A performance evaluation method for short packet coding communication

Abstract

This application proposes a performance evaluation method for short packet coding communication, which belongs to the field of short packet communication technology. This method includes the following steps: constructing a system model for short packet coding communication, where the system model includes a ground base station, an unmanned aerial vehicle (UAV), a legitimate user terminal, and a non-target user terminal connected to the communication, and a reconfigurable intelligent surface is configured on the UAV; based on all channel state information of the system model, the system model is sequentially analyzed for average achievable confidentiality rate, average decoding error probability, and confidentiality interruption probability, and the performance evaluation of the system model is performed based on all analysis results. This application can improve the security and reliability of performance evaluation of short packet coding communication.

Description

Technical Field

[0001] The present application relates to the technical field of short packet communications, and in particular to a performance evaluation method for short packet coding communications. Background Art

[0002] The deployment of 5G / 6G networks is driving the widespread application of various large-scale IoT applications. One of the key technical indicators is Ultra-Reliable and Low-Latency Communication (URLLC), which is specifically used to support applications that require extremely high reliability and an average decoding error probability of no more than 10%. , and ultra-low latency (typically less than 1ms) communication scenarios. Short packet communication networks often play a crucial role in achieving ultra-reliable low-latency communications. They typically involve the transmission of small data packets ranging from tens to hundreds of bytes, which facilitates high-frequency data exchange. However, small data packets bring many limitations and face the following challenges: First, the traditional Shannon theorem is usually based on the assumption of infinitely long data blocks and is therefore not applicable to ultra-reliable low-latency communication scenarios; second, the performance evaluation of short packet communication networks often lacks a systematic verification method, which has become a problem that needs to be addressed urgently.

[0003] Reconfigurable Intelligent Surfaces (RIS) are a key technology in 5G / 6G communication systems and are widely used in various wireless communication scenarios. Unlike traditional technologies, RIS do not perform analog-to-digital conversion on the radio frequency link. Instead, they redirect the incident signal by adjusting the phase and amplitude of multiple reflective elements, optimizing the propagation path and improving the signal quality and spectral efficiency at the receiver. Unmanned Aerial Vehicles (UAVs), due to their compact size and high maneuverability, can be combined with RIS to form a UAV-RIS system, which can be used to address communication scenarios requiring flexible network deployment. Due to the open transmission environment of UAVs, private information can be easily intercepted by non-target users. Therefore, the UAV-RIS system can quickly establish line-of-sight links in complex environments, effectively protecting private information. Compared with traditional relay security equipment, the UAV-RIS system can significantly reduce network construction and maintenance costs, providing strong technical support for high-security data transmission in ultra-reliable, low-latency communications.

[0004] However, existing technologies lack performance evaluation methods for short packet communication scenarios involving non-target users, mainly due to the following problems:

[0005] First, when evaluating the performance of long packet communication networks involving UAV-RIS systems and non-targeted user attacks, the existing techniques involve the infinite block length theory, which has not yet been fully extended to the finite block length theory, and the reliability of short packet communication networks has not been fully explored.

[0006] Secondly, in the performance evaluation of a short packet communication network that includes the UAV-RIS system but does not include non-target user-end attacks, the existing technology shows that the UAV-RIS system as a relay system can meet the reliability requirements of the short packet communication network, but fails to consider the information security issues in actual data transmission.

[0007] Therefore, it is necessary to propose a solution to improve one or more problems existing in the above-mentioned related technical solutions.

[0008] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0009] The present invention provides a method for evaluating the performance of short packet coding communication, which includes the following steps:

[0010] Constructing a system model for short packet coding communication, the system model includes a ground base station, a drone, a legitimate user terminal and a non-target user terminal connected in communication, and the drone is equipped with a reconfigurable smart surface;

[0011] According to all channel state information of the system model, performing average achievable confidentiality rate analysis, average decoding error probability analysis and confidentiality interruption probability analysis on the system model in sequence;

[0012] The performance of the system model is evaluated based on the results of the average achievable confidentiality rate analysis, the results of the average decoding error probability analysis, and the results of the confidentiality interruption probability analysis.

[0013] Furthermore, the reconfigurable smart surface comprises a reflective array composed of a plurality of reflective elements of the same structure, each of the reflective elements is respectively configured with a single antenna; the ground base station, the legitimate user terminal and the non-target user terminal are respectively configured with the single antenna;

[0014] The expression of the phase shift matrix of the reconfigurable smart surface is:

[0015] (1)

[0016] in, represents the phase shift matrix of the reconfigurable smart surface, It means constructing a diagonal matrix, represents the imaginary unit, Reconfigurable smart surface Phase shift of each reflective element;

[0017] The ground base station is used The position of the ground base station is represented by express, , Indicates the ground base station Axis coordinates, Indicates the ground base station axis coordinates;

[0018] The drone is used The position of the UAV is represented by express, , Indicates drone Axis coordinates, Indicates drone Axis coordinates, Indicates the altitude of the drone;

[0019] The legal user terminal uses Indicates that the location of the legal user terminal is express, , Indicates a legitimate user terminal Axis coordinates, Indicates a legitimate user terminal axis coordinates;

[0020] The non-target user terminal uses Indicates that the location of the non-target user terminal is express, , Indicates non-target user end Axis coordinates, Indicates non-target user end Axis coordinates.

[0021] Furthermore, the legitimate user terminal and the non-target user terminal both receive two channel signals, the first channel signal being a direct link signal transmitted from the ground base station, and the second channel signal being a reflected link signal from the reconfigurable smart surface;

[0022] The expression of the received signal of the legal user terminal is:

[0023] (2)

[0024] in, Indicates the received signal of the legal user end, Indicates the signal transmission power of the ground base station, Represents the transmission signal of the ground base station, and satisfies , express The expected value of Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, x S→R,n ∈(0,2π] , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is x R,n→D ∈(0,2π] , Reconfigurable smart surface The phase shift of the reflective element, Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the interference noise of the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, x S→D ∈(0,2π] ;

[0025] The expression of the received signal of the non-target user terminal is:

[0026] (3)

[0027] in, Indicates the received signal of non-target user end, represents the path loss coefficient of the channel signal from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is x R,n→I ∈(0,2π] , Indicates the channel distance from the ground base station to the non-target user terminal, Indicates the channel distance from the UAV to the non-target user end, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the interference noise of non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, x S→I ∈(0,2π] .

[0028] Furthermore, the step of performing the average achievable confidentiality rate analysis on the system model according to all the channel state information of the system model includes:

[0029] Adjusting the reconfigurable smart surface to achieve an optimal phase shift according to the received signal of the legitimate user terminal, and obtaining an optimal signal-to-noise ratio of the legitimate user terminal and an instantaneous signal-to-noise ratio of the non-target user terminal respectively;

[0030] Modeling the optimal signal-to-noise ratio of the legitimate user terminal as a randomly distributed variable of a gamma function, and deriving a probability density function and a cumulative distribution function of the legitimate user terminal using a moment matching method;

[0031] Modeling the instantaneous signal-to-noise ratio of the non-target user terminal as an exponential distribution variable of a gamma function, and deriving the probability density function and cumulative distribution function of the non-target user terminal using a moment matching method;

[0032] The closed form of the average achievable confidentiality rate of the system model is determined by using the achievable confidentiality rate of the system model, the probability density function of the legitimate user terminal, and the probability density function of the non-target user terminal.

[0033] Furthermore, the probability density function of the legitimate user terminal is expressed as:

[0034] (4)

[0035] in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is The probability density function when Indicates the signal transmission power of the ground base station, Indicates the signal-to-noise ratio of the received signal of the legitimate user end, represents the optimal signal-to-noise ratio of the legitimate user end, , , Reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, x S→R,n ∈(0,2π] , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is x R,n→D ∈(0,2π] , , Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, , Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, x S→D ∈(0,2π] , represents the shape parameter, represents the exponential function, represents the scale parameter, represents the gamma function;

[0036] The cumulative distribution function of the legal user terminal is expressed as:

[0037] (5)

[0038] in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is Cumulative distribution function when ;

[0039] The probability density function of the non-target user terminal is expressed as:

[0040] (6)

[0041] in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is The probability density function when Indicates the signal-to-noise ratio of the received signal of the non-target user end, represents the rate parameter of the exponential distribution, represents the instantaneous signal-to-noise ratio of the non-target user end, , , represents the path loss coefficient of the channel signal from the UAV to the non-target user end, Indicates the channel distance from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is x R,n→I ∈(0,2π] , represents the net phase difference of the non-target user end, , Indicates the channel distance from the ground base station to the non-target user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, x S→I ∈(0,2π] ;

[0042] The cumulative distribution function of the non-target user terminal is expressed as:

[0043] (7)

[0044] in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is The cumulative distribution function when .

[0045] Furthermore, the step of determining the closed-form average achievable confidentiality rate of the system model using the achievable confidentiality rate of the system model, the probability density function of the legitimate user terminal, and the probability density function of the non-target user terminal comprises:

[0046] When the length of the channel block of the ground base station is constant, the decoding error probability of the legal user terminal is less than or equal to the decoding error probability threshold, and the information leakage rate of the legal user terminal is less than or equal to the information leakage rate threshold, if , then the achievable confidentiality rate is 0, and the achievable confidentiality rate is express, Indicates the length of the channel block of the ground base station, represents the decoding error probability of the legitimate user end, Indicates the information leakage rate of legitimate users;

[0047] like , the closed-form expression of the achievable confidentiality rate is:

[0048] (8)

[0049] in, The closed form of the achievable confidentiality rate of the system model is expressed as, represents the achievable confidentiality rate of the legitimate user end, , represents the achievable confidentiality rate of the non-target user end, , Indicates the confidentiality rate of long packet coding communication, , represents the channel dispersion of the legal user end, , represents the channel dispersion of the non-target user end, , express The inverse function of a function, , express The inverse function of a function, , represents the integral variable;

[0050] The closed-form expression of the average achievable confidentiality rate is:

[0051] (9)

[0052] in, The closed form of the average achievable confidentiality rate is expressed as, represents the Meijer'G function, represents the Fox-H function, , , Represents an accumulated variable, when hour, ,when hour, .

[0053] Furthermore, the step of performing the average decoding error probability analysis on the system model according to all the channel state information of the system model includes:

[0054] when When , the decoding error probability is equal to 1;

[0055] when When , the expression of the decoding error probability is:

[0056] (10)

[0057] in, represents the right tail function of the standard normal distribution, , express variables, Indicates the number of bits of the transmission signal of the ground base station;

[0058] Using linear approximation theory, let Approximate linear function To simplify the computational complexity of formula (10), , the linear function The expression is:

[0059] (11)

[0060] in, , , represents the lower bound of the achievable confidentiality rate, represents the upper bound of the achievable confidentiality rate, , Represents a linear function variables;

[0061] make ,Will Re-expressed as Then, the linear function Substitute into formula (10) and calculate the average of all the decoding error probabilities to obtain the average decoding error probability;

[0062] The expression of the average decoding error probability is:

[0063] (12)

[0064] in, represents the average decoding error probability, represents auxiliary variables, represents the cumulative distribution function of the legitimate user terminal at the lower bound of the achievable confidentiality rate, represents the cumulative distribution function of the legitimate user terminal at the upper bound of the achievable confidentiality rate, Indicates that when the variable equal A linear function of

[0065] Solve using the distribution integral method , and use the Riemann integral to convert Rewrite to obtain the closed form of the average decoding error probability;

[0066] The closed-form expression of the average decoding error probability is:

[0067] (13)

[0068] in, The closed form of the average decoding error probability is expressed as, , Represents the cumulative distribution function of legal user terminals.

[0069] Furthermore, the step of performing the confidentiality interruption probability analysis on the system model according to all the channel state information in the system model includes:

[0070] When the length of the channel block of the ground base station is , and the threshold of the achievable confidentiality rate is ,and When , the closed form of the probability of confidentiality interruption is calculated using the moment matching method;

[0071] Calculating the cumulative distribution function of the legitimate user terminal under the condition of a large signal-to-noise ratio of the system model;

[0072] By using the cumulative distribution function of the legal user terminal under the condition of large signal-to-noise ratio of the system model, the closed form of the asymptotic secrecy interruption probability and the positive secrecy asymptotic capacity are derived respectively.

[0073] Furthermore, the closed-form expression of the confidentiality interruption probability is:

[0074] F ACR a ACR th =U{ ACR th - ACR a ¯ Ψ(AC R a ) }= 1 2πΨ( ACR a ) ∫ -∞ ACR th exp{- [ t- ACR a ¯ ] 2 2Ψ( ACR a ) }dt (14)

[0075] in, The closed form of the probability of confidentiality interruption is expressed as, represents the variance of the closed form of the achievable confidentiality rate, represents the Gaussian function;

[0076] When the system model is under a high signal-to-noise ratio condition, the cumulative distribution function of the legal user terminal is expressed as:

[0077] (15)

[0078] in, It represents the cumulative distribution function of the legal user end under the condition of large signal-to-noise ratio of the system model. Indicates the signal-to-noise ratio of the received signal at the legitimate user end when the system model has a large signal-to-noise ratio.

[0079] The closed-form expression of the asymptotic privacy outage probability is:

[0080] (16)

[0081] in, The closed form for the asymptotic probability of confidentiality outage is, ;

[0082] The expression of the positive secret asymptotic capacity is:

[0083] (17)

[0084] in, Indicates the positive confidentiality of the progressive capacity, represents the probability when the achievable confidentiality rate is positive, .

[0085] Furthermore, the step of evaluating the performance of the system model according to the results of the achievable confidentiality rate analysis, the results of the average decoding error probability analysis, and the results of the confidentiality interruption probability analysis includes:

[0086] When the result of the achievable confidentiality rate analysis is greater than or equal to , and the result of the average decoding error probability analysis is less than or equal to , and the result of the confidentiality interruption probability analysis is less than or equal to When , the performance of the system model is judged to be excellent;

[0087] When the result of the achievable confidentiality rate analysis is and the result of the average decoding error probability analysis is greater than and less than or equal to , and the result of the confidentiality interruption probability analysis is greater than and less than or equal to When , the performance of the system model is judged to be medium;

[0088] When the result of the achievable confidentiality rate analysis is less than or equal to , and the result of the average decoding error probability analysis is greater than and less than or equal to , and the result of the confidentiality interruption probability analysis is greater than and less than or equal to , the performance of the system model is judged to be poor.

[0089] This application provides a performance evaluation method for short packet coding communication, which has at least the following beneficial effects:

[0090] (1) The performance evaluation method for short packet coding communication proposed in this application improves the communication quality of the entire system model by configuring a reconfigurable smart surface on the drone and using it as a passive forwarding relay station for signal transmission;

[0091] (2) This application analyzes the average achievable confidentiality rate, average decoding error probability and confidentiality interruption probability of the system model in turn based on all channel state information of the system model, thereby forming a complete performance evaluation scheme to achieve optimal resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0093] Figure 1 A schematic diagram illustrating the steps of a performance evaluation method for short packet coding communication in an exemplary embodiment of the present application is shown;

[0094] Figure 2 A schematic diagram illustrating a structure of a system model for short packet coding communication in an exemplary embodiment of the present application is shown;

[0095] Figure 3 A comparison curve diagram showing the average achievable confidentiality rate when the signal-to-noise ratio of the transmission signal is changed under conditions of different numbers of reflective elements and lengths of channel blocks in an exemplary embodiment of the present application;

[0096] Figure 4 A comparative curve diagram showing average decoding error probability when the length of a channel block is changed under conditions of different numbers of reflective elements and Ricean factors in an exemplary embodiment of the present application;

[0097] Figure 5 A comparative curve diagram showing the probability of privacy interruption when the signal-to-noise ratio of the transmission signal is changed under different thresholds of the achievable privacy rate in an exemplary embodiment of the present application;

[0098] Figure 6 A comparative curve chart showing the positive secrecy asymptotic capacity when the signal-to-noise ratio of the transmission signal is changed under different conditions of the number of reflective elements, channel block length, decoding error probability, information leakage rate and drone flight altitude in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0099] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0100] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0101] The following describes in more detail a performance evaluation method for short packet coding communication proposed in this exemplary embodiment.

[0102] This example embodiment provides a performance evaluation method for short packet coding communication, such as Figure 1 As shown, the method may include the following steps:

[0103] In this embodiment, step S101 is to construct a system model for short packet coding communication.

[0104] In step S101 of this embodiment, Figure 2 As shown in Figure 1, the system model includes a ground base station, a UAV, a legitimate user terminal, and a non-target user terminal. A reconfigurable smart surface is configured on the UAV.

[0105] Furthermore, the ground base station, the legitimate user end and the non-target user end are each equipped with a single antenna. The location of the ground base station is represented by express, , Indicates the ground base station Axis coordinates, Indicates the ground base station Axis coordinates. For drones The position of the drone is represented by express, , Indicates drone Axis coordinates, Indicates drone Axis coordinates, Indicates the altitude of the drone. Indicates that the location of the legal user terminal is express, , Indicates a legitimate user terminal Axis coordinates, Indicates a legitimate user terminal Axis coordinates. For non-target users Indicates that the location of non-target users is express, , Indicates non-target user end Axis coordinates, Indicates non-target user end Axis coordinates.

[0106] Furthermore, the reconfigurable smart surface is a rectangular reflective array composed of multiple reflective elements of the same structure arranged evenly, and each reflective element is equipped with a single antenna. The phase shift matrix of the reconfigurable smart surface is expressed as:

[0107] (1)

[0108] in, represents the phase shift matrix of the reconfigurable smart surface, It means constructing a diagonal matrix, represents the imaginary unit, i.e. , Reconfigurable smart surface The phase shift of each reflective element.

[0109] Furthermore, the distance between the air and the ground is significantly greater than the spacing between the reflective elements of the reconfigurable smart surface. Therefore, assuming that the distance between each reflective element and each ground node is uniform, both legitimate and non-target users can receive two channel signals. The first channel signal is the direct link signal transmitted from the ground base station; the second channel signal is the reflected link signal from the reconfigurable smart surface. The direct link signal is modeled as a Rayleigh fading channel, and the reflected link signal is modeled as a Ricean fading channel.

[0110] It can be obtained that the expression of the received signal of the legal user end is:

[0111] (2)

[0112] in, Indicates the received signal of the legal user end, Indicates the signal transmission power of the ground base station, Represents the transmission signal of the ground base station, and satisfies , express The expected value of Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, x S→R,n ∈(0,2π] , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is x R,n→D ∈(0,2π] , Reconfigurable smart surface The phase shift of the reflective element, Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the interference noise of the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, x S→D ∈(0,2π] .

[0113] The expression of the received signal of the non-target user end is:

[0114] (3)

[0115] in, Indicates the received signal of non-target user end, represents the path loss coefficient of the channel signal from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is x R,n→I ∈(0,2π] , Indicates the channel distance from the ground base station to the non-target user terminal, Indicates the channel distance from the UAV to the non-target user end, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the interference noise of non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, x S→I ∈(0,2π] .

[0116] In step S102 of this embodiment, based on all channel state information (CSI) of the system model, the average achievable confidentiality rate analysis, average decoding error probability analysis, and confidentiality interruption probability analysis are sequentially performed on the system model. In this embodiment, step S102 may include the following sub-steps:

[0117] Sub-step S1021: Analyze the average achievable confidentiality rate of the system model to determine the closed form of the average achievable confidentiality rate of the system model. The specific process is as follows:

[0118] Firstly, the reconfigurable smart surface is adjusted to achieve the optimal phase shift according to the received signal of the legitimate user end, and the optimal signal-to-noise ratio of the legitimate user end and the instantaneous signal-to-noise ratio of the non-target user end are obtained respectively.

[0119] Furthermore, in this embodiment, adjusting the reconfigurable smart surface to achieve the optimal phase shift means adjusting the phase of the reconfigurable smart surface to eliminate the phase error, thereby enabling the reconfigurable smart surface to achieve the optimal phase shift. At this point, the optimal signal-to-noise ratio of the legitimate user end can be obtained: ,in, , , The instantaneous signal-to-noise ratio of the non-target user end is: ,in, , .

[0120] Furthermore, the optimal phase shift of the reconfigurable smart surface is Indicates that the expression is The net phase difference of the non-target user end is Indicates that the expression is: Here 、 、 、 and The value range of (0,2π] .

[0121] Secondly, the optimal signal-to-noise ratio of the legitimate user end is modeled as a randomly distributed variable of the gamma function, and the probability density function and cumulative distribution function of the legitimate user end are derived respectively using the moment matching method.

[0122] Here, we define a random variable ,make , and introduce the moment matching method to directly derive the random variable The cumulative distribution function of the random variable The distribution of is approximately the same as the Gamma distribution with the same parameters. The Gamma distribution has two parameters, one of which is the shape parameter. , and the other is the scale parameter The two parameters satisfy , the transformation relationship of Gamma distribution is: and . Make the scale parameter , After substituting the Gamma distribution, we can obtain the probability density function and cumulative distribution function of the legal user end. express variance; express The mean of express The probability density function of express The cumulative distribution function of .

[0123] Furthermore, the probability density function of the legitimate user terminal is expressed as:

[0124] (4)

[0125] in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is The probability density function when Indicates the signal transmission power of the ground base station, Indicates the signal-to-noise ratio of the received signal of the legitimate user end, represents the optimal signal-to-noise ratio of the legitimate user end, , , Reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, x S→R,n ∈(0,2π] , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is x R,n→D ∈(0,2π] , , Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, , Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, x S→D ∈(0,2π] , represents the shape parameter, represents the exponential function, represents the scale parameter, represents the gamma function.

[0126] The expression of the cumulative distribution function of the legal user terminal is:

[0127] (5)

[0128] in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is The cumulative distribution function when .

[0129] Then, the instantaneous signal-to-noise ratio of the non-target user end is modeled as an exponential distribution variable of the gamma function, and the probability density function and cumulative distribution function of the non-target user end are derived respectively using the moment matching method.

[0130] Here, we define another random variable ,make , assuming ,when hour, It can be approximated as a mean of 0 and a variance of The complex Gaussian distribution of . From this, we can get the probability density function and cumulative distribution function of the non-target user end.

[0131] Furthermore, the probability density function of the non-target user end is expressed as:

[0132] (6)

[0133] in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is The probability density function when Indicates the signal-to-noise ratio of the received signal of the non-target user end, represents the rate parameter of the exponential distribution, , represents the instantaneous signal-to-noise ratio of the non-target user end, , , represents the path loss coefficient of the channel signal from the UAV to the non-target user end, Indicates the channel distance from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is x R,n→I ∈(0,2π] , represents the net phase difference of the non-target user end, , Indicates the channel distance from the ground base station to the non-target user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, x S→I ∈(0,2π] .

[0134] The expression of the cumulative distribution function of the non-target user end is:

[0135] (7)

[0136] in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is The cumulative distribution function when .

[0137] Finally, the closed form of the average achievable confidentiality rate of the system model is determined by using the achievable confidentiality rate of the system model, the probability density function of the legitimate user end and the probability density function of the non-target user end.

[0138] Furthermore, when the length of the channel block of the ground base station is constant, and the decoding error probability of the legitimate user end is less than or equal to the decoding error probability threshold, and the information leakage rate of the legitimate user end is less than or equal to the information leakage rate threshold, if , then the achievable confidentiality rate is 0, and the achievable confidentiality rate is express, Indicates the length of the channel block of the ground base station, represents the decoding error probability of the legitimate user end, Indicates the information leakage rate of legitimate users;

[0139] like , then the closed-form expression of the achievable confidentiality rate is:

[0140] (8)

[0141] in, The closed form of the achievable confidentiality rate of the system model is expressed as, represents the achievable confidentiality rate of the legitimate user end, , represents the achievable confidentiality rate of the non-target user end, , Indicates the confidentiality rate of long packet coding communication, , represents the channel dispersion of the legal user end, , represents the channel dispersion of the non-target user end, , express The inverse function of a function, , express The inverse function of a function, , represents the integration variable.

[0142] The closed-form expression of the average achievable confidentiality rate is:

[0143] (9)

[0144] in, The closed form of the average achievable confidentiality rate is expressed as, represents the Meijer'G function, represents the Fox-H function, , , Represents an accumulated variable, when hour, ,when hour, .

[0145] The closed form of average achievable confidentiality rate is used as a security evaluation indicator to analyze the ability of the entire system model to resist interception by non-target users.

[0146] Sub-step S1022: Analyze the average decoding error probability of the system model, and use the right tail function of the standard normal distribution, linear approximation theory, and Riemann integral to determine the closed form of the average decoding error probability of the system model. The specific process is as follows:

[0147] when When , the decoding error probability is equal to 1;

[0148] when When , the expression of decoding error probability is:

[0149] (10)

[0150] in, represents the right tail function of the standard normal distribution, , express variables, Indicates the number of bits of the transmission signal of the ground base station;

[0151] Using linear approximation theory, let Approximate linear function To simplify the computational complexity of formula (10), , linear function The expression is:

[0152] (11)

[0153] in, , , represents the lower bound of the achievable confidentiality rate, , represents the upper bound of the achievable confidentiality rate, , Represents a linear function variables.

[0154] make ,Will Re-expressed as Then, the linear function Substitute into formula (10) and calculate the average of all decoding error probabilities to obtain the average decoding error probability;

[0155] The expression of average decoding error probability is:

[0156] (12)

[0157] in, represents the average decoding error probability, represents auxiliary variables, represents the cumulative distribution function of the legitimate user terminal at the lower bound of the achievable confidentiality rate, represents the cumulative distribution function of the legitimate user terminal at the upper bound of the achievable confidentiality rate, Indicates that when the variable equal A linear function of .

[0158] Solve using the distribution integral method , and use the Riemann integral to convert Rewrite it to get the closed form of the average decoding error probability;

[0159] The closed-form expression for the average decoding error probability is:

[0160] (13)

[0161] in, The closed form of the average decoding error probability is expressed as, , Represents the cumulative distribution function of legal user terminals.

[0162] The closed form of the average decoding error probability can be used to evaluate the security and reliability of the entire system model.

[0163] Sub-step S1023: Analyzing the confidentiality interruption probability of the system model can solve the problem of unknown achievable confidentiality rate distribution. In addition, analyzing the asymptotic performance of the confidentiality capability of the system model's confidentiality interruption probability can evaluate the performance of the system model in high signal-to-noise ratio scenarios. This specifically includes the following process:

[0164] First, during short packet coding communications, since the length of the ground base station's channel block is limited, constraints can be placed on the probability of decoding errors and information leakage to ensure the security and reliability of network transmission. The probability of confidentiality interruption can be defined as: given a channel block length and a threshold for the achievable confidentiality rate, the achievable confidentiality rate is less than the threshold. If the constraints are violated, the distribution of the achievable confidentiality rate cannot be determined and directly solved. For problems with unknown distributions, moment matching methods are used to calculate all moments, which can replace the unknown parameters. and They are independent of each other, and it is simpler to solve all order moments separately, so the achievable confidentiality rate is approximated as a Gaussian distribution to obtain its parameters.

[0165] Therefore, when the length of the channel block of the ground base station is , and the threshold of the achievable confidentiality rate is ,and When , the closed form of the confidentiality interruption probability is calculated using the moment matching method.

[0166] Furthermore, the closed-form expression of the confidentiality interruption probability is:

[0167] F ACR a ACR th =U{ ACR th - ACR a ¯ Ψ(AC R a ) }= 1 2πΨ( ACR a ) ∫ -∞ ACR th exp{- [ t- ACR a ¯ ] 2 2Ψ( ACR a ) }dt (14)

[0168] in, The closed form of the probability of confidentiality interruption is expressed as, represents the variance of the achievable confidentiality rate, represents the Gaussian function.

[0169] Secondly, the cumulative distribution function of the legal user end of the system model is calculated under the condition of large signal-to-noise ratio.

[0170] The parameters in the closed form of the confidentiality interruption probability contain complex forms of multiplication and accumulation, which is difficult to solve when analyzing the performance of the system model. Therefore, a method based on the signal-to-noise ratio is proposed. The probability of progressive confidentiality interruption in the scenario of is: , , Taylor expand the cumulative distribution function of the legal user end, ignore the higher-order terms, and only keep the first-order terms, where, express The channel dispersion, express or .

[0171] Therefore, the expression of the cumulative distribution function of the legal user end under the condition of large signal-to-noise ratio of the system model can be obtained as follows:

[0172] (15)

[0173] in, It represents the cumulative distribution function of the legal user end under the condition of large signal-to-noise ratio of the system model. It represents the signal-to-noise ratio of the received signal of the legal user end when the system model is under the condition of large signal-to-noise ratio.

[0174] Finally, the closed form of the asymptotic secrecy outage probability and the positive secrecy asymptotic capacity are derived using the cumulative distribution function of the legitimate user end under the condition of large signal-to-noise ratio in the system model.

[0175] Furthermore, the closed-form expression of the asymptotic secrecy outage probability is:

[0176] (16)

[0177] in, The closed form for the asymptotic probability of confidentiality outage is, .

[0178] The expression of positive secrecy asymptotic capacity is:

[0179] (17)

[0180] in, Indicates the positive confidentiality of the progressive capacity, represents the probability when the achievable confidentiality rate is positive, .

[0181] Under high signal-to-noise ratio (SNR) conditions, the positive asymptotic capacity of a system model can be defined as the probability that the achievable secrecy rate is greater than zero. This asymptotic capacity can be used as a metric to evaluate the secrecy performance of the entire system model. A high SNR refers to a signal-to-noise ratio greater than 30dB.

[0182] In this embodiment, step S103: based on the results of the average achievable confidentiality rate analysis, the results of the average decoding error probability analysis, and the results of the confidentiality interruption probability analysis, the performance of the system model is evaluated. In this embodiment, step S103 may include the following sub-steps:

[0183] Sub-step S1031: When the result of the achievable confidentiality rate analysis, i.e. the closed form of the average achievable confidentiality rate, is greater than or equal to , and the result of the average decoding error probability analysis, that is, the closed form of the average decoding error probability is less than or equal to , and the result of the confidentiality interruption probability analysis, that is, the closed form of the confidentiality interruption probability is less than or equal to , the performance of the system model is judged to be excellent.

[0184] Sub-step S1032: When the result of the achievable confidentiality rate analysis is and the average decoding error probability analysis result is greater than and less than or equal to , and the result of confidentiality interruption probability analysis is greater than and less than or equal to , the performance of the system model is judged to be medium.

[0185] Sub-step S1033: When the result of the achievable confidentiality rate analysis is less than or equal to , and the result of the average decoding error probability analysis is greater than and less than or equal to , and the result of confidentiality interruption probability analysis is greater than and less than or equal to , the performance of the system model is judged to be poor.

[0186] When the confidentiality performance of the system model is poor, the results of the confidentiality interruption probability analysis can be enhanced by increasing the number of reflective elements in the reconfigurable smart surface, and the confidentiality performance of the system model can be improved by increasing the length of the channel block of the ground base station and increasing the proportion of line-of-sight links.

[0187] In order to verify the applicability and superiority of the performance evaluation method for short packet coding communication proposed in this application, the following simulation experiments were carried out.

[0188] In the simulation, the ground base station's communication tower was positioned at (0, 0), the legitimate user was positioned at (60, 60 m), the non-target user was positioned at (56, 85 m), and the drone carrying the reconfigurable smart surface was positioned at (40, 40 m). The path loss factor was set to 2.3, and the number of bits transmitted per unit time was set to 100.

[0189] The data under the following six conditions were counted:

[0190] the number of reflective elements of the reconfigurable smart surface;

[0191] The length of the channel block of the ground base station;

[0192] Rice factor;

[0193] the threshold of achievable confidentiality rate;

[0194] The drone's flight altitude;

[0195] Safety constraints and reliability constraints.

[0196] Based on this simulation experiment, we get:

[0197] Figure 3 The relationship between the signal-to-noise ratio of the transmission signal and the average achievable confidentiality rate of the system model is shown. Figure 3 It can be seen that as the signal-to-noise ratio of the transmitted signal increases, more reflective elements on the reconfigurable smart surface regulate the channel, thereby enhancing the signal of the legitimate user end while suppressing the signal of the non-target user end, significantly improving the average achievable confidentiality rate of the system model. Figure 3 middle Indicates the length of the channel block of the ground base station, Represents the number of reflective elements in the reconfigurable smart surface.

[0198] Increasing the length of the ground base station's channel block can support efficient coding and accurate channel observation. However, very short channel blocks become unreliable because they compress a lot of error correction information, which will lead to a decline in the performance of the system model, especially when the number of reflective elements on the reconfigurable smart surface is insufficient, which will aggravate the attenuation. Thus, this simulation experiment verifies that the coordinated optimization between the number of reflective elements on the reconfigurable smart surface and the length of the ground base station's channel block plays a key role in balancing security and transmission rate. It also verifies the effectiveness of the average achievable confidentiality rate analysis framework for the system model in this application.

[0199] Figure 4 The system model shows that the average decoding error probability decreases as the length of the ground base station's channel block increases. Longer channel blocks are more reliable because they contain more error correction information, and the channel gain gradually saturates. Shorter channel blocks, due to insufficient coding redundancy, lead to a sharp increase in the average decoding error probability, making it difficult to meet the ultra-low decoding error rate requirements of short packet coding communication scenarios.

[0200] Number of reflective elements of reconfigurable smart surfaces and Rice factor Jointly regulate the performance of the system model. The larger the number of reflective elements or the Ricean factor, the stronger the path control and line-of-sight components of the channel signal, which significantly reduces the average decoding error probability. When the number of reflective elements or the Ricean factor becomes smaller and smaller, the performance of the system model will gradually deteriorate. This simulation experiment shows that the length of the channel block of the ground base station needs to balance the delay and reliability. If the channel block is short, it is necessary to increase the compensation performance of the reflective element or the Ricean factor, and enhancing the line-of-sight component is crucial to improving the security of the system model. The Monte Carlo results are consistent with the theoretical trend, which also verifies the effectiveness of the average decoding error probability analysis framework for the system model in this application.

[0201] Figure 5 It shows that as the signal-to-noise ratio of the transmitted signal increases, the probability of confidentiality interruption of the system model will decrease significantly. This is because the expansion of the channel quality difference between the legitimate user end and the non-target user end will enhance the confidentiality performance of the system model. When the threshold of the confidentiality rate is reached Very low, for example, When , the probability of confidentiality interruption of the system model is lower, which indicates that the security requirements of the system model are more easily met. The Monte Carlo simulation results are highly consistent with the Gaussian approximation results, which verifies the effectiveness of the theoretical method of this application.

[0202] Figure 6It is shown that increasing the number of reflective elements on the reconfigurable smart surface and the length of the channel block of the ground base station can significantly improve the positive asymptotic capacity of the system model. Increasing the number of reflective elements on the reconfigurable smart surface can achieve performance gains in the system model by optimizing the quality of the signal channel and interference suppression; increasing the length of the channel block of the ground base station enhances the data processing capability, thereby improving the confidentiality performance of the system model. However, stricter reliability constraints (i.e., the probability of decoding errors at the legitimate user end) ) and security constraints (i.e., the information leakage rate of legitimate users ) Additional power compensation is required to maintain the positive confidentiality asymptotic capacity level of the system model. Increasing the altitude of the UAV , which will weaken the confidentiality performance of the system model due to the path loss of the channel signal and environmental changes. The simulation results show that it is necessary to coordinately optimize the number of reflective elements. , the length of the channel block , signal power, layout and other parameters, so as to achieve global optimization of the safety performance of the system model under complex conditions.

[0203] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise clearly specified.

[0204] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0205] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of the present application.

[0206] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

Claims

1. A performance evaluation method for short packet coding communication, characterized in that: The method comprises the following steps: Constructing a system model for short packet coding communication, the system model includes a ground base station, a drone, a legitimate user terminal and a non-target user terminal connected in communication, and the drone is equipped with a reconfigurable smart surface; According to all channel state information of the system model, performing average achievable confidentiality rate analysis, average decoding error probability analysis and confidentiality interruption probability analysis on the system model in sequence; The step of performing the average achievable confidentiality rate analysis on the system model according to all the channel state information of the system model includes: Adjusting the reconfigurable smart surface to achieve an optimal phase shift according to the received signal of the legitimate user terminal, and obtaining an optimal signal-to-noise ratio of the legitimate user terminal and an instantaneous signal-to-noise ratio of the non-target user terminal respectively; Modeling the optimal signal-to-noise ratio of the legitimate user terminal as a randomly distributed variable of a gamma function, and deriving a probability density function and a cumulative distribution function of the legitimate user terminal using a moment matching method; Modeling the instantaneous signal-to-noise ratio of the non-target user terminal as an exponential distribution variable of a gamma function, and deriving the probability density function and cumulative distribution function of the non-target user terminal using a moment matching method; Determining a closed form of an average achievable confidentiality rate of the system model using the achievable confidentiality rate of the system model, the probability density function of the legitimate user terminal, and the probability density function of the non-target user terminal; The probability density function of the legal user terminal is expressed as: (4) in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is The probability density function when Indicates the signal transmission power of the ground base station, Indicates the signal-to-noise ratio of the received signal of the legitimate user end, represents the optimal signal-to-noise ratio of the legitimate user end, , , Represents the reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is , , Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, , Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, , represents the shape parameter, represents the exponential function, represents the scale parameter, represents the gamma function; The cumulative distribution function of the legal user terminal is expressed as: (5) in, Indicates that the signal-to-noise ratio of the received signal at the legitimate user end is Cumulative distribution function when ; The probability density function of the non-target user terminal is expressed as: (6) in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is The probability density function when Indicates the signal-to-noise ratio of the received signal of the non-target user end, represents the rate parameter of the exponential distribution, represents the instantaneous signal-to-noise ratio of the non-target user end, , , represents the path loss coefficient of the channel signal from the UAV to the non-target user end, Indicates the channel distance from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is , represents the net phase difference of the non-target user end, , Indicates the channel distance from the ground base station to the non-target user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, ; The cumulative distribution function of the non-target user terminal is expressed as: (7) in, Indicates that the signal-to-noise ratio of the non-target user end when receiving the signal is Cumulative distribution function when ; The closed-form step of determining an average achievable confidentiality rate of the system model using the achievable confidentiality rate of the system model, the probability density function of the legitimate user terminal, and the probability density function of the non-target user terminal comprises: When the length of the channel block of the ground base station is constant, the decoding error probability of the legal user terminal is less than or equal to the decoding error probability threshold, and the information leakage rate of the legal user terminal is less than or equal to the information leakage rate threshold, if , then the achievable confidentiality rate is 0, and the achievable confidentiality rate is express, Indicates the length of the channel block of the ground base station, represents the decoding error probability of the legitimate user end, Indicates the information leakage rate of legitimate users; like , then the closed-form expression of the achievable confidentiality rate is: (8) in, The closed form of the achievable confidentiality rate of the system model is expressed as, represents the achievable confidentiality rate of the legitimate user end, , represents the achievable confidentiality rate of the non-target user end, , Indicates the confidentiality rate of long packet coding communication, , represents the channel dispersion of the legal user end, , represents the channel dispersion of the non-target user end, , express The inverse function of a function, , express The inverse function of a function, , represents the integral variable; The closed-form expression of the average achievable confidentiality rate is: (9) in, The closed form of the average achievable confidentiality rate is expressed as, represents the Meijer'G function, represents the Fox-H function, , , Represents an accumulated variable, when hour, ,when hour, ; The step of performing the average decoding error probability analysis on the system model according to all the channel state information of the system model includes: when When , the decoding error probability is equal to 1; when When , the expression of the decoding error probability is: (10) in, represents the right tail function of the standard normal distribution, , express variables, Indicates the number of bits of the transmission signal of the ground base station; Using linear approximation theory, let Approximate linear function To simplify the computational complexity of formula (10), , the linear function The expression is: (11) in, , , represents the lower bound of the achievable confidentiality rate, , represents the upper bound of the achievable confidentiality rate, , Represents a linear function variables; make ,Will Re-expressed as Then, the linear function Substitute into formula (10) and calculate the average of all the decoding error probabilities to obtain the average decoding error probability; The expression of the average decoding error probability is: (12) in, represents the average decoding error probability, represents auxiliary variables, represents the cumulative distribution function of the legitimate user terminal at the lower bound of the achievable confidentiality rate, represents the cumulative distribution function of the legitimate user terminal at the upper bound of the achievable confidentiality rate, Indicates that when the variable equal A linear function of Solve using the distribution integral method , and use the Riemann integral to convert Rewrite to obtain the closed form of the average decoding error probability; The closed-form expression of the average decoding error probability is: (13) in, The closed form of the average decoding error probability is expressed as, , represents the cumulative distribution function of legal user terminals; The step of performing the confidentiality interruption probability analysis on the system model according to all the channel state information in the system model comprises: When the length of the channel block of the ground base station is , and the threshold of the achievable confidentiality rate is ,and When , the closed form of the probability of confidentiality interruption is calculated using the moment matching method; Calculating the cumulative distribution function of the legitimate user terminal under the condition of a large signal-to-noise ratio of the system model; Using the cumulative distribution function of the legitimate user terminal under the condition of large signal-to-noise ratio of the system model, the closed form of the asymptotic privacy interruption probability and the positive privacy asymptotic capacity are derived respectively; The performance of the system model is evaluated based on the results of the average achievable confidentiality rate analysis, the results of the average decoding error probability analysis, and the results of the confidentiality interruption probability analysis.

2. The performance evaluation method for short packet coding communication according to claim 1, characterized in that The reconfigurable smart surface comprises a reflective array composed of a plurality of reflective elements of the same structure, each of the reflective elements is respectively configured with a single antenna; the ground base station, the legitimate user terminal and the non-target user terminal are respectively configured with the single antenna; The expression of the phase shift matrix of the reconfigurable smart surface is: (1) in, represents the phase shift matrix of the reconfigurable smart surface, It means constructing a diagonal matrix, represents the imaginary unit, Represents the reconfigurable smart surface Phase shift of each reflective element; The ground base station is used The position of the ground base station is represented by express, , Indicates the ground base station Axis coordinates, Indicates the ground base station axis coordinates; The drone is used The position of the UAV is represented by express, , Indicates drone Axis coordinates, Indicates drone Axis coordinates, Indicates the altitude of the drone; The legal user terminal uses Indicates that the location of the legal user terminal is express, , Indicates a legitimate user terminal Axis coordinates, Indicates a legitimate user terminal axis coordinates; The non-target user terminal uses Indicates that the location of the non-target user terminal is express, , Indicates non-target user end Axis coordinates, Indicates non-target user end Axis coordinates.

3. The performance evaluation method for short packet coding communication according to claim 1, characterized in that The legitimate user terminal and the non-target user terminal both receive two channel signals, the first channel signal being a direct link signal transmitted from the ground base station, and the second channel signal being a reflected link signal from the reconfigurable smart surface; The expression of the received signal of the legal user terminal is: (2) in, Indicates the received signal of the legal user end, Indicates the signal transmission power of the ground base station, Represents the transmission signal of the ground base station, and satisfies , express The expected value of Indicates the channel distance from the ground base station to the drone, Indicates the channel distance from the drone to the legal user end, Represents the path loss coefficient of the channel signal from the ground base station to the UAV, Represents the path loss coefficient of the channel signal from the drone to the legitimate user end, Represents the reconfigurable smart surface A reflective element, represents the number of all reflective elements of the reconfigurable smart surface, represents the complex channel gain from the ground base station to the reconfigurable smart surface on the UAV, , The first step of the reconfigurable smart surface from the ground base station to the UAV The channel phase shift coefficient of the reflecting element, , represents the imaginary unit, represents the complex channel gain from the reconfigurable smart surface on the UAV to the legitimate user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the legitimate user end is , Represents the reconfigurable smart surface The phase shift of the reflective element, Indicates the channel distance from the ground base station to the legal user terminal, Represents the path loss coefficient of the channel signal from the ground base station to the legitimate user end, represents the interference noise of the legitimate user end, represents the complex channel gain from the ground base station to the legal user end, , represents the channel phase shift coefficient from the ground base station to the legal user end, ; The expression of the received signal of the non-target user terminal is: (3) in, Indicates the received signal of non-target user end, represents the path loss coefficient of the channel signal from the UAV to the non-target user end, represents the complex channel gain from the reconfigurable smart surface on the UAV to the non-target user end, , Reconfigurable smart surfaces on drones The channel phase shift coefficient from the reflective element to the non-target user end is , Indicates the channel distance from the ground base station to the non-target user terminal, Indicates the channel distance from the UAV to the non-target user end, Represents the path loss coefficient of the channel signal from the ground base station to the non-target user end, represents the interference noise of non-target user end, represents the complex channel gain from the ground base station to the non-target user end, , represents the channel phase shift coefficient from the ground base station to the non-target user end, .

4. The performance evaluation method for short packet coding communication according to claim 1, characterized in that The closed-form expression of the confidentiality interruption probability is: (14) in, The closed form of the probability of confidentiality interruption is expressed as, represents the closed-form variance of the achievable confidentiality rate, represents the Gaussian function; When the system model is under a high signal-to-noise ratio condition, the cumulative distribution function of the legal user terminal is expressed as: (15) in, It represents the cumulative distribution function of the legal user end under the condition of large signal-to-noise ratio of the system model. Indicates the signal-to-noise ratio of the received signal at the legitimate user end when the system model has a large signal-to-noise ratio. The closed-form expression of the asymptotic privacy outage probability is: (16) in, The closed form for the asymptotic probability of confidentiality outage is, ; The expression of the positive secret asymptotic capacity is: (17) in, Indicates the positive confidentiality of the progressive capacity, represents the probability when the achievable confidentiality rate is positive, .

5. The performance evaluation method for short packet coding communication according to claim 1, characterized in that The step of evaluating the performance of the system model according to the results of the achievable confidentiality rate analysis, the results of the average decoding error probability analysis, and the results of the confidentiality interruption probability analysis comprises: When the result of the achievable confidentiality rate analysis is greater than or equal to , and the result of the average decoding error probability analysis is less than or equal to , and the result of the confidentiality interruption probability analysis is less than or equal to When , the performance of the system model is judged to be excellent; When the result of the achievable confidentiality rate analysis is and the result of the average decoding error probability analysis is greater than and less than or equal to , and the result of the confidentiality interruption probability analysis is greater than and less than or equal to When , the performance of the system model is judged to be medium; When the result of the achievable confidentiality rate analysis is less than or equal to , and the result of the average decoding error probability analysis is greater than and less than or equal to , and the result of the confidentiality interruption probability analysis is greater than and less than or equal to , the performance of the system model is judged to be poor.

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