled in the art should understand that the content disclosed in the embodiments can be provided as a method, a system, or a computer program product. Therefore, this solution can be implemented in the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, this solution can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program code.

[0139] This solution is described with reference to the flowcharts and / or block diagrams of methods and computer program products according to embodiments of this solution. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or block diagrams Figure 1 or a device for implementing the functions specified in multiple blocks.

[0140] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one process Figure 1 one process or multiple processes and / or block diagrams Figure 1 or a device for implementing the functions specified in multiple blocks.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or block diagrams Figure 1 or a device for implementing the functions specified in multiple blocks.

[0142] Those of ordinary skill in the art can understand that all or part of the processes for implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0143] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A flexible access method for space-air-ground integrated network based on stochastic network calculus, characterized in that: The method includes: Step 1: Divide the ground users into K clusters based on channel correlation and difference. Each cluster of users communicates with the satellite with the assistance of a UAV relay. Among them, the UAV uses NOMA technology to serve two users within the cluster, uses SDMA technology to improve the system capacity, and uses the amplify-and-forward technology to forward the signals received from the users to the satellite. Step 2: When the satellite uses SIC technology to decode the user signals, analyze the signal-to-interference-plus-noise ratio (SINR) expressions of each user; and design the UAV receive and transmit beamforming based on the channel angle information. Step 3: Use the stochastic network calculus theory to characterize the delay performance in a time-varying channel environment; construct a user data queue, model the violation probability of the queue delay as a dynamic function of the arrival process and the service process, and derive a closed-form expression for the upper bound of the delay violation probability to quantitatively describe the delay performance. Step 4: Under the conditions of meeting the QoS delay guarantee and transmission stability, with the goal of minimizing the total system transmit power and the queue delay threshold and the delay violation probability threshold as the constraint conditions, construct an optimization problem, and use the alternating iterative search algorithm to solve it to dynamically adjust the transmit power allocation of the users.

2. The flexible access method for space-air-ground integrated network based on stochastic network calculus according to claim 1, wherein The said Step 1 includes: Step 101: Divide the ground users into K user clusters based on channel correlation and channel gain difference. Among them, two users with large channel similarity and large channel gain gap are divided into one cluster for uplink NOMA transmission; two users within the cluster use NOMA technology, and SDMA technology is used between clusters. Step 102. The channel model is the wireless channel from U k,i (k = 1, …, K, i = 1, 2) to the satellite, including: the channel vector h k,i (t) of the user-drone link and the channel vector h RS (t) of the drone-satellite link; After the UAV receives the superimposed signals of all users, beamforming is performed, and its received signal is expressed as: Among them, x k,i represents the signal of user U k,i , P k,i represents the transmit power, w k represents the receive beamforming weight vector, n UR (t) represents additive white Gaussian noise with a mean of 0 and a variance of σ 2 ; Step 103: The UAV adopts the amplify-and-forward protocol with a fixed gain factor: After amplifying, forwarding, and beamforming the received signal and sending it to the satellite, the satellite received signal is expressed as: Among them, P R represents the transmission power of the UAV, and n RS (t) represents AWGN with a mean of 0 and a variance of σ 2 , and w RS represents the transmit beamforming weight vector in the UAV-satellite channel.

3. The flexible access method for space-air-ground integrated network based on stochastic network calculus according to claim 1, characterized in that, The said Step 2 includes: Step 201, when the satellite decodes users using SIC technology, user U k,1 and user U k,2 's channel gains satisfy |h k,1 (t)| 2 >|h k,2 (t)| 2 , then the power satisfies P k,1 >P k,2 ; At this time, the satellite first decodes the signal x k,1 of user U k,1 , and regards the signal x k,2 of user U k,2 as interference; Then, after successfully decoding the signal x k,1 of user U k,1 , the signal x k,1 is subtracted from the superimposed signal using SIC technology. At this time, the signal x k,2 of user U k,2 is only affected by noise; Therefore, at time slot t, user U k,1 and user U k,2 have the following signal-to-interference-plus-noise ratios respectively: Step 202: To eliminate the interference between clusters, a zero-forcing beamforming scheme is adopted, and then w k is expressed as: Among them, is the channel response matrix, Step 203. When the angle information θ of the UAV-satellite channel RS is known, the transmit beamforming weight vector w RS is expressed as: Therefore, the beamforming is simplified to obtain:

4. The flexible access method for space-air-ground integrated network based on stochastic network calculus according to claim 1, characterized in that, The said Step 3 includes: Step 301: Construct the data to be transmitted by the user into a data queue. The queue of user U k,i The cumulative arrival process, service process, and departure process from time slot τ to time slot t-1 are respectively defined as and Step 302. Assume that the queue follows the first-come-first-served principle. Then, at time slot t, the queue delay ω k,i of user U k,i (t) is defined as the time taken for the information bits arriving at time slot t to be successfully transmitted. Therefore, the queue delay is expressed as: ω k,i (t) = inf{u ≥ 0 : A k,i (0, t) ≤ D k,i (0, t + u)}; The delay violation probability is expressed as: Among them, represents the Mellin transform of the arrival process, represents the Mellin transform of the service process; by deriving the Mellin transform expressions of the arrival process and the service process a closed-form expression for the delay violation probability can be obtained.

5. The flexible access method for space-air-ground integrated network based on stochastic network calculus according to claim 1, characterized in that, The said Step 4 includes: Step 401: The constructed optimization problem is expressed as: Among them, P max represents the maximum transmit power of the user; Step 402: Obtain the delay violation probability in Step 302, then the performance upper bound optimization problem of the delay violation probability is further expressed as: Among them, represents the steady-state condition that needs to be satisfied when there exists an upper bound on the delay violation probability; Step 403: Use the alternating iterative search method to solve the above optimization problem: First, initialize the user power, then fix the power of user one to optimize the power of user two, and then fix the power of user two to optimize the power of user one; update the power value after each iteration, and check whether the objective function satisfies the constraint conditions and converges; through alternating iterative search, finally obtain the minimum total transmit power required under the conditions of meeting the delay violation probability constraint and the stability constraint, thereby completing the optimization design of the system power control, increasing the network capacity and improving the spectrum utilization efficiency on the premise of saving power consumption.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the steps in a space-air-ground integrated flexible access method based on stochastic network calculus as described in any one of claims 1-5.

7. A computer device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps in a space-air-ground integrated flexible access method based on stochastic network calculus as described in any one of claims 1-5.

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