Robust information age optimization method for monitoring short packet communication of Internet of Things under non-ideal channel state information

Abstract

The invention discloses a robust information age optimization method for monitoring short packet communication of the internet of things under non-ideal channel state information, which comprises the following steps of: establishing a robust resource allocation model of which the weighted average information age of a system is minimized under the constraint of maximum transmitting power of a base station, and respectively considering two conditions of uncertain bounded channels and uncertain statistical channels; corresponding constraints are converted into linear matrix inequalities by using S-process and Bernstein inequalities, and then an original problem is converted into a convex optimization problem to be solved by using a positive semidefinite relaxation method; according to the robust information age optimization method disclosed by the invention, aiming at the problem of deterioration of machine node state updating performance under the condition of non-ideal channel state information in short packet communication of the monitored Internet of Things, two common channel uncertainty representation forms are fully considered, and the method is closer to a complex actual condition; the method can be widely applied to a monitoring Internet of Things state updating scene of non-ideal state information.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wireless communication state update, and particularly relates to a robust information age optimization method for monitoring Internet of Things (IoT) short packet communication under non-ideal channel state information. BACKGROUND

[0002] In emergency monitoring IoT scenarios such as disaster relief and forest fire prevention, machine type nodes (MTCD) play an important role in rescue and monitoring tasks due to their high flexibility and wide coverage. For example, in the forest fire prevention scenario, MTCDs collect and send real-time environmental information (such as temperature, fire, terrain, etc.) to the base station. The base station analyzes the data returned by the MTCD and broadcasts control instructions to each node in time to play a real-time monitoring role in controlling the fire. The freshness of the control instructions received by the MTCD is very important for the entire emergency monitoring IoT. Traditional indicators such as latency and throughput cannot fully measure the freshness of the information. In order to better describe the information freshness of the state update system, the present application uses a new indicator called information age proposed by Kaul et al. in the paper (S. Kaul, R. Yates, and M. Gruteser, "Real-time status: How often should one update?" in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Mar. 2012, pp. 2731-2735.) to represent the time elapsed since the generation of the most recently received status packet at the destination.

[0003] One of the main features of the emergency monitoring Internet of Things is the existence of a large number of short data packets for monitoring, so short packet communication (SPC) appears, which can be as low as hundreds of bits in the amount of control instruction information transmitted. More importantly, SPC uses code words with limited block length for encoding, so there will be a large packet error rate. Therefore, the theoretical methods based on Shannon's theorem cannot be well applied to the monitoring Internet of Things scene dominated by short packet communication. There have been some articles on the performance of short packet communication. (B. Yu, Y. Cai and D. Wu, "Joint Access Control and Resource Allocation for Short-Packet-Based mMTC in Status Update Systems," in IEEE Journal on Selected Areas in Communications, vol. 39, no. 3, pp. 851-865, March 2021.) studies the status update process in the large-scale machine type short packet communication scene, and improves the status update performance by optimizing access control, subchannel allocation and frame structure. (B. Tadele, V. Shyianov, F. Bellili, A. Mezghani and E. Hossain, "Age-Limited Capacity of Massive MIMO," in IEEE Transactions on Communications, vol. 70, no. 11, pp. 7384-7399, Nov. 2022.) based on the ideal channel state information assumption, studies the trade-off between information age and spectral efficiency in large-scale MIMO systems.

[0004] However, most of the above research assumes that the system can obtain ideal channel state information to solve the problem, ignoring the impact of non-ideal channel state information on system transmission performance. Due to factors such as channel feedback delay and quantization error in complex propagation environments, it is difficult to obtain ideal channel state information in actual communication processes, and the resource allocation algorithm designed under the condition of ideal channel state information is too idealistic, which may produce serious interruption events. Therefore, it is of great significance to consider the impact of non-ideal channel state information on information age in short packet monitoring Internet of Things communication systems. SUMMARY

[0005] In view of the above problems, the purpose of the present application is to provide a robust information age optimization method for monitoring Internet of Things short packet communication under non-ideal channel state information, a robust resource allocation model for minimizing the system weighted average information age is established under the constraint of the maximum transmission power of the base station, the bounded channel uncertainty and the statistical channel uncertainty are considered respectively, the corresponding constraints are converted into linear matrix inequalities by using the S-process and Bernstein inequality method, and then the semi-definite relaxation method is used to convert the original problem into a convex optimization problem for solving.

[0006] The specific technical scheme for achieving the purpose of the present application is:

[0007] A robust information age optimization method for monitoring Internet of Things short packet communication under non-ideal channel state information, comprising the following steps:

[0008] Step 1, based on the actual communication scene, the system channel uncertainty under non-ideal channel state information is modeled;

[0009] Step 2, determine the signal-to-noise ratio of the machine node controlled by the base station;

[0010] Step 3, determine the packet error rate and the system average information age;

[0011] Step 4, construct a robust resource optimization model for minimizing the system average weighted information age based on the constraint of the maximum transmission power of the base station;

[0012] Step 5, solve the robust resource optimization model for minimizing the system average weighted information age constructed in step 4 to obtain the optimization result.

[0013] Compared with the prior art, the present application has the following advantages:

[0014] The scheme of the present application is aimed at the problem of performance degradation of machine node state update in the case of non-ideal channel state information in the monitoring Internet of Things short packet communication, and fully considers two common forms of channel uncertainty, which is more close to the complex actual situation;

[0015] In addition, the maximum transmission power constraint of the base station is considered when constructing the model, and a robust resource allocation model for minimizing the system weighted average information age is established, and under the conditions of bounded channel uncertainty and statistical channel uncertainty, the problem is converted into a linear matrix inequality by using the S-process and Bernstein inequality method, and then the semi-definite relaxation method is used to solve the problem, which can be widely applied to the state update scene of the monitoring Internet of Things with non-ideal state information.

[0016] The present application will be further described below in conjunction with the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Flow chart of the method for monitoring robust information age optimization of short packet communication of Internet of Things under non-ideal channel state information of the application.

[0018] Figure 2 System schematic diagram of the communication scenario in the embodiment of the application.

[0019] Figure 3 Information age change schematic diagram of the data packet transmission process in the embodiment of the application.

[0020] Figure 4 Average information age change schematic diagram with sending power under the bounded channel state information error model in the embodiment of the application.

[0021] Figure 5 Average information age change schematic diagram with sending power under the statistical channel state information error model in the embodiment of the application.

[0022] Figure 6 Average information age change schematic diagram with antenna number and user number under the bounded channel state information error model in the embodiment of the application.

[0023] Figure 7 Average information age change schematic diagram with antenna number and user number under the statistical channel state information error model in the embodiment of the application. DETAILED DESCRIPTION

[0024] Embodiment

[0025] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. The described embodiments are only some of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work under the premise that the application falls within the scope of protection.

[0026] As shown in the present application and claims, unless the context clearly indicates otherwise, the words “one”, “an”, “a” and / or “the” do not specifically refer to the singular, but also include the plural. Generally, the terms “comprise” and “include” only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0027] The relative arrangement of parts and steps, numerical expressions, and numerical values set forth in the examples herein are not intended to limit the scope of the present application unless specifically so stated. It is to be understood that the actual dimensions of the various parts shown in the drawings are not necessarily to scale, and that the dimensions can be arbitrarily set forth for the clarity of presentation and because it is the purpose of the drawings only to illustrate certain aspects of the application. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art can not be discussed in detail but can be assumed by those of ordinary skill in the art to be part of a design consideration for the purposes of the balance of the present application. In all examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation on the scope of the exemplary embodiments. Thus, other examples of the exemplary embodiments can have different values. It is to be noted that like numbers and letters refer to like elements throughout the several views of the drawings and that the drawings are not necessarily to scale as the primary purpose of the drawings is to illustrate certain aspects of the application.

[0028] In conjunction Figure 1 A robust information age optimization method for monitoring Internet of Things short packet communication under non-ideal channel state information includes the following steps:

[0029] Step 1, based on the actual communication scene, the system channel uncertainty under non-ideal channel state information is modeled:

[0030] According to the large-scale monitoring Internet of Things state updating system shown in FIG. Figure 2 The system contains a base station configured with L antennas and K single-antenna machine nodes. In the emergency monitoring scene, the base station can simultaneously send control instructions to multiple machine nodes in the form of broadcast. At the same time, since the control instruction packet sent by the base station to the machine node is generally short, the short packet communication scene is considered. The channel is considered to be a quasi-static block fading channel, and the fading coefficient remains unchanged during each transmission block, and changes independently from one transmission block to another. The base station uses a suitable precoding matrix to precode and send information to the machine node, so that the average weighted information age of the entire system is minimized.

[0031] In the actual communication scene, in the complex electromagnetic environment, when the base station broadcasts control instructions to all machine nodes, it is difficult for the base station to accurately obtain the channel state information of each node, that is, the channel state information of the machine node is uncertain, so there is a channel error, and the non-ideal channel state information of all links is considered in the present application;

[0032] The base station broadcasts control instructions to all machine nodes, and the system channel uncertainty is modeled in two forms, including bounded channel estimation error And statistical channel estimation error The channel error is estimated:

[0033]

[0034] wherein, denotes the actual estimated channel, Δh k denotes the corresponding channel estimation error, ε is an upper bound of the channel estimation error, Ω k is the covariance matrix of Δh k .

[0035] Step 2, determine the signal-to-noise ratio of the base station controlled machine node:

[0036] The base station sends a state data packet with an information amount D to the machine node, and the data packet length is N d CU, and the system bandwidth is B; define the set of machine nodes as is the antenna set at the base station, and the base station can simultaneously send control instructions to multiple machine nodes in the form of broadcast. Considering that the channel is a quasi-static block fading channel, the fading coefficient remains unchanged during each transmission block, and changes independently from one transmission block to another;

[0037] The received signal of the kth machine node is represented as:

[0038]

[0039] wherein, x k is the transmission signal at the kth machine node, satisfying represents the noise at the kth machine node with a mean of 0 and a variance of k G is the downlink transmission signal of the control node to user k, w is the Lx1 beamforming vector of the signal x , which is the same at each node, and P represents the transmission power of the base station;

[0040] The signal-to-noise ratio at the kth machine node is:

[0041]

[0042] Step 3, determine the packet error rate and system average information age:

[0043] Step 3-1, packet error rate analysis: in a large-scale monitoring Internet of Things, the control instructions sent by the base station to the machine node are generally short, in which case the short packet communication theory is used to analyze the packet error rate, and the maximum achievable transmission rate of the machine node can be approximately represented as:

[0044]

[0045] wherein, ε represents the packet error rate. In addition, V = 1-(1+γ k ) -2 represents information dispersion, and Q(·) is a Gaussian function Q-1 is the inverse function of Q function;

[0046] Then the instantaneous packet error rate expression at the machine nodes is

[0047]

[0048] Due to the existence of Q function, it is difficult to obtain the closed expression of average packet error rate. The packet error rate can be approximated as

[0049]

[0050] where is an intermediate variable;

[0051] Assuming that the noise power at each node is the same, that is, the scenario of fixed interference, let The approximate value of average packet error rate at the machine node is

[0052]

[0053] where, The first-order Riemann integral approximation method (FORIA) is adopted, which has the characteristics of high precision and low complexity, that is, Therefore, the average packet error rate can be approximated as

[0054]

[0055] Step 3-2, information age analysis: the information age change diagram of the data packet transmission process is shown in Figure 3 The present application adopts the broken line method to describe the change process of average information age AoI, and the average AoI of the kth machine node can be expressed as

[0056]

[0057] The average AoI is equal to the area under the AoI broken line divided by the total time. In the mth state update process, the area under the AoI broken line is represented by Q k,m , that is, the area of the shaded part, therefore, the average AoI can also be expressed as:

[0058]

[0059] Where, S k (τ) is the number of valid information data packets successfully received by the kth machine node before time τ. Therefore, is the average arrival rate of valid information data packets. To solve , it is necessary to calculate and λ k , in combination with Figure 3 It can be known that Qk,m may be expressed as:

[0060]

[0061] where S k,m is the number of data packets sent by the base station to the kth machine node in the mth state update process;

[0062] To calculate First, calculate the probability distribution of S k,m is:

[0063]

[0064] Thus, we can get and are:

[0065]

[0066] Therefore, we can get is

[0067]

[0068] From Figure 3 we can get the expression of λ k

[0069]

[0070] Finally, determine the average information age of each machine node and the weighted average information age of the system:

[0071]

[0072] where, represents the average information age of node k, represents the weighted average information age of the system.

[0073] Step 4, construct a system average weighted information age minimization robust resource optimization model based on the maximum transmit power constraint of the base station:

[0074] To minimize the system average weighted information age, construct a robust resource optimization model to optimize the precoding matrix w:

[0075]

[0076] where P MAX represents the maximum transmit power of the base station.

[0077] Step 5, solve the system average weighted information age minimization robust resource optimization model constructed in step 4 to get the optimization result, which is:​

[0078] The objective function is optimized as follows:

[0079] The weighted average information age of the system can be simplified as:

[0080]

[0081] According to the optimization model, the upper bound of F needs to be minimized, according to F K According to the properties of (x) represents the maximum term in

[0082] Therefore, we have:

[0083]

[0084] where represents the maximum term in

[0085] Since δ, P, σ0 are fixed values, the objective function of the optimization model can be converted to:

[0086]

[0087] From the above analysis, the problem can be converted to:

[0088]

[0089] Let W = ww H , and the power is the maximum power, then the optimization model is converted to

[0090]

[0091] In order to convert the constraint containing channel uncertainty into a deterministic linear matrix inequality, consider the case of bounded channel estimation error, and introduce the following S-process method:

[0092] S-process (S-Procedure): Definition Where Then The condition is that there exists a ≥ 0, and the following formula is established

[0093]

[0094] Based on the above method, considering the case of bounded channel estimation error, let A1 = I, b1 = 0, A2 = W, There are

[0095] ​​

[0096] In this case, the optimization model can be transformed into:

[0097]

[0098] where a k is the slack variable;

[0099] In the case of considering statistical channel estimation error, Bernstein inequality is introduced to transform the optimization model into a tractable deterministic form. According to the literature [WANG K-Y, SO A M-C, CHANG T-H, et al. Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization [J]. IEEE Transactions on Signal Processing, 2014, 62(21): 5690-5705], it is assumed that f(v) = v H Av + 2Re{u H v} + c, where For any ρ ∈ [0, 1], define x and y as slack variables, the following inequality holds:

[0100]

[0101] Define the covariance matrix of Δh k as The channel uncertainty parameter is a Gaussian random variable. Define x k and y k as slack variables, the above formula can be rewritten as:

[0102]

[0103] In this case, the optimization problem can be transformed into:

[0104]

[0105] where x k and y k are slack variables, is the covariance matrix of Δh k , the channel uncertainty parameter is a Gaussian random variable, and ρ0 represents the introduction coefficient;

[0106] Solve the optimization model in the above two cases, respectively, to obtain the optimal beamforming vector in the case of bounded channel estimation error and statistical channel estimation error

[0107] Specifically, the rank-one constraint relaxation of the above two optimization objective functions is summarized, two convex semi-definite programming problems can be obtained, which can be solved by using the CVX toolbox. However, the optimal beamforming matrix obtained by the two problems may not satisfy the rank-one constraint. It can be known that if the optimal beamforming matrix obtained satisfies then the optimal beamforming vector can be obtained by eigenvalue decomposition If the rank-one constraint is not satisfied, that is, then the approximate optimal solution can be obtained by the Gaussian randomization method.

[0108] The parameters in the two cases in the embodiment and the optimization results obtained based on the method are shown in the following table:

[0109]

[0110]

[0111]

[0112] The parameter settings in the embodiment are as follows:

[0113] The upper bound of channel uncertainty is [0, 0.2], and the scene considered by the application is mostly emergency Internet of Things, for example, forest fire prevention scene, so the noise at the machine node is generally larger than usual, and the noise power σ0 at the information receiving end is considered 2 =-110dBm, the channel bandwidth is B=180kHz. The state information amount is D=300nats, and the data packet length is N d =200cu. Figure 3 and Figure 4 The Rician channel model is considered, the Rician factor is 3dB, the users are randomly distributed on a circle with a distance of 1000m from the BS, and the number of machine nodes K=12, and the number of antennas at the BS L=32. Figure 5 and Figure 6 It is assumed that the users are randomly distributed on a circle with a distance of 1000m from the BS, the maximum transmission power P MAX =35dBm, the bounded channel estimation error coefficient is e0=0.2, the statistical channel estimation variance is 0.05, and the outage probability threshold ρ=0.05.

[0114] Figure 4The average AoI change relation with the sending power under different algorithms is given in the case of bounded channel estimation error. As can be seen from the figure, with the increase of the maximum transmission power, the average weighted AoI of the system is smaller, and the state update performance is better. This is because the increase of the maximum transmission power can reduce the packet error rate of the system, thereby improving the AoI performance of the system. In addition, it can be known from the figure that the algorithm proposed in the application can still maintain good robust AoI performance in the case of channel error, and the performance is much better than that of the random beamforming scheme. When the error coefficient increases, it can be obviously seen that the state update performance of the system is weakening. This is because when the error coefficient increases continuously, more communication resources will be used to overcome the uncertainty of the channel.

[0115] Figure 5 The average AoI change relation with the sending power under different algorithms is given in the case of statistical channel estimation error. Where e represents the variance of the error matrix, and p represents the interruption probability coefficient. The smaller p is, the larger the interruption probability threshold 1-p is. At this time, the system must have a higher probability greater than a threshold. As can be seen from the figure, with the increase of the transmission power, the AoI performance of the system is better. This is the same as the explanation of Figure 4 . Similarly, the algorithm proposed in the application can still maintain good robustness in the case of channel error. When the error variance e increases, the AoI performance of the system decreases. This is also because more communication resources will be used to overcome the uncertainty of the new channel state. When the interruption requirement is relaxed (p changes from 0.05 to 0.2), the AoI performance of the algorithm is improved. Because at this time, only a lower probability is required to meet the condition of the interruption probability threshold, and therefore the AoI performance is improved.

[0116] Figure 6 The average AoI change relation with the number of antennas and the number of users is depicted in the case of bounded channel estimation error. The figure describes the change diagram of the ideal channel state information and the algorithm proposed in the application under the condition that the number of users is 10 and 20. As can be seen from the figure, even in the case of non-ideal channel state information, the system still has good robust AoI performance. As can be seen from the figure, when the number of users is fixed, the number of antennas at the base station is increased, the average weighted AoI of the system is continuously reduced, and the performance of the system is continuously improved. When the number of antennas is fixed, the number of users is increased, the state update performance of the system is deteriorated. This is because increasing the number of antennas at the base station can improve the channel capacity and transmission reliability of the system, and increasing the number of antennas can make the beam narrower, and can point to the target user with higher angular resolution, thereby improving the beam gain, and thus improving the state update performance of the system. However, when the number of antennas remains unchanged, the number of users is increased, which means that the system needs to allocate limited beam resources among more users, and the base station needs to generate more beams, thereby reducing the state update frequency of each user, and thus reducing the average weighted AoI performance of the system.

[0117] Figure 7 The relationship change graph of average AoI and the number of antennas and the number of users under the condition of statistical channel estimation error is depicted. Similarly, when the number of users is fixed and the number of antennas at the base station is increased, the average weighted AoI of the system is continuously reduced, and the performance of the system is continuously improved. When the number of antennas is fixed, increasing the number of users will make the state update performance of the system worse. This is also because increasing the number of antennas at the base station can increase the channel gain, improve the system channel capacity and transmission reliability, so as to improve the state update performance of the system. However, when the number of antennas remains unchanged, increasing the number of users means that the system needs to allocate limited resources among more users, so that the state update performance of the system is reduced.

[0118] The application also provides a robust information age optimization system for monitoring short packet communication of an Internet of Things under non-ideal channel state information, comprising the following modules

[0119] A channel uncertainty modeling module is used to model system channel uncertainty under non-ideal channel state information based on an actual communication scenario.

[0120] A parameter determination module is used to determine the signal-to-noise ratio, packet error rate and system average information age of a machine node controlled by a base station.

[0121] An optimization module is used to construct a robust resource optimization model for minimizing the system average weighted information age based on the maximum transmission power constraint of the base station, and to solve the model to obtain an optimization result.

[0122] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0123] Step 1: Model system channel uncertainty under non-ideal channel state information based on an actual communication scenario.

[0124] Step 2: Determine the signal-to-noise ratio of a machine node controlled by a base station.

[0125] Step 3: Determine the packet error rate and system average information age.

[0126] Step 4: Construct a robust resource optimization model for minimizing the system average weighted information age based on the maximum transmission power constraint of the base station.

[0127] Step 5: Solve the robust resource optimization model for minimizing the system average weighted information age constructed in step 4 to obtain an optimization result.

[0128] A computer storage medium, which stores a computer program, the computer program is executed by a processor to implement the following steps:

[0129] Step 1, based on the actual communication scene, the system channel uncertainty under non-ideal channel state information is modeled;

[0130] Step 2, determine the signal-to-noise ratio of the base station controlled machine node;

[0131] Step 3, determine the packet error rate and system average information age;

[0132] Step 4, construct a system average weighted information age minimization robust resource optimization model based on the maximum transmission power constraint of the base station;

[0133] Step 5, solve the system average weighted information age minimization robust resource optimization model constructed in step 4 to obtain the optimization result.

[0134] The above-mentioned embodiment only expresses one embodiment of the present application, and the description is more specific and detailed, but it cannot be understood as the limitation of the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A robust information age optimization method for monitoring short packet communication of Internet of Things under non-ideal channel state information, characterized in that, The method comprises the following steps: Step 1, based on the actual communication scenario, the system channel uncertainty modeling under non-ideal channel state information is carried out; Step 2, the signal-to-noise ratio of the base station controlled machine node is determined; Step 3, the packet error rate and the system average information age are determined; Step 4, a system average weighted information age minimization robust resource optimization model based on the maximum transmission power constraint of the base station is constructed; Step 5, the system average weighted information age minimization robust resource optimization model constructed in step 4 is solved, and the optimization result is obtained.

2. The robust information age optimization method for monitoring IoT short packet communication under non-ideal channel state information according to claim 1, characterized in that, The system channel uncertainty modeling in step 1 is specifically: In actual communication scenarios, when a base station broadcasts control instructions to all machine nodes, due to uncertain channel state information of the machine nodes, there is a channel error, which includes a bounded channel estimation error and a statistical channel estimation error Estimate the channel error: wherein, denotes the actual estimated channel, Ah k denotes the corresponding channel estimation error, ε is an upper bound on the channel estimation error, Ω k is the covariance matrix of Ah k .

3. The robust information age optimization method for monitoring IoT short packet communication under non-ideal channel state information according to claim 1, characterized in that, The signal-to-noise ratio of the base station controlled machine node in step 2 is specifically: The base station sends a status data packet with an information amount D to the machine node, the data packet length being N d CU, the system bandwidth being B; define a set of machine nodes as for a set of antennas at the base station, The received signal of the kth machine node is represented as: where x k is the transmitted signal at the kth machine node, satisfying represents the noise with mean 0 and variance at the kth machine node, G is the downlink transmission signal from the control node to user k, w is the L 1 beamforming vector of the signal x k , which is the same at each node, and P denotes the transmit power of the base station; The signal-to-noise ratio at the kth machine node is:

4. The robust information age optimization method for monitoring IoT short packet communication under non-ideal channel state information according to claim 3, characterized in that, The packet error rate and information age analysis in step 3 is specifically: Step 3-1, the packet error rate and average packet error rate of each machine node are determined: where ε k denotes the packet error rate of node k, δ = exp(D / N d )-1, is an intermediate variable, and Q(·) is the Gaussian function denotes the average packet error rate of each node; Step 3-2, the average information age of each machine node and the weighted average information age of the system are determined: where B denotes the bandwidth, denotes the average information age of node k, denotes the weighted average information age of the system.

5. The robust information age optimization method for monitoring IoT short packet communication under non-ideal channel state information according to claim 4, characterized in that, The construction of the system average weighted information age minimization robust resource optimization model in step 4 is specifically: A robust resource optimization model is constructed with the minimum system average weighted information age as the target: where P MAX denotes the maximum transmit power of the base station.

6. The robust information age optimization method for monitoring IoT short packet communication under non-ideal channel state information according to claim 5, characterized in that, The solution of the robust resource optimization model in step 5 is specifically: The weighted average information age of the system is simplified as: According to: Therefore: wherein, wherein denotes the maximum term in Let W = ww H , and the power is the maximum power, the optimization model is converted to t≥0 W＞0 Tr(W)≤1 When considering the bounded channel estimation error, the optimization model is converted into: a k ≥0 t k ≥0 W＞0 Tr(W)≤1 Rank(W)=1 wherein a k is a slack variable; When considering the statistical channel estimation error, the optimization model is converted into: W＞0 t k ≥0 Tr(W)≤1 Rank(W)=1 wherein x k and y k are slack variables, is the covariance matrix of Δh k , the channel uncertainty parameter is a Gaussian random variable, and ρ0represents an introduction coefficient; Solving the optimization model in the above two cases, the optimal beamforming vector is obtained in the case of bounded channel estimation error and statistical channel estimation error, respectively 7. A robust information age optimization system for monitoring IoT short packet communication under non-ideal channel state information, characterized in that, The method comprises the following modules The channel uncertainty modeling module is used for system channel uncertainty modeling under non-ideal channel state information based on the actual communication scenario; The parameter determination module is used for determining the signal-to-noise ratio, packet error rate and system average information age of the base station controlled machine node; The optimization module is used for constructing a system average weighted information age minimization robust resource optimization model based on the maximum transmission power constraint of the base station, and solving, to obtain the optimization result.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 1-6.

9. A computer storable medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the steps of the method in any one of claims 1-6.

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