An active intelligent reflecting surface assisted short packet communication method in an electromagnetic interference environment

Abstract

An active intelligent reflector-assisted short packet communication method is disclosed for use in electromagnetic interference environments. In an active reflector-assisted short packet communication system, due to building obstruction, there is no direct transmission link between the wireless access point and the user equipment, requiring communication via an active reflector-assisted intelligent reflector. An electromagnetic interference source exists in the system. It is assumed that the transmission power of the electromagnetic interference source and the transmission channel characteristics from the electromagnetic interference source to the intelligent reflector and the user equipment are known. First, the signal-to-interference-plus-noise ratio (SIR) statistical characteristics of the user's decoding are determined using statistical channel state information. Second, the average block error rate of the user under fading channels is determined according to relevant formulas for short packet communication. Finally, under reliability constraints, the minimum common block length of the system is determined. This invention achieves highly reliable, low-latency communication.

Description

Technical Field

[0001] This invention relates to a method for optimizing the design of an active reflective intelligent reflective surface (IRS)-assisted short packet communication system in an environment with electromagnetic interference, belonging to the field of next-generation mobile communication technology. Background Technology

[0002] Ultra-Reliable and Low-Latency Communication (URLLC) is one of the main application scenarios for future mobile communication technologies, aiming to provide ultra-high reliability. (above) and ultra-low latency ( How to achieve URLLC (milliseconds) remains one of the challenges facing the field of wireless communication.

[0003] Short packet communication and IRS (Interactive Reflective Switching) are considered key technologies for achieving URLLC (URLLC). Short packet communication refers to communication with very short data blocks, thus effectively reducing system transmission latency (Y. Yu, H. Chen, et al. “On the performance of non-orthogonal multiple access in short-packet communications,” IEEE Commun. Lett., vol. 22, no. 3, pp. 590–593, Mar. 2018.). IRS includes various types such as reflective IRS and simultaneous transmission-reflection IRS. Reflective IRS is further subdivided into passive reflective IRS and active reflective IRS. Compared to passive reflective IRS, active reflective IRS can solve the high path loss problem caused by "multiplicative fading" in the IRS auxiliary cascade channel in passive reflective IRS-assisted communication by actively amplifying the reflected signal (Z. Zhang, S. Nie, Y. Wang, X. Mu, J. Wang, and J. Song, "Active RIS vs. Passive RIS: Which Will Prevail in 6G?", IEEE Trans. Commun., vol. 71, no. 3, pp. 1707–1725, Mar. 2023.).

[0004] Chinese invention patent CN115442816B discloses a passive reflective IRS-assisted short packet communication (URLLC) implementation method. It designs the system using statistical channel state information for a two-user scenario and proposes a power allocation and transmission block length optimization algorithm. Chinese invention patent CN120128945A discloses a passive reflective IRS-assisted short packet communication method under electromagnetic interference (EMI) conditions. It uses EMI to optimize power allocation and transmission block length for a two-user scenario to achieve URLLC. The above research on IRS-assisted short packet communication focuses on passive reflective IRSs. Currently, research on how to optimize the design of active reflective IRS-assisted short packet communication systems to achieve URLLC under EMI conditions is relatively limited. Therefore, it is essential to study the optimization design of active reflective IRS-assisted short packet communication systems to achieve URLLC under EMI conditions. Summary of the Invention

[0005] This invention provides an active intelligent reflector-assisted short packet communication method for electromagnetic interference environments, achieving highly reliable low-latency communication (URLLC).

[0006] The objective of this invention is achieved through the following technical solution:

[0007] An active intelligent reflector-assisted short packet communication method under electromagnetic interference environment, the specific implementation steps are as follows:

[0008] Step A: Establish an active reflective intelligent reflector (IRS)-assisted downlink short packet communication system. This system includes a radio access point (AP) with M antennas, a single-antenna user equipment (UE), and a system with... The system comprises an active reflective IRS for each unit. Due to building obstruction, there is no direct transmission link between the AP and UE, and communication is achieved through the active reflective IRS. The system includes an electromagnetic interference source. It is assumed that the transmission power of the electromagnetic interference source and the transmission channel characteristics from the electromagnetic interference source to the active reflective IRS and the UE are known. At the same time, the electromagnetic interference source will affect the received signals of the UE and the active reflective IRS respectively.

[0009] Step B: Based on the system model established in Step A, derive the UE's received signal under electromagnetic interference environment; based on the statistical channel state information, further write out the instantaneous signal-to-interference-plus-noise ratio (SINR).

[0010] Step C: Based on the channel parameters, the number of AP antennas M, and the number of active reflective IRS units. The active reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and the distribution characteristics of instantaneous SINR are analyzed and presented; its specific implementation is as follows:

[0011] The end-to-end channel gain from AP to UE is The end-to-end channel gain from the electromagnetic interference source to the UE is Thermal noise term is ,in Represents the modulus of a complex number or the absolute value of a real number. , These represent the end-to-end channel fading coefficients from AP to UE and from the electromagnetic interference source to UE, respectively. This represents the channel coefficient between the AP and the active reflective IRS. For complex fields, This represents the channel coefficient between the active reflective IRS and the UE. This represents the channel coefficient between the interference source and the active reflective IRS. This represents the channel coefficient between the interference source and the UE. This represents the reflection coefficient matrix of an active reflective IRS. Describing the Frobenius norm, This represents the thermal noise power of an active reflective IRS; when using optimal continuous phase adjustment, according to the moment matching method, Approximate shape parameters are and scale parameters are The gamma distribution, in which , , , and Represents Rice factor, The path loss is measured at a reference distance of 1 meter. This represents the amplitude coefficient of an active reflective IRS. and These represent the distances from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. and These represent the path loss indices from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. Obtains a shape parameter of 1 and a scale parameter of 1. The gamma distribution, in which , and These represent the distances from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively. and Let these represent the path loss exponents from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively; by the central limit theorem, we can obtain... Approximately follows the mean variance is The Gaussian distribution, where , , This represents the confluence hypergeometry function.

[0012] The instantaneous SINR at the UE can be calculated based on the probability density functions of the channel gain and thermal noise terms above. The cumulative distribution function, i.e.

[0013]

[0014] in, , , This represents the power of additive complex white Gaussian noise. Represents the gamma function. Represents the Gaussian hypergeometry function. , This represents the incomplete gamma function. Represents the error function. , , This indicates the number of terms truncated by the Gauss-Chebyshev integral method.

[0015] Step D: Obtain the BLER expression for the average block error rate of the UE based on short packet communication theory.

[0016] Step E: Based on the expression obtained in Step D, the minimum transmission block length is further solved under reliability constraints.

[0017] Further:

[0018] The specific implementation of step A is as follows:

[0019] An active reflective IRS is deployed high on the building surface, enabling line-of-sight (LoS) transmission links between the IRS and the access point (AP) and user unit (UE). Based on this, the channel coefficient between the AP and the IRS is... and the channel coefficient between IRS and UE Modeled as a Rice distribution, which are respectively

[0020]

[0021]

[0022] in, and For the line-of-sight transmission components of the corresponding link, and For the non-line-of-sight transmission components of the corresponding link, and Each element in the array is independent and follows a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1; because the AP antenna and IRS unit use a uniform linear array, the array response at the AP and IRS is... ,in , For carrier wavelength, For unit spacing, Indicates the transmission angle or angle of arrival of the signal; based on this, and The Los components are respectively and ,in and These represent the emission angles at AP and IRS, respectively. The angle of arrival at the IRS is represented by: The channel coefficients from the interference source to the IRS and the UE are respectively expressed as: and ,in and Each element in the distribution is independent and follows a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

[0023] The specific implementation of step B is as follows:

[0024] Based on the above system model, it can be seen that the signal received by the UE in active reflective IRS-assisted communication is

[0025]

[0026] in, and These represent the transmit power of the AP and the interference source, respectively. and These represent the normalized power signals transmitted by the AP and the interference source, respectively. Follows the pattern with mean 0 and variance of The cyclic complex Gaussian distribution represents the thermal noise introduced by the active reflective IRS. This indicates that the mean is 0 and the variance is... Additive complex white Gaussian noise, This represents the reflection coefficient matrix of an active reflective IRS. This represents a diagonal matrix with the elements within the parentheses forming its diagonal elements. ( ) indicates the active IRS. Phase shift of each reflecting unit This represents the power-normalized beamforming vector at AP.

[0027] IRS uses statistical channel state information to perform optimal continuous phase adjustment to obtain... , Represents the remainder when x is divided by y; when the AP adopts the maximum ratio transmission strategy based on statistical channel state information, the optimal beamforming vector at the AP is .

[0028] When electromagnetic interference is present, the instantaneous SINR of the UE in active IRS-assisted communication under fading channels is:

[0029] .

[0030] The specific implementation of step D is as follows:

[0031] For a given received signal-to-noise ratio BLER and transport block length ,when When the number of symbols is 1, the maximum achievable rate of short packet communication in an additive complex white Gaussian noise channel is approximately:

[0032]

[0033] in, Indicates Shannon capacity. Represents channel divergence, express The inverse function;

[0034] Assume the physical layer information rate of the UE is ,in Indicates the number of information bits; UE decoding signal The instantaneous BLER approximation is as follows: ;

[0035] Based on instantaneous BLER and instantaneous SINR, the average BLER in the fading channel Represented as:

[0036]

[0037] in, express The probability density function;

[0038] use Approximation of the average BLER in fading channels Further expressed as:

[0039]

[0040] in , , , , , , , ;

[0041] Further employing the Gauss-Chebyshev integration method, the average BLER at the UE can be obtained. for:

[0042]

[0043] in Let be the approximate order of the Gauss-Chebyshev integral. , , , .

[0044] The specific implementation of step E is as follows:

[0045] The target average BLER of the UE is expressed as The specific steps for determining the minimum transport block length are as follows:

[0046] 1.1 Given system parameters , , , , , , , , , , , , , , , , , , , , and Initialize block length Limit the block length to ,make , ;

[0047] 1.2 Length of block , Substitute into the average BLER formula to calculate the corresponding average BLER for the UE. , ;

[0048] 1.3 If the calculated average BLER satisfies Let the minimum block length be This means there is no solution;

[0049] 1.4 If the calculated average BLER satisfies Then let the minimum block length be... ;

[0050] 1.5 If none of the above conditions are met, then if the conditions are met... Under the condition that, let Calculate the corresponding average BLER , This represents the floor function;

[0051] 1.6 When satisfied Then let Otherwise ;

[0052] 1.7 Update or Continue with steps 1.5 to 1.6 until... By stopping the iteration, the minimum transport block length can be obtained. .

[0053] The core of this invention lies in the first deriving of a closed-form expression for the cumulative distribution function of the instantaneous SINR of the UE decoding in an active IRS-assisted short packet communication system under electromagnetic interference environment, and further deriving a closed-form expression for the average BLER. Finally, under reliability constraints, the minimum transmission block length of the system is optimized to achieve URLLC. Attached Figure Description

[0054] Figure 1 This is the system model of the present invention;

[0055] Figure 2 This is a logic flowchart of the present invention;

[0056] Figure 3 This invention relates to a passive reflective IRS (i.e., IRS) with different interference source transmission powers. , The relationship between the minimum transport block length and the number of IRS units in an auxiliary short packet communication system, where... , , , , , , , , , , , , , , , , , , , , , ;

[0057] Figure 4This invention relates to the minimum transmission block length and the number of IRS units in an active reflective IRS-assisted short packet communication system under different interference source transmit powers. , , , , , , , , , , , , , , , , , , , , , , . Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings, both in terms of theory and specific implementation.

[0059] Reference Figure 1 , Figure 2 An active intelligent reflector-assisted short packet communication method under electromagnetic interference environment, the specific implementation steps are as follows:

[0060] Step A: Establish an active reflective intelligent reflective surface (IRS) assisted downlink short packet communication system. This system includes an M-antenna wireless access point (AP), a single-antenna user equipment (UE), and a system with... The system consists of an active reflective IRS unit. Due to building obstruction, there is no direct transmission link between the AP and UE, so communication is achieved through the active reflective IRS. The system includes an electromagnetic interference source. It is assumed that the transmission power of the electromagnetic interference source and the transmission channel characteristics from the electromagnetic interference source to the active reflective IRS and the UE are known. At the same time, the electromagnetic interference source will affect the received signals of the UE and the active reflective IRS respectively.

[0061] Step B: Based on the system model established in Step A, derive the UE's received signal under electromagnetic interference environment; based on statistical channel state information, further write out the instantaneous signal to interference plus noise ratio (SINR).

[0062] Step C: Based on the channel parameters, the number of AP antennas M, and the number of active reflective IRS units. The active reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and the distribution characteristics of instantaneous SINR are analyzed and presented; its specific implementation is as follows:

[0063] The end-to-end channel gain from AP to UE is The end-to-end channel gain from the electromagnetic interference source to the UE is Thermal noise term is ,in Represents the modulus of a complex number or the absolute value of a real number. , These represent the end-to-end channel fading coefficients from AP to UE and from the electromagnetic interference source to UE, respectively. This represents the channel coefficient between the AP and the active reflective IRS. For complex fields, This represents the channel coefficient between the active reflective IRS and the UE. This represents the channel coefficient between the interference source and the active reflective IRS. This represents the channel coefficient between the interference source and the UE. This represents the reflection coefficient matrix of an active reflective IRS. Describing the Frobenius norm, This represents the thermal noise power of an active reflective IRS; when using optimal continuous phase adjustment, according to the moment matching method, Approximate shape parameters are and scale parameters are The gamma distribution, in which , , , and Represents Rice factor, The path loss is measured at a reference distance of 1 meter. This represents the amplitude coefficient of an active reflective IRS. and These represent the distances from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. and These represent the path loss indices from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. Obtains a shape parameter of 1 and a scale parameter of 1. The gamma distribution, in which , and These represent the distances from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively. and Let these represent the path loss exponents from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively; by the central limit theorem, we can obtain... Approximately follows the mean variance is The Gaussian distribution, where , , This represents the confluence hypergeometry function.

[0064] The instantaneous SINR at the UE can be calculated based on the probability density functions of the channel gain and thermal noise terms above. The cumulative distribution function, i.e.

[0065]

[0066] in, , , This represents the power of additive complex white Gaussian noise. Represents the gamma function. Represents the Gaussian hypergeometry function. , This represents the incomplete gamma function. Represents the error function. , , This indicates the number of terms truncated by the Gauss-Chebyshev integral method.

[0067] Step D: Obtain the average block error rate (BLER) of the UE based on short packet communication theory.

[0068] Step E: Based on the expression obtained in Step D, the minimum transmission block length is further solved under reliability constraints.

[0069] Further:

[0070] The specific implementation of step A is as follows:

[0071] An active reflective IRS is deployed high on the building surface, enabling a line-of-sight (LoS) transmission link between the IRS and the access point (AP) and user unit (UE). Based on this, the channel coefficient between the AP and the IRS is... and the channel coefficient between IRS and UE Modeled as a Rice distribution, which are respectively

[0072]

[0073]

[0074] in, and For the line-of-sight transmission components of the corresponding link, and For the non-line-of-sight transmission components of the corresponding link, and Each element in the array is independent and follows a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1; because the AP antenna and IRS unit use a uniform linear array, the array response at the AP and IRS is... ,in , For carrier wavelength, For unit spacing, Indicates the transmission angle or angle of arrival of the signal; based on this, and The Los components are respectively and ,in and These represent the emission angles at AP and IRS, respectively. The angle of arrival at the IRS is represented by: The channel coefficients from the interference source to the IRS and the UE are respectively expressed as: and ,in and Each element in the distribution is independent and follows a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

[0075] The specific implementation of step B is as follows:

[0076] Based on the above system model, it can be seen that the signal received by the UE in active reflective IRS-assisted communication is

[0077]

[0078] in, and These represent the transmit power of the AP and the interference source, respectively. and These represent the normalized power signals transmitted by the AP and the interference source, respectively. Follows the pattern with mean 0 and variance of The cyclic complex Gaussian distribution represents the thermal noise introduced by the active reflective IRS. This indicates that the mean is 0 and the variance is... Additive complex white Gaussian noise, This represents the reflection coefficient matrix of an active reflective IRS. This represents a diagonal matrix with the elements within the parentheses forming its diagonal elements. ( ) indicates the active IRS. Phase shift of each reflecting unit This represents the power-normalized beamforming vector at AP.

[0079] IRS uses statistical channel state information to perform optimal continuous phase adjustment to obtain... , Represents the remainder when x is divided by y; when the AP adopts the maximum ratio transmission strategy based on statistical channel state information, the optimal beamforming vector at the AP is .

[0080] When electromagnetic interference is present, the instantaneous SINR of the UE in active IRS-assisted communication under fading channels is:

[0081] .

[0082] The specific implementation of step D is as follows:

[0083] For a given received signal-to-noise ratio BLER and transport block length ,when When the number of symbols is 1, the maximum achievable rate of short packet communication in an additive complex white Gaussian noise channel is approximately:

[0084]

[0085] in, Indicates Shannon capacity. Represents channel divergence, express The inverse function;

[0086] Assume the physical layer information rate of the UE is ,in Indicates the number of information bits; UE decoding signal The instantaneous BLER approximation is as follows: ;

[0087] Based on instantaneous BLER and instantaneous SINR, the average BLER in the fading channel Represented as:

[0088]

[0089] in, express The probability density function;

[0090] use Approximation of the average BLER in fading channels Further expressed as:

[0091]

[0092] in , , , , , , , ;

[0093] Further employing the Gauss-Chebyshev integration method, the average BLER at the UE can be obtained. for:

[0094]

[0095] in Let be the approximate order of the Gauss-Chebyshev integral. , , , .

[0096] The specific implementation of step E is as follows:

[0097] The target average BLER of the UE is expressed as The specific steps for determining the minimum transport block length are as follows:

[0098] 1.1 Given system parameters , , , , , , , , , , , , , , , , , , , , and Initialize block length Limit the block length to ,make , ;

[0099] 1.2 Length of block , Substitute into the average BLER formula to calculate the corresponding average BLER for the UE. , ;

[0100] 1.3 If the calculated average BLER satisfies Let the minimum block length be This means there is no solution;

[0101] 1.4 If the calculated average BLER satisfies Then let the minimum block length be... ;

[0102] 1.5 If none of the above conditions are met, then if the conditions are met... Under the condition that, let Calculate the corresponding average BLER , This represents the floor function;

[0103] 1.6 When satisfied Then let Otherwise ;

[0104] 1.7 Update or Continue with steps 1.5 to 1.6 until... By stopping the iteration, the minimum transport block length can be obtained. .

[0105] The specific implementation process of this invention is as follows:

[0106] An active intelligent reflector-assisted short packet communication method under electromagnetic interference environment, the specific implementation steps are as follows:

[0107] Step A: Establish an intelligent reflective surface (IRS)-assisted downlink short packet communication system. This system includes an access point (AP) with M=4 antennas, a user equipment (UE) with a single antenna, and a device with... The system comprises an active reflective IRS for each unit. Due to building obstruction, there is no direct transmission link between the AP and UE, requiring communication via the active reflective IRS. The system includes an electromagnetic interference (EMI) source. It is assumed that the EMI source's transmit power, the transmission channel characteristics from the EMI source to the active reflective IRS and the UE are known. Simultaneously, the EMI source will affect the received signals of both the UE and the active reflective IRS. The channel coefficient between the AP and the IRS is... and the channel coefficient between IRS and UE Modeled as a Rice distribution, which are respectively , ,

[0108] in and For the line-of-sight transmission components of the corresponding link, and For the non-line-of-sight transmission components of the corresponding link, and Each element in the dataset is independent and follows a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1. and Represents Rice factor, The path loss is measured at a reference distance of 1 meter. This represents the amplitude coefficient of an active reflective IRS. and These represent the distances from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. and These represent the path loss indices from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. , , , , , , Because the AP antenna and IRS unit use a uniform linear array, the array response at the AP and IRS is: ,in , Centimeters is the carrier wavelength. Centimeters represent the unit spacing. This indicates the signal's transmission angle or angle of arrival. Based on this, and The Los components are respectively and ,in and These represent the emission angles at AP and IRS, respectively. Indicates the angle of arrival at the IRS, and , , The channel coefficients from the interference source to the IRS and UE are expressed as follows: and ,in and Each element in the dataset is independent and follows a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1. and These represent the distances from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively. and These represent the path loss indices from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively. , , , .

[0109] Step B: Based on the system model established in Step A, derive the UE's received signal under electromagnetic interference environment; based on statistical channel state information, further write out the instantaneous signal to interference plus noise ratio (SINR).

[0110] Step C: Based on the channel parameters, the number of AP antennas M, and the number of active reflective IRS units. Active reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and the distribution characteristics of instantaneous SINR are analyzed and given.

[0111] Step D: Calculate the UE's average block error rate (BLER) according to the formula in short packet communication.

[0112] Step E: Based on the expression calculated in Step D, under reliability constraints... Next, the minimum transport block length is further solved by using a binary search method and a one-dimensional traversal search algorithm.

[0113] Figure 3 The relationship between the minimum transport block length and the number of IRS units in a passive reflective IRS-assisted short packet communication system under different interference source transmit powers is presented. Figure 3 When the interference source transmits power is At that time, the minimum transfer block length should be reduced to The required number of IRS units is 545, and when the interference source's transmit power increases to At that time, the minimum transport block length is reduced to The required number of IRS units has increased to 903, an increase of 358. Figure 4 The relationship between the minimum transport block length and the number of IRS units in an active reflective IRS-assisted short packet communication system under different interference source transmit powers is presented. Figure 4 When the interference source transmits power is At that time, the minimum transfer block length should be reduced to The required number of IRS units is 155, and when the interference source's transmit power increases to At that time, the minimum transport block length is reduced to The required number of IRS units increased to 290, requiring only 135 additional units. This result demonstrates that, compared to passive reflective IRS, active reflective IRS provides stronger anti-interference capability for short packet communication systems.

Claims

1. A method for short packet communication assisted by an active intelligent reflector under electromagnetic interference environment, characterized in that, The specific implementation steps are as follows: Step A: Establish an active reflective intelligent reflector (IRS)-assisted downlink short packet communication system. This system includes a radio access point (AP) with M antennas, a single-antenna user equipment (UE), and a system with... The system consists of an active reflective IRS unit. Due to building obstruction, there is no direct transmission link between the AP and UE, so communication is achieved through the active reflective IRS. The system includes an electromagnetic interference source. It is assumed that the transmission power of the electromagnetic interference source and the transmission channel characteristics from the electromagnetic interference source to the active reflective IRS and the UE are known. At the same time, the electromagnetic interference source will affect the received signals of the UE and the active reflective IRS respectively. Step B: Based on the system model established in Step A, derive the UE's received signal under electromagnetic interference environment; based on the statistical channel state information, further write out the instantaneous signal-to-interference-plus-noise ratio (SINR); Step C: Based on the channel parameters, the number of AP antennas M, and the number of active reflective IRS units. The active reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and the distribution characteristics of instantaneous SINR are analyzed and presented; its specific implementation is as follows: The end-to-end channel gain from AP to UE is The end-to-end channel gain from the electromagnetic interference source to the UE is Thermal noise term is ,in Represents the modulus of a complex number or the absolute value of a real number. , These represent the end-to-end channel fading coefficients from AP to UE and from the electromagnetic interference source to UE, respectively. This represents the channel coefficient between the AP and the active reflective IRS. For complex fields, This represents the channel coefficient between the active reflective IRS and the UE. This represents the channel coefficient between the interference source and the active reflective IRS. This represents the channel coefficient between the interference source and the UE. This represents the reflection coefficient matrix of an active reflective IRS. Denotes the Frobenius norm. This represents the thermal noise power of an active reflective IRS; when using optimal continuous phase adjustment, according to the moment matching method, Approximate shape parameters are and scale parameters are The gamma distribution, in which , , , and Represents Rice factor, The path loss is measured at a reference distance of 1 meter. This represents the amplitude coefficient of an active reflective IRS. and These represent the distances from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. and These represent the path loss indices from the AP antenna center to the IRS center and from the IRS center to the UE, respectively. Obtains a shape parameter of 1 and a scale parameter of 1. The gamma distribution, in which , and These represent the distances from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively. and Let these represent the path loss exponents from the electromagnetic interference source to the IRS center and from the electromagnetic interference source to the UE, respectively; by the central limit theorem, we can obtain... Approximately follows the mean variance is The Gaussian distribution, where , , Represents the confluence hypergeometry function; The instantaneous SINR at the UE can be calculated based on the probability density functions of the channel gain and thermal noise terms above. The cumulative distribution function, i.e. ； in, , , This represents the power of additive complex white Gaussian noise. Represents the gamma function. Represents the Gaussian hypergeometry function. , This represents the incomplete gamma function. Represents the error function. , , This indicates the number of terms truncated by the Gauss-Chebyshev integral method; Step D: Obtain the BLER expression for the average block error rate of the UE based on short packet communication theory; Step E: Based on the expression obtained in Step D, the minimum transmission block length is further solved under reliability constraints.

2. The method for active intelligent reflector-assisted short packet communication under electromagnetic interference environment according to claim 1, wherein step A is specifically implemented as follows: An active reflective IRS is deployed high on the building surface, enabling line-of-sight (LoS) transmission links between the IRS and the access point (AP) and user unit (UE). Based on this, the channel coefficient between the AP and the IRS is... and the channel coefficient between IRS and UE Modeled as a Rice distribution, which are respectively ； ； in, and For the line-of-sight transmission components of the corresponding link, and For the non-line-of-sight transmission components of the corresponding link, and Each element in the array is independent and follows a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1; because the AP antenna and IRS unit use a uniform linear array, the array response at the AP and IRS is... ,in , For carrier wavelength, For unit spacing, Indicates the transmission angle or angle of arrival of the signal; based on this, and The Los components are respectively and ,in and These represent the emission angles at AP and IRS, respectively. The angle of arrival at the IRS is represented by: The channel coefficients from the interference source to the IRS and the UE are respectively expressed as: and ,in and Each element in the distribution is independent and follows a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

3. The active intelligent reflector-assisted short packet communication method under electromagnetic interference environment according to claim 1, wherein step B is specifically implemented as follows: Based on the above system model, it can be seen that the signal received by the UE in active reflective IRS-assisted communication is ； in, and These represent the transmit power of the AP and the interference source, respectively. and These represent the normalized power signals transmitted by the AP and the interference source, respectively. Follows the pattern with mean 0 and variance of The cyclic complex Gaussian distribution represents the thermal noise introduced by the active reflective IRS. This indicates that the mean is 0 and the variance is... Additive complex white Gaussian noise, This represents the reflection coefficient matrix of an active reflective IRS. This represents a diagonal matrix with the elements within the parentheses forming its diagonal elements. ( ) indicates the active IRS. Phase shift of each reflecting unit This represents the power-normalized beamforming vector at AP; IRS uses statistical channel state information to perform optimal continuous phase adjustment to obtain... , Represents the remainder when x is divided by y; when the AP adopts the maximum ratio transmission strategy based on statistical channel state information, the optimal beamforming vector at the AP is ; When electromagnetic interference is present, the instantaneous SINR of the UE in active IRS-assisted communication under fading channels is: 。 4. The intelligent reflector-assisted short packet communication method under electromagnetic interference environment according to claim 1, wherein step D is specifically implemented as follows: For a given received signal-to-noise ratio BLER and transport block length ,when When the number of symbols is 1, the maximum achievable rate of short packet communication in an additive complex white Gaussian noise channel is approximately: ； in, Indicates Shannon capacity, Represents channel divergence, express The inverse function; Assume the physical layer information rate of the UE is ,in Indicates the number of information bits; UE decoding signal The instantaneous BLER approximation is as follows: ; Based on instantaneous BLER and instantaneous SINR, the average BLER in the fading channel Represented as: ； in, express The probability density function; use Approximation of the average BLER in fading channels Further expressed as: ； in , , , , , , , ; Further employing the Gauss-Chebyshev integration method, the average BLER at the UE can be obtained. for: ； in Let be the approximate order of the Gauss-Chebyshev integral. , , , .

5. The intelligent reflector-assisted short packet communication method under electromagnetic interference environment according to claim 1, wherein step E is specifically implemented as follows: The target average BLER of the UE is expressed as The specific steps for determining the minimum transport block length are as follows: 1.1 Given system parameters , , , , , , , , , , , , , , , , , , , , and Initialize block length Limit the block length to ,make , ; 1.2 Length of block , Substitute into the average BLER formula to calculate the corresponding average BLER for the UE. , ; 1.3 If the calculated average BLER satisfies Let the minimum block length be This means there is no solution; 1.4 If the calculated average BLER satisfies Then let the minimum block length be... ; 1.5 If none of the above conditions are met, then if the conditions are met... Under the condition that, let Calculate the corresponding average BLER , This represents the floor function; 1.6 When satisfied Then let Otherwise ; 1.7 Update or Continue with steps 1.5 to 1.6 until... The minimum transport block length can be obtained by stopping the iteration. .

Images