Low earth orbit satellite internet of things access method and apparatus

Abstract

The application provides a low-orbit satellite Internet of Things access method and device, and belongs to the technical field of satellite communication. The method is applied to a satellite end and comprises the following steps: according to uplink channel observation information, a target prediction algorithm is used to dynamically estimate the number of future effective competitive terminals; according to the number of future effective competitive terminals and a system throughput model, an optimal access probability is calculated with the goal of optimizing the system successful communication efficiency, and the optimal access probability is encapsulated as a probability control factor; and the probability control factor is broadcast to ground terminals in the coverage area of the satellite end, and is used for the ground terminals to randomly decide whether to initiate uplink transmission at the next access opportunity according to the probability control factor in the case of having data transmission demand, so as to access the low-orbit satellite Internet of Things of the satellite end. Through the closed loop of “observation-prediction-optimization-control”, the application realizes distributed and forward-looking accurate regulation and control of the access behavior of a large number of terminals.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and in particular to a method and apparatus for accessing low-Earth orbit satellite Internet of Things (IoT). Background Technology

[0002] With the widespread application of the Internet of Things (IoT) in areas without terrestrial network coverage, such as oceans, deserts, and emergency response, low-orbit satellite IoT systems have become a key solution due to their advantages of wide coverage and low cost.

[0003] However, the system faces a core contradiction: the extreme limitation of satellite channel resources versus the large number of ground terminals and their highly unpredictable access. Traditional Aloha-type random access protocols experience a sharp increase in collision probability when the number of terminals surges, leading to a sudden drop in throughput or even system paralysis, a phenomenon known as the "avalanche effect."

[0004] Therefore, there is an urgent need for a low-Earth orbit satellite IoT access method that has forward-looking control capabilities and can adaptively adjust terminal access behavior to keep the system throughput stable in the optimal range over the long term. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and apparatus for accessing low-orbit satellite Internet of Things (IoT). By constructing a closed loop of "observation-prediction-optimization-control," it achieves distributed, forward-looking, and precise regulation of the access behavior of massive numbers of terminals, effectively overcoming the lag and inefficiency of traditional random access protocols under dynamic loads, and significantly improving system throughput, stability, and resource utilization.

[0006] In a first aspect, the present invention provides a method for accessing a low-Earth orbit satellite Internet of Things (IoT) system, the method being applied to a satellite; the method includes the following steps: Based on uplink channel observation information, the target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated and encapsulated as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite. Upon receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0007] According to a low-Earth orbit satellite Internet of Things access method provided by the present invention, the target prediction algorithm is a Kalman filter algorithm, the Kalman filter algorithm includes a state transition model and an observation model, and the uplink channel observation information includes channel observation information at the current access time; The step of dynamically estimating the number of effective competing terminals for future access opportunities using a target prediction algorithm based on uplink channel observation information includes: Based on the estimated number of effective competing terminals at the previous moment and the channel observation information of the current access timing, the estimated number of effective competing terminals at the current access timing is updated using the state transition model and the observation model. Based on the estimated number of valid competing terminals at the current access time, the estimated number of valid competing terminals at future access times is predicted.

[0008] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, the step of updating the estimated number of effective competing terminals at the current access time using the state transition model and the observation model based on the estimated number of effective competing terminals at the previous moment and the channel observation information at the current access time includes: Based on the estimated number of valid competing terminals in the previous moment, the prior prediction value of the current access opportunity is calculated using the state transition model; Substitute the prior prediction value of the current access timing into the observation model to obtain the predicted observation of the current access timing, and calculate the observation residual of the current access timing based on the predicted observation of the current access timing and the channel observation information of the current access timing. The prior prediction value of the current access opportunity is corrected based on the observation residual of the current access opportunity to obtain the estimated value of the number of effective competing terminals for the current access opportunity.

[0009] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, the step of predicting an estimated number of effective competing terminals for future access opportunities based on an estimated number of effective competing terminals for the current access opportunity includes: Using the state transition model, the estimated number of valid competing terminals at the current access time is taken as input to calculate the estimated number of valid competing terminals at the future access time.

[0010] According to the present invention, a low-orbit satellite Internet of Things access method is provided, wherein the system's successful communication efficiency is represented by the expected number of terminals that successfully communicate in the system, and the system throughput model is used to characterize the functional relationship between the expected number of terminals that successfully communicate in the system and the access probability and the number of competing terminals under given channel resource conditions. The system throughput model is constructed based on the time-slot Aloha protocol and the capture effect model. The step of calculating the optimal access probability based on the estimated number of effective competing terminals at the future access opportunity and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, includes: With the goal of maximizing the expected number of terminals that successfully communicate in the system, the functional relationship is solved to obtain the optimal access probability.

[0011] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, the ground terminal, in response to receiving the probability control factor, randomly decides whether to initiate uplink transmission at the next access opportunity based on the probability control factor when there is a data transmission demand, including: In response to the probability control factor, the ground terminal generates a random number at each uplink decision point; the random number is greater than 0 and less than 1. If the random number is determined to be less than the probability control factor, the ground terminal determines to initiate uplink transmission at the next access opportunity. If the random number is determined to be greater than or equal to the probability control factor, the ground terminal determines to enter a backoff state and waits for the next uplink decision point.

[0012] According to a low-Earth orbit satellite Internet of Things access method provided by the present invention, the step of broadcasting the probability control factor to the ground terminal within the coverage area of ​​the satellite includes: Control signaling carrying the probability control factor and synchronization information is transmitted to ground terminals within the coverage area of ​​the satellite via the downlink broadcast channel.

[0013] Secondly, the present invention provides a low-orbit satellite Internet of Things access method, the method being applied to a ground terminal; the method includes the following steps: The probability control factor received from the satellite is obtained by the satellite using uplink channel observation information and a target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities. Based on this estimate and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system communication efficiency, and then encapsulates this optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. In response to receiving the probability control factor, and when there is a data transmission requirement, a random decision is made based on the probability control factor as to whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0014] Thirdly, the present invention also provides a low-orbit satellite Internet of Things access device, which is applied to a satellite; the device includes the following modules: The prediction module is used to dynamically estimate the number of effective competing terminals for future access opportunities based on uplink channel observation information and target prediction algorithm. The probability control factor calculation module is used to calculate the optimal access probability based on the estimated number of effective competing terminals for the future access opportunity and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, and encapsulate the optimal access probability as a probability control factor; the probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has a data transmission requirement. The broadcast module is used to broadcast the probability control factor to the ground terminal within the coverage area of ​​the satellite. In response to receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0015] Fourthly, the present invention also provides a low-orbit satellite Internet of Things access device, which is applied to a ground terminal; the device includes: The receiving module is used to receive the probability control factor broadcast by the satellite. The probability control factor is obtained by the satellite dynamically estimating the number of effective competing terminals for future access opportunities using a target prediction algorithm based on uplink channel observation information. Based on this estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, the optimal access probability is calculated with the goal of optimizing system successful communication efficiency. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The decision module is used to respond to the received probability control factor and, when there is a data transmission requirement, randomly decide whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0016] Fifthly, the present invention also provides an electronic device, including 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 any of the low-orbit satellite Internet of Things access methods described above.

[0017] In a sixth aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the low-Earth orbit satellite Internet of Things access method as described above.

[0018] In a seventh aspect, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the low-orbit satellite Internet of Things access methods described above.

[0019] This invention provides a method and apparatus for accessing a low-Earth orbit (LEO) satellite Internet of Things (IoT). First, based on uplink channel observation information, a target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Then, based on the estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, with the goal of optimizing system communication efficiency, the optimal access probability is calculated. This optimal access probability is encapsulated as a probability control factor, which represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to ground terminals within the satellite's coverage area. Upon receiving the probability control factor, the ground terminals, when they have data transmission needs, randomly decide whether to initiate uplink transmission in the next access opportunity to access the LEO satellite IoT.

[0020] This invention estimates the number of competing terminals in the future using a target prediction algorithm and calculates a probability control factor accordingly. This overcomes the lag inherent in traditional methods based on historical data and enables faster response to sudden changes in access load. The adaptive learning mechanism of the target prediction algorithm allows for continuous model optimization, ensuring the system remains highly efficient and stable in the face of changes in terminal service patterns. By constructing a closed loop of "observation-prediction-optimization-control," this invention achieves distributed, forward-looking, and precise regulation of massive terminal access behavior, effectively overcoming the lag and inefficiency of traditional random access protocols under dynamic loads, and significantly improving system throughput, stability, and resource utilization. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is one of the flowcharts illustrating the low-orbit satellite IoT access method provided by the present invention.

[0023] Figure 2This is the second flowchart of the low-orbit satellite Internet of Things access method provided by the present invention.

[0024] Figure 3 This is a schematic diagram of the simulation results of the low-orbit satellite Internet of Things access method provided by the present invention.

[0025] Figure 4 This is the third flowchart of the low-orbit satellite Internet of Things access method provided by the present invention.

[0026] Figure 5 This is one of the structural schematic diagrams of the low-orbit satellite Internet of Things access device provided by the present invention.

[0027] Figure 6 This is the second structural schematic diagram of the low-orbit satellite Internet of Things access device provided by the present invention.

[0028] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this 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 this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, not limited in number; for example, a first node can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0031] The following is combined with Figures 1 to 7 The present invention describes a low-orbit satellite Internet of Things access method and apparatus.

[0032] Figure 1 This is one of the flowcharts illustrating the low-Earth orbit satellite IoT access method provided by the present invention, which is applied to the satellite end; such as Figure 1 As shown, the method includes the following: Step 101: Based on the uplink channel observation information, use the target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities.

[0033] It should be noted that the execution entity of the low-orbit satellite IoT access method provided by the present invention can be the satellite end.

[0034] This low-Earth orbit satellite IoT access method is implemented through the following steps: The uplink channel observation information includes, for example, the observation vector. This includes at least one of the following: the number of data packets successfully decoded in the current time slot. Total number of data packet attempts detected (including collisions) Channel received signal strength indication .

[0035] First, within each predefined uplink time slot or time window, the satellite receives and processes the uplink signal, extracting the observation vector for the current time slot. .

[0036] The satellite maintains an internal estimate of the system state. This is used to characterize the number of effective competing terminals under the current satellite beam coverage (i.e., an estimate of the number of terminals attempting to transmit data). The observation vector of the extracted time slot is used... Subsequently, the satellite uses a target prediction algorithm (such as Kalman filtering, particle filtering, or a machine learning-based time-series prediction model) and the state estimate from the previous time slot to perform the prediction. and the observation vector of the current time slot The update and prediction steps are performed to obtain an estimate of the number of competing terminals for future access opportunities, such as the number of competing terminals in the next time slot. .

[0037] Step 102: Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, calculate the optimal access probability and encapsulate the optimal access probability as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs.

[0038] Specifically, after predicting the estimated number of effective competing terminals for future access opportunities, the probability control factor is calculated by further combining it with the system throughput model.

[0039] The system throughput model characterizes the functional relationship between the expected number of terminals that can successfully communicate in the system under given channel resources, the access probability, and the number of competing terminals.

[0040] The physical meaning of optimal access probability is: a "threshold" or "transmission permission probability" broadcast by the satellite to all terminals within its coverage area to maximize the overall communication efficiency of the system. If the access probability is too high, a large number of terminals will transmit simultaneously, resulting in severe collisions and a decrease in the number of successful transmissions; if the access probability is too low, channel resources will be idle, and the number of successful transmissions will also be low. The optimal access probability ensures that the system operates at the peak of the throughput curve. When it is predicted that the number of competing terminals will increase in the future, the satellite will reduce the broadcast probability to suppress the impulse to transmit; when the number of competing terminals decreases, the satellite will increase the broadcast probability to make full use of channel resources.

[0041] For example, in the slotted ALOHA protocol, if the number of channels is n and the number of competing terminals is m, then the optimal access probability p* approximately satisfies: Physical intuition: p∗ is approximately equal to n / m or min(1,n / m).

[0042] Meaning: The optimal probability value is chosen such that the expected number of terminals that actually attempt to send, m·p∗, matches the number of channel resources, n.

[0043] In other words, by broadcasting this probability, the satellite adjusts the "sending impulse" of a massive number of terminals to a level that the channel can handle efficiently, neither overloaded nor idle.

[0044] In practical applications, with the goal of optimizing the system's successful communication efficiency (e.g., maximizing the expected number of terminals successfully communicating), the functional relationship is solved to obtain the optimal access probability p*. Furthermore, the optimal access probability p* is used as a probability control factor to indicate the probability that each ground terminal is allowed to initiate uplink transmission at the next access opportunity when it has a data transmission need. That is, the probability control factor is a distributed access control factor. The ground terminal decides whether to "transmit" or "not transmit" by comparing a random number with this probability. The probability control factor may be, for example, 20% or 40%.

[0045] Step 103: Broadcast the probability control factor to the ground terminals within the coverage area of ​​the satellite. When the ground terminals receive the probability control factor, they can randomly decide whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things if they have a data transmission requirement.

[0046] Specifically, after calculating the probability control factor, it is broadcast to all ground terminals within the satellite's coverage area. Upon receiving the probability control factor, any ground terminal, if it has a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity based on the probability control factor.

[0047] The ground terminal can achieve adaptive control based solely on probabilistic control factors because the satellite undertakes all system perception, prediction, and optimization calculation tasks, compressing complex control decisions into a simple "admission threshold" broadcast to the terminal. The terminal then accurately realizes the satellite's control intent in a statistical sense through local randomization. This is a closed-loop control architecture of "centralized perception and calculation, distributed random execution."

[0048] Assuming there are m terminals within the current coverage area that require data transmission, and each makes an independent decision with probability p*, then: The actual number of terminals N attempting to send data follows a binomial distribution B(m,p*); Its expected value is E[N] = m × p*.

[0049] The key point is that the p* of satellite broadcasting is based on the prediction of m, calculated as the "optimal value" through a throughput optimization model. Therefore: E[N] = m × p ≈ number of channel resources (or optimal contention intensity).

[0050] When the number of actual attempts to send by the group is controlled within the optimal range, the system throughput naturally tends to be maximized.

[0051] Although the decision-making rules of the terminals are fixed, the probability control factor p* changes dynamically with the load (p decreases when the load is high and increases when the load is low); all terminals respond synchronously to the new p* value; therefore, the behavior of the terminal group can be adjusted in real time to follow the control commands broadcast by the satellite. This manifests as follows: under high load, the transmission probability is automatically reduced to reduce collisions; under low load, the transmission probability is automatically increased to make full use of the channel.

[0052] The method provided in this embodiment of the invention is applied to the satellite end. First, based on uplink channel observation information, a target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Then, based on the estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated. The optimal access probability is encapsulated as a probability control factor, which represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to ground terminals within the coverage area of ​​the satellite end. In response to receiving the probability control factor, the ground terminals, when they have data transmission needs, randomly decide whether to initiate uplink transmission in the next access opportunity to access the low-Earth orbit satellite Internet of Things (IoT) on the satellite end.

[0053] This invention estimates the number of competing terminals in the future using a target prediction algorithm and calculates a probability control factor accordingly. This overcomes the lag inherent in traditional methods based on historical data and enables faster response to sudden changes in access load. The adaptive learning mechanism of the target prediction algorithm allows for continuous model optimization, ensuring the system remains highly efficient and stable in the face of changes in terminal service patterns. By constructing a closed loop of "observation-prediction-optimization-control," this invention achieves distributed, forward-looking, and precise regulation of massive terminal access behavior, effectively overcoming the lag and inefficiency of traditional random access protocols under dynamic loads, and significantly improving system throughput, stability, and resource utilization.

[0054] It should be noted that each implementation method of this application can be freely combined, rearranged, or executed individually, and does not need to rely on or depend on a fixed execution order.

[0055] According to the present invention, a low-orbit satellite Internet of Things access method is provided, wherein the target prediction algorithm is a Kalman filter algorithm, the Kalman filter algorithm includes a state transition model and an observation model, and the uplink channel observation information includes the channel observation information of the current access timing; Based on uplink channel observation information, the estimated number of effective competing terminals for future access opportunities is obtained dynamically using a target prediction algorithm, including: Based on the estimated number of effective competing terminals in the previous moment and the channel observation information at the current access time, the estimated number of effective competing terminals at the current access time is updated using the state transition model and the observation model. Based on the current estimated number of competing terminals for access, the estimated number of competing terminals for access in the future can be predicted.

[0056] Specifically, in some embodiments, the target prediction algorithm is a Kalman filter algorithm, which includes a state transition model and an observation model.

[0057] The state transition model and the observation model are constructed based on the time-slotted ALOHA protocol principle, wherein: the state transition model is used to describe the changing relationship between the number of effective competing terminals at different access times, and the state transition model is constructed based on at least one of the following information: terminal service activation pattern, terminal mobility mode, or terminal silent period, and is used to characterize the dynamic characteristics of the number of effective competing terminals evolving over time; the observation model is used to describe the functional relationship between the number of successfully received data packets, the number of detected data packet collisions, and the number of effective competing terminals.

[0058] The process of predicting the number of effective competing terminals in step 101 specifically includes the following steps: (1) Phase 1 update: This step is "data fusion", which aims to use current observation information to correct the prediction of the previous moment and obtain a more accurate estimate of the current state.

[0059] Specifically, based on the estimated number of effective competing terminals at the previous moment and the channel observation information at the current access moment, the estimated number of effective competing terminals at the current access moment is updated using the state transition model and the observation model.

[0060] For example, using the channel observation information Z at the current access time. k An estimate of the previous time step (i.e., an estimate of the number of effective competing terminals in the previous time step). The optimal estimate for the current moment is obtained by making corrections (i.e., the estimated number of valid competing terminals at the current access time). .

[0061] (2) Stage 2 prediction: This step is "extrapolation". It uses the system evolution law to extrapolate the state of future moments from the current optimal estimate for forward control.

[0062] Specifically, based on the estimated number of effective competing terminals at the current access time, the estimated number of effective competing terminals at future access times is predicted.

[0063] For example, the optimal estimate based on the current moment (i.e., the estimate of the number of valid competing terminals at the current access time). Extrapolating the future state based on the system's evolutionary patterns, that is, estimating the number of effective competing terminals at future access opportunities. .

[0064] In practical applications, these two steps can be executed cyclically for each access cycle (such as a superframe), or the execution frequency can be adjusted according to the satellite's processing capacity and the channel's rate of change.

[0065] The method provided in this invention has an update step (outputting the current estimate) that relies on current observations to ensure that the state estimate closely follows actual load changes; and a prediction step (future estimate) that relies on the updated current estimate to ensure that the inference of future load is based on the latest optimal estimate. These two steps are executed alternately, forming a closed loop of "prediction—control—observation—update—re-prediction," thereby achieving the "proactive and precise regulation" of this invention.

[0066] According to the low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, based on the estimated number of effective competing terminals at the previous moment and the channel observation information at the current access time, the estimated number of effective competing terminals at the current access time is updated using a state transition model and an observation model, including: Based on the estimated number of valid competing terminals in the previous moment, the prior prediction value of the current access opportunity is calculated using the state transition model; Substitute the prior prediction value of the current access timing into the observation model to obtain the predicted observation of the current access timing, and calculate the observation residual of the current access timing based on the predicted observation of the current access timing and the channel observation information of the current access timing. The prior prediction value for the current access opportunity is corrected based on the observation residuals of the current access opportunity to obtain an estimate of the number of effective competing terminals for the current access opportunity.

[0067] Specifically, in some embodiments, updating the estimated number of valid competing terminals at the current access time (update step) is achieved through the following steps: Step 1: Obtain the prior prediction value.

[0068] Based on the estimated number of valid competing terminals from the previous time step, the prior prediction of the current access opportunity is calculated using a state transition model. For example, firstly, the state transition model is used to calculate the estimated value from the previous time step. The prediction is obtained from the prior prediction (that is, the prior estimate at the current moment). : in, For the state transition function (state transition model), such as This indicates that the change in the number of terminals is a random walk.

[0069] Step 2: Calculate the observation residuals.

[0070] First, the prior prediction value of the current access timing is substituted into the observation model to obtain the predicted observation value of the current access timing.

[0071] For example, first consider the prior prediction (i.e., the prior estimate at the current moment). Substituting the data into the observation model h(·), we obtain the predicted observations for the current access timing. =h( ).

[0072] Obtain channel observation information Z for the current access timing from the satellite receiver. k The observation residual for the current access timing is calculated based on the predicted observations and channel observations for the current access timing, such as: The observation residuals (new information) at the current access timing are: New interest = Z k - The residual reflects the difference between current observations and predictions based on historical information.

[0073] Step 3: Update the state estimate.

[0074] The step of correcting the prior prediction value based on the observation residual specifically includes: calculating the Kalman gain based on the covariance of the prior prediction value and the observation noise covariance; multiplying the observation residual by the Kalman gain and then adding it to the prior prediction value to obtain the estimated number of effective competing terminals at the current time.

[0075] For example, the Kalman gain is first calculated: based on the prediction covariance, the observation noise covariance, and the Jacobian matrix H of the observation model, the Kalman gain K is calculated. k Kalman gain K k Used to balance the weights of "prediction" and "observation".

[0076] Furthermore, combining the Kalman gain K k The prior prediction for the current access opportunity is corrected based on the observation residuals at the current access opportunity to obtain an estimate of the number of effective competing terminals at the current access opportunity. For example, the observation residuals (news) at the current access opportunity are multiplied by the Kalman gain K. k Then, it is superimposed on the prior prediction (that is, the prior estimate at the current moment). The above yields the optimal estimate for the current moment, which is the estimate of the number of effective competing terminals at the current moment. : At the same time, the covariance matrix is ​​updated for use in the next iteration.

[0077] If the current observation Z k With model prediction h( If the correlation is high, the innovation is small, and the estimated value basically remains the predicted value; if there is an unexpected sudden access (a sudden increase in the observed value), the innovation is large, and the Kalman gain is high. It will correct the estimated value towards the observed value, thereby responding quickly to load changes.

[0078] The method provided in this invention relies on current observations to ensure that the current state estimate closely follows actual load changes. The update and prediction steps are executed alternately, forming a closed loop of "prediction-control-observation-update-repreneurial prediction", thereby achieving "proactive and precise regulation".

[0079] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, based on an estimate of the number of effective competing terminals at the current access opportunity, an estimate of the number of effective competing terminals at the future access opportunity is predicted, including: Using a state transition model, the estimated number of effective competing terminals at the current access opportunity is taken as input to calculate the estimated number of effective competing terminals at the future access opportunity.

[0080] Specifically, in some embodiments, the estimation of the number of effective competing terminals for future access opportunities (prediction step) is achieved through the following steps: 1. Obtain the optimal estimate at the current moment.

[0081] That is, the result obtained in the previous step , representing the estimated number of effective competing terminals at the current moment.

[0082] 2. Apply the state transition model.

[0083] Based on prior knowledge of terminal service behavior (such as activation probability, terminal beam ingress / egress rates due to mobility, etc.), a state transition function L(·) is defined. The predicted value for the future time k+1 is: In its simplest form, assuming the number of terminals changes steadily over a short period and there is no external control input, then: That is, the current estimate is used directly as the prediction for the next moment.

[0084] 3. Optional: Consider dynamic business models.

[0085] If the system knows the average activation period or mobility model of the terminal service, this information can be incorporated into the transfer function. For example: If it is known that the average value per period is Δ + Individual terminal activation, Δ - If each terminal completes transmission or moves out of the beam, it can be modeled as follows: This modeling approach can improve prediction accuracy, especially when terminal behavior exhibits clear patterns.

[0086] 4. Output the predicted value.

[0087] Received This is an estimate of the number of effective competing terminals for future access opportunities, used to calculate the optimal access probability.

[0088] The method provided in this invention relies on the updated current estimate to ensure that the inference of future load is based on the latest optimal estimate. The update and prediction steps are executed alternately to form a closed loop of "prediction-control-observation-update-repreneurial", thereby achieving "proactive and precise regulation".

[0089] According to the low-orbit satellite Internet of Things access method provided by the present invention, the system successful communication efficiency is represented by the expected number of terminals that successfully communicate in the system. The system throughput model is used to characterize the functional relationship between the expected number of terminals that successfully communicate in the system and the access probability and the number of competing terminals under given channel resource conditions. The system throughput model is constructed based on the time-slot Aloha protocol and the capture effect model. Based on the estimated number of effective competing terminals for future access and the pre-existing system throughput model, the optimal access probability is calculated with the goal of optimizing the system's successful communication efficiency, including: With the goal of maximizing the expected number of terminals that successfully communicate in the system, the functional relationship is solved to obtain the optimal access probability.

[0090] Specifically, in some embodiments, the system's successful communication efficiency is represented by the expected number of terminals successfully communicating. The system throughput model is constructed based on the slotted Aloha protocol and the capture effect model. The system throughput model is used to characterize the functional relationship between the expected number of terminals successfully communicating E (total system throughput) and the access probability p and the number of competing terminals m under given channel resource conditions (given number of channels n). For example, the functional relationship is expressed as follows: in, The expected number of terminals that successfully communicate with the system. Let p be the access probability. To compete for the number of terminals, Given the number of channels.

[0091] The optimal access probability is obtained through the following steps: Based on the estimated number of effective competing terminals for future access opportunities obtained in step 101 The satellite solves the following optimization problem: given and a given number of channels Under the condition of all possible access probabilities Find the optimal access probability p* that maximizes the predicted throughput (the expected number E of terminals that successfully communicate in the system). That is: in, Optimal access probability, This is an estimate of the number of effective competing terminals for future access opportunities. Given a number of channels, For access probability, The independent variable that makes the function reach its maximum value .

[0092] The following example illustrates how to solve for the optimal access probability p*: Assume the simplified throughput function is: Where m=100 (number of predicted terminals), when hour, ,when hour, ,when hour, ,when hour, ,when hour, ,when hour, .

[0093] As can be seen, when hour, , This is the maximum value at this point, therefore: That is, the optimal access probability p* is 0.20.

[0094] The throughput model built based on the slotted ALOHA protocol is a single-peaked curve: when When the channel is idle for too long, When the value is too large, collisions are severe, and the peak point corresponds to the optimal probability of maximizing system throughput.

[0095] By solving Satellites are capable of: It maximizes the expected number of terminals that successfully communicate with the system and accurately locks the peak point; it does not rely on manually configured fixed probabilities or simple inverse proportional formulas (such as p=1 / m), but is based on theoretical optimal values, which can approach the theoretical limit under different loads and avoid subjective experience settings.

[0096] Furthermore, the calculated optimal access probability p* can be encapsulated as a probability control factor and then broadcast to the terminal. This p* is the control parameter that maximizes the predicted throughput and is the core output of the entire closed-loop control.

[0097] The method provided in this invention, by predicting the number of competing terminals in the future, adjusts the access probability in advance before the load arrives, achieving the following: proactively reducing the terminal transmission probability before a surge in load to avoid collision avalanche; even if the load changes abruptly, the satellite can complete the adjustment within one prediction period (usually at the superframe level), with a response speed far faster than traditional methods based on historical statistics. Thus, this invention, through a collaborative design of "prediction-driven + theoretical optimization + distributed execution + closed-loop correction," achieves a comprehensive technical effect of high throughput, strong robustness, low overhead, and scalability in low-Earth orbit satellite IoT access control.

[0098] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, in response to receiving a probability control factor, the ground terminal, when there is a data transmission demand, randomly decides whether to initiate uplink transmission at the next access opportunity based on the probability control factor, including: The ground terminal responds to the probability control factor and generates a random number at each uplink decision point; the random number is greater than 0 and less than 1. If the random number is determined to be less than the probability control factor, the ground terminal will initiate uplink transmission at the next access opportunity. If the random number is determined to be greater than or equal to the probability control factor, the ground terminal will enter a backoff state and wait for the next uplink decision point.

[0099] Specifically, in some embodiments, the process by which the ground terminal randomly decides whether to initiate uplink transmission at the next access opportunity based on a probability control factor is implemented through the following steps: First, in response to the probability control factor p*, the ground terminal generates a random number ξ at each uplink decision point. Each terminal's generated random number ξ is a random variable uniformly distributed within the interval [0,1). The random number ξ represents the terminal's "random draw" result at the current access time; it is a "lucky value" or "token number" for the terminal itself. Due to the uniform distribution, the probability that ξ falls within any subinterval of [0,1) is equal to the length of that interval. For example, P(ξ<0.3)=0.3, P(ξ<0.8)=0.8. This characteristic allows random numbers to serve as a medium for probabilistic decision-making.

[0100] Furthermore, the terminal compares the random number ξ with the probability control factor p* broadcast by the satellite. If ξ < p*, the ground terminal determines to initiate uplink transmission at the next access opportunity, that is, the terminal decides to send; if ξ ≥ p*, the ground terminal determines to enter the backoff state and waits for the next uplink decision point, that is, the terminal decides not to send (backoff).

[0101] Based on the property of uniform distribution, the probability that the terminal actually sends a message is exactly p*: P(send) = P(ξ) <p)=p* This mechanism can be understood as a lottery for entry: The satellite broadcasts an "entry probability" p*, such as p*=0.20, meaning "only those with a probability of 0.20 can enter the competition." Each terminal draws a number (generating a random number ξ). If the drawn number is less than 0.2, it enters (sends the signal); otherwise, it waits outside. Overall, the final number of terminals entering the competition is exactly 20% of the total demand, consistent with the satellite's control target.

[0102] In summary, the satellite precisely controls the probability of each terminal actually initiating a transmission by broadcasting a probability value p*. Specifically: 1. From the perspective of a single terminal: Each terminal independently decides to send a message with probability p*. The terminal does not need to know the status of other terminals, nor does it need complex calculations; it only needs to compare a random number.

[0103] 2. From the perspective of the system as a whole: Assuming there are m terminals in the current coverage area that need to send data, each sending with probability p*, then the expected number of terminals that actually attempt to send is m×p. This expected value is exactly the target value that the satellite wants to control with the optimal probability p*.

[0104] 3. Matching with the optimal access probability p*: When calculating p*, the satellite's optimization objective is to maximize the system throughput (the expected number of terminals successfully communicating). The optimized p* solution precisely satisfies that: m×p ≈ number of channel resources (or optimal contention strength). Therefore, when the terminal group makes random decisions according to probability p*, the actual competition intensity is "tuned" to near the optimal range, which avoids both overload leading to severe collisions and idleness leading to resource waste.

[0105] The method provided in this invention, with its "broadcast probability + local random comparison" mechanism, is the technical foundation for achieving distributed, low-overhead, and precisely controllable access regulation.

[0106] According to a low-Earth orbit satellite Internet of Things access method provided by the present invention, a probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite, comprising: Control signaling carrying probability control factors and synchronization information is transmitted to ground terminals within the coverage area of ​​the satellite via the downlink broadcast channel.

[0107] In some embodiments, necessary synchronization information refers to the auxiliary information required in a satellite communication system to ensure that the ground terminal can correctly receive broadcast signaling, understand the timing of the probability control factor's activation, and initiate uplink transmission on the correct time and frequency resources. Specifically, it includes one or more of the following aspects: (1) Time synchronization information.

[0108] This is the most crucial synchronization information, ensuring that the terminal knows exactly which point in time corresponds to the "next access opportunity".

[0109] Examples include the start timestamp or system frame number of the next access opportunity (superframe / slot start time), the access opportunity number corresponding to the current broadcast (access opportunity index), and the delay compensation value caused by transmission distance (timing advance).

[0110] (2) Frequency synchronization information.

[0111] Ensure that the terminal's uplink transmission frequency is aligned with the satellite's receiving frequency. This includes, for example, the uplink center frequency and frequency offset correction value.

[0112] (3) Resource allocation information.

[0113] Inform the terminal of available channel resources to avoid transmitting on incorrect resources. This includes, for example, available channel / slot indication and spreading code / preamble resources.

[0114] (4) Beam / coverage area identification.

[0115] Ensure that the terminal confirms that the broadcast signaling applies to its own beam. This may include, for example, the beam identifier corresponding to the current broadcast.

[0116] (5) The beam identifier corresponding to the current broadcast.

[0117] Ensure that the terminal uses the latest control parameters. These include, for example, the control signaling sequence number / version number and validity period (the effective period of this probability control factor).

[0118] If this synchronization information is missing: No time synchronization: The terminal does not know when to send, which may cause it to miss the access opportunity or be out of time slot with other terminals; No frequency synchronization: The satellite receiver cannot demodulate correctly, and even if it transmits, it will likely fail. No resource indication: The terminal may be sending data on the wrong channel, causing interference or waste; No version / expiration date: The terminal may use an expired factor for a long time and cannot adapt to changes in load.

[0119] In practical applications, the probability control factor p* and necessary synchronization information are encapsulated into control signaling, and the control signaling is transmitted to all ground terminals within the coverage area of ​​the satellite via the downlink broadcast channel.

[0120] The method provided in this invention provides "necessary synchronization information" to ensure that the probability control factor can be executed accurately, uniformly, and orderly by the terminal, which is the fundamental support for the closed-loop control of this invention to achieve the expected results.

[0121] Optionally, the low-Earth orbit satellite IoT access method further includes an adaptive learning mechanism: the satellite periodically compares the predicted system throughput E with the actual value S obtained through subsequent observations. k+1 The prediction error is calculated. This error is then used to fine-tune the prediction model (such as the process noise or observation noise parameters of the Kalman filter) or the system throughput model online, making the model more closely match the actual dynamic characteristics of the network.

[0122] In another aspect, the present invention provides a system for implementing the above method, comprising a satellite-side processing module (satellite side) and a terminal-side control module (terminal side).

[0123] 1) The satellite-side processing module includes: an observation unit, a prediction and estimation unit, an optimization calculation unit, and a broadcast generation and transmission unit.

[0124] 2) The terminal-side control module includes: a broadcast receiving and parsing unit and a random access decision unit.

[0125] Figure 2 This is the second flowchart illustrating the low-orbit satellite IoT access method provided by the present invention, as shown below. Figure 2 As shown, the method includes: Satellite side: The observation unit obtains the observation vector Z. K The prediction and estimation unit (such as the Kalman filter algorithm) outputs the predicted load X. ｛k+1|k｝ The optimization calculation unit calculates the optimal probability p* based on the system throughput model; the broadcast generation and transmission unit sends control signaling, that is, the downlink broadcast probability control factor p*, to the ground terminal side.

[0126] Ground terminal side: The broadcast receiving and parsing unit obtains the probability control factor p*; the random access decision unit generates a random number ξ; it determines whether ξ < p*; if so, it sends data to the satellite, specifically by uplinking data transmission or attempting to transmit to the observation unit; if not, it backs off and waits.

[0127] Figure 3 This is a schematic diagram of the simulation results of the low-orbit satellite IoT access method provided by the present invention. Specifically, as shown in the figure... Figure 3The diagram shown represents the results of a simulation of dynamic access control for low-Earth orbit satellite IoT (number of channels: 100). It includes sub-graphs on the number of terminals, system throughput, access probability, and terminal number prediction. The horizontal axis represents the simulation time slot.

[0128] In the subplot of terminal count, the blue curve represents the actual number of competing terminals, the purple box represents the actual number of competing terminals corresponding to stage 1: medium load, the pink box represents the actual number of competing terminals corresponding to stage 2: high load, and the purple box represents the actual number of competing terminals corresponding to stage 3: medium to high load.

[0129] In the system throughput subplot, the red curve represents the system throughput curve of the existing technology (static feedback), the green curve represents the system throughput curve of the present invention (dynamic prediction), the blue dashed curve represents the theoretical optimal upper bound, and the pink box represents the system throughput corresponding to the high load stage. Based on the displayed simulation results, the performance index analysis is as follows: The global average throughput is as follows: Prior art: 28.31; This invention: 34.06; Compared with the prior art, the throughput of this invention is increased by 20.3% (that is, the performance improvement is +20.3%).

[0130] The average throughput during high load periods is as follows: Prior art: 21.07; This invention: 32.64; This invention improves throughput by 55.0% (i.e., performance improvement: +55.0%) compared to existing technologies. This is a direct result of global optimization.

[0131] In the subplot of access probability, the red curve represents the access probability curve of the prior art, and the green curve represents the access probability curve of the present invention. The simulation results in the figure show the following probability stability (standard deviation): Existing technology: 0.1494; This invention: 0.1872.

[0132] In the subplot of terminal number prediction, the blue curve represents the actual number of terminals, and the green dashed curve represents the predicted value of this invention. The simulation results show the prediction performance as follows: Mean absolute error: 58.22; Terminal relative error: 32.2%.

[0133] Figure 4 This is the third flowchart of the low-orbit satellite IoT access method provided by the present invention, which is applied to a ground terminal; such as Figure 4 As shown, the method includes the following steps: Step 401: Receive the probability control factor broadcast by the satellite. The probability control factor is obtained by the satellite dynamically estimating the number of effective competing terminals for future access opportunities based on uplink channel observation information and using a target prediction algorithm. Based on the estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, the optimal access probability is calculated with the goal of optimizing the system's successful communication efficiency. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. Step 402: In response to receiving the probability control factor, if there is a data transmission requirement, randomly decide whether to initiate uplink transmission at the next access opportunity based on the probability control factor in order to access the low-orbit satellite Internet of Things at the satellite end.

[0134] It should be noted that the execution subject of the low-orbit satellite Internet of Things access method provided by the present invention is a ground terminal, specifically, any ground terminal within the coverage area of ​​the satellite.

[0135] This low-Earth orbit satellite IoT access method is implemented through the following steps: First, the system receives the probability control factor p* broadcast by the satellite. This probability control factor p* is a dynamically estimated value of the number of competing terminals for future access opportunities, calculated by the satellite using a target prediction algorithm based on uplink channel observation information. The optimal access probability is then calculated based on this estimated number of competing terminals and a pre-stored system throughput model, with the goal of optimizing system communication efficiency. This optimal access probability is then encapsulated. Further, in response to the received probability control factor, and given a data transmission requirement, the system randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-Earth orbit satellite IoT.

[0136] The ground terminal can achieve adaptive control based solely on probabilistic control factors because the satellite undertakes all system perception, prediction, and optimization calculation tasks, compressing complex control decisions into a simple "admission threshold" broadcast to the terminal. The terminal then accurately realizes the satellite's control intent in a statistical sense through local randomization. This is a closed-loop control architecture of "centralized perception and calculation, distributed random execution."

[0137] The method provided in this embodiment of the invention achieves the following technical effects: I. Simplified decision-making on the terminal side to achieve low power consumption and low cost.

[0138] The ground terminal only needs to perform two actions: receiving broadcasts and comparing random numbers. The terminal does not need to detect channel status, count collisions, or understand the behavior of other terminals. The terminal is not involved in prediction algorithms, optimization solutions, or state maintenance; it only performs one random number generation and comparison. The terminal does not need to record historical access results or maintain backoff counters (unless simple backoff is selectively added).

[0139] The terminal hardware has extremely low cost and power consumption, perfectly meeting the core requirements of IoT terminals: "low cost, low power consumption, and long standby time".

[0140] II. Distributed autonomous decision-making enables high scalability for massive numbers of terminals.

[0141] Terminals do not need to interact with each other and make independent decisions. The decision results are statistically precisely subject to the probability control factor of satellite broadcasting.

[0142] No centralized scheduling overhead: No satellite needs to allocate time slots or identification for each terminal, and signaling overhead is decoupled from the number of terminals.

[0143] No interaction conflicts: There is no negotiation, handshake or competition between terminals to resolve communication, avoiding secondary overhead.

[0144] Linear scalability: Even if the number of terminals grows from thousands to millions, the system only needs to maintain the same broadcast period and probability update frequency, and there will be no control signaling explosion.

[0145] Technical benefits: The system can support a massive number of concurrent terminals and has a strong capacity for large-scale deployment.

[0146] Third, respond to globally optimal instructions to achieve efficient system-level communication.

[0147] Although the terminal's decision-making rules are simple, the probability control factor p* on which the decision is based is the optimal value calculated by the satellite through global perception, dynamic prediction, and theoretical optimization.

[0148] Statistically precise execution: All terminals transmit independently with probability p*, so that the expected value of the actual number of terminals attempting to transmit, m×p, is precisely controlled within the optimal range of channel resources.

[0149] Global optimal approximation: The behavior of the terminal group statistically achieves the goal of maximizing the throughput of the satellite, making the system's successful communication efficiency approach the theoretical limit.

[0150] Technical effect: Without increasing the complexity on the terminal side, it achieves global optimal control at the system level, achieving the ideal state of "simple terminal and efficient system".

[0151] IV. Rapid response and dynamic adjustment to achieve forward-looking self-adaptation.

[0152] Before each access opportunity begins, the terminal uses the latest received probability control factor to make a decision.

[0153] Immediate effect: The terminal does not need to wait for multiple cycles or accumulated statistical information; the new probability is applied immediately after the broadcast is received.

[0154] Synchronous response: All terminals within the coverage area synchronously switch to the new transmission probability, and the group behavior quickly follows the satellite's control instructions.

[0155] Proactive adaptation: Since the probability control factor is based on the prediction of future load, the terminal is "pre-adjusted" to the appropriate behavior pattern before the actual load arrives.

[0156] Technical effect: The system can complete the adjustment at the first access time of load change, avoiding the lag loss of the traditional method of "collision first, adjustment later", and significantly improving the throughput stability.

[0157] V. Perceived Differentiation Processing to Achieve Terminal Fairness All terminals receive the same probability control factor, adopt the same random decision-making rules, and have statistically equal opportunities to send data.

[0158] Long-term fairness: Each terminal has an equal probability of being allowed to send data during long-term operation, and there will be no situation where some terminals are "starved".

[0159] No identification required: No need to distinguish terminal priority or type, suitable for large-scale homogeneous IoT scenarios.

[0160] Technical effect: While ensuring the overall efficiency of the system, fair access between terminals is achieved, avoiding the problem of unfair resource allocation.

[0161] VI. Decoupling from the satellite side enables deployment flexibility.

[0162] The implementation on the terminal side is completely decoupled from the specific prediction algorithms and optimization models on the satellite side.

[0163] Terminal transparency: The terminal does not need to know whether the satellite uses Kalman filtering, particle filtering, or neural networks, nor does it need to understand the specific form of the throughput model.

[0164] Protocol compatibility: As long as the broadcast protocol definition is consistent, the terminal can be adapted to satellite payloads of different versions or manufacturers.

[0165] Technical benefits: The terminal can be developed independently, mass-produced, and used for a long time without needing to be updated with satellite algorithm upgrades, thus reducing system deployment and maintenance costs.

[0166] In summary, this method places all the complex system-level optimizations on the satellite side, while the terminal side only performs them in a very simple way of "receiving broadcasts + random comparisons". While achieving low power consumption, low cost and high scalability of the terminal, it achieves the technical effect of maximizing system throughput by accurately responding to the global optimal probability. It is an ideal distributed control architecture for massive IoT terminal access.

[0167] The low-Earth orbit satellite Internet of Things (IoT) access device provided by the present invention is described below. The low-Earth orbit satellite IoT access device described below can be referred to in correspondence with the low-Earth orbit satellite IoT access method described above.

[0168] Figure 5 This is one of the structural schematic diagrams of the low-orbit satellite IoT access device provided by the present invention, which is applied to the satellite end; such as Figure 5 As shown, the low-orbit satellite IoT access device 500 includes the following modules: Prediction module 510 is used to dynamically estimate the number of effective competing terminals for future access opportunities based on uplink channel observation information and target prediction algorithm. The probability control factor calculation module 520 is used to calculate the optimal access probability based on the estimated number of effective competing terminals for the future access opportunity and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, and encapsulate the optimal access probability as a probability control factor; the probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has a data transmission requirement. The broadcast module 530 is used to broadcast the probability control factor to the ground terminal within the coverage area of ​​the satellite. In response to receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0169] This invention estimates the number of competing terminals in the future using a target prediction algorithm and calculates a probability control factor accordingly. This overcomes the lag inherent in traditional methods based on historical data and enables faster response to sudden changes in access load. The adaptive learning mechanism of the target prediction algorithm allows for continuous model optimization, ensuring the system remains highly efficient and stable in the face of changes in terminal service patterns. By constructing a closed loop of "observation-prediction-optimization-control," this invention achieves distributed, forward-looking, and precise regulation of massive terminal access behavior, effectively overcoming the lag and inefficiency of traditional random access protocols under dynamic loads, and significantly improving system throughput, stability, and resource utilization.

[0170] According to the present invention, a low-orbit satellite Internet of Things access device 500 is provided, wherein the target prediction algorithm is a Kalman filter algorithm, the Kalman filter algorithm includes a state transition model and an observation model, and the uplink channel observation information includes channel observation information of the current access timing; The prediction module 510 is specifically used for: Based on the estimated number of effective competing terminals at the previous moment and the channel observation information of the current access timing, the estimated number of effective competing terminals at the current access timing is updated using the state transition model and the observation model. Based on the estimated number of valid competing terminals at the current access time, the estimated number of valid competing terminals at future access times is predicted.

[0171] According to a low-Earth orbit satellite Internet of Things access device 500 provided by the present invention, the prediction module 510 is further used for: Based on the estimated number of valid competing terminals in the previous moment, the prior prediction value of the current access opportunity is calculated using the state transition model; Substitute the prior prediction value of the current access timing into the observation model to obtain the predicted observation of the current access timing, and calculate the observation residual of the current access timing based on the predicted observation of the current access timing and the channel observation information of the current access timing. The prior prediction value of the current access opportunity is corrected based on the observation residual of the current access opportunity to obtain the estimated value of the number of effective competing terminals for the current access opportunity.

[0172] According to a low-Earth orbit satellite Internet of Things access device 500 provided by the present invention, the prediction module 510 is further used for: Using the state transition model, the estimated number of valid competing terminals at the current access time is taken as input to calculate the estimated number of valid competing terminals at the future access time.

[0173] According to the present invention, a low-orbit satellite Internet of Things access device 500 is provided, wherein the system's successful communication efficiency is represented by the expected number of terminals that successfully communicate in the system, and the system throughput model is used to characterize the functional relationship between the expected number of terminals that successfully communicate in the system and the access probability and the number of competing terminals under given channel resource conditions. The system throughput model is constructed based on the time-slot Aloha protocol and the capture effect model. The probability control factor calculation module 520 is specifically used for: With the goal of maximizing the expected number of terminals that successfully communicate in the system, the functional relationship is solved to obtain the optimal access probability.

[0174] According to a low-Earth orbit satellite Internet of Things (IoT) access method provided by the present invention, the ground terminal, in response to receiving the probability control factor, randomly decides whether to initiate uplink transmission at the next access opportunity based on the probability control factor when there is a data transmission demand, including: In response to the probability control factor, the ground terminal generates a random number at each uplink decision point; the random number is greater than 0 and less than 1. If the random number is determined to be less than the probability control factor, the ground terminal determines to initiate uplink transmission at the next access opportunity. If the random number is determined to be greater than or equal to the probability control factor, the ground terminal determines to enter a backoff state and waits for the next uplink decision point.

[0175] According to the present invention, a low-Earth orbit satellite Internet of Things access device 500 is provided, wherein the broadcast module 530 is specifically used for: Control signaling carrying the probability control factor and synchronization information is transmitted to ground terminals within the coverage area of ​​the satellite via the downlink broadcast channel.

[0176] Figure 6 This is the second structural schematic diagram of the low-orbit satellite IoT access device provided by the present invention, which is applied to a ground terminal; as shown below. Figure 6 As shown, the low-orbit satellite IoT access device 600 includes: The receiving module 610 is used to receive a probability control factor broadcast by the satellite. The probability control factor is obtained by the satellite dynamically estimating the number of effective competing terminals for future access opportunities using a target prediction algorithm based on uplink channel observation information. Based on this estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system successful communication efficiency, and then encapsulates the optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The decision module 620 is used to respond to the received probability control factor and, when there is a data transmission requirement, randomly decide whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0177] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 7As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communications interface 720, and the memory 730 communicate with each other via the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a low-Earth orbit satellite Internet of Things access method, which is applied to the satellite end; the method includes: Based on uplink channel observation information, the target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated and encapsulated as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite. Upon receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0178] Alternatively, a low-Earth orbit satellite IoT access method may be implemented, the method being applied to a ground terminal; the method includes: The probability control factor received from the satellite is obtained by the satellite using uplink channel observation information and a target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities. Based on this estimate and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system communication efficiency, and then encapsulates this optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. In response to receiving the probability control factor, and when there is a data transmission requirement, a random decision is made based on the probability control factor as to whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0179] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the low-Earth orbit satellite Internet of Things access method provided by the above methods, the method being applied to a satellite; the method includes: Based on uplink channel observation information, the target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated and encapsulated as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite. Upon receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0181] Alternatively, the low-Earth orbit satellite IoT access method provided by the above methods can be implemented, and the method is applied to a ground terminal; the method includes: The probability control factor received from the satellite is obtained by the satellite using uplink channel observation information and a target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities. Based on this estimate and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system communication efficiency, and then encapsulates this optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. In response to receiving the probability control factor, and when there is a data transmission requirement, a random decision is made based on the probability control factor as to whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0182] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the low-Earth orbit satellite Internet of Things access method provided by the methods described above, the method being applied to a satellite; the method includes: Based on uplink channel observation information, the target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated and encapsulated as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite. Upon receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

[0183] Alternatively, the low-Earth orbit satellite IoT access method provided by the above methods can be implemented, and the method is applied to a ground terminal; the method includes: The probability control factor received from the satellite is obtained by the satellite using uplink channel observation information and a target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities. Based on this estimate and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system communication efficiency, and then encapsulates this optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. In response to receiving the probability control factor, and when there is a data transmission requirement, a random decision is made based on the probability control factor as to whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

[0184] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for accessing low-Earth orbit satellite Internet of Things (IoT), characterized in that, Applied to the satellite end; the method includes: Based on uplink channel observation information, the target prediction algorithm is used to dynamically estimate the number of effective competing terminals for future access opportunities. Based on the estimated number of effective competing terminals for future access opportunities and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, the optimal access probability is calculated and encapsulated as a probability control factor. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The probability control factor is broadcast to the ground terminal within the coverage area of ​​the satellite. Upon receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

2. The low-orbit satellite IoT access method according to claim 1, characterized in that, The target prediction algorithm is a Kalman filter algorithm, which includes a state transition model and an observation model. The uplink channel observation information includes channel observation information for the current access timing. The step of dynamically estimating the number of effective competing terminals for future access opportunities using a target prediction algorithm based on uplink channel observation information includes: Based on the estimated number of effective competing terminals at the previous moment and the channel observation information of the current access timing, the estimated number of effective competing terminals at the current access timing is updated using the state transition model and the observation model. Based on the estimated number of valid competing terminals at the current access time, the estimated number of valid competing terminals at future access times is predicted.

3. The low-orbit satellite IoT access method according to claim 2, characterized in that, The step of updating the estimated number of effective competing terminals for the current access opportunity using the state transition model and the observation model based on the previous time-of-flight estimate of the number of terminals and the channel observation information of the current access opportunity includes: Based on the estimated number of valid competing terminals in the previous moment, the prior prediction value of the current access opportunity is calculated using the state transition model; Substitute the prior prediction value of the current access timing into the observation model to obtain the predicted observation of the current access timing, and calculate the observation residual of the current access timing based on the predicted observation of the current access timing and the channel observation information of the current access timing. The prior prediction value of the current access opportunity is corrected based on the observation residual of the current access opportunity to obtain the estimated value of the number of effective competing terminals for the current access opportunity.

4. The low-orbit satellite IoT access method according to claim 2, characterized in that, The step of predicting the estimated number of effective competing terminals for future access opportunities based on the estimated number of effective competing terminals for the current access opportunity includes: Using the state transition model, the estimated number of valid competing terminals at the current access time is taken as input to calculate the estimated number of valid competing terminals at the future access time.

5. The low-Earth orbit satellite Internet of Things access method according to any one of claims 1-4, characterized in that, The system's successful communication efficiency is represented by the expected number of terminals that successfully communicate with the system. The system throughput model is used to characterize the functional relationship between the expected number of terminals that successfully communicate with the system and the access probability and the number of competing terminals under given channel resource conditions. The system throughput model is constructed based on the slotted Aloha protocol and the capture effect model. The step of calculating the optimal access probability based on the estimated number of effective competing terminals at the future access opportunity and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, includes: With the goal of maximizing the expected number of terminals that successfully communicate in the system, the functional relationship is solved to obtain the optimal access probability.

6. The low-Earth orbit satellite Internet of Things access method according to any one of claims 1-4, characterized in that, In response to receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity based on the probability control factor, including: In response to the probability control factor, the ground terminal generates a random number at each uplink decision point; the random number is greater than 0 and less than 1. If the random number is determined to be less than the probability control factor, the ground terminal determines to initiate uplink transmission at the next access opportunity. If the random number is determined to be greater than or equal to the probability control factor, the ground terminal determines to enter a backoff state and waits for the next uplink decision point.

7. The low-Earth orbit satellite Internet of Things access method according to any one of claims 1-4, characterized in that, The step of broadcasting the probability control factor to the ground terminal within the coverage area of ​​the satellite includes: Control signaling carrying the probability control factor and synchronization information is transmitted to ground terminals within the coverage area of ​​the satellite via the downlink broadcast channel.

8. A method for accessing low-Earth orbit satellite Internet of Things (IoT), characterized in that, Applied to ground terminals; the method includes: The probability control factor received from the satellite is obtained by the satellite using uplink channel observation information and a target prediction algorithm to dynamically estimate the number of effective competing terminals for future access opportunities. Based on this estimate and a pre-stored system throughput model, the satellite calculates the optimal access probability with the goal of optimizing system communication efficiency, and then encapsulates this optimal access probability. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. In response to receiving the probability control factor, and when there is a data transmission requirement, a random decision is made based on the probability control factor as to whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

9. A low-orbit satellite Internet of Things access device, characterized in that, Applications on satellites; The device includes: The prediction module is used to dynamically estimate the number of effective competing terminals for future access opportunities based on uplink channel observation information and target prediction algorithm. The probability control factor calculation module is used to calculate the optimal access probability based on the estimated number of effective competing terminals for the future access opportunity and the pre-stored system throughput model, with the goal of optimizing the system's successful communication efficiency, and encapsulate the optimal access probability as a probability control factor; the probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has a data transmission requirement. The broadcast module is used to broadcast the probability control factor to the ground terminal within the coverage area of ​​the satellite. In response to receiving the probability control factor, the ground terminal, when there is a data transmission requirement, randomly decides whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things of the satellite.

10. A low-orbit satellite Internet of Things access device, characterized in that, Applied to ground terminals; the device includes: The receiving module is used to receive the probability control factor broadcast by the satellite. The probability control factor is obtained by the satellite dynamically estimating the number of effective competing terminals for future access opportunities using a target prediction algorithm based on uplink channel observation information. Based on this estimated number of effective competing terminals for future access opportunities and a pre-stored system throughput model, the optimal access probability is calculated with the goal of optimizing system successful communication efficiency. The probability control factor represents the probability that each ground terminal is allowed to initiate uplink transmission in the next access opportunity when it has data transmission needs. The decision module is used to respond to the received probability control factor and, when there is a data transmission requirement, randomly decide whether to initiate uplink transmission at the next access opportunity to access the low-orbit satellite Internet of Things at the satellite end.

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