Active monitoring method based on short packet communication

Abstract

The invention discloses an active monitoring method based on short packet communication, and the method comprises the steps: firstly, building a communication system model which comprises monitoring equipment, a target transmitting end and a target receiving end, transmitting a power-adjustable artificial noise signal by the monitoring equipment, and actively interfering a communication link between the target transmitting end and the target receiving end; then, an optimization problem is established by taking maximization of the average effective monitoring rate of monitoring equipment as an optimization target, and meanwhile, the condition that the average decoding error rate of a target receiving end is maintained at a preset target value is taken as a constraint condition; and finally, solving an optimization problem, determining an optimal transmission rate and corresponding artificial noise transmitting power, and guiding monitoring equipment to perform optimal active monitoring. On the premise of ensuring the communication quality of the target link, a more favorable channel condition is created for the monitoring party, and the monitoring performance is remarkably improved compared with the traditional interference-free passive monitoring scheme and the active monitoring scheme with fixed interference power.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless communication and information security, and specifically relates to a monitoring method for short packet communication, in particular, a dynamic method for maximizing monitoring performance by transmitting active interference signals and optimizing system parameters in a limited block length transmission environment. BACKGROUND

[0002] With the rapid development of Internet of Things (IoT), Internet of Vehicles, and industrial automation applications, Ultra-Reliable and Low-Latency Communication (URLLC) has become a key scenario for next-generation wireless communication. In such scenarios, information interaction is usually in the form of short data packets, i.e., short packet communication. Traditional wireless communication theory is mainly based on Shannon's theorem, which states that as long as the transmission rate is lower than the channel capacity, error-free transmission can be achieved under the assumption of infinite code length. However, this theory is not entirely applicable to short packet communication.

[0003] In short packet communication, due to the limited code length (i.e., block length) of transmission, even if the transmission rate is lower than the channel capacity, there is a non-negligible error probability when the receiving end decodes. This inherent decoding error rate is an intrinsic characteristic of limited block length transmission, which sacrifices a certain reliability in exchange for lower transmission latency. This characteristic poses new challenges to the design of wireless communication systems, especially in the field of monitoring and information acquisition that requires high reliability.

[0004] Existing wireless monitoring techniques, whether passive or active, are mostly based on long packet communication models. For example, passive monitoring schemes directly eavesdrop on the target communication link, but in the short packet communication scenario, due to the inherent high decoding error rate, the monitor is difficult to effectively and reliably obtain information. Some active monitoring schemes change the channel environment by transmitting interference signals to create more favorable monitoring conditions, but these schemes do not consider the decoding error characteristics brought by limited block length when optimizing the interference strategy, resulting in a significant reduction in applicability and performance in short packet communication scenarios.

[0005] Therefore, when suspicious communication uses short packet transmission, if the monitor still follows the traditional monitoring scheme designed based on the long packet communication principle, it will not achieve the ideal monitoring effect. Currently, there is a lack of an active monitoring method specifically designed for the characteristics of short packet communication in the technical field. This method needs to be able to recognize and utilize the decoding error characteristics of limited block length, and through the active application of interference signals, effectively improve the success rate of monitoring and the reliability of information acquisition. SUMMARY

[0006] The present application aims to provide a short packet communication monitoring method and system based on active interference and a storage medium to solve the problem of poor performance and low reliability of the existing monitoring scheme in the short packet communication scenario mentioned in the background art.

[0007] To achieve the above-mentioned purpose, the technical scheme adopted by the present application is: a short packet communication monitoring method based on active interference, comprising the following steps:

[0008] Step 1: establishing a communication system model, the communication system model comprising a monitoring device, a target transmitting end and a target receiving end, wherein the target transmitting end sends short packet information of limited block length to the target receiving end.

[0009] Step 2: the monitoring device transmits an artificial noise signal with adjustable power to actively interfere with the communication link between the target transmitting end and the target receiving end.

[0010] Step 3: establishing an optimization problem, the optimization problem taking maximizing the average effective monitoring rate of the monitoring device as the optimization objective, and maintaining the average decoding error rate of the target receiving end at a preset target value as the constraint condition.

[0011] Step 4: solving the optimization problem to determine the optimal system transmission rate and the corresponding artificial noise transmission power, thereby guiding the monitoring device to perform optimal active monitoring activities.

[0012] As a preferred, in the step 4, it is proved by theoretical analysis that the objective function of the optimization problem is a quasi-concave function with respect to the transmission rate, and the function has a unique optimal solution. When solving, the bisection search algorithm is used to solve the first derivative of the objective function, and when the derivative value is zero, the corresponding transmission rate is the optimal transmission rate. This method can obtain the optimal performance solution in theory, but the integral operation of the Gaussian Q function is involved in the solving process, and the calculation complexity is high.

[0013] As a preferred, to reduce the calculation complexity, in the step 4, a solving scheme based on linear approximation is adopted. This scheme first uses a linear function to approximate the average decoding error rate, thereby constructing an approximate objective function that does not contain integral operation; then, the bisection search algorithm is also used to solve the first derivative of the approximate objective function, thereby obtaining a low-complexity approximate solution of the optimal transmission rate.

[0014] As a preference, in order to further reduce the calculation complexity and obtain a closed-form solution, in the step 4, a solution scheme based on a moment approximation is adopted. The scheme uses the first moment to approximate the integral term of the average decoding error rate, and then deduces a closed-form analytical expression about the optimal transmission rate, which can be directly calculated by the Lambert W function. The method avoids the integral operation and iterative search, and has the highest calculation efficiency.

[0015] The beneficial effects of the present application are as follows:

[0016] The present application introduces an active interference power adjustment mechanism, which creates more favorable channel conditions for the listener under the premise of ensuring the communication quality of the target link. The listening performance is significantly improved compared with the traditional passive listening scheme without interference and the active listening scheme with fixed interference power. In particular, the present application is specially designed for the decoding error characteristics of short packet communication. By incorporating this feature into the system modeling and optimization problem, the incompatibility problem of existing listening methods based on long packet communication theory in the short packet scenario is effectively solved. In the implementation scheme, the present application not only provides a complete path to obtain the theoretical optimal solution, but also innovatively proposes two low-complexity solution schemes, linear approximation and moment approximation, which can be flexibly selected according to different performance and computing resource requirements in practical applications. This design not only ensures the optimization of the listening effect, but also significantly reduces the calculation complexity of the algorithm, significantly improves the practicability and deployability of the method, and provides reliable technical support for active listening needs in different application scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0017] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the accompanying drawings are used to illustrate the embodiments of the present application only, so that those skilled in the art can better understand the technical solutions of the present application. Those skilled in the art can obtain other embodiments from the accompanying drawings without departing from the core idea of the present application.

[0018] Figure 1 is a method flowchart according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of an active listening system model based on short packet communication according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of the relationship between the effective listening rate and the transmission rate R in the optimal listening scheme according to an embodiment of the present application;

[0021] Figure 4 is a performance comparison diagram of the effective listening rate of the three schemes proposed by the present application under the condition of different transmission block lengths N according to an embodiment of the present application;

[0022] Figure 5 is according to the embodiment of the present application, the present application proposes three kinds of scheme and two traditional scheme under different channel condition effective monitoring rate performance comparison chart;

[0023] Figure 6 is according to the embodiment of the present application, under the condition of different decoding error rate constraint δ, the best effective monitoring rate performance comparison chart of three kinds of scheme proposed by the present application.

[0024] The specific implementation details of the above drawings can be further understood in combination with the embodiment part of the present application. Those skilled in the art can adjust the system configuration and parameters shown in the drawings according to the actual application scene, but these adjustments all belong to the protection scope of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and specific embodiments.

[0026] The present application provides a method for improving monitoring performance by actively transmitting interference signals in the short packet communication scenario of limited block length transmission. Referring to Figure 1 , the method provided by the present application mainly includes the following steps:

[0027] Firstly, an active monitoring model based on short packet communication is established;

[0028] Then, multiple schemes for solving the optimal monitoring strategy are proposed for the model, such as the optimal algorithm based on binary search (scheme A), the low complexity algorithm based on linear approximation (scheme B) and the simplified algorithm based on matrix approximation (scheme C);

[0029] Finally, the performance of the proposed scheme is verified and analyzed by simulation tool. The following embodiments will describe these steps and schemes in detail.

[0030] Embodiment 1:

[0031] A. Optimal solution scheme:

[0032] Referring to Figure 2 , the present application first establishes an active monitoring system model. The system includes a target transmitting end, a target receiving end and a monitoring device. The target transmitting end sends short packet information with block length N to the target receiving end. In order to improve the monitoring effect, the monitoring device actively transmits artificial noise signals to interfere with the communication link (hereinafter referred to as "suspected link") from the target transmitting end to the target receiving end.

[0033] Assume that all channels in the communication system model are Rayleigh fading channels. The channel coefficient from the target transmitting end to the target receiving end is denoted as hsd The channel coefficient from the target transmitter to the monitoring device is denoted as h sm The channel coefficient from the monitoring device to the target receiver is denoted as h md The channel power gain |h ij | 2 The probability density function (PDF) of h

[0034]

[0035] where λ ij is the parameter of the exponential distribution, h ij includes three types of h sd , h sm and h md .

[0036] The target transmitter sends a signal x s , with the average power E[|x s | 2 ] = 1. The received signals at the target receiver and the monitoring device can be represented as:

[0037]

[0038] where p s is the transmit power of the target transmitter, p m is the artificial noise power transmitted by the monitoring device, x m is the artificial noise signal transmitted by the monitoring device, which follows a complex Gaussian distribution, n d and n m are the additive white Gaussian noise (AWGN) at the target receiver and the monitoring device, respectively, with variances and

[0039] Therefore, the signal-to-interference-plus-noise ratio (SINR) at the target receiver and the signal-to-noise ratio (SNR) at the monitoring device are:

[0040]

[0041] According to the short packet communication theory, under the condition of given transmission rate R and block length N, the decoding error rates ∈ d and ∈ m at the target receiver and the monitoring device can be approximated as:

[0042]

[0043] where Q is the complementary cumulative distribution function (i.e., Q function) of the standard Gaussian distribution, and is the channel dispersion.

[0044] The core purpose of the present application is to maximize the effective monitoring rate R m of the monitoring device, under the constraint that the average decoding error rate of the target receiver is equal to a preset value δ, and the system transmission rate R m .

[0045] The effective monitoring rate R m of the monitoring device is defined as:

[0046]

[0047] Where, is the average decoding error rate of the monitoring device.

[0048] The average decoding error rate of the target receiver is:

[0049]

[0050] In summary, the optimization problem to be solved by the present application can be formally described as:

[0051] max R m

[0052]

[0053] Where is the maximum transmission power of the monitoring device. Given R, there is only one p m that satisfies the constraint Therefore, for the constraint can be equivalently transformed into:

[0054] R min ≤ R ≤ R max

[0055] To solve this problem, through analysis, it is known that ∈ d is a monotonically increasing function with respect to R and p m . This means that for any given R, there is a unique p m that satisfies the constraint condition. Therefore, the original problem can be transformed into a single-variable optimization problem only about variable R.

[0056] The objective function R m is a quasi-convex function with respect to the transmission rate R, as shown in Figure 3 . This means that there is a unique optimal solution R o , which can be obtained by solving the equation whose first derivative is zero:

[0057]

[0058] Since it is difficult to directly obtain the analytical solution of this equation, the bisection search algorithm is used to numerically solve R o .

[0059] R o can be solved by the bisection search method in the interval [R min , R max ] . Considering the constraint of R, i.e., R min ≤ R ≤ R max , the optimal solution of the optimization problem is:

[0060] min{max{R min , R o}, R max}

[0061] After obtaining the optimal transmission rate R o , the optimal artificial noise power R is solved according to the constraint condition Finally, the monitoring device performs active monitoring according to the calculated R .

[0062] B. Low complexity scheme based on linear approximation:

[0063] This scheme uses a linear function to approximate the decoding error rate, avoiding direct calculation and integration of the Q function. The average bit error rate of the monitoring device is linearly approximated using the following equation:

[0064]

[0065] where:

[0066]

[0067] Substituting this approximate equation into the average decoding error rate ∈ m of the monitoring device, we can get:

[0068]

[0069] where F denotes the cumulative distribution function (CDF) of γ m . The CDF of γ m is:

[0070]

[0071] Therefore, the linear approximation expression of R m is:

[0072]

[0073] After the integral is solved, the present scheme can obtain:

[0074]

[0075] where

[0076] Subsequently, the present scheme obtains the first derivative, and can obtain:

[0077]

[0078] where:

[0079]

[0080] Then, the approximate optimal transmission rate is found by solving the first derivative. The first derivative can be solved by the dichotomy method.

[0081] After the optimal transmission rate is obtained, the optimal artificial noise power is solved according to the constraint condition. Finally, the monitoring device performs active monitoring according to the calculated .

[0082] Through this linear approximation processing, the present scheme can greatly reduce the computational complexity under the premise of ensuring the monitoring performance, so that scheme B is more suitable for deployment and application in actual systems.

[0083] C. Ultra-low complexity scheme based on rectangular approximation:

[0084] The present scheme further adopts the midpoint rectangle method (Riemann integral approximation) to simplify the calculation on the basis of the linear approximation of scheme B. Specifically, the integral term is approximated as:

[0085]

[0086] Through this approximation processing, the effective monitoring rate R m of the monitoring device can be simplified to a more concise expression:

[0087]

[0088] The derivative of the approximate objective function is taken and set to zero, and the closed-form analytical solution of the optimal transmission rate can be obtained:

[0089]

[0090] where W(·) is the Lambert function, defined as the complex-valued function satisfying z = W(z)e {W(z)} in mathematics and engineering for solving equations containing exponential and logarithmic terms. Thus, the solution of the matrix approximation is:

[0091]

[0092] After obtaining the optimal transmission rate , the optimal artificial noise power is solved according to the constraint condition Finally, the monitoring device performs active monitoring according to the calculated .

[0093] This scheme can directly calculate the approximate optimal solution, and this ultra-low complexity feature makes it very suitable for deployment on resource-constrained monitoring devices.

[0094] Embodiment 2

[0095] This embodiment aims to verify the performance and beneficial effects of the method described in Embodiment 1 through simulation experiments.

[0096] The simulation environment is built based on Matlab R2022b, and the simulation parameters are set as follows: the target transmitter's transmit signal-to-noise ratio The maximum transmit power of the monitoring device The channel fading parameter λ of the suspicious link sd = 1, the channel fading parameter of the monitoring-related link (including the transmitter to the monitoring device and the monitoring device to the receiver) The target average decoding error rate constraint δ of the target receiver is 0.05.

[0097] Refer to Figure 4This embodiment first verifies the performance of the three schemes proposed in this invention (i.e., the optimal scheme, the linear approximation scheme, and the moment approximation scheme) under different short block lengths N. It can be seen that as N increases, the gap between the optimal active monitoring scheme and the active monitoring schemes based on linear approximation and moment approximation decreases. When N = 40, the effective monitoring rate of the optimal active monitoring scheme is about 0.0001 bps / Hz higher than that of the active monitoring scheme based on moment approximation. It can also be seen that, among the two proposed approximation schemes, the active monitoring scheme based on linear approximation can achieve an effective monitoring rate close to that of the optimal active monitoring scheme when the block length N is greater than or equal to 40; and the active monitoring scheme based on moment approximation can achieve an effective monitoring rate close to that of the optimal active monitoring scheme when the block length N is greater than or equal to 120.

[0098] Reference Figure 5 This embodiment demonstrates the different λ values ​​when N = 50. sd Performance comparison of effective listening rates of different schemes under / λ value. Figure 5 In addition to Figure 4 In addition to the three schemes mentioned above, this invention also compares a passive monitoring scheme without interference, denoted as "passive monitoring," and an active monitoring scheme with fixed interference power, denoted as "active monitoring with constant interference power." For the passive monitoring scheme without interference, this invention allows... For active monitoring schemes with fixed interference power, this invention enables... from Figure 5 It can be observed that when λ sd When / λ is relatively small, i.e., when the link from the target transmitter to the listening device and the link from the listening device to the target receiver are poor, the effective listening rate of the active listening scheme with fixed interference power is almost the same as the effective listening rate of the three listening schemes proposed in this invention. This is because when λ sd When / λ is very small, the optimal artificial noise power is This means that the monitoring equipment needs to use the maximum artificial noise power to achieve optimal monitoring performance. When λ sd When λ is relatively large, meaning the links from the target transmitter to the listening device and from the listening device to the target receiver are good, the effective listening rate of the interference-free passive listening scheme is almost the same as the effective listening rate of the three listening schemes proposed in this invention. This is because when λ... sd When / λ is very large, the optimal artificial noise power is p. m =0, meaning the monitoring device can achieve optimal monitoring performance without emitting artificial noise. Simulation results show that under different channel conditions (by the horizontal axis)... The performance curves of the three schemes of the present application basically coincide and are significantly better than those of the two prior art schemes, which proves that the method of the present application has obvious advantages over the prior art.

[0099] With reference to Figure 6 , this embodiment further verifies the influence of the decoding error rate constraint δ of the target receiving end on the monitoring performance. From Figure 6 , it can be seen that when the constraint condition δ = 0.01, the optimal effective monitoring rate is 0.59 bps / Hz. Considering the constraint of R, the optimal solution at this time is mainly subject to [R min ,R max ]. When the constraint condition δ is very small, the optimal solution is related to δ. When the constraint condition δ gradually becomes larger, from Figure 6 , it can be seen that the optimal solution at this time has fallen within the range of [R min ,R max ], so the optimal effective monitoring rate no longer depends on the constraint condition δ. In addition, Figure 6 , it is illustrated that the active monitoring scheme proposed in the present application can achieve better performance at a lower average block error rate (Block Error Rates, BLER) of the target receiving end. This result proves that the method of the present application can work stably under different and more stringent reliability constraints, which embodies the robustness of the method.

[0100] In summary, the simulation results of this embodiment powerfully prove that the active monitoring method proposed in the present application has significant performance advantages and robustness compared with the prior art under different channels, different block lengths, and different reliability constraints. The technical scheme of the present application not only can obtain the optimal monitoring performance in theory, but also provides two low-complexity approximation algorithms, so that it has high efficiency and practicality in actual engineering deployment, and has high industrial application value.

Claims

1. An active monitoring method based on short packet communication, characterized in that, The method comprises the following steps: Step 1: establishing a communication system model, comprising a monitoring device, a target transmitting end and a target receiving end; Step 2: the monitoring device transmits an adjustable-power artificial noise signal to actively interfere with a communication link between the target transmitting end and the target receiving end; Step 3: establishing an optimization problem with the maximum average effective monitoring rate of the monitoring device as an optimization target, and maintaining the average decoding error rate of the target receiving end at a preset target value as a constraint condition; Step 4: solving the optimization problem to determine an optimal transmission rate and a corresponding artificial noise transmission power, and guiding the monitoring device to perform optimal active monitoring.

2. The method of claim 1, wherein, In the communication system model, the target transmitting end transmits short packet information with a limited block length to the target receiving end.

3. The method of claim 2, wherein, The specific process of step 4 is as follows: Assume that all channels in the communication system model are Rayleigh fading channels; the channel coefficient from the target transmitting end to the target receiving end is denoted as h sd , the channel coefficient from the target transmitting end to the monitoring device is denoted as h sm , and the channel coefficient from the monitoring device to the target receiving end is denoted as h md ; the probability density function (PDF) of the channel power gain |h ij | 2 is represented as: where λ ij is the parameter of the exponential distribution, h ij includes h sd , h sm and h md three categories; Target transmitting end sends signal x s , whose average power E[|x s | 2 ] = 1; the signals received by the target receiving end and the monitoring device are respectively represented as: wherein p s is the transmit power of the target transmitter, p m is the artificial noise power transmitted by the monitoring device, x m is the artificial noise signal transmitted by the monitoring device subject to complex Gaussian distribution, n d and n m are the additive white Gaussian noise at the target receiver and the monitoring device, respectively, with variances and The signal-to-interference-and-noise ratio (SINR) of the target receiver and the signal-to-noise ratio (SNR) of the monitoring device are calculated according to the variances as γ d and γ m , respectively. According to the short packet communication theory, under the condition of a given transmission rate R and block length N, the decoding error rate ∈ of the target receiving end and the monitoring device is approximately: d and ∈ m approximately: where Q(z) is the complementary cumulative distribution function of the standard Gaussian distribution, is the channel dispersion; The optimization problem is to maximize the effective monitoring rate R of the monitoring device by adjusting the artificial noise power p m and the system transmission rate R, under the constraint that the average decoding error rate of the target receiver equals a preset value δ, to satisfy the target receiver m ; Effective listening rate of a listening device wherein, is the average decoding error rate of the listening device; the average decoding error rate of the target receiver is: In summary, the optimization problem is formulated as: max R m wherein Pmax is the maximum transmit power of the listening device; given R, there is only one p m satisfying the constraint for the constraint is equivalent to: R min ≤ R ≤ R max ; Objective function R m is a quasi-concave function in the transmission rate R, which has a unique optimal solution R o which is obtained by solving the equation of its first derivative being zero, which is numerically solved for R o using a bisection search algorithm; R o In the interval [R min ,R max ] by binary search method , the optimal solution of the optimization problem is: min{max{R min ,R o}, R max}. The optimal transmission rate R is obtained o Then, the optimal artificial noise power is solved The optimal artificial noise power is solved Finally, the monitoring device monitors according to the calculated active monitoring.

4. The method of claim 3, wherein, Step 4 can also be implemented through the following process: A linear function is used to approximate the decoding error rate, and the average error rate of the monitoring device is linearly approximated using the following equation: Substituting this approximation equation into the average decoding error rate ∈ of the monitoring device m , we obtain: wherein represents γ m cumulative distribution function CDF; so R m linear approximation expression for After the integrals are evaluated, we obtain: where Then, the approximately optimal transmission rate is found by solving the first derivative is obtained by binary search; In obtaining the optimal transmission rate Then, according to the constraint condition Solving the inverse problem to obtain the optimal artificial noise power Finally, the monitoring device according to the calculated Active monitoring.

5. The method of claim 4, wherein, Step 4 can also be implemented through the following process: The midpoint rectangle method is used to simplify the calculation, and the integral term is approximated as: Effective listening rate R of the listening device m Simplifies to: The closed-form analytical solution of the optimal transmission rate is obtained by taking the derivative of the approximated objective function and setting it to zero: where W(·) is Lambert function and the solution of the matrix approximation is: In obtaining the optimal transmission rate Then, according to the constraint condition Solving the inverse problem to obtain the optimal artificial noise power Finally, the monitoring device according to the calculated Active monitoring.

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