Information transmission method of intelligent reflector-assisted short packet non-orthogonal multiple access system

Abstract

The invention discloses an information transmission method of an intelligent reflector-assisted short packet non-orthogonal multiple access system. The information transmission method comprises the steps of constructing a transmission signal model, determining an imperfect serial interference elimination coefficient and a hardware damage coefficient, determining an intelligent reflector shift coefficient, determining a cumulative distribution function, and determining an average block error rate and system throughput. Due to the fact that the intelligent reflecting surface and the non-optimal continuous phase shift are adopted, imperfect serial interference elimination and hardware damage are considered, a transmission signal model conforming to reality is established, an information transmission method is provided, the intelligent reflecting surface is utilized, the average block error rate of the system is reduced, and the throughput of the system is improved. The method has the advantages that the transmission scheme conforms to reality, implementation is easy, and the average block error rate is low.

Description

Technical Field

[0001] The present invention belongs to the technical field of short-packet communication, and specifically relates to information transmission in an intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system. Background Art

[0002] As one of the potential core technologies of 6G systems, intelligent reflecting surfaces can be applied to a variety of new scenarios, including vehicle-to-everything (V2X) communication, terahertz communication, and physical layer security. Compared with traditional relays, appropriate configuration can solve the problems of fading and interference in wireless channels. An intelligent reflecting surface is a reconfigurable metamaterial composed of basic units (called scattering particles or meta-atoms). Each intelligent surface unit of the intelligent reflecting surface can independently absorb energy or adjust the signal phase, thereby affecting the wavefront characteristics of the incident signal. By intelligently reconstructing the wireless propagation environment and simultaneously expanding the transmission range, the intelligent reflecting surface realizes flexible three-dimensional metasurface deployment, improves the spectrum utilization rate, and reduces energy consumption.

[0003] At the same time, non-orthogonal multiple access is regarded as one of the key technologies in 6G networks due to its excellent spectrum efficiency and large-scale connection capabilities. Different from orthogonal multiple access that uses orthogonal resource allocation, non-orthogonal multiple access significantly improves the spectrum utilization rate by allocating non-orthogonal resources to multiple users within the same resource block. Specifically, non-orthogonal multiple access adopts superposition coding at the transmitter and uses successive interference cancellation at the receiver to suppress inter-user interference and extract the desired signal. With good user connection capabilities, low latency characteristics, and excellent spectrum and energy efficiency, non-orthogonal multiple access has broad application prospects in future wireless communication systems.

[0004] Short-packet communication uses finite block length coding, which can significantly reduce transmission latency and improve transmission reliability, so it is widely used in the fields of the Internet of Things and machine-type communication.

[0005] In the short-packet non-orthogonal multiple access scenario, the use of an intelligent reflecting surface can reduce the average block error rate and improve the user communication quality. However, in practical applications, it is difficult for the intelligent reflecting surface to achieve optimal phase shift adjustment, and perfect hardware is also difficult to implement.

[0006] Therefore, it is very necessary to study the information transmission method of an intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to fully consider the actual application scenario and propose an information transmission method for an intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system with a simple transmission method, easy implementation, and good performance.

[0008] The technical solution adopted to solve the above technical problem consists of the following steps:

[0009] (1) Construct a transmission signal model

[0010] The short-packet non-orthogonal multiple access system consists of one base station, one near user U1 and one far user U2, and an intelligent reflecting surface with N elements. N is a finite positive integer. The base station, near user U1 and far user U2 are equipped with single antennas, and all nodes operate in half-duplex mode to construct a transmission signal model.

[0011] (2) Determine the imperfect successive interference cancellation coefficient and hardware impairment coefficient

[0012] According to the non-orthogonal multiple access technology, after the near user U1 receives the mixed information sent by the base station, it first needs to decode and delete the information required by the far user U2, and then decode its own required information. However, in actual applications, due to imperfect successive interference cancellation, the information required by the far user U2 cannot be completely decoded and deleted by the near user U1. Therefore, the near user U1 will continue to be affected by the residual interference of the information required by the far user U2 when decoding its own required information. For this reason, the imperfect successive interference cancellation coefficient is defined as ε, and the value of ε is [0,1].

[0013] In an ideal situation, a communication system usually assumes that the hardware is perfect, that is, signal processing and transmission will not introduce additional distortion. However, in an actual wireless communication system, due to the imperfection of the hardware, the signal will be affected by additional interference. For example, the nonlinearity of the radio frequency front end, phase noise, etc. will inevitably affect the system performance. Therefore, ignoring hardware impairment may lead to a significant deviation between theoretical analysis and the performance of the actual system. To make the transmission signal model more in line with actual applications, the hardware impairment coefficient is defined as θ, and the value of θ is [0,0.5].

[0014] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0015] Determine the phase shift coefficient of the intelligent reflecting surface according to Equation (1):

[0016]

[0017] where, arg(h si ) represents the angle of h si , arg(h si ) represents 's angle, arg(h si ) represents 's angle, ψ n is the phase error when the intelligent reflecting surface adopts a non-optimal continuous phase shift scheme with phase estimation error, ψ n obeys the von Mises distribution with a mean of 0 and a central parameter of ξ, ξ takes values in (0,10), g si is from the base station to user U iChannel coefficient, d si Denote the distance from the base station to user U i , i ∈ {1, 2} Denote as the channel coefficient between the base station and the nth reflection element of the intelligent reflecting surface, d sr Denote the distance between the base station and the intelligent reflecting surface Represent the channel coefficient between the nth reflection element of the intelligent reflecting surface and user U i , d ri Denote the distance between the nth reflection element of the intelligent reflecting surface and user U i , where β is the path loss exponent and β takes values in [2, 4].

[0018] (4) Determine the cumulative distribution function

[0019] Determine the cumulative distribution function of the near user U1 according to Equations (2) and (3):

[0020]

[0021] E[|H1| 4 = E[|h s1 | 4 + E[U1 4 + 6 × E[|h s1 | 2 × E[U1 2

[0022] + 4 × E[|h s1 | 3 × E[U1] + 4 × E[|h s1 |] × E[U1 3

[0023] + 2 × E[V1 2 × E[|h s1 | 2 + 2 × E[V1 2 × E[U1 2

[0024] + 4 × E[V1 2 × E[|h s1 |] × E[U1] + E[V1 4

[0025]

[0026] U1 = (d sr × d r1 ) -β / 2 × U a

[0027] ​​​​

[0028] V1 = (d sr × d r1 ) -β / 2 × V a

[0029]

[0030] where diag(·) represents a diagonal matrix, ρ is the transmission signal-to-noise ratio, ρ ranges from [30 dB, 50 dB], α n is the power allocation factor for the near user U1, α n ranges from (0, 0.5), α f is the power allocation factor for the far user U2, α f ranges from (0.5, 1), γ 1,1 is the signal-to-interference-plus-noise ratio for the near user U1 to decode the information it needs, γ 1,2 is the signal-to-interference-plus-noise ratio for the near user U1 to decode the information needed by the far user U2, m s1 、m r1 、m sr are channel fading parameters, m s1 、m r1 、m sr all range from [2, 3].

[0031] Determine the cumulative distribution function of the far user U2 according to Equation (4):

[0032]

[0033]

[0034] U2 = (d sr × d r2 ) -β / 2 × U b

[0035]

[0036] V2 = (d sr × d r2 ) -β / 2 × V b

[0037]

[0038] where γ 2,2 is the signal-to-interference-plus-noise ratio for the far user U2 to decode the information it needs, where m s2 、m r2 are channel fading parameters, m s2 、m r2The value ranges are all [2, 3].

[0039] (5) Determine the average block error rate and system throughput

[0040] The average block error rate of the near user U1 determined according to Equation (5):

[0041]

[0042]

[0043] w2 = h2 - (2 × μ2) -1

[0044] τ2 = h2 + (2 × μ2) -1

[0045]

[0046] λ1 = α n ρ + ρθ 2

[0047]

[0048] w1 = h1 - (2 × μ1) -1

[0049] τ1 = h1 + (2 × μ1) -1

[0050]

[0051] λ2 = εα f ρ + ρθ 2

[0052] where G is a trade-off parameter for balancing computational complexity and analysis accuracy, with a value range of [200, 400], η i is the number of information bits required by user U i η i takes values in [50 bits, 150 bits], L1 is the block length required for the base station to send information to user U1, L1 takes values in [100, 300], and L2 is the block length required for the base station to send information to user U2, L2 takes values in [100, 300].

[0053] The average block error rate of the far user U2 determined according to Equation (6):

[0054]

[0055] The system throughput determined according to Equation (7):

[0056]

[0057] Analyze the variation of the average block error rate of users with the change of signal-to-noise ratio and the variation of the average block error rate of users with the change of the number N of reflection elements of the intelligent reflecting surface; the smaller the average block error rate, the better the system performance; calculate the system throughput T, the larger the number N of reflection elements of the intelligent reflecting surface, the better the system performance and the better the system throughput.

[0058] In the (2) determination of the imperfect successive interference cancellation coefficient and the hardware impairment coefficient of the present invention, ε is the imperfect successive interference cancellation coefficient, and the optimal value of ε is 0.05, and θ is the hardware impairment coefficient, and the optimal value of θ is 0.2.

[0059] In the (3) determination of the phase shift coefficient of the intelligent reflecting surface of the present invention, β is the path loss exponent, and the optimal value of β is 3.2, and ξ is the central parameter of the von Mises distribution, and the optimal value of ξ is 8.

[0060] In the (4) determination of the cumulative distribution function of the present invention, ρ is the transmission signal-to-noise ratio, and the optimal value of ρ is 30 dB, and α n is the power allocation factor for the near user U1, and α n has an optimal value of 0.2, and α f is the power allocation factor for the far user U2, and α f has an optimal value of 0.8, and m s1 、m s2 、m r1 、m r2 、m sr are channel fading parameters, and the optimal values of m s1 、m s2 、m r1 、m r2 、m sr are all 2.

[0061] In the (5) determination of the average block error rate and the system throughput of the present invention, η i is the number of information bits required by user U i , and the optimal value of η i is 50 bits, and L i is the block length required for the base station to send information to user U i , and the optimal value of L i is 100.

[0062] Since the present invention adopts a short-packet communication system, a reasonable transmission signal model is established, and an information transmission method is proposed. By using an intelligent reflecting surface, the average block error rate of the system is reduced and the system throughput is improved. Compared with the prior art, when the number N of intelligent reflecting surface elements is 20 and the transmission signal-to-noise ratio is 30 dB to 50 dB, the average block error rate of the system is reduced by 0 to 0.118243. As the number of intelligent reflecting surface elements increases, the system performance is better. The present invention has the advantages of simple transmission method, easy implementation, good performance, etc., and can be used in the field of short-packet communication technology. Description of the Drawings

[0063] Figure 1 It is the process flow diagram of Embodiment 1 of the present invention.

[0064] Figure 2 It is the simulation curve of Embodiment 1 of the present invention. Detailed Embodiments

[0065] The present invention will be further described below in conjunction with the drawings and specific embodiments, but the present invention is not limited to the following embodiments.

[0066] Embodiment 1

[0067] In Figure 1 the information transmission method of the intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system in this embodiment consists of the following steps:

[0068] (1) Construct a transmission signal model

[0069] The short-packet non-orthogonal multiple access system consists of 1 base station, 1 near user U1, 1 far user U2, and an intelligent reflecting surface containing N elements. N is a finite positive integer. In this embodiment, N is taken as 20. The base station, near user U1, and far user U2 are equipped with single antennas, and all nodes operate in a half-duplex mode to construct a transmission signal model.

[0070] (2) Determine the imperfect successive interference cancellation coefficient and the hardware impairment coefficient

[0071] According to the non-orthogonal multiple access technology, after the near user U1 receives the mixed information sent by the base station, it first needs to decode and delete the information required by the far user U2, and then decode the information required by itself. However, in actual applications, due to imperfect successive interference cancellation, the information required by the far user U2 cannot be completely decoded and deleted by the near user U1. Therefore, the near user U1 will continue to be affected by the residual interference of the information required by the far user U2 when decoding the information required by itself. For this reason, the imperfect successive interference cancellation coefficient is defined as ε, and the value of ε is [0, 1]. In this embodiment, the value of ε is taken as 0.05.

[0072] In an ideal scenario, communication systems typically assume that the hardware is perfect, i.e., signal processing and transmission do not introduce additional distortion. However, in actual wireless communication systems, due to the imperfection of the hardware, signals are subject to additional interference. For example, the non-linearity, phase noise, etc. of the radio frequency front-end inevitably affect the system performance. Therefore, ignoring hardware impairments may lead to a significant deviation between theoretical analysis and the actual system performance. To make the transmission signal model more in line with practical applications, the hardware impairment coefficient is defined as θ, where θ ranges from [0, 0.5], and in this embodiment, θ is taken as 0.2.

[0073] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0074] Determine the phase shift coefficient of the intelligent reflecting surface according to Equation (1):

[0075]

[0076] where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n is the phase error when the intelligent reflecting surface adopts a non-optimal continuous phase shift scheme with phase estimation error, ψ n follows the von Mises distribution with a mean of 0 and a central parameter of ξ, where ξ ranges from (0, 10), and in this embodiment, ξ is taken as 8, g si is the channel coefficient from the base station to user U i , d si represents the distance from the base station to user U i , i ∈ {1, 2}, represents the channel coefficient between the base station and the nth reflection element of the intelligent reflecting surface, d sr represents the distance between the base station and the intelligent reflecting surface, represents the channel coefficient between the nth reflection element of the intelligent reflecting surface and user U i , d ri represents the distance between the nth reflection element of the intelligent reflecting surface and user U i , β is the path loss exponent, β ranges from [2, 4], and in this embodiment, β is taken as 3.2.

[0077] (4) Determine the cumulative distribution function

[0078] Determine the cumulative distribution function of the near user U1 according to Equations (2) and (3):

[0079]

[0080]

[0081]

[0082] U1 = (d sr × d r1 ) -β / 2 × U a

[0083]

[0084] V1 = (d sr × d r1 ) -β / 2 × V a

[0085]

[0086] where diag(·) represents a diagonal matrix, ρ is the transmission signal-to-noise ratio, the value range of ρ is [30 dB, 50 dB], the value of ρ in this embodiment is 30 dB, α n is the power allocation factor for the near user U1, α n takes values in (0, 0.5), the value of α in this embodiment n is 0.2, α f is the power allocation factor for the far user U2, α f takes values in (0.5, 1), the value of α in this embodiment f is 0.8, γ 1,1 is the signal-to-interference-plus-noise ratio for the near user U1 to decode its own required information, γ 1,2 is the signal-to-interference-plus-noise ratio for the near user U1 to decode the required information of the far user U2, m s1 , m r1 , m sr are channel fading parameters, m s1 , m r1 , m sr all take values in [2, 3], the values of m s1 , m r1 , m sr in this embodiment are all 2.

[0087] Determine the cumulative distribution function of the far user U2 according to Equation (4):

[0088]

[0089]

[0090] U2 = (d sr × d r2 ) -β / 2 × U b

[0091]

[0092] V2 = (d sr × d r2 ) -β / 2 × V b

[0093]

[0094] where γ 2,2 is the signal-to-interference-plus-noise ratio for the far user U2 to decode its own required information, where m s2 , m r2 are channel fading parameters, and the value ranges of m s2 , m r2 are both [2, 3]. In this embodiment, the values of m s2 , m r2 are both 2.

[0095] (5) Determine the average block error rate and system throughput

[0096] The average block error rate of the near user U1 determined according to Equation (5):

[0097]

[0098] w2 = h2 - (2 × μ2) -1

[0099] τ2 = h2 + (2 × μ2) -1

[0100]

[0101] λ1 = α n ρ + ρθ 2

[0102]

[0103] w1 = h1 - (2 × μ1) -1

[0104] τ1 = h1 + (2 × μ1) -1

[0105]

[0106] λ2 = εα f ρ + ρθ 2

[0107] where G is a trade-off parameter for balancing computational complexity and analysis accuracy, and its value range is [200, 400]. In this embodiment, the value of G is 200, and η i is for user Ui The number of information bits required, η i The value range is [50 bits, 150 bits], and η in this embodiment i The value is 50 bits. L1 is the block length required for the base station to send information to user U1, and the value range of L1 is [100, 300]. In this embodiment, the value of L1 is 100. L2 is the block length required for the base station to send information to user U2, and the value range of L2 is [100, 300]. In this embodiment, the value of L2 is 100.

[0108] Determine the average block error rate of the far user U2 according to Equation (6):

[0109]

[0110] Determine the system throughput according to Equation (7):

[0111]

[0112] Analyze the change of the average block error rate of users with the change of the signal-to-noise ratio and the change of the average block error rate of users with the change of the number N of reflecting elements of the intelligent reflecting surface; the smaller the average block error rate, the better the system performance; calculate the system throughput T, the larger the number N of reflecting elements of the intelligent reflecting surface, the better the system performance and the better the system throughput.

[0113] Complete the information transmission method of the intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system.

[0114] Embodiment 2

[0115] The information transmission method of the intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system in this embodiment consists of the following steps:

[0116] (1) Construct a transmission signal model

[0117] The short-packet non-orthogonal multiple access system consists of 1 base station, 1 near user U1, 1 far user U2, and an intelligent reflecting surface containing N elements. N is a finite positive integer. In this embodiment, the value of N is 40. The base station, near user U1, and far user U2 are equipped with single antennas, and all nodes operate in half-duplex mode to construct a transmission signal model.

[0118] (2) Determine the imperfect serial interference cancellation coefficient and the hardware impairment coefficient

[0119] This step is the same as that in Embodiment 1.

[0120] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0121] Determine the phase shift coefficient of the intelligent reflecting surface according to Equation (1):

[0122]

[0123] where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n is the phase error when the intelligent reflecting surface adopts a non - optimal continuous phase shift scheme with phase estimation error. ψ n follows the von Mises distribution with a mean of 0 and a concentration parameter of ξ. ξ takes values in (0, 10), and in this embodiment, ξ takes the value of 4. g si is the channel coefficient from the base station to user U i , d si represents the distance from the base station to user U i , i ∈ {1, 2}, is expressed as the channel coefficient between the base station and the nth reflecting element of the intelligent reflecting surface, d sr represents the distance between the base station and the intelligent reflecting surface, represents the channel coefficient between the nth reflecting element of the intelligent reflecting surface and user U i , d ri represents the distance between the nth reflecting element of the intelligent reflecting surface and user U i , β is the path loss exponent, β takes values in [2, 4], and in this embodiment, β takes the value of 3.

[0124] (4) Determine the cumulative distribution function

[0125] This step is the same as in Embodiment 1.

[0126] (5) Determine the average block error rate and system throughput

[0127] The average block error rate of the near user U1 determined according to Equation (5):

[0128]

[0129]

[0130] w2 = h2 - (2×μ2) -1

[0131] τ2 = h2 + (2×μ2) -1

[0132]

[0133] λ1 = α n ρ + ρθ 2

[0134]

[0135] w1 = h1 - (2×μ1) -1

[0136] τ1 = h1 + (2×μ1) -1

[0137]

[0138] λ2 = εα f ρ + ρθ 2

[0139] Where G is a trade - off parameter for balancing computational complexity and analysis accuracy, and its value range is [200, 400]. In this embodiment, G takes the value of 300, η i is the number of bits of information required by user U i and η i takes values in [50 bits, 150 bits]. In this embodiment, η i takes the value of 100 bits. L1 is the block length required for the base station to send information to user U1, and L1 takes values in [100, 300]. In this embodiment, L1 takes the value of 200. L2 is the block length required for the base station to send information to user U2, and L2 takes values in [100, 300]. In this embodiment, L2 takes the value of 200.

[0140] Determine the average block error rate of the far user U2 according to Equation (6):

[0141]

[0142] Determine the system throughput according to Equation (7):

[0143]

[0144] Complete the information transmission method of the intelligent reflecting surface - assisted short - packet non - orthogonal multiple access system.

[0145] Embodiment 3

[0146] The information transmission method of the intelligent reflecting surface - assisted short - packet non - orthogonal multiple access system in this embodiment consists of the following steps:

[0147] (1) Construct a transmission signal model

[0148] The short - packet non - orthogonal multiple access system consists of 1 base station, 1 near user U1, 1 far user U2, and an intelligent reflecting surface containing N elements. N is a finite positive integer. In this embodiment, N takes the value of 60. The base station, near user U1, and far user U2 are equipped with single antennas, and all nodes operate in half - duplex mode to construct a transmission signal model.

[0149] (2) Determine the imperfect successive interference cancellation coefficient and the hardware impairment coefficient

[0150] This step is the same as that in Embodiment 1.

[0151] (3) Determine the intelligent reflecting surface phase shift coefficient

[0152] Determine the intelligent reflecting surface phase shift coefficient according to Equation (1):

[0153]

[0154] where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n is the phase error when the intelligent reflecting surface adopts a non-optimal continuous phase shift scheme with phase estimation error, ψ n follows the von Mises distribution with a mean of 0 and a central parameter of ξ, ξ takes values in (0, 10), and ξ in this embodiment takes the value of 2, g si is the channel coefficient from the base station to user U i , d si represents the distance from the base station to user U i , i ∈ {1, 2}, represents the channel coefficient between the base station and the nth reflecting element of the intelligent reflecting surface, d sr represents the distance between the base station and the intelligent reflecting surface, represents the channel coefficient between the nth reflecting element of the intelligent reflecting surface and user U i , d ri represents the distance between the nth reflecting element of the intelligent reflecting surface and user U i , β is the path loss exponent, β takes values in [2, 4], and β in this embodiment takes the value of 2.

[0155] (4) Determine the cumulative distribution function

[0156] This step is the same as that in Embodiment 1.

[0157] (5) Determine the average block error rate and system throughput

[0158] Determine the average block error rate of the near user U1 according to Equation (5):

[0159]

[0160] w2 = h2 - (2 × μ2) -1

[0161] τ2 = h2+(2×μ2) -1

[0162]

[0163] λ1 = α n ρ + ρθ 2

[0164]

[0165] w1 = h1-(2×μ1) -1

[0166] τ1 = h1+(2×μ1) -1

[0167]

[0168] λ2 = εα f ρ + ρθ 2

[0169] where G is a trade-off parameter for balancing computational complexity and analysis accuracy, with a value range of [200, 400], and the value of G in this embodiment is 400, η i is the number of bits of information required by user U i and η i takes values in [50 bits, 150 bits], and the value of η i in this embodiment is 150 bits. L1 is the block length required for the base station to send information to user U1, and L1 takes values in [100, 300], and the value of L1 in this embodiment is 300. L2 is the block length required for the base station to send information to user U2, and L2 takes values in [100, 300], and the value of L2 in this embodiment is 300.

[0170] Determine the average block error rate of the far user U2 according to Equation (6):

[0171]

[0172] Determine the system throughput according to Equation (7):

[0173]

[0174] Complete the information transmission method of the intelligent reflecting surface assisted short packet non-orthogonal multiple access system.

[0175] To verify the beneficial effects of the present invention, the inventors conducted a comparative simulation experiment using the information transmission method of the intelligent reflecting surface-assisted short-packet non-orthogonal multiple access system in Embodiment 1 of the present invention (hereinafter referred to as the method of Embodiment 1) and the method without an intelligent reflecting surface (hereinafter referred to as the comparative experiment method). The experimental results are shown in Figure 2 . Figure 2 is the influence of the transmission signal-to-noise ratio on the average block error rate. As Figure 2 can be seen, compared with the comparative experiment method, when N = 20 and the transmission signal-to-noise ratio is 30 dB to 50 dB, the average block error rate of the system in the method of Embodiment 1 is reduced by 0 to 0.118243.

Claims

1. An information transmission method for an intelligent reflecting surface-assisted short packet non-orthogonal multiple access system, characterized in that It consists of the following steps: (1) Construct a transmission signal model The short-packet non-orthogonal multiple access system consists of 1 base station, 1 near user U1, 1 far user U2, and an intelligent reflecting surface containing N elements, where N is a finite positive integer. The base station, near user U1, and far user U2 are equipped with single antennas, and all nodes operate in half-duplex mode to construct a transmission signal model; (2) Determine the imperfect successive interference cancellation coefficient and the hardware impairment coefficient According to non-orthogonal multiple access technology, after the near user U1 receives the mixed information sent by the base station, it first needs to decode and delete the information required by the far user U2, and then decode the information it needs. However, in practical applications, due to imperfect successive interference cancellation, the information required by the far user U2 cannot be completely decoded and deleted by the near user U1. Therefore, the near user U1 will continue to be affected by the residual interference of the information required by the far user U2 when decoding the information it needs. For this reason, the imperfect successive interference cancellation coefficient is defined as ε, and the value of ε is in the range of [0,1]; In an ideal situation, a communication system usually assumes that the hardware is perfect, that is, signal processing and transmission will not introduce additional distortion. However, in an actual wireless communication system, due to the imperfection of the hardware, the signal will be affected by additional interference. For example, the nonlinearity of the radio frequency front end, phase noise, etc. will inevitably affect the system performance. Therefore, ignoring hardware impairment may lead to a significant deviation between theoretical analysis and the actual system performance. To make the transmission signal model more in line with practical applications, the hardware impairment coefficient is defined as θ, and the value of θ is in the range of [0,0.5]; (3) Determine the phase shift coefficient of the intelligent reflecting surface Determine the phase shift coefficient of the intelligent reflecting surface according to Equation (1): where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n is the phase error when the intelligent reflecting surface adopts a non - optimal continuous phase shift scheme with phase estimation error. ψ n obeys the von Mises distribution with a mean of 0 and a concentration parameter of ξ. The value of ξ is (0, 10), g si is the channel coefficient from the base station to user U i , d si represents the distance from the base station to user U i , i ∈ {1, 2}, represents the channel coefficient between the base station and the n - th reflecting element of the intelligent reflecting surface, d sr represents the distance between the base station and the intelligent reflecting surface, represents the channel coefficient between the n - th reflecting element of the intelligent reflecting surface and user U i , d ri represents the distance between the n - th reflecting element of the intelligent reflecting surface and user U i , and β is the path loss exponent with a value range of [2, 4]; (4) Determine the cumulative distribution function Determine the cumulative distribution function of the near user U1 according to Equations (2) and (3): where diag(·) represents a diagonal matrix, ρ is the transmission signal-to-noise ratio, ρ takes values in [30 dB, 50 dB], and α n is the power allocation factor for the near user U1, and α n takes values in (0, 0.5), and α f is the power allocation factor for the far user U2, and α f takes values in (0.5, 1), and γ 1,1 is the signal-to-interference-plus-noise ratio for the near user U1 to decode its own required information, and γ 1,2 is the signal-to-interference-plus-noise ratio for the near user U1 to decode the required information of the far user U2, and m s1 、m r1 、m sr are channel fading parameters, and m s1 、m r1 、m sr all take values in [2, 3]. Determine the cumulative distribution function of the far user U2 according to Equation (4): where γ 2,2 is the signal-to-interference-plus-noise ratio for the far user U2 to decode its required information, where m s2 , m r2 are channel fading parameters, and the value ranges of m s2 , m r2 are both [2, 3]; (5) Determine the average block error rate and system throughput The average block error rate of the near user U1 determined according to Equation (5): Among them, G is a trade-off parameter for balancing the computational complexity and analysis accuracy, and its value range is [200, 400], η i is the number of information bits required by user U i η i takes values in [50 bits, 150 bits]. L1 is the block length required for the base station to send information to user U1, and L1 takes values in [100, 300]. L2 is the block length required for the base station to send information to user U2, and L2 takes values in [100, 300]; The average block error rate of the far user U2 determined according to Equation (6): Determine the system throughput according to Equation (7): Analyze the change of the user average block error rate with the change of the signal-to-noise ratio, and the change of the user average block error rate with the change of the number N of reflecting elements of the intelligent reflecting surface; the smaller the average block error rate, the better the system performance; calculate the system throughput T, the larger the number N of reflecting elements of the intelligent reflecting surface, the better the system performance and the system throughput.

2. The information transmission method of the intelligent reflecting surface assisted short packet non-orthogonal multiple access system according to claim 1, characterized in that: In (2) determining the imperfect successive interference cancellation coefficient and the hardware impairment coefficient, the ε is the imperfect successive interference cancellation coefficient, and the value of ε is 0.

05. The θ is the hardware impairment coefficient, and the value of θ is 0.

2.

3. The information transmission method of the intelligent reflecting surface assisted short packet non-orthogonal multiple access system according to claim 1, characterized in that: In (3) determining the phase shift coefficient of the intelligent reflecting surface, the β is the path loss exponent, and the value of β is 3.

2. ξ is the central parameter of the von Mises distribution, and the value of ξ is 8.

4. The information transmission method of the intelligent reflecting surface-assisted short packet non-orthogonal multiple access system according to claim 1, characterized in that: In (4) determining the cumulative distribution function, ρ is the transmission signal-to-noise ratio, and the value of ρ is 30 dB, α n is the power allocation factor for the near user U1, α n takes the value of 0.2, α f is the power allocation factor for the far user U2, α f takes the value of 0.8, m s1 、m s2 、m r1 、m r2 、m sr are channel fading parameters, and the values of m s1 、m s2 、m r1 、m r2 、m sr are all 2.

5. The information transmission method of the intelligent reflecting surface assisted short packet non-orthogonal multiple access system according to claim 1, characterized in that: In (5) determining the average block error rate and system throughput, η i is the number of information bits required for user U i , and η i takes a value of 50 bits. L i is the block length required for the base station to send information to user U i , and L i takes a value of 100.

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