Intelligent reflecting surface assisted short packet communication method in electromagnetic interference environment

Abstract

The application discloses a smart reflecting surface assisted short packet communication method in an electromagnetic interference environment, and relates to a reflecting type smart reflecting surface assisted two-user non-orthogonal multiple access system, in which a smart reflecting surface assists a far user to communicate, a wireless access point sends short data packets to the two users to realize high-reliability low-latency communication, and an electromagnetic interference source exists in the system; it is assumed that the transmission channel characteristics of the electromagnetic interference source to the smart reflecting surface and the two users are known; firstly, statistical channel state information is used to determine the statistical characteristics of the signal-to-noise ratio of decoding of the two users; secondly, according to short packet communication related formulas, the average block error rate of the two users in a fading channel is determined; and finally, under the reliability constraint, the optimal power allocation coefficient and the minimum common block length of the system are determined. The application realizes high-reliability low-latency communication.

Description

Technical Field

[0001] This invention relates to a method for optimizing the design of a short packet communication system assisted by an Intelligent Reflecting Surface (IRS) 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 99.999%) and ultra-low latency (≤1ms). How to achieve URLLC remains one of the challenges facing the current wireless communication field.

[0003] Short packet communication, IRS, and non-orthogonal multiple access (NOMA) are all considered key technologies for achieving URLLC. Short packet communication refers to communication where the data block length is very short, usually only a few hundred bits. Therefore, short packet communication can effectively reduce 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 (Inductively Reflected Surface) consists of a large number of programmable passive units that can actively adjust the reflection or transmission of incident waves. IRS-assisted communication improves communication quality by altering the wireless propagation environment (Q. Wu, S. Zhang, B. Zheng, et al. "Intelligent reflecting surface-aided wireless communications: A tutorial," IEEE Trans. Commun., vol. 69, no. 5, pp. 3313-3351, May 2021.). In multi-user scenarios, NOMA (Normally Access Multiple Access) technology is considered one of the most promising multiple access technologies, significantly improving spectral efficiency by allowing different users to share the same time-frequency resources.

[0004] Chinese invention patent CN115442816B discloses a method for implementing NOMA short packet communication with a reflective IRS-assisted system. It designs the system using statistical channel state information for a two-user scenario and proposes an algorithm for NOMA power allocation and transmission block length optimization. Chinese invention patent CN116170856A discloses a design method for a NOMA short packet communication system with a simultaneous transmission reflective IRS-assisted system. It uses statistical channel state information to optimize the coefficients of the simultaneous transmission reflective IRS, NOMA power allocation, and transmission block length for a two-user scenario to achieve URLLC. Both of these IRS-assisted NOMA short packet communication studies assume no additional electromagnetic interference in the transmission environment. In actual wireless communication systems, in addition to being affected by fading and Gaussian white noise, communication systems are often affected by co-channel interference signals. Since IRS can enhance both useful and interference signals, the performance of IRS-assisted NOMA short packet communication systems designed in environments without additional electromagnetic interference deteriorates significantly when co-channel interference is present. Therefore, it is necessary to redesign parameters such as NOMA power allocation and transmission block length of the IRS-assisted NOMA short packet communication system to achieve URLLC in electromagnetic interference environments.

[0005] Currently, there is relatively little research on how to optimize the design of an IRS-assisted NOMA short packet communication system to achieve URLLC in environments with electromagnetic interference. Therefore, it is essential to study the optimization design of an IRS-assisted NOMA short packet communication system to achieve URLLC in environments with electromagnetic interference. Summary of the Invention

[0006] This invention provides a smart reflector-assisted short packet communication method for electromagnetic interference environments, achieving highly reliable, low-latency communication (URLLC).

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A method for intelligent reflector-assisted short packet communication under electromagnetic interference environment, the specific implementation steps are as follows:

[0009] Step A: Establish a downlink two-user non-orthogonal multiple access (NOMA) short packet communication system assisted by an Intelligent Reflecting Surface (IRS). This system includes a single-antenna access point (AP), two single-antenna users: near user UE1 and far user UE2, and a reflective IRS with N passive elements. The AP communicates directly with near user UE1 via a direct link (AP-UE1), while the AP communicates with far user UE2 via a cascaded channel using the reflective IRS. This cascaded channel includes two communication links: AP-IRS and IRS-UE2. Simultaneously, an electromagnetic interference source will affect the received signals of UE1, the reflective IRS, and UE2. 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 reflective IRS and the two users are known.

[0010] Step B: Based on the system model established in Step A, derive the received signals of the two users under electromagnetic interference environment; determine the order of continuous interference cancellation based on statistical channel state information, and further write out the instantaneous signal-to-interference-plus-noise ratio (SINR).

[0011] Step C: Based on the channel parameters and the number of reflective IRS units N, the reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and analyzes and gives the distribution characteristics of instantaneous SINR; its specific implementation is as follows:

[0012] The end-to-end channel gains from AP to UE1 and from AP to UE2 are |g0| respectively. 2 and |G| 2 The end-to-end channel gains from the electromagnetic interference source to UE1 and UE2 are |g5| respectively. 2 and |H| 2 Where |·| represents the modulus of the complex number or the absolute value of the real number, and g0, G, g5, and H represent the end-to-end channel fading coefficients from AP to UE1, AP to UE2, the electromagnetic interference source to UE1, and the electromagnetic interference source to UE2, respectively. When optimal continuous phase adjustment is used, according to the central limit theorem, |G| follows a Ricean distribution, and its average power and Ricean factor are respectively... and Where β0 represents the path loss at a reference distance of 1 meter, β represents the path loss exponent, d1 is the distance from AP to the center of the reflective IRS, d2 is the distance from the center of the reflective IRS to UE2, K1 and K2 are Rice factors, and K c = (K1+1)(K2+1); H follows a pattern with mean 0 and variance of . The cyclic symmetric complex Gaussian distribution, where d3 and d4 are the distances from the electromagnetic interference source to the center of the reflective IRS and UE2, respectively; since H and g5 both follow a cyclic symmetric complex Gaussian distribution, |H| 2 and |g5| 2 It follows a shape parameter of 1 and a scale parameter of μ. y The gamma distribution of μ5, where μ5=β0d5 -β d5 represents the distance from the electromagnetic interference source to UE1; both |G| and |g0| follow a Rice distribution, therefore |G| 2 and |g0| 2 They all follow a mixed gamma distribution, and their probability density functions are respectively and Where I and V represent the order of the summation. and These are the weighting coefficients.

[0013] Γ(·) represents the gamma function, and K0 is the Rice factor. This indicates the mean calculation, where d0 is the distance from AP to UE1;

[0014] The instantaneous SINRγ can be calculated based on the probability density function of the channel gain above. 12 γ 11 γ 22 The cumulative distribution function, i.e.

[0015]

[0016] in For γ lι The cumulative distribution function of (l, ι∈{1,2}), P I This indicates the transmission power of the electromagnetic interference source. For the power allocation factor corresponding to UE1, satisfying P represents the transmit power of AP, 2F1(·) represents the Gaussian hypergeometric function, and γ(·) represents the lower incomplete gamma function;

[0017] Step D: Calculate the average block error rate (BLER) for both users according to the formula in short packet communication.

[0018] Step E: Based on the expression calculated in step D, under reliability constraints, further solve for the NOMA optimal power allocation coefficient and minimum common block length.

[0019] Further:

[0020] The specific implementation of step A is as follows:

[0021] Modeling Channel Coefficients: Communication links AP-UE1, AP-IRS, and IRS-UE2 have the same path loss exponent β, and their small-scale fading follows a Ricean distribution. The channel coefficients between AP and UE1 are expressed as follows: The channel coefficients between the AP and the nth (n∈{1,2,…N}) cell of the reflective IRS are expressed as follows: The channel coefficient between the nth unit of the reflective IRS and UE2 is expressed as: ),in and This represents the corresponding line-of-sight transmission component. and The corresponding non-line-of-sight transmission components follow a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1. and Let h represent the channel coefficients from the electromagnetic interference source to the nth unit of the reflective IRS, UE2, and UE1, respectively, where h 3n h4 and h5 follow a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

[0022] The specific implementation of step B is as follows:

[0023] The received signals at UE1 and UE2 are as follows:

[0024]

[0025] in It is a NOMA superimposed signal sent by the AP, s l This represents the power normalization signal sent by the AP to the UE, s I This represents the power-normalized interference signal emitted by the electromagnetic interference source.

[0026] G1 = [g 11 ,g 12 ,...,g 1N ], G2 = [g 21 ,g 22 ,...,g 2N ] T and G3 = [g 31 ,g 32 ,…,g 3N [g] represents the channel coefficient matrix between AP and reflective IRS, reflective IRS and UE2, and electromagnetic interference source and reflective IRS, respectively. 21 ,g 22 ,...,g 2N ]T Represents vector [g] 21 ,g 22 ,...,g 2N The transpose of ], where θ represents the phase offset matrix of the reflective IRS, is Indicates Let φ be a diagonal matrix with diagonal elements. n This represents the phase offset value of the nth cell in a reflective IRS, when the optimal continuous phase adjustment strategy is used. arg(·) represents the complex angle function; w1 and w2 represent functions with a mean of 0 and a variance of 0. Additive complex Gaussian white noise;

[0027] Furthermore, the end-to-end channel fading coefficient G between AP and UE2 and the end-to-end channel fading coefficient H between the electromagnetic interference source and UE2 are respectively:

[0028]

[0029] AP uses statistical channel state information for system design, when At that time, according to the NOMA principle, the AP allocates a small power allocation factor to users with large channel gain, therefore UE1 needs to correctly decode and remove UE2's signal s2 before decoding its own signal s1. UE2, having allocated a larger power factor, can treat s1 as interference and directly decode its own signal s2. Therefore, the SINR values ​​of UE1's decoded signals s2 and s1, and UE2's decoded signal s2, are respectively:

[0030]

[0031] The specific implementation of step D is as follows:

[0032] According to the relevant formulas for short packet communication, given the signal-to-interference-plus-noise ratio γ and the short data packet block size T... d When using BLERε for short data packets, the maximum achievable rate of short data packets, expressed in bits per symbol, is approximately:

[0033]

[0034] Where C(γ)=log2(1+γ) represents the Shannon capacity, and V(γ)=(1-(1+γ)) -2 (log2e) 2 Q represents the channel divergence. -1 (·)express The inverse function;

[0035] Assume the physical layer information rate between the two users is R. ι =F ι / T d , where F ι The instantaneous BLER of UE1 decoded signal s2, s1 and UE2 decoded signal s2 is approximately expressed as:

[0036] Based on instantaneous BLER and instantaneous SINR, the average BLER in the fading channel is... Represented as:

[0037]

[0038] in, Indicates γ lι The probability density function;

[0039] according to An approximation of the average value in the fading channel. Further expressed as:

[0040]

[0041] in

[0042] Further using the Gauss-Chebyshev integration method, we can obtain:

[0043]

[0044] Where J is the approximate order of the Gauss-Chebyshev integral.

[0045] Therefore, the average BLER of UE1 and UE2 is:

[0046]

[0047] in and They are respectively:

[0048]

[0049] The specific implementation of step E is as follows:

[0050] Represent the target BLER of the two users as The specific steps for determining the optimal power allocation coefficient and the minimum common block length are as follows:

[0051] 1.1 Given system parameters N, K0, K1, K2, d0, d1, d2, d3, d4, d5, β0, β, P, P I , F1, F2 and Initialize block length T d Limit the block length to [M] min M max ], let T - =M min T + =M max ;

[0052] 1.2 Set the block length T - T + Substitute into the average BLER formula to calculate the corresponding average for the two users.

[0053] 1.3 If the calculated average BLER satisfies or Let the minimum block length be T min =-1, which means there is no solution;

[0054] 1.4 If the calculated average BLER simultaneously satisfies and Let the minimum block length be T. min =M min ;

[0055] 1.5 If none of the above conditions are met, then if T is satisfied... + -T - Under the condition that ≠ 1, let Calculate the corresponding average and This represents the floor function;

[0056] 1.6 When satisfied and Then let Otherwise

[0057] 1.7 Update T - or T + Continue with steps 1.5 to 1.6 until T. + -T - Stop iteration when the value equals 1, and the minimum block length T can be obtained. min =T + ;

[0058] 1.8 Choosing a step size of Δ for traversal Given the range of values ​​for T, repeat steps 1.1 to 1.7 to obtain the minimum block length T corresponding to each power allocation coefficient. min So let The corresponding power allocation coefficient is the optimal power allocation coefficient. min(·) means calculating the minimum element in an array.

[0059] The core of this invention lies in the first derivation of the closed-form expression of the cumulative distribution function of instantaneous SINR of two-user decoding under electromagnetic interference environment, and further the derivation of the closed-form expression of average BLER. Finally, under reliability constraints, URLLC is achieved by jointly optimizing the NOMA power allocation coefficient and the minimum common block length. Attached Figure Description

[0060] Figure 1 This is the system model of the present invention;

[0061] Figure 2 This is a logic flowchart of the present invention;

[0062] Figure 3 The transmission power P of the electromagnetic interference source in this invention is free from electromagnetic interference. I The relationship between the minimum common block length and the optimal power allocation coefficient when N = 0, where N = 64, β0 = -20dB, K0 = K1 = K2 = 5, d0 = 30 m, d1 = 50 m, d2 = 60 m, d3 = d4 = d5 = 50 m. β = 2, P = 55 dBm F1 = F2 = 200;

[0063] Figure 4 The present invention contains electromagnetic interference and the emission power P of the electromagnetic interference source. I The relationship between the minimum common block length and the optimal power distribution coefficient when N = 64, β0 = -20dB, K0 = K1 = K2 = 5, d0 = 30 m, d1 = 50 m, d2 = 60 m, d3 = d4 = d5 = 50 m. β = 2, P = 55 dBm F1 = F2 = 200. Detailed Implementation

[0064] The present invention will be further described below with reference to the accompanying drawings, both in terms of theory and specific implementation.

[0065] Reference Figure 1 , Figure 2 A method for intelligent reflector-assisted short packet communication under electromagnetic interference environment, the specific implementation steps are as follows:

[0066] Step A: Establish a downlink two-user non-orthogonal multiple access (NOMA) short packet communication system assisted by an Intelligent Reflecting Surface (IRS). This system includes a single-antenna access point (AP), two single-antenna users: near user UE1 and far user UE2, and a reflective IRS with N passive elements. The AP communicates directly with near user UE1 via a direct link AP-UE1, while the AP communicates with far user UE2 via a cascaded channel using the reflective IRS. This cascaded channel includes two communication links: AP-IRS and IRS-UE2. Simultaneously, an electromagnetic interference source will affect the received signals of UE1, the reflective IRS, and UE2. The system includes an electromagnetic interference source. Assume that the transmission power of the electromagnetic interference source and the transmission channel characteristics from the electromagnetic interference source to the reflective IRS and the two users are known.

[0067] Step B: Based on the system model established in Step A, derive the received signals of the two users under electromagnetic interference environment; determine the order of continuous interference cancellation based on statistical channel state information, and further write out the instantaneous signal-to-interference-plus-noise ratio (SINR).

[0068] Step C: Based on the channel parameters and the number of reflective IRS units N, the reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and analyzes and gives the distribution characteristics of instantaneous SINR; its specific implementation is as follows:

[0069] The end-to-end channel gains from AP to UE1 and from AP to UE2 are |g0| respectively. 2 and |G| 2 The end-to-end channel gains from the electromagnetic interference source to UE1 and UE2 are |g5| respectively. 2 and |H| 2 Where |·| represents the modulus of the complex number or the absolute value of the real number, and g0, G, g5, and H represent the end-to-end channel fading coefficients from AP to UE1, AP to UE2, the electromagnetic interference source to UE1, and the electromagnetic interference source to UE2, respectively. When optimal continuous phase adjustment is used, according to the central limit theorem, |G| follows a Ricean distribution, and its average power and Ricean factor are respectively... and Where β0 represents the path loss at a reference distance of 1 meter, β represents the path loss exponent, d1 is the distance from AP to the center of the reflective IRS, d2 is the distance from the center of the reflective IRS to UE2, K1 and K2 are Rice factors, and K c = (K1+1)(K2+1); H follows a pattern with mean 0 and variance of . The cyclic symmetric complex Gaussian distribution, where d3 and d4 are the distances from the electromagnetic interference source to the center of the reflective IRS and UE2, respectively; since H and g5 both follow a cyclic symmetric complex Gaussian distribution, |H| 2 and |g5| 2 It follows a shape parameter of 1 and a scale parameter of μ. y The gamma distribution of μ5, where μ5=β0d5 -β d5 represents the distance from the electromagnetic interference source to UE1; both |G| and |g0| follow a Rice distribution, therefore |G| 2 and |g0| 2 They all follow a mixed gamma distribution, and their probability density functions are respectively and Where I and V represent the order of the summation. and These are the weighting coefficients.

[0070] Γ(·) represents the gamma function, and K0 is the Rice factor. This indicates the mean calculation, where d0 is the distance from AP to UE1;

[0071] The instantaneous SINRγ can be calculated based on the probability density function of the channel gain above. 12 γ 11 γ 22 The cumulative distribution function, i.e.

[0072]

[0073]

[0074] in For γ lι The cumulative distribution function of (l, ι∈{1,2}), P I This indicates the transmission power of the electromagnetic interference source. For the power allocation factor corresponding to UE1, satisfying P represents the transmit power of AP, 2F1(·) represents the Gaussian hypergeometric function, and γ(·) represents the lower incomplete gamma function;

[0075] Step D: Calculate the average block error rate (BLER) for both users according to the formula in short packet communication.

[0076] Step E: Based on the expression calculated in step D, under reliability constraints, further solve for the NOMA optimal power allocation coefficient and minimum common block length.

[0077] Further:

[0078] The specific implementation of step A is as follows:

[0079] Modeling Channel Coefficients: Communication links AP-UE1, AP-IRS, and IRS-UE2 have the same path loss exponent β, and their small-scale fading follows a Ricean distribution. The channel coefficients between AP and UE1 are expressed as follows: The channel coefficients between the AP and the nth (n∈{1,2,…N}) cell of the reflective IRS are expressed as follows: The channel coefficient between the nth unit of the reflective IRS and UE2 is expressed as: ),in and This represents the corresponding line-of-sight transmission component. and The corresponding non-line-of-sight transmission components follow a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1. and Let h represent the channel coefficients from the electromagnetic interference source to the nth unit of the reflective IRS, UE2, and UE1, respectively, where h 3n h4 and h5 follow a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

[0080] The specific implementation of step B is as follows:

[0081] The received signals at UE1 and UE2 are as follows:

[0082]

[0083] in It is a NOMA superimposed signal sent by the AP, s l This represents the power normalization signal sent by the AP to the UE, s I This represents the power-normalized interference signal emitted by the electromagnetic interference source.

[0084] G1 = [g 11 ,g 12 ,...,g 1N ], G2 = [g 21 ,g 22 ,...,g 2N ] T and G3 = [g 31 ,g 32 ,…,g 3N[g] represents the channel coefficient matrix between AP and reflective IRS, reflective IRS and UE2, and electromagnetic interference source and reflective IRS, respectively. 21 ,g 22 ,...,g 2N ] T Represents vector [g] 21 ,g 22 ,...,g 2N The transpose of ], where θ represents the phase offset matrix of the reflective IRS, is Indicates Let φ be a diagonal matrix with diagonal elements. n This represents the phase offset value of the nth cell in a reflective IRS, when the optimal continuous phase adjustment strategy is used. arg(·) represents the complex angle function; w1 and w2 represent functions with a mean of 0 and a variance of 0. Additive complex Gaussian white noise;

[0085] Furthermore, the end-to-end channel fading coefficient G between AP and UE2 and the end-to-end channel fading coefficient H between the electromagnetic interference source and UE2 are respectively:

[0086]

[0087] AP uses statistical channel state information for system design, when At that time, according to the NOMA principle, the AP allocates a small power allocation factor to users with large channel gain, therefore UE1 needs to correctly decode and remove UE2's signal s2 before decoding its own signal s1. UE2, having allocated a larger power factor, can treat s1 as interference and directly decode its own signal s2. Therefore, the SINR values ​​of UE1's decoded signals s2 and s1, and UE2's decoded signal s2, are respectively:

[0088]

[0089] The specific implementation of step D is as follows:

[0090] According to the relevant formulas for short packet communication, given the signal-to-interference-plus-noise ratio γ and the short data packet block size T... d When using BLERε for short data packets, the maximum achievable rate of short data packets, expressed in bits per symbol, is approximately:

[0091]

[0092] Where C(γ)=log2(1+γ) represents the Shannon capacity, and V(γ)=(1-(1+γ)) -2 (log2e) 2 Q represents the channel divergence.-1 (·)express The inverse function;

[0093] Assume the physical layer information rate between the two users is R. ι =F ι / T d , where F ι The instantaneous BLER of UE1 decoded signal s2, s1 and UE2 decoded signal s2 is approximately expressed as:

[0094] Based on instantaneous BLER and instantaneous SINR, the average BLER in the fading channel is... Represented as:

[0095]

[0096] in, Indicates γ lι The probability density function;

[0097] according to An approximation of the average value in the fading channel. Further expressed as:

[0098]

[0099] in Further using the Gauss-Chebyshev integration method, we can obtain:

[0100]

[0101] Where J is the approximate order of the Gauss-Chebyshev integral.

[0102] Therefore, the average BLER of UE1 and UE2 is:

[0103]

[0104] in and They are respectively:

[0105]

[0106] The specific implementation of step E is as follows:

[0107] Represent the target BLER of the two users as Determine the optimal power allocation factor and the minimum common block length. The specific steps for determining the optimal power allocation factor and the minimum common block length are as follows:

[0108] 1.1 Given system parameters N, K0, K1, K2, d0, d1, d2, d3, d4, d5, β0, β, P, P I , F1, F2 and Initialize block length T d Limit the block length to [M] min M max ], let T - =M min T + =M max ;

[0109] 1.2 Set the block length T - T + Substitute into the average BLER formula to calculate the corresponding average for the two users. 1.3 If the calculated average BLER satisfies or Let the minimum block length be T min =-1, which means there is no solution;

[0110] 1.4 If the calculated average BLER simultaneously satisfies and Let the minimum block length be T. min =M min ;

[0111] 1.5 If none of the above conditions are met, then if T is satisfied... + -T - Under the condition that ≠ 1, let Calculate the corresponding average and This represents the floor function;

[0112] 1.6 When satisfied and Then let Otherwise

[0113] 1.7 Update T - or T + Continue with steps 1.5 to 1.6 until T. + -T - Stop iteration when the value equals 1, and the minimum block length T can be obtained. min =T + ;

[0114] 1.8 Choosing a step size of Δ for traversal Given the range of values ​​for T, repeat steps 1.1 to 1.7 to obtain the minimum block length T corresponding to each power allocation coefficient. min So let The corresponding power allocation coefficient is the optimal power allocation coefficient. min(·) means calculating the minimum element in an array.

[0115] The specific implementation process of this invention is as follows:

[0116] A method for intelligent reflector-assisted short packet communication under electromagnetic interference environment, the specific implementation steps are as follows:

[0117] Step A: Construct a downlink two-user non-orthogonal multiple access system consisting of a single-antenna wireless access point (AP), two single-antenna users, and a reflective IRS with N=64 passive elements. The near user UE1 communicates directly with the AP, while the far user UE2 communicates with the AP via the reflective IRS. There is no direct link between the AP and UE2; communication is only possible through a cascaded channel using the reflective IRS. This cascaded channel includes two communication links: AP-IRS and IRS-UE2. A direct link, AP-UE1, exists between the AP and UE1. Electromagnetic interference sources will affect the received signals of UE1, the reflective IRS, and UE2 respectively. The communication links AP-UE1, AP-IRS, and IRS-UE2 have the same path loss exponent β, and their small-scale fading follows a Ricean distribution with β=2. The channel coefficient between the AP and UE1 is expressed as... The channel coefficients between the AP and the nth (n∈{1,2,…N}) cell of the reflective IRS are expressed as follows: The channel coefficient between the nth unit of the reflective IRS and UE2 is expressed as: ), where β0 represents the path loss at a reference distance of 1 meter, d0 is the distance from AP to UE1, d1 is the distance from AP to the center of the reflective IRS, d2 is the distance from the center of the reflective IRS to UE2, and K0, K1, and K2 are Rice factors. and This represents the corresponding line-of-sight transmission component. and The corresponding non-line-of-sight transmission component follows a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1, and β0 = -20 dB, d0 = 30 m, d1 = 50 m, d2 = 60 m, K0 = K1 = K2 = 5; the average power of g0 is defined as... Where |·| represents the modulus of the complex number or the absolute value of the real number. This represents the mean operation; and Let d3, d4, and d5 represent the channel coefficients from the electromagnetic interference source to the nth unit of the reflective IRS, UE2, and UE1, respectively, where d3, d4, and d5 are the distances from the electromagnetic interference source to the center of the reflective IRS, UE2, and UE1, respectively, and h is the distance from the electromagnetic interference source to the center of the reflective IRS, UE2, and UE1, respectively. 3n h3, h4, and h5 follow a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1, and d3 = d4 = d5 = 50 meters.

[0118] Step B: Based on the system model established in Step A, derive the received signals of the two users under electromagnetic interference environment; determine the order of continuous interference cancellation based on statistical channel state information, and further write out the instantaneous SINR.

[0119] Step C: Based on the channel parameters and the number of reflective IRS units N, the reflective IRS performs optimal continuous phase adjustment based on statistical channel state information, and analyzes and gives the distribution characteristics of instantaneous SINR.

[0120] Step D: Calculate the average BLER expression for the two users according to the formula in short packet communication;

[0121] Step E: Based on the expression calculated in step D, under reliability constraints... With a step size Δ = 0.01, the relationship between the average BLER and NOMA optimal power allocation coefficients and the minimum common block length is further obtained through the bisection method and one-dimensional traversal search algorithm.

[0122] Assuming the AP transmit power P = 55 dBm, and the noise power... Figure 3 The emission power P of the electromagnetic interference source is given. I The minimum common block length under different power allocation coefficients when = 0 is determined by... Figure 3 The optimal power allocation coefficient under reliability constraints can be obtained as follows: The length of the least common block is The symbol is used to input the obtained optimal power allocation coefficient and the minimum common block length into P. I When the BLER is 25dBm, the average BLER of UE1 can be obtained from the formula for calculating the average BLER of the two users. The average BLER of UE2 is None of them meet the reliability constraints, indicating that the NOMA power distribution and transmission block length designed in an environment without electromagnetic interference are not applicable in an environment with electromagnetic interference, and need to be redesigned for electromagnetic interference environments. Figure 4 The emission power P of the electromagnetic interference source is given. I The minimum common block length under different power distribution factors when = 25dBm is determined by: Figure 4 The optimal power allocation coefficient under reliability constraints can be obtained as follows: The length of the least common block is The symbol is used to input the obtained optimal power allocation coefficient and the minimum common block length into P. I When the BLER is 25dBm, the average BLER of UE1 can be obtained from the formula for calculating the average BLER of the two users. The average BLER of UE2 is All satisfy reliability constraints. As can be seen from the embodiments of the present invention, compared with an environment without electromagnetic interference, the minimum common block length required by the system to satisfy user reliability constraints increases significantly in an environment with electromagnetic interference.

Claims

1. A method for intelligent reflector-assisted short packet communication under electromagnetic interference environment, characterized in that, The specific implementation steps are as follows: Step A: Establish a downlink two-user non-orthogonal multiple access (NOMA) short packet communication system with an intelligent reflecting surface (Intelligent Reflecting Surface) and IRS-assisted system. This system includes a single-antenna access point (AP), two single-antenna users: near user UE1 and far user UE2, and a [missing information - likely a device or mechanism]. A passive unit reflective IRS; The AP communicates directly with the nearby user UE1 via a direct link AP-UE1, while the AP communicates with the distant user UE2 via a cascaded channel using a reflective IRS. This cascaded channel includes two communication links: AP-IRS and IRS-UE2. The system includes an electromagnetic interference source. It is assumed that the transmission power of the electromagnetic interference source, the transmission channel characteristics from the electromagnetic interference source to the reflective IRS, and the two users are known. The electromagnetic interference source will affect the received signals of UE1, the reflective IRS, and UE2 respectively. Step B: Based on the system model established in Step A, derive the received signals of the two users under electromagnetic interference environment; determine the order of continuous interference cancellation based on statistical channel state information, and further write out the instantaneous signal to interference plus noise ratio (SINR). Step C: Based on channel parameters and the number of reflective IRS units The 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 gains from AP to UE1 and from AP to UE2 are respectively and The end-to-end channel gains from the electromagnetic interference source to UE1 and UE2 are respectively and ,in Represents the modulus of a complex number or the absolute value of a real number. , , and These represent the end-to-end channel fading coefficients from AP to UE1, AP to UE2, electromagnetic interference source to UE1, and electromagnetic interference source to UE2, respectively. When optimal continuous phase adjustment is used, according to the central limit theorem, It follows a Rice distribution, with average power and Rice factor respectively. and ,in This represents the path loss at a reference distance of 1 meter. This represents the path loss index. The distance from the AP to the center of the reflective IRS. The distance from the center of the reflective IRS to UE2. , Rice factor, ; Follows the pattern with mean 0 and variance of The cyclic symmetric complex Gaussian distribution, where , , These are the distances from the electromagnetic interference source to the center of the reflective IRS and UE2, respectively; because and They all follow a cyclically symmetric complex Gaussian distribution, therefore and Obtaining a shape parameter of 1 and scale parameters of respectively and The gamma distribution, in which , , This indicates the distance from the electromagnetic interference source to UE1; and They all follow a Rice distribution, therefore and They all follow a mixed gamma distribution, and their probability density functions are respectively and ,in and This indicates the order of the summation. and These are the weighting coefficients. , , , , , , , , , , , , Represents the gamma function. Rice factor, , This represents the mean operation. This is the distance from AP to UE1; The instantaneous SINR can be calculated based on the probability density function of the channel gain above. , , The cumulative distribution function, where Indicates UE1 decoding signal Instantaneous SINR, Indicates UE1 decoding signal Instantaneous SINR, Indicates UE2 decoding signal The instantaneous SINR, i.e. ； ； ； in for ( The cumulative distribution function of ) This indicates the transmission power of the electromagnetic interference source. , , For the corresponding UE The power allocation coefficient satisfies P represents the transmit power of the AP. Represents the Gaussian hypergeometry function. This represents the incomplete gamma function. This represents the power of additive complex white Gaussian noise; Step D: Calculate the average block error rate (Block Error Rate) for both users using the formula in short packet communication, and use the BLER expression. Step E: Based on the expression calculated in step D, under reliability constraints, further solve for the NOMA optimal power allocation coefficient and minimum common block length.

2. The method for intelligent reflector-assisted short packet communication under electromagnetic interference environment according to claim 1, wherein step A is specifically implemented as follows: Modeling channel coefficients: Communication links AP-UE1, AP-IRS, and IRS-UE2 have the same path loss index. Furthermore, the small-scale fading follows a Rice distribution, and the channel coefficients between AP and UE1 are expressed as follows: AP and reflective IRS ( The channel coefficients between ) units are expressed as Reflective IRS The channel coefficients between each unit and UE2 are expressed as follows: ,in , and This represents the corresponding line-of-sight transmission component. , and The corresponding non-line-of-sight transmission components follow a cyclic symmetric complex Gaussian distribution with mean 0 and variance 1. , and These represent the electromagnetic interference source to the reflective IRS. The channel coefficients of each unit, UE2, and UE1, where , and It follows a cyclic symmetric complex Gaussian distribution with a mean of 0 and a variance of 1.

3. The method for intelligent reflector-assisted short packet communication under electromagnetic interference environment according to claim 1, wherein step B is specifically implemented as follows: The received signals at UE1 and UE2 are as follows: ； ； in It is a NOMA superimposed signal sent by the AP. This indicates that the AP sends the message to the UE. The power normalized signal, This represents the power-normalized interference signal emitted by the electromagnetic interference source. , and These represent the channel coefficient matrices between AP and reflective IRS, reflective IRS and UE2, and electromagnetic interference source and reflective IRS, respectively. Representing vectors transpose, This represents the phase offset matrix of a reflective IRS, which is... Indicates A diagonal matrix with diagonal elements. Indicates the reflective IRS number The phase offset value of each unit, when the optimal continuous phase adjustment strategy is adopted. , This represents the function for finding complex angles; and This indicates that the mean is 0 and the variance is... Additive complex Gaussian white noise; Furthermore, the end-to-end channel fading coefficient between AP and UE2 can be obtained. and the end-to-end channel fading coefficient between the electromagnetic interference source and UE2 They are respectively: ； ； AP uses statistical channel state information for system design, when At that time, according to the NOMA principle, the AP allocates a small power allocation factor to users with large channel gain, therefore UE1 needs to be correctly decoded first, and the decoding signal of UE2 needs to be removed. Then decode its own signal. UE2, due to its larger power factor, can... Treat it as interference signal and directly decode its own signal. ; Therefore, the UE1 decoding signal and and UE2 decoding signal The SINR values ​​are as follows: ； ； 。 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: According to the relevant formulas for short packet communication, when the signal-to-interference-plus-noise ratio is given Short data packet block size and short data packets BLER At that time, the maximum achievable rate of short data packets, measured in bits per symbol, is approximately expressed as: ； in, Indicates Shannon capacity, Represents channel divergence, express The inverse function; Assume the physical layer information rate of the two users is ,in Indicates the number of information bits; UE1 decoding signal , and UE2 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; according to Approximation of the average BLER in fading channels Further expressed as: ； in , , , , , , , ; Further, using the Gauss-Chebyshev integration method, we can obtain: ； in Let be the approximate order of the Gauss-Chebyshev integral. , , , ; Therefore, the average BLER of UE1 and UE2 is: ； ； in , and They are respectively: ； ； 。 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: Represent the target BLER of the two users as The specific steps for determining the optimal power allocation coefficient and the minimum common block length are as follows: 1.1 Given system parameters , , , , , , , , , , , , , , , , and ,in and These represent the number of information bits sent to UE1 and UE2 respectively, and the initial block length. Limit the block length to ,make , ; 1.2 Length of block , Substitute into the average BLER formula to calculate the average BLER for the two users. , ; 1.3 If the calculated average BLER satisfies or Let the minimum block length be This means there is no solution; 1.4 If the calculated average BLER simultaneously satisfies and 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 and , This represents the floor function; 1.6 When satisfied and Then let Otherwise ; 1.7 Update or Continue with steps 1.5 to 1.6 until... Stopping the iteration allows you to find the minimum block length. ; 1.8 Select step size Traversal Given the range of values, repeat steps 1.1 to 1.7 to obtain the minimum block length corresponding to each power allocation coefficient. So let , The corresponding power allocation coefficient is the optimal power allocation coefficient. ,in This indicates that the minimum element in an array is calculated.

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