Time delay minimization method for intelligent reflector-assisted short packet non-orthogonal multiple access

Abstract

The invention relates to a time delay minimization method for auxiliary short packet non-orthogonal multiple access of an intelligent reflecting surface. The time delay minimization method comprises the steps of constructing a short packet communication system, determining a channel model, determining an intelligent reflecting surface shift coefficient, determining a progressive average block error rate, optimizing a block length and determining a minimum common block length. Due to the fact that discrete phase shifting is adopted in the intelligent reflecting surface, imperfect serial interference elimination and hardware damage are considered at the same time, a transmission signal model meeting the reality is established, a time delay minimization method is provided, the non-orthogonal multiple access technology and the intelligent reflecting surface are utilized, and the time delay of the system is reduced by reducing the transmission block length of the system. The transmission method has the advantages of being practical, easy to implement, low in system delay and the like, and can be used in the technical field of short packet communication.

Description

Technical Field

[0002] The present invention belongs to the technical field of short-packet communication, and specifically relates to the delay minimization of intelligent reflecting surface assisted short-packet non-orthogonal multiple access. Background Art

[0003] An intelligent reflecting surface is a programmable artificial metamaterial information surface that can change the propagation direction of electromagnetic waves by intelligently regulating its electromagnetic characteristics, thereby achieving functions that are difficult to achieve with traditional metamaterials. The intelligent reflecting surface consists of a large number of low-cost controllable passive units and precisely adjusts its phase and amplitude through an intelligent controller, enabling it to actively optimize the wireless propagation environment, enhance signal quality, and reduce signal distortion caused by obstacles or interference. With the help of passive beamforming technology, the intelligent reflecting surface can guide and enhance the signal propagation in a specific direction, thereby improving the stability and reliability of the communication system. In addition, since the intelligent reflecting surface relies on passive units for signal modulation, its hardware cost and power consumption are much lower than those of traditional active relay technologies, giving it significant advantages in expanding the coverage and capacity of wireless communication networks.

[0004] At the same time, non-orthogonal multiple access technology has received extensive attention because it provides higher spectral efficiency through non-orthogonal allocation of limited communication resources. Non-orthogonal multiple access technology introduces the concept of power domain on the basis of orthogonal frequency division multiple access technology. Multiple user signals can be accommodated simultaneously in the same time-frequency resource unit to achieve non-orthogonal multiplex transmission. Non-orthogonal multiple access uses superposition coding at the transmitter and serial interference cancellation at the receiver to suppress interference between users and extract the desired signal. With its good user connection ability, low delay characteristics, and excellent spectral and energy efficiency, non-orthogonal multiple access has broad application prospects in future wireless communication systems.

[0005] Short-packet communication uses finite block length coding, which can significantly reduce transmission delay and improve transmission reliability, so it is widely used in the fields of the Internet of Things and machine-type communication.

[0006] Although non-orthogonal multiple access technology can reduce system delay, in actual engineering, it is difficult to achieve perfect serial interference cancellation for non-orthogonal multiple access technology, and signal processing and transmission will introduce additional distortion, causing hardware damage, which leads to a decline in system performance and an increase in delay. The impact on the system caused by imperfect serial interference cancellation and hardware damage can be compensated by using an intelligent reflecting surface. However, the optimal continuous phase shift technology currently used for intelligent reflecting surfaces has disadvantages such as high hardware cost and great implementation difficulty. By using discrete phase shift technology, not only can the system performance under the optimal continuous phase shift technology be achieved, but also the hardware cost can be reduced and the implementation difficulty can be lowered.

[0007] Therefore, in the scenario of imperfect successive interference cancellation and hardware impairments, it is very necessary to study the method for minimizing the latency of intelligent reflecting surface-assisted short-packet non-orthogonal multiple access. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to overcome the above-mentioned disadvantages of the prior art and provide a method for minimizing the latency of intelligent reflecting surface-assisted short-packet non-orthogonal multiple access with a simple transmission method, easy implementation, and low latency.

[0009] The technical solution adopted to solve the above technical problem consists of the following steps:

[0010] (1) Construct a short-packet communication system

[0011] The short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N is a finite positive integer, a base station that transmits superimposed signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0012] (2) Determine the channel model

[0013] Determine the channel model h according to Equation (1) sr :

[0014]

[0015] Among them, represents the channel coefficient between the base station and the Nth reflecting element of the intelligent reflecting surface, and d sr represents the distance from the base station to the intelligent reflecting surface, and β is the path loss exponent, and the value range of β is 3 to 5.

[0016] Determine the channel model h according to Equation (2) ri :

[0017]

[0018] Among them represents the channel coefficient between the Nth reflecting element of the intelligent reflecting surface and the user U i , and d ri represents the distance between the intelligent reflecting surface and the user U i , and the value of i is 1 or 2.

[0019] Determine the channel model h according to Equation (3) si :

[0020]

[0021] Among them, g si is the channel coefficient between the base station and the user U i , and d si represents the distance between the base station and the user Ui The distance between

[0022] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0023] Determine the phase shift coefficient φ of the intelligent reflecting surface according to Equation (4) n :

[0024]

[0025] where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n represents the phase error when the intelligent reflecting surface adopts a discrete phase shift scheme with q-bit quantization, and the value range of q is an integer from 1 to 4.

[0026] (4) Determine the asymptotic average block error rate

[0027] Determine the asymptotic average block error rate of the near user U1 according to Equation (5)

[0028] λ2 = ε × α f × ρ + ρ × θ 2

[0029] λ1 = α n × ρ + ρ × θ 2

[0030]

[0031]

[0032] U1 = (d sr × d r1 ) -β / 2 × U a

[0033]

[0034]

[0035] V1 = (d sr × d r1 ) -β / 2 × V a

[0036]

[0037] where diag(·) represents a diagonal matrix, is a coefficient, Θ is the reflection coefficient matrix of the intelligent reflecting surface, ρ is the transmission signal-to-noise ratio, and its value range is 40 - 50 dB. α n is the power allocation factor for the near user U1, α n has a value range of 0.1 - 0.3, α f is the power allocation factor for the far user U2, α f has a value range of 0.5 - 7, ε is the imperfect successive interference cancellation coefficient, and its value range is 0 - 0.1. θ is the hardware impairment coefficient, and its value range is 0 - 0.4. η1 is the number of information bits required by user U1, and its value range is 50 - 200 bits. η2 is the number of information bits required by user U2, and its value range is 50 - 200 bits. L1 is the block length required for the base station to send information to user U1, and its value is 100 - 300. L2 is the block length required for the base station to send information to user U2, and its value is 100 - 300. m s1 , m r1 , m sr are channel fading parameters, m s1 , m r1 , m sr all have a value range of 2 - 4.

[0038] Determine the

[0039]

[0040]

[0041] U2 = (d sr × d r2 ) -β / 2 × U b

[0042]

[0043]

[0044] where, m s2 , m r2 are channel fading parameters, m s2 , m r2 all have a value range of 2 - 4.

[0045] (5) Block length optimization

[0046] Construct the common block length optimization model according to Equation (7)

[0047]

[0048] χ1 = δ1 × k1!

[0049]

[0050] χ2 = δ2 × k2!

[0051] Wherein, L is the common block length required for the system to send information.

[0052] The optimization conditions of the above model are as shown in Equation (8):

[0053]

[0054] 0 < α n < 0.5

[0055] Wherein, δ1 is the maximum threshold value of the block error rate of the near user U1, and the value range of δ1 is 10 -10 ~10 -6 ; δ2 is the maximum threshold value of the block error rate of the far user U2, and the value range of δ2 is 10 -10 ~10 -6 , and the existing golden section method is adopted to obtain the optimal value of α n .

[0056] (6) Determine the minimum common block length

[0057] Determine the minimum common block length L in the case of non-orthogonal multiple access according to Equation (9) * :

[0058]

[0059] Wherein, is the optimal value of α obtained by solving Equation (7) n .

[0060] Analyze the change of the minimum common block length with the change of the signal-to-noise ratio, the change of the minimum common block length with the change of the imperfect successive interference cancellation coefficient, and the change of the minimum common block length with the change of the hardware impairment coefficient.

[0061] In the step (2) of the present invention to determine the channel model, the β is the path loss exponent, and the optimal value of β is 4.

[0062] In the step (3) of the present invention to determine the intelligent reflecting surface phase shift coefficient, the optimal value of q is 3.

[0063] In the step (4) of the present invention to determine the asymptotic average block error rate, the ρ is the transmission signal-to-noise ratio, the optimal value of ρ is 45 dB, α n is the power allocation factor of the near user U1, α n has an optimal value of 0.2, α fis the power allocation factor for the far user U2, α f The optimal value is 0.6, ε is the imperfect successive interference cancellation coefficient, the optimal value of ε is 0.04, θ is the hardware impairment coefficient, the optimal value of θ is 0.2, η1 is the number of information bits required by the near user U1, the value of η1 is optimally 100 bits, η2 is the number of information bits required by the far user U2, the optimal value of η2 is 100 bits, L1 is the block length required for the base station to send information to the near user U1, the value of L1 is 200, L2 is the block length required for the base station to send information to the far user U2, the optimal value of L2 is 200, 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 optimally 3.

[0064] In the block length optimization of step (5) of the present invention, δ1 is the maximum threshold of the block error rate of the near user U1, and the optimal value of δ1 is 10 -8 , δ2 is the maximum threshold of the block error rate of the far user U2, and the optimal value of δ2 is 10 -8 .

[0065] In step (1) of constructing a short-packet communication system of the present invention, the short-packet communication system is composed of an intelligent reflecting surface with N reflecting elements, where N ranges from 20 to 60, a base station that sends superposed signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0066] In step (1) of constructing a short-packet communication system of the present invention, the short-packet communication system is composed of an intelligent reflecting surface with N reflecting elements, where the optimal value of N is 40, a base station that sends superposed signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0067] Since the present invention adopts a short-packet communication system, takes into account the impact of imperfect successive interference cancellation and hardware impairments on the system, and the phase shift of the intelligent reflecting surface adopts discrete phase shifts with low hardware cost and low implementation difficulty, there are both a direct transmission link and a reflection link assisted by the intelligent reflecting surface between the base station and the user, establishing a more practical transmission signal model. In order to reduce the system delay, non-orthogonal multiple access technology is utilized to reduce the common block length of the system, thereby reducing the system delay. Compared with the prior art, when the transmission signal-to-noise ratio ranges from 40 dB to 50 dB, the minimum common block length of the system is reduced by 31.025 to 15.745. The present invention has the advantages of simple transmission method, easy implementation, conforming to reality, and low delay, and can be used in the field of short-packet communication technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is the process flow diagram of Embodiment 1 of the present invention.

[0069] Figure 2 is the simulation curve of Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] 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.

[0071] Embodiment 1

[0072] In Figure 1 the delay minimization method for intelligent reflecting surface-assisted short-packet non-orthogonal multiple access in this embodiment consists of the following steps:

[0073] (1) Construct a short-packet communication system

[0074] The short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N ranges from 20 to 60, N in this embodiment is 40, a base station that transmits superimposed signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0075] (2) Determine the channel model

[0076] Determine the channel model h according to Equation (1) sr :

[0077]

[0078] where represents the channel coefficient between the base station and the Nth reflecting element of the intelligent reflecting surface, d sr represents the distance from the base station to the intelligent reflecting surface, β is the path loss exponent, and the value range of β is 3 to 5, and β in this embodiment is 4;

[0079] Determine the channel model \(h\) according to Equation (2) ri :

[0080]

[0081] where represents the channel coefficient between the \(N\)th reflection element of the intelligent reflecting surface and user \(U\) i , \(d\) ri represents the distance between the intelligent reflecting surface and user \(U\) i , and \(i\) takes the value of 1 or 2;

[0082] Determine the channel model \(h\) according to Equation (3) si :

[0083]

[0084] where \(g\) si is the channel coefficient between the base station and user \(U\) i , \(d\) si represents the distance between the base station and user \(U\) i .

[0085] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0086] Determine the phase shift coefficient \(\varphi\) of the intelligent reflecting surface according to Equation (4) n :

[0087]

[0088] where \(\arg(h\) si ) represents the angle of \(h\) si , represents 's angle, represents 's angle, \(\psi\) n represents the phase error when the intelligent reflecting surface adopts a discrete phase shift scheme with \(q\)-bit quantization. The value range of \(q\) is an integer from 1 to 4, and in this embodiment, \(q\) takes the value of 3.

[0089] (4) Determine the asymptotic average block error rate

[0090] Determine the asymptotic average block error rate of the near user \(U1\) according to Equation (5)

[0091]

[0092] \(\lambda_2=\varepsilon\times\alpha\) f \(\times\rho+\rho\times\theta\) 2

[0093] \(\lambda_1=\alpha\) n \(\times\rho+\rho\times\theta\) 2

[0094]

[0095]

[0096] U1 = (d sr × d r1 ) -β / 2 × U a

[0097]

[0098]

[0099] V1 = (d sr × d r1 ) -β / 2 × V a

[0100]

[0101] where diag(·) represents a diagonal matrix, (E[|H1| 2 ) 2 、E[|H1| 4 , E[|h s1 |], E[|h s1 | 2 , E[|h s1 | 3 , E[|h s1 | 4 have been disclosed in "Outage Performance of Multi-RIS-Assisted NOMA Systems under Imperfect Serial Interference Cancellation" ([J]. Journal of Internet of Things, 2024, 8(01): 153 - 160); E[U1], E[V1 2 , E[V1 4 have been disclosed in "T.-H. Vu, T.-V. Nguyen, Q.-V. Pham, D. B. da Costa, and S. Kim, “STAR RIS-enabled short-packet NOMA systems,”" (IEEE Trans. Veh. Technol., vol. 72, no. 10, pp. 13764–13769, May 2023), is a coefficient, k takes values from 1 to 4, and in this embodiment, takes values 1, 2, 3, 4 in sequence, takes values 1, 3, 6 in sequence, takes values 1, 4 in sequence, takes the value of 1; Θ is the reflection coefficient matrix of the intelligent reflecting surface, ρ is the transmission signal-to-noise ratio, the value range of ρ is 40 - 50 dB, and the value of ρ in this embodiment is 45 dB, α n is the power allocation factor for the near user U1, α n has a value range of 0.1 - 0.3, and 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 has a value range of 0.5 - 0.7, and the value of α in this embodiment f is 0.6, ε is the imperfect successive interference cancellation coefficient, the value range of ε is 0 - 0.1, and the value of ε in this embodiment is 0.04, θ is the hardware impairment coefficient, the value range of θ is 0 - 0.4, and the value of θ in this embodiment is 0.2, η1 is the number of information bits required by user U1, the value range of η1 is 50 - 200 bits, and the value of η1 in this embodiment is 100 bits, η2 is the number of information bits required by user U2, the value range of η2 is 50 - 200 bits, and the value of η2 in this embodiment is 100 bits, L1 is the block length required for the base station to send information to user U1, L1 takes values from 100 to 300, and the value of L1 in this embodiment is 200, L2 is the block length required for the base station to send information to user U2, L2 takes values from 100 to 300, and the value in this embodiment is 200, m s1 、m r1 、m sr are channel fading parameters, m s1 、m r1 、m sr all have a value range of 2 - 4, and the values of m s1 、m r1 、m sr in this embodiment are 3.

[0102] Determine the

[0103]

[0104]

[0105] U2 of the far user U2 according to Equation (6) sr ×d r2 ) -β / 2 ×U b

[0106]

[0107] V2=(d sr ×d r2 ) -β / 2 ×V b

[0108]

[0109] Among them, m s2 , m r2 is the channel fading parameter, and the value ranges of m s2 , m r2 are both 2 to 4. In this embodiment, the values of m s2 , m r2 are 3;

[0110] (5) Block length optimization

[0111] Construct the common block length optimization model according to Equation (7)

[0112]

[0113] χ1 = δ1 × k1!

[0114]

[0115] χ2 = δ2 × k2!

[0116] Among them, L is the common block length required for the system to send information;

[0117] The optimization conditions of the above model are as shown in Equation (8):

[0118]

[0119] 0 < α n < 0.5

[0120] Among them, δ1 is the maximum threshold of the block error rate of the near user U1, and the value range of δ1 is 10 -10 ~10 -6 , and the value of δ1 in this embodiment is 10 -8 , δ2 is the maximum threshold of the block error rate of the far user U2, and the value range of δ2 is 10 -10 ~10 -6 , and the value of δ2 in this embodiment is 10 -8 , and the optimal value of α n is obtained by using the existing golden section method;

[0121] (6) Determine the minimum common block length

[0122] Determine the minimum common block length L in the case of non-orthogonal multiple access according to Equation (9) * :

[0123]

[0124] Among them, The optimal value of α obtained by solving Equation (7) n ;

[0125] Analyze the variation of the minimum common block length with the signal-to-noise ratio, the variation of the minimum common block length with the imperfect successive interference cancellation coefficient, and the variation of the minimum common block length with the hardware impairment coefficient.

[0126] Complete the method for minimizing the latency of intelligent reflecting surface-assisted short-packet non-orthogonal multiple access

[0127] Embodiment 2

[0128] The method for minimizing the latency of intelligent reflecting surface-assisted short-packet non-orthogonal multiple access in this embodiment consists of the following steps:

[0129] (1) Construct a short-packet communication system

[0130] The short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N ranges from 20 to 60 and N is 20 in this embodiment, a base station that transmits superimposed signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0131] (2) Determine the channel model

[0132] Determine the channel model h according to Equation (1) sr :

[0133] The expression of Equation (1) is the same as that in Embodiment 1.

[0134] In Equation (1), represents the channel coefficient between the base station and the Nth reflecting element of the intelligent reflecting surface, and d sr represents the distance from the base station to the intelligent reflecting surface, and β is the path loss exponent, where β ranges from 3 to 5 and β is 3 in this embodiment.

[0135] Determine the channel model h according to Equation (2) ri :

[0136] The expression of Equation (2) is the same as that in Embodiment 1.

[0137] In Equation (2), represents the channel coefficient between the Nth reflecting element of the intelligent reflecting surface and user U i , and d ri represents the distance between the intelligent reflecting surface and user U i , and i takes the value of 1 or 2.

[0138] Determine the channel model h according to Equation (3) si :

[0139]

[0140] In (3), the meanings represented by the coefficients, parameters, and variables are the same as those in Embodiment 1, and the value ranges are the same as those in Embodiment 1.

[0141] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0142] Determine the phase shift coefficient φ of the intelligent reflecting surface according to Equation (4) n :

[0143] The expression of Equation (4) is the same as that in Embodiment 1.

[0144] In Equation (4), ψ n represents the phase error when the intelligent reflecting surface adopts the discrete phase shift method with q-bit quantization. The value range of q is an integer from 1 to 4, and the value of q in this embodiment is 1. The meanings represented by the coefficients, parameters, and variables are the same as those in Embodiment 1, and the value ranges are the same as those in Embodiment 1.

[0145] (4) Determine the asymptotic average block error rate

[0146] Determine the asymptotic average block error rate of the near user U1 according to Equation (5)

[0147] The expression of Equation (5) is the same as that in Embodiment 1.

[0148] In Equation (5),

[0149]

[0150] ρ is the transmission signal-to-noise ratio. The value range of ρ is 40 - 50 dB, and the value of ρ in this embodiment is 40 dB. α n is the power allocation factor of the near user U1. α n has a value range of 0.1 - 0.3, and the value of α in this embodiment n is 0.1. α f is the power allocation factor of the far user U2. α f has a value range of 0.5 - 0.7, and the value of α in this embodiment fThe value of is 0.5, ε is the imperfect successive interference cancellation coefficient, and the value range of ε is 0 to 0.1. In this embodiment, the value of ε is 0. θ is the hardware impairment coefficient, and the value range of θ is 0 to 0.4. In this embodiment, the value of θ is 0. η1 is the number of bits of information required by user U1, and the value range of η1 is 50 to 200 bits. In this embodiment, the value of η1 is 50 bits. η2 is the number of bits of information required by user U2, and the value range of η2 is 50 to 200 bits. In this embodiment, the value of η2 is 50 bits. L1 is the block length required for the base station to send information to user U1, and the value of L1 is 100 to 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 of L2 is 100 to 300. In this embodiment, the value is 100, m s1 and m r1 and m sr are channel fading parameters, m s1 and m r1 and m sr The value ranges of all are 2 to 4. In this embodiment, m s1 and m r1 and m sr take the value of 2.

[0151]

[0152] m s2 and m r2 are channel fading parameters, m s2 and m r2 The value ranges of all are 2 to 4. In this embodiment, m s2 and m r2 take the value of 2.

[0153] The other expressions in Equation (5) are the same as those in Embodiment 1. The meanings and value ranges of the parameters, coefficients, and variables represented by the other expressions in Equation (5) are the same as those in Embodiment 1.

[0154] (5) Block length optimization

[0155] Construct the common block length optimization model according to Equation (7)

[0156] The expression of Equation (7) is the same as that in Embodiment 1.

[0157] In Equation (7), the meanings and value ranges of the parameters, coefficients, and variables represented are the same as those in Embodiment 1.

[0158] The optimization conditions of the above model are as shown in Equation (8):

[0159] The expression of Equation (8) is the same as that in Embodiment 1.

[0160] In Equation (8), δ1 is the maximum threshold of the block error rate of the near user U1, and the value range of δ1 is 10 -10 ~10 -6 ; in this embodiment, the value of δ1 is 10 -10 ; δ2 is the maximum threshold of the block error rate of the far user U2, and the value range of δ2 is 10 -10 ~10 -6 ; in this embodiment, the value of δ2 is 10 -10 , and the optimal value of α n is obtained by using the existing golden section method;

[0161] Other steps are the same as those in Embodiment 1. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access is completed.

[0162] Embodiment 3

[0163] The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access in this embodiment consists of the following steps:

[0164] (1) Construct a short packet communication system

[0165] The short packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N ranges from 20 to 60, and the value of N in this embodiment is 60, a base station that transmits superposition signals with a single antenna and operates in a half-duplex mode, a near user U1, and a far user U2.

[0166] (2) Determine the channel model

[0167] Determine the channel model h according to Equation (1) sr :

[0168] The expression of Equation (1) is the same as that in Embodiment 1.

[0169] In Equation (1), represents the channel coefficient between the base station and the Nth reflecting element of the intelligent reflecting surface, d sr represents the distance from the base station to the intelligent reflecting surface, β is the path loss exponent, and the value range of β is 3 to 5. In this embodiment, the value of β is 5.

[0170] Determine the channel model h according to Equation (2) ri :

[0171] The expression of Equation (2) is the same as that in Embodiment 1.

[0172] In Equation (2), represents the channel coefficient between the Nth reflecting element of the intelligent reflecting surface and the user U i , d ri represents the distance between the intelligent reflecting surface and the user U i , and i takes the value of 1 or 2.

[0173] Determine the channel model \(h\) according to Equation (3) si :

[0174]

[0175] In (3), the meanings represented by the coefficients, parameters, and variables are the same as those in Embodiment 1, and the value ranges are the same as those in Embodiment 1.

[0176] (3) Determine the phase shift coefficient of the intelligent reflecting surface

[0177] Determine the phase shift coefficient \(\varphi\) of the intelligent reflecting surface according to Equation (4) n :[[]]

[0178] The expression of Equation (4) is the same as that in Embodiment 1.

[0179] In Equation (4), \(\psi\) n represents the phase error when the intelligent reflecting surface adopts a discrete phase shift method with \(q\)-bit quantization. The value range of \(q\) is an integer from 1 to 4, and the value range of \(q\) in this embodiment is 4. The meanings represented by the coefficients, parameters, and variables are the same as those in Embodiment 1, and the value ranges are the same as those in Embodiment 1.

[0180] (4) Determine the asymptotic average block error rate

[0181] Determine the asymptotic average block error rate of the near user \(U1\) according to Equation (5)

[0182] The expression of Equation (5) is the same as that in Embodiment 1.

[0183] In Equation (5),

[0184]

[0185]

[0186] In Equation (5), \(\rho\) is the transmission signal-to-noise ratio, and the value range of \(\rho\) is 40 - 50 dB. The value of \(\rho\) in this embodiment is 50 dB, and \(\alpha\) n is the power allocation factor of the near user \(U1\), and \(\alpha\) n has a value range of 0.1 - 0.3. The value of \(\alpha\) in this embodiment n is 0.3, and \(\alpha\) f is the power allocation factor of the far user \(U2\), and \(\alpha\) f has a value range of 0.5 - 0.7. The value of \(\alpha\) in this embodiment fThe value is 0.7, ε is the imperfect successive interference cancellation coefficient, and the value range of ε is 0 to 0.1. In this embodiment, the value of ε is 0.1. θ is the hardware impairment coefficient, and the value range of θ is 0 to 0.4. In this embodiment, the value of θ is 0.4. η1 is the number of information bits required by user U1, and the value range of η1 is 50 to 200 bits. In this embodiment, the value of η1 is 200 bits. η2 is the number of information bits required by user U2, and the value range of η2 is 50 to 200 bits. In this embodiment, the value of η2 is 200 bits. L1 is the block length required for the base station to send information to user U1, and L1 takes values from 100 to 300. In this embodiment, the value of L1 is 300. L2 is the block length required for the base station to send information to user U2, and L2 takes values from 100 to 300. In this embodiment, the value of L2 is 300, m s1 and m r1 and m sr are channel fading parameters, and m s1 and m r1 and m sr all have a value range of 2 to 4. In this embodiment, m s1 and m r1 and m sr take the value of 4.

[0187]

[0188] m s2 and m r2 are channel fading parameters, and m s2 and m r2 all have a value range of 2 to 4. In this embodiment, m s2 and m r2 take the value of 4.

[0189] The other expressions in Equation (5) are the same as those in Embodiment 1. The meanings and value ranges of the parameters, coefficients, and variables represented by the other expressions in Equation (5) are the same as those in Embodiment 1.

[0190] (5) Block length optimization

[0191] Construct a common block length optimization model according to Equation (7)

[0192] The expression of Equation (7) is the same as that in Embodiment 1.

[0193] In Equation (7), the meanings and value ranges of the parameters, coefficients, and variables represented are the same as those in Embodiment 1.

[0194] The optimization conditions of the above model are as shown in Equation (8):

[0195] The expression of Equation (8) is the same as that in Embodiment 1.

[0196] In Equation (8), δ1 is the maximum threshold value of the block error rate of the near user U1, and the value range of δ1 is 10 -10 ~10 -6 , and the value of δ1 in this embodiment is 10 -6 , δ2 is the maximum threshold value of the block error rate of the far user U2, and the value range of δ2 is 10 -10 ~10 -6 , and the value of δ2 in this embodiment is 10 -6 , and the optimal value of α is obtained by using the existing golden section method; n

[0197] Other steps are the same as those in Embodiment 1. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access is completed.

[0198] The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access is completed.

[0199] To verify the beneficial effects of the present invention, the inventor carried out a comparative simulation experiment by using the method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access in Embodiment 1 of the present invention (hereinafter referred to as the method of Embodiment 1) and orthogonal multiple access (hereinafter referred to as the comparative experiment). The experimental results are shown in Figure 2 . Figure 2 is the influence curve of the transmission signal-to-noise ratio on the minimum common block length. As can be seen from Figure 2 , compared with the comparative experiment method, when N is 20 and the transmission signal-to-noise ratio is 40 dB to 50 dB, the minimum common block length of the system is reduced by 31.025 to 15.745.​

Claims

1. A method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access, characterized in that It consists of the following steps: (1) Construct a short-packet communication system The short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N is a finite positive integer, a base station that transmits superimposed signals equipped with a single antenna and operating in a half-duplex mode, a near user U1, and a far user U2; (2) Determine the channel model Determine the channel model h according to Equation (1) sr :[[]]END]] Among them, represents the channel coefficient between the base station and the Nth reflection element of the intelligent reflecting surface, and d sr represents the distance from the base station to the intelligent reflecting surface, β is the path loss exponent, and the value range of β is 3 to 5; Determine the channel model h according to Equation (2) ri :[[]]END]] Among them 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 intelligent reflecting surface and user U i The distance between them, and i takes the value of 1 or 2; Determine the channel model h according to Equation (3). si : where g si is the channel coefficient between the base station and user U i and d si represents the distance between the base station and user U i ; (3) Determine the phase shift coefficient of the intelligent reflecting surface Determine the phase shift coefficient φ of the intelligent reflecting surface according to Equation (4) n :[[-END]] where arg(h si ) represents the angle of h si , represents 's angle, represents 's angle, ψ n represents the phase error when the intelligent reflecting surface adopts a discrete phase shift scheme with q-bit quantization, and the value range of q is an integer from 1 to 4; (4) Determine the asymptotic average block error rate Determine the asymptotic average block error rate of the near user U1 according to Equation (5). where, diag(·) represents a diagonal matrix, is a coefficient, Θ is the reflection coefficient matrix of the intelligent reflecting surface, ρ is the transmission signal-to-noise ratio, and its value range is 40 to 50 dB, α n is the power allocation factor for the near user U1, α n has a value range of 0.1 to 0.3, α f is the power allocation factor for the far user U2, α f has a value range of 0.5 to 0.7, ε is the imperfect successive interference cancellation coefficient, and its value range is 0 to 0.1, θ is the hardware impairment coefficient, and its value range is 0 to 0.4, η1 is the number of information bits required by user U1, and its value range is 50 to 200 bits, η2 is the number of information bits required by user U2, and its value range is 50 to 200 bits, L1 is the block length required for the base station to send information to user U1, and L1 takes a value of 100 to 300, L2 is the block length required for the base station to send information to user U2, and L2 takes a value of 100 to 300, m s1 、m r1 、m sr are channel fading parameters, and the value ranges of m s1 、m r1 、m sr are all 2 to 4; Determine that of the remote user U2 according to formula (6) where m s2 and m r2 are channel fading parameters, and the value ranges of m s2 and m r2 are both from 2 to 4; (5) Block length optimization Construct the common block length optimization model according to Equation (7) where L is the common block length required for the system to transmit information; The optimization condition of the above model is as shown in Equation (8): Among them, δ1 is the maximum threshold value of the block error rate of the near user U1, and the value range of δ1 is 10 -10 ~10 -6 ; δ2 is the maximum threshold value of the block error rate of the far user U2, and the value range of δ2 is 10 -10 ~10 -6 . By using the existing golden section method, the optimal value of α n is obtained; (6) Determine the minimum common block length Determine the minimum common block length \(L\) in the case of non - orthogonal multiple access according to Equation (9) * : Among them, is the optimal value of α obtained by solving Equation (7); n ​ Analyze the variation of the minimum common block length with the signal-to-noise ratio, the variation of the minimum common block length with the imperfect successive interference cancellation coefficient, and the variation of the minimum common block length with the hardware impairment coefficient.

2. The method for minimizing the delay of intelligent reflecting surface-assisted short packet non-orthogonal multiple access according to claim 1, wherein: In step (2) of determining the channel model, β is the path loss exponent, and the value of β is 4.

3. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access according to claim 1, wherein: In step (3) of determining the phase shift coefficient of the intelligent reflecting surface, the value of q is 3.

4. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access according to claim 1, characterized in that: In step (4) for determining the progressive average block error rate, ρ is the transmission signal-to-noise ratio, and the value of ρ is 45 dB, α n is the power allocation factor for the near user U1, α n has a value of 0.2, α f is the power allocation factor for the far user U2, α f has a value of 0.6, ε is the imperfect successive interference cancellation coefficient, the value of ε is 0.04, θ is the hardware impairment coefficient, the value of θ is 0.2, η1 is the number of information bits required by the near user U1, the value of η1 is 100 bits, η2 is the number of information bits required by the far user U2, the value of η2 is 100 bits, L1 is the block length required for the base station to send information to the near user U1, the value of L1 is 200, L2 is the block length required for the base station to send information to the near user U2, the value of L2 is 200, m s1 、m s2 、m r1 、m r2 、m sr are channel fading parameters, m s1 、m s2 、m r1 、m r2 、m sr all have a value of 3.

5. The method for minimizing the delay of intelligent reflecting surface-assisted short packet non-orthogonal multiple access according to claim 1, wherein: In the block length optimization of step (5), δ1 is the maximum threshold value of the block error rate of the near user U1, and the value of δ1 is 10 -8 , δ2 is the maximum threshold value of the block error rate of the far user U2, and the value of δ2 is 10 -8 .

6. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access according to claim 1, wherein: In step (1) of constructing the short-packet communication system, the short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N ranges from 20 to 60, a base station that transmits superimposed signals equipped with a single antenna and operating in a half-duplex mode, a near user U1, and a far user U2.

7. The method for minimizing the delay of intelligent reflecting surface assisted short packet non-orthogonal multiple access according to claim 1 or 6, characterized in that: In step (1) of constructing the short-packet communication system, the short-packet communication system consists of an intelligent reflecting surface with N reflecting elements, where N = 40, a base station that transmits superimposed signals equipped with a single antenna and operating in a half-duplex mode, a near user U1, and a far user U2.

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