A satellite internet of things random access adaptive method, device and storage medium

Abstract

The present invention discloses a satellite Internet of Things random access adaptive method, device and storage medium, including: based on a pre-built low-orbit satellite Internet of Things model, performing spread spectrum anti-interference analysis, combining the interference situation in the channel at the current moment, determining whether to use spread spectrum and spread spectrum code length in the random access mode; obtaining preamble code state information, estimating the current time slot load by the maximum likelihood estimation method according to the preamble code state information, integrating the estimated current time slot load as the historical data of machine learning, using machine learning to predict the load of several future time slots to obtain the future load prediction value; according to whether to use spread spectrum and spread spectrum code length in the random access mode and the future load prediction value, determining the adaptive switching random access mode or maintaining the current mode. The throughput of the system can be improved, the communication quality can be guaranteed even when the signal-to-interference ratio deteriorates, and the throughput can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite Internet of Things, and relates to a satellite Internet of Things random access adaptive method, device and storage medium. Background Art

[0002] In recent years, with the continuous improvement of people's demand for message transmission and the continuous progress of information and communication technology, the concept of the Internet of Things has attracted widespread attention. Since the Internet of Things can network and informatize the real physical world, integrate and communicate the two separate worlds of the physical world and the information world, the Internet of Things has become an important part of supporting the intelligent industry, realizing many functions such as intelligent environmental monitoring, waste management, traffic control, intelligent metering, identification and positioning, and tracking management, promoting the rapid development of various intelligent industries, and providing more and more convenience for people's lives. The Internet of Things has become an important part of the new generation of information technology. It is regarded as the third industrial revolution in the development of the world's information industry after computers and the Internet, and has become one of the important driving forces for the development of today's society, with broad market prospects.

[0003] However, IoT services based on ground base stations are usually restricted by geographical environments (such as oceans and deserts), because ground base stations in these areas are difficult to build and maintain, and they are also easily destroyed when natural disasters (such as earthquakes) occur.

[0004] To solve this problem, scholars began to study the application potential of long-range wide area networks in low-orbit satellites as a supplement to the ground-based Internet of Things.

[0005] For satellite IoT systems, their services are somewhat sudden and random, and the data volume is small. The resource allocation of multiple access technologies based on fixed allocation is not flexible enough, which will cause unnecessary waste of resources and is not suitable for application in satellite IoT systems. Compared with multiple access technologies based on fixed allocation, contention-based multiple access protocols have the advantages of high flexibility and easy implementation, and their signaling overhead is small and the transmission efficiency is high. They are suitable communication protocols for satellite IoT systems. However, under medium and high load conditions, the throughput of contention-based multiple access protocol systems will decline significantly or even sharply. Summary of the invention

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a satellite Internet of Things random access adaptive method. Under the low-orbit satellite Internet of Things scenario model, the most suitable spread spectrum code length is used for anti-interference processing for channel environments with different interference conditions. By comparing the predicted value of the load estimation with the upper and lower thresholds, combined with the selected spread spectrum code length, the appropriate random access method is adaptively selected to maximize the system throughput while being able to resist interference in the channel.

[0007] The present invention provides a satellite Internet of Things random access adaptive scheme based on environmental perception, including low-orbit Internet of Things modeling and anti-interference analysis, load estimation and prediction, and random access adaptive scheme. Low-orbit Internet of Things modeling and anti-interference analysis refer to first establishing a low-orbit Internet of Things scenario, and analyzing the improvement of anti-interference capabilities of different spread spectrum situations in this scenario; load estimation and prediction is to estimate the load situation of the current time slot according to the prior time slot state information during the communication process, and integrate all the estimation results as historical data for machine learning, and then use machine learning to predict the load situation of future time slots; the random access adaptive scheme is to set the upper and lower thresholds of the adaptive scheme on the premise of anti-interference analysis to determine whether to spread the spectrum and the length of the spread spectrum code, and adaptively switch the random access scheme through the load prediction value. This method can adaptively switch different random access schemes according to the interference situation and the load situation, and can improve the system throughput when the interference can be ignored, and can also ensure the communication quality and improve the throughput when the signal-to-interference ratio deteriorates.

[0008] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0009] In a first aspect, a satellite Internet of Things random access adaptive method is provided, comprising:

[0010] Based on the pre-built low-orbit satellite IoT model, spread spectrum anti-interference analysis is performed, and combined with the interference situation in the channel at the current moment, it is determined whether to use spread spectrum and the spread spectrum code length in the random access mode;

[0011] Obtain preamble status information, estimate the current time slot load using the maximum likelihood estimation method based on the preamble status information, integrate the estimated current time slot load as historical data for machine learning, and use machine learning to predict the load of several future time slots to obtain a future load prediction value;

[0012] According to the determination of whether to use the spreading and the spreading code length in the random access mode and the future load prediction value, it is determined whether to adaptively switch the random access mode or maintain the current mode.

[0013] In some embodiments, the low-orbit satellite Internet of Things model includes:

[0014] In the low-orbit satellite IoT model, IoT terminals covered by satellite beams send data packets to low-orbit satellites through uplinks using random access; satellite receivers receive and demodulate IoT terminal data packets to obtain demodulated information; the demodulated information is transmitted to the ground gateway through intersatellite links and feeder links;

[0015] The low-orbit satellite is at a height of H from the subsatellite point, with a beam radius of R and an area of ​​V = πR 2 A total of N t The terminal’s transmission power is expressed as P t , then the large-scale path loss g(d k )for:

[0016]

[0017] Where: d k is the distance between the kth terminal and the LEO satellite, is the wavelength, c is the speed of light, f c is the carrier frequency, η is the path loss coefficient;

[0018] The terminal's transmitted signal is s 1 , the received signal r at the LEO satellite receiver 1 for:

[0019]

[0020] Where: s k is the signal transmitted by the kth terminal, N 0 is the system noise power, G r represents the receiving gain of the low-orbit satellite antenna, which satisfies the following formula:

[0021]

[0022] Among them: μ=2.07123sin(θ) / sin(θ 3dB ), θ 3dB is the beam gain at an angle corresponding to a 3dB attenuation at the beam center, J 1 and J 3 are the first-order and third-order Bessel functions of the first kind, D is the antenna aperture, and ξ is the antenna efficiency.

[0023] In some embodiments, the processing gain G of the receiver p for

[0024]

[0025] Among them B bis the bandwidth occupied by the signal before spread spectrum processing, B ss It is the bandwidth occupied by the signal after spread spectrum processing; (S / N) out is the output signal-to-noise power ratio of the receiver despreader, (S / N) in is the receiver input signal-to-noise power ratio.

[0026] In some embodiments, obtaining preamble status information, and estimating the current time slot load using a maximum likelihood estimation method according to the preamble status information, includes:

[0027] Analyze the probability of occurrence of different preamble code states;

[0028] Use s i Indicates the state of the i-th preamble: s i =0 means that the leading code is not selected, that is, it is in idle state; s i =1 means that the preamble code is just selected by a terminal, that is, the random access process can be completed, and this state is called a successful state; s i ≥2 means that two or more terminals have selected the preamble code and it is detected as a conflicting state. Then the probabilities of the i-th preamble code being in these three states are:

[0029]

[0030] Among them, N p Indicates the number of available preamble codes in the current time slot; n a Indicates the number of access applications in the current time slot;

[0031] After completing a random access time slot, the satellite detects all preamble information, that is, the number of idle preambles n 1 、Number of successful access preamble codes n 2 and the number of collision preambles n 3 ; The maximum likelihood estimation method is used to estimate the access scale of the current time slot. The maximum likelihood function P is expressed as:

[0032]

[0033] When P reaches the maximum value, N a The value of is the estimated value of the access request in the current time slot Right now:

[0034]

[0035] In some embodiments, determining whether to adaptively switch the random access mode or maintain the current mode according to the determination of whether to use spreading and spreading code length and load prediction value in the random access mode includes:

[0036] Determine the upper threshold of the adaptive random access scheme based on the selected spreading code length ceiling and lower threshold floor ;

[0037] The obtained future load forecast value is compared with the determined upper threshold value ceiling and lower threshold floor Make a comparison;

[0038] In response to the future load forecast value being greater than the upper threshold ceiling , adaptively select the corresponding spread spectrum SA or SA mode;

[0039] In response to the future load forecast value being less than the lower threshold floor , adaptively select the corresponding spread spectrum CRDSA or CRDSA mode;

[0040] In response to the future load forecast value being between an upper threshold ceiling and lower threshold floor Keep the current access mode unchanged.

[0041] In some embodiments, the upper threshold is (1+e)X, the lower threshold is (1-e)X, X represents the normalized load value at the intersection of the throughputs of the two selected access modes, and e represents the error of the load estimation method.

[0042] In a second aspect, a satellite Internet of Things random access adaptive device is provided, characterized in that it includes:

[0043] The spread spectrum determination module is configured to: perform spread spectrum anti-interference analysis based on a pre-built low-orbit satellite Internet of Things model, and determine whether to use spread spectrum and spread spectrum code length in the random access mode in combination with the interference situation in the channel at the current moment;

[0044] The future load prediction module is configured to: obtain preamble state information, estimate the current time slot load by using the maximum likelihood estimation method according to the preamble state information, integrate the estimated current time slot load as historical data for machine learning, and use machine learning to predict the load of several future time slots to obtain a future load prediction value;

[0045] The access mode determination module is configured to determine whether to adaptively switch the random access mode or maintain the current mode according to whether to use the spreading and spreading code length and the future load prediction value in the random access mode.

[0046] In a third aspect, a satellite Internet of Things random access adaptive device is provided, including a processor and a storage medium;

[0047] The storage medium is used to store instructions;

[0048] The processor is used to operate according to the instructions to execute the steps of the method according to the first aspect.

[0049] According to a fourth aspect, a storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0050] Beneficial effects: The present invention provides a random access adaptive method based on satellite Internet of Things. Low-orbit Internet of Things modeling and anti-interference analysis refer to first establishing a low-orbit Internet of Things scenario, and analyzing the improvement of anti-interference capabilities of different spread spectrum situations in this scenario; load estimation and prediction is to estimate the load situation of the current time slot according to the prior time slot state information during the communication process, and integrate all estimation results as historical data for machine learning, and then use machine learning to predict the load situation of future time slots; the random access adaptive scheme is to set the upper and lower thresholds of the adaptive scheme on the premise of determining whether to spread the spectrum and the length of the spread spectrum code under the anti-interference analysis, and adaptively switch the random access scheme through the load prediction value. This method can adaptively switch different random access schemes according to the interference situation and load situation, and can improve the system throughput when the interference can be ignored, and can also ensure the communication quality and improve the throughput when the signal-to-interference ratio deteriorates. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flowchart of the implementation process of the method of the embodiment of the present invention;

[0052] Figure 2 A scene model diagram in an embodiment of the present invention;

[0053] Figure 3 A comparison simulation diagram of the spread spectrum anti-interference capability of the method according to the embodiment of the present invention;

[0054] Figure 4 is a maximum likelihood load estimation simulation diagram of the method according to an embodiment of the present invention;

[0055] Figure 5 It is a machine learning load prediction simulation diagram of the method of the embodiment of the present invention;

[0056] Figure 6 This is a simulation diagram of the random access adaptation solution results of the embodiment method of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0058] In the description of the present invention, "several" means more than one, "many" means more than two, "greater than", "less than", "exceed" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0059] In the description of the present invention, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0060] Example 1

[0061] A satellite Internet of Things random access adaptive method, comprising:

[0062] Based on the pre-built low-orbit satellite IoT model, spread spectrum anti-interference analysis is performed, and combined with the interference situation in the channel at the current moment, it is determined whether to use spread spectrum and the spread spectrum code length in the random access mode;

[0063] Obtain preamble status information, estimate the current time slot load using the maximum likelihood estimation method based on the preamble status information, integrate the estimated current time slot load as historical data for machine learning, and use machine learning to predict the load of several future time slots to obtain a future load prediction value;

[0064] According to the determination of whether to use the spreading and the spreading code length in the random access mode and the future load prediction value, it is determined whether to adaptively switch the random access mode or maintain the current mode.

[0065] In some specific embodiments, Figure 1 As shown in the figure, a satellite IoT random access adaptive scheme based on environment perception is provided, which includes three parts: low-orbit IoT modeling and anti-interference analysis, load estimation and prediction, and random access adaptive scheme. The specific steps are as follows:

[0066] 1. Low-orbit Internet of Things Modeling and Anti-interference Analysis

[0067] The process of low-orbit IoT modeling and anti-interference analysis includes the following steps:

[0068] (1.1) Construct a low-orbit satellite IoT model. First, the IoT terminal covered by the satellite beam sends data packets to the low-orbit satellite through the uplink using random access; then, the satellite receiver demodulates the IoT terminal data packet; finally, the demodulated information is transmitted to the ground gateway through the inter-satellite link and feeder link.

[0069] We consider a low-orbit satellite with a height of H from the subsatellite point, a beam radius of R, and an area of ​​V = πR 2 A total of N t terminals, we denote the terminal’s transmit power as P t , then the large-scale path loss can be written as:

[0070]

[0071] Where: d k is the distance between the kth terminal and the LEO satellite, is the wavelength, c is the speed of light, f c is the carrier frequency, η is the path loss coefficient. We consider the terminal’s transmitted signal to be s 1 , the received signal r at the LEO satellite receiver 1 can be written as:

[0072]

[0073] Where: s k is the signal transmitted by the kth terminal, N 0 is the system noise power, G r represents the receiving gain of the low-orbit satellite antenna, which satisfies the following formula:

[0074]

[0075] Among them: μ=2.07123sin(θ) / sin(θ 3dB ), θ 3dB is the beam gain at an angle corresponding to a 3dB attenuation at the beam center, J 1 and J 3 are the first-order and third-order Bessel functions of the first kind, D is the antenna aperture, and ξ is the antenna efficiency.

[0076] (1.2) We consider analyzing the improvement of the anti-interference ability of the system by using different lengths of spread spectrum codes. When measuring the anti-interference ability of a spread spectrum communication system, the concept of "processing gain" is usually introduced to describe it. Processing gain G pDefined as the output signal-to-noise power ratio (S / N) of the receiver despreader out The signal-to-noise power ratio (S / N) of the receiver input in The ratio of

[0077]

[0078] Consider that the power spectral density of interference and noise entering the receiver correlation despreader is uniformly distributed, and the spectral density is N 0 , then the power of interference and noise at the receiver's related despreader input is N in =N 0 B ss , where B ss is the bandwidth of the receiver. Assume that the signal power of the receiver-correlated despreader input is P, then the signal-to-noise ratio of the receiver-correlated despreader input is (S / N) in =P / N 0 B ss After the relevant processing of the receiver despreader, the signal can pass through the bandwidth B without distortion. b The signal energy is not lost through the filter, and the power of the receiver output signal is still P. Only a small amount of energy from interference and noise can pass through the bandwidth B. b filter, and most of the energy is filtered out by the filter, so the processing gain of the receiver is G p for

[0079]

[0080] Where B b is the bandwidth occupied by the signal before spread spectrum processing, B ss It is the bandwidth occupied by the signal after the spread spectrum processing. This shows that the processing gain of the spread spectrum receiver is proportional to its spread spectrum signal bandwidth B ss (unilateral bandwidth of the signal before despreading) is proportional to the bandwidth B of the unspread signal b (the single-sided bandwidth of the despread signal).

[0081] 2. Load Estimation and Forecasting

[0082] Load estimation and prediction specifically includes the following steps:

[0083] (2.1) First, analyze the probability of occurrence of different preamble states. i Indicates the state of the i-th preamble. i =0 means that the leading code is not selected, that is, it is in idle state; s i =1 means that the preamble code is just selected by a terminal, that is, the random access process can be completed, and this state is called a successful state; s i≥2 means that two or more terminals have selected the preamble code and it is detected as a conflicting state. Then the probabilities of the i-th preamble code being in these three states are:

[0084]

[0085] Among them, N p Indicates the number of available preamble codes in the current time slot; n a Indicates the number of access applications in the current time slot.

[0086] (2.2) After completing a random access time slot, the satellite can detect all preamble information, that is, the number of idle preambles n 1 、Number of successful access preamble codes n 2 and the number of collision preambles n 3 We use the maximum likelihood estimation method to estimate the access scale of the current time slot. The maximum likelihood function expression is:

[0087]

[0088] When the above formula reaches its maximum value, N a The value of is the estimated value of the access request in the current time slot Right now:

[0089]

[0090] (2.3) Through the maximum likelihood estimation method adopted in (2.2), we can obtain the load conditions of all time slots before the current time slot. Then, we integrate the load results of each time slot and send them to the machine learning module. By analyzing historical data through machine learning, we can predict the load conditions of several future time slots.

[0091] 3. Random Access Adaptive Scheme

[0092] The random access adaptive scheme specifically includes the following steps:

[0093] (3.1) Based on the analysis of (1.2) and the interference situation in the channel at the current moment, decide whether to adopt spread spectrum means. If spread spectrum is required, select the appropriate spread spectrum code length.

[0094] (3.2) In order to reduce the impact of load estimation errors and frequent access mode switching on system efficiency, upper and lower thresholds can be set in the adaptive switching scheme, and the area between the upper and lower thresholds is used as the free switching zone. Because the possibility of selecting the wrong access mode based on the load estimation value is extremely low in the interval (0, (1-e)X) and the interval ((1+e)X, maxG), we consider setting a free switching zone (Xa, X+a) symmetrical about X in the interval ((1-e)X, (1+e)X). X represents the normalized load value at the intersection of the throughput of the two selected access modes, e represents the error of the load estimation method, and maxG represents the maximum load value considered in the study.

[0095] When the value of a changes within the interval (0, eX), we find that for the interval (Xa, X+a), the error probability of selecting a solution based on the load estimation value is 50%, so we conclude that within the considered interval ((1-e)X, (1+e)X), as the free-conversion zone increases, the accuracy of selecting a solution based on the load estimation value will not increase, but the frequency of mode conversion can be reduced, that is, the system efficiency can be improved. Therefore, it is most appropriate to select ((1-e)X, (1+e)X) as the free-conversion zone, that is, the upper threshold is (1+e)X and the lower threshold is (1-e)X.

[0096] (3.3) Determine the upper threshold of the adaptive random access scheme based on the spreading code length selected in (3.1) ceiling and lower threshold floor The error of the load prediction scheme in this paper is about 2%, so e is set to 2%, the upper threshold is 1.02·X, and the lower threshold is 0.98·X. Table 1 shows the upper and lower thresholds corresponding to some spreading code lengths.

[0097] (3.4) The future load forecast value obtained in (2.3) is compared with the upper threshold value determined in (3.2) ceiling and lower threshold floor For comparison, greater than the upper threshold ceiling Adaptively select the corresponding spread spectrum SA or SA mode, which is less than the lower threshold threshold floor Adaptively select the corresponding spread spectrum CRDSA or CRDSA mode. If it is between the upper and lower thresholds, the previous round of access mode remains unchanged.

[0098] Table 1

[0099]

[0100] Figure 3 The relevant parameters of the simulation are set as follows:

[0101] parameter Parameter Value parameter Parameter Value Data length 10000000 Carrier to Interference Ratio(dB) -20～10 Modulation BPSK Spreading code type Walsh Code

[0102] Figure 4 The relevant parameters of the simulation are set as follows:

[0103]

[0104]

[0105] Figure 5 The relevant parameters of the simulation are set as follows:

[0106] parameter Parameter Value parameter Parameter Value Number of terminals 100,000 Study time slots 300～2300 Number of observation slots 4000 Prediction time slot 2300～2500

[0107] Figure 6 The relevant parameters of the simulation are set as follows:

[0108] parameter Parameter Value parameter Parameter Value Load range 0～10 Monte Carlo times 5000

[0109] Figure 2 It is a low-orbit satellite IoT scenario modeling, which includes different geographical environments and IoT terminal types.

[0110] Figure 3 It is the bit error rate of different spread spectrum code lengths under different interference levels.

[0111] Figure 4 It is the maximum likelihood loading estimation simulation diagram.

[0112] Figure 5 This is a simulation diagram of machine learning load prediction.

[0113] Figure 6 The following are simulation diagrams of the throughput and packet loss rate of the adaptive random access scheme under different spreading code lengths. By comparing several simulation diagrams, we can see that when interference can be ignored, the adaptive scheme can also improve throughput and reduce packet loss rate without spreading. When the signal-to-interference ratio deteriorates, we introduce spreading. As the spreading code length increases, the throughput also increases and the packet loss rate decreases.

[0114] Example 2

[0115] This embodiment provides a satellite Internet of Things random access adaptive device, which is characterized by comprising:

[0116] The spread spectrum determination module is configured to: perform spread spectrum anti-interference analysis based on a pre-built low-orbit satellite Internet of Things model, and determine whether to use spread spectrum and spread spectrum code length in the random access mode in combination with the interference situation in the channel at the current moment;

[0117] The future load prediction module is configured to: obtain preamble state information, estimate the current time slot load by using the maximum likelihood estimation method according to the preamble state information, integrate the estimated current time slot load as historical data for machine learning, and use machine learning to predict the load of several future time slots to obtain a future load prediction value;

[0118] The access mode determination module is configured to determine whether to adaptively switch the random access mode or maintain the current mode according to whether to use the spreading and spreading code length and the future load prediction value in the random access mode.

[0119] This embodiment also provides a satellite Internet of Things random access adaptive device, including a processor and a storage medium;

[0120] The storage medium is used to store instructions;

[0121] The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0122] Example 3

[0123] This embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A satellite Internet of Things random access adaptive method, characterized in that: include: Based on the pre-built low-orbit satellite IoT model, spread spectrum anti-interference analysis is performed, and combined with the interference situation in the channel at the current moment, it is determined whether to use spread spectrum and the spread spectrum code length in the random access mode; Acquire the preamble status information, estimate the current time slot load by using the maximum likelihood estimation method according to the preamble status information, integrate the estimated current time slot load as the historical data of machine learning, and use machine learning to predict the load of several future time slots to obtain the future load prediction value; wherein, acquire the preamble status information, estimate the current time slot load by using the maximum likelihood estimation method according to the preamble status information, specifically including: Analyze the probability of occurrence of different preamble code states; Use s i Indicates the state of the i-th preamble: s i =0 means that the leading code is not selected, that is, it is in idle state; s i =1 means that the preamble code is just selected by a terminal, and the random access process is completed, and this state is called a successful state; s i ≥2 means that two or more terminals have selected the preamble code and it is detected as a conflicting state. Then the probabilities of the i-th preamble code being in these three states are: Among them, N p Indicates the number of available preamble codes in the current time slot; n a Indicates the number of access applications in the current time slot; After completing a random access time slot, the satellite detects all preamble information, namely the number of idle preambles n1, the number of successful access preambles n2 and the number of collision preambles n3; the maximum likelihood estimation method is used to estimate the access scale of the current time slot, and the maximum likelihood function P is expressed as: When P reaches the maximum value, N a The value of is the estimated value of the access request in the current time slot Right now: Determining, according to the determination of whether to use spread spectrum and spread spectrum code length and load prediction value in the random access mode, adaptively switching the random access mode or maintaining the current mode specifically includes: Determine the upper threshold of the adaptive random access scheme based on the selected spreading code length ceiling and lower threshold floor ; The obtained future load forecast value is compared with the determined upper threshold value ceiling and lower threshold floor Make a comparison; In response to the future load forecast value being greater than the upper threshold ceiling , adaptively select the corresponding spread spectrum SA or SA mode; In response to the future load forecast value being less than the lower threshold floor , adaptively select the corresponding spread spectrum CRDSA or CRDSA mode; In response to the future load forecast value being between an upper threshold ceiling and lower threshold floor Keep the current access mode unchanged.

2. The satellite Internet of Things random access adaptive method according to claim 1, characterized in that: Low-orbit satellite IoT model, including: In the low-orbit satellite IoT model, IoT terminals covered by satellite beams send data packets to low-orbit satellites through uplinks using random access; satellite receivers receive and demodulate IoT terminal data packets to obtain demodulated information; the demodulated information is transmitted to the ground gateway through intersatellite links and feeder links; The low-orbit satellite is at a height of H from the subsatellite point, with a beam radius of R and an area of ​​V = πR 2 A total of N t The terminal’s transmission power is expressed as P t , then the large-scale path loss g(d k )for: Where: d k is the distance between the kth terminal and the LEO satellite, is the wavelength, c is the speed of light, f c is the carrier frequency, η is the path loss coefficient; The terminal's transmitted signal is s1, and the received signal r1 at the low-orbit satellite receiver is: Where: s k is the signal transmitted by the kth terminal, N0 is the system noise power, G r represents the receiving gain of the low-orbit satellite antenna, which satisfies the following formula: Among them: μ=2.07123sin(θ) / sin(θ 3dB ), θ 3dB is the beam gain at the angle corresponding to a 3 dB attenuation relative to the beam center, J1 and J3 are the first-order and third-order Bessel functions of the first kind, D is the antenna aperture, and ξ is the antenna efficiency.

3. The satellite Internet of Things random access adaptive method according to claim 2, characterized in that: The processing gain of the receiver is G p for Among them B b is the bandwidth occupied by the signal before spread spectrum processing, B ss It is the bandwidth occupied by the signal after spread spectrum processing; (S / N) out is the output signal-to-noise power ratio of the receiver despreader, (S / N) in is the receiver input signal-to-noise power ratio.

4. The satellite Internet of Things random access adaptive method according to claim 1, characterized in that: The upper threshold is (1+e)X, and the lower threshold is (1-e)X, where X represents the normalized load value at the intersection of the throughputs of the two selected access modes, and e represents the error of the load estimation method.

5. A satellite Internet of Things random access adaptive device, characterized in that: include: The spread spectrum determination module is configured to: perform spread spectrum anti-interference analysis based on a pre-built low-orbit satellite Internet of Things model, and determine whether to use spread spectrum and spread spectrum code length in the random access mode in combination with the interference situation in the channel at the current moment; The future load prediction module is configured to: obtain the preamble state information, estimate the current time slot load by the maximum likelihood estimation method according to the preamble state information, integrate the estimated current time slot load as the historical data of machine learning, and use machine learning to predict the load of several future time slots to obtain the future load prediction value; specifically used for: Analyze the probability of occurrence of different preamble code states; Use s i Indicates the state of the i-th preamble: s i =0 means that the leading code is not selected, that is, it is in idle state; s i =1 means that the preamble code is just selected by a terminal, and the random access process is completed, and this state is called a successful state; s i ≥2 means that two or more terminals have selected the preamble code and it is detected as a conflicting state. Then the probabilities of the i-th preamble code being in these three states are: Among them, N p Indicates the number of available preamble codes in the current time slot; n a Indicates the number of access applications in the current time slot; After completing a random access time slot, the satellite detects all preamble information, namely the number of idle preambles n1, the number of successful access preambles n2 and the number of collision preambles n3; the maximum likelihood estimation method is used to estimate the access scale of the current time slot, and the maximum likelihood function P is expressed as: When P reaches the maximum value, N a The value of is the estimated value of the access request in the current time slot Right now: The access mode determination module is configured to: determine whether to use the spread spectrum and the spread spectrum code length in the random access mode and the future load prediction value, and adaptively switch the random access mode or maintain the current mode, specifically for: Determine the upper threshold of the adaptive random access scheme based on the selected spreading code length ceiling and lower threshold floor ; The obtained future load forecast value is compared with the determined upper threshold value ceiling and lower threshold floor Make a comparison; In response to the future load forecast value being greater than the upper threshold ceiling , adaptively select the corresponding spread spectrum SA or SA mode; In response to the future load forecast value being less than the lower threshold floor , adaptively select the corresponding spread spectrum CRDSA or CRDSA mode; In response to the future load forecast value being between an upper threshold ceiling and lower threshold floor Keep the current access mode unchanged.

6. A satellite Internet of Things random access adaptive device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 4.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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