Intelligent reflector assisted dynamic cache network parameter configuration method and device

Abstract

The invention provides an intelligent reflector assisted dynamic cache network parameter configuration method and device. According to the method, initial fixed parameters of an intelligent metasurface auxiliary cache network are counted to serve as input parameters of an average network coverage performance estimation algorithm; under the condition that the network coverage performance reaches a given threshold value, reducing the base station cache capacity improvement cost through I RS deployment enhanced coverage, and obtaining a base station cache capacity and I RS deployment density configuration scheme in the network; according to the method, for the coverage performance of the intelligent reflecting surface auxiliary cache network, a theoretical expression of the network average cache coverage probability is obtained by adopting a reasonable network model for modeling by taking the coverage probability reaching a threshold value as an optimization target, and by comparing the influence of IRS deployment and base station cache capacity on the coverage performance in different scenes, the network average cache coverage probability is obtained. And a network deployment parameter scheme is obtained, so that the deployment density configuration of the IRS of the coverage-performance-oriented intelligent reflector-assisted cache network is completed, and the coverage performance of the network with the limited cache capacity of the base station is ensured.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method and device for configuring network parameters for intelligent reflective surface-assisted dynamic caching in future fifth-generation mobile communications (Beyond 5th generation, B5G) and sixth-generation mobile communications (6th Generation, 6G). Background Art

[0002] Nowadays, the driving force behind the exponential growth of mobile network traffic has fundamentally shifted from the steady growth of demand for traditional connection-centric communications to the explosive growth of content-centric communications. As an effective solution for content offloading, caching content at the base station (BS) can reduce the backhaul burden of the core network and improve the quality of service (QoS) of the user equipment (UE). However, when the content transmission link is in deep fading, the beneficial effects of content caching cannot be fully realized. Therefore, the content caching design naturally creates a coupling between wireless communication and caching strategies, and performance needs to be improved from the perspective of communication.

[0003] Fortunately, recent breakthroughs in micro-electromechanical systems and metamaterials have established intelligent reflecting surfaces (IRS) as a promising technology to enhance the propagation environment of wireless networks. IRSs consist of numerous low-cost passive components that can independently control the phase and amplitude of the incident signal to achieve three-dimensional (3D) passive beamforming. In IRS-assisted networks, IRSs are densely deployed and can coordinate with BSs to reconstruct the channel environment, bringing richer access opportunities and a new dimension of signal processing. By actively shaping the wireless propagation environment, IRSs can be used to improve the quality of content delivery links, especially in the field of wireless edge or mobility management. However, existing caching research rarely introduces IRSs to improve caching performance through signal improvement. Therefore, although IRS-assisted networks with caching are promising, the theoretical analysis of the caching performance gain achieved by using IRS-assisted communication remains an open research problem. Summary of the invention

[0004] The present invention proposes a method and device for configuring parameters of an intelligent reflective surface-assisted dynamic cache network. In the method, firstly, it is necessary to count the initial deployment parameters of the intelligent reflective surface-assisted cache network; next, the useful signal strength distribution of a typical user in the intelligent reflective surface-assisted cache network under the conditions of IRS beamforming, random scattering and the absence of a reflective surface closest to the user is analyzed and derived; then, the interference signal power distribution of a typical user in the intelligent reflective surface-assisted cache network is derived; finally, a network coverage probability expression is obtained, and according to the changing relationship between the cache coverage probability index and different network deployment parameters, the network deployment parameters under the maximum coverage probability are obtained.

[0005] A method for configuring network parameters of an intelligent reflective surface-assisted dynamic cache network of the present invention comprises the following steps:

[0006] Step 200, obtaining the initial fixed parameters in the target intelligent reflector-assisted dynamic caching network scenario mainly includes the base station deployment density λ in the network BS , IRS typical unit number N, IRS service distance D 1 and D 2 , channel path loss index α, carrier frequency f c , Zipf index w, probability q(m) of a user requesting file m, probability p of a typical base station storing file m c (m), signal-to-interference-noise ratio threshold T.

[0007] Consider an IRS-assisted network scenario that supports caching, such as the following Figure 1 As shown in the figure, the location distribution of BS and IRS obeys two independent homogeneous Poisson point processes (HPPP), that is, the densities are λ BS and λ IRS of and Assume that the BS and UE are equipped with a single antenna and each IRS has N reflection units. Assume that only at a distance D 1 The IRS in the UE provides O(N 2 ) channel gain beamforming, at the threshold distance D 2 (D 2 >D 1 ) contributes signal and interference to the UE by randomly scattering O(N) channel gain.

[0008] For any BSi, the signal power received directly from BSi by a typical user at the origin O is Where P t represents the transmission power of BS, h d,i represents the BS-UE direct channel, r i -αis the path loss associated with the BS-UE distance r, G i represents small-scale fading, assuming Rayleigh fading, that is, G i ~exp(1). represents the average channel power gain, where c represents the speed of light, and f c is the carrier frequency. Therefore, the amplitude |h d,i |Follows the scale parameter Rayleigh distribution, and h d,i Following the mean of zero and covariance of g d,i The circularly symmetric complex Gaussian (CSCG) distribution of

[0009] Depend on and Denote the baseband equivalent channels of the BS-IRS link and the IRS-UE link respectively. represents the phase shift matrix of an IRS with N reflection units, where diag{x} represents a diagonal matrix, φ N ∈[0,2π] represents the phase shift of diagonal unit N, and j represents the imaginary unit. The BS-IRS-UE cascade channel can be modeled as h b ,Φ and h r The cascade of

[0010]

[0011] in represents the BS-IRS-UE channel reflected by unit n.

[0012] The channel power gain of the BS-nIRS reflection unit link and the nIRS reflection unit-UE link is expressed as:

[0013] |h b,n | 2 =g b G c,n =βl -α G b,n ,|h r,n | 2 =g r G r,n =βd -α G r,n (2)

[0014] where g b and l represent the average channel power gain and BS-IRS distance of the BS-IRS channel, g r and d represent the average channel power gain and IRS-UE distance of the IRS-UE channel, Gb,n and G r,n represents a unit mean exponential random variable (RV) considering small-scale Rayleigh fading. b,n |Follow scale parameters Rayleigh distribution, amplitude |h r,n |Follows the scale parameter The Rayleigh distribution of .

[0015] The cache file library consists of M files of the same size, and the content index set is represented as q(m) indicates that the user requests the mth file f m The probability of The request probability is Where w (0<w<2) is the Zipf index. Each base station can store C (C<M) files. Let p c (m) indicates file f m The probability of caching at any BS, the cache location of all content at the BS is denoted as p c =[p c (1),p c (2),…,p c (M)]. The cache at the BS is subject to the upper limit of the cache capacity:

[0016] Step 210, analyzing and deriving the useful signal strength distribution of a typical user in the IRS beamforming, random scattering and intelligent reflector-assisted dynamic caching network in the absence of a reflector, which is closest to the user.

[0017] When the distance between UE 0 and its serving IRS 0 satisfies d 0 ≤D 1 When IRS 0 provides beamforming gain for BS-IRS signal, other UEs with distance D except IRS 0 2 The IRS within the range is a random scattered signal. The distance r between BS and UE 0 0 、The distance d between IRS j and UE 0 j The distance l from BS-IRS j 0,r satisfy relationship, among which is the BS-UE 0-IRSj link angle projected on the ground plane, assuming l 0,j ≈r 0 ,therefore The useful signal strength S at this time bf The expression is:

[0018]

[0019] where h d,0 is the BS-UE direct connection channel, is the BS-IRSj-UE cascade channel, Indicates that the distance to the UE is less than D 2 IRS collection, Assume that the BS 0-UE 0 direct channel phase ∠h d,0 It is known that IRS 0 can perform a general phase shift such that and h d,0 are in phase and therefore superimposed at UE 0. Other IRS Randomly scatters the signal, generating random phase The combined channel h of the BS 0-UE 0 direct path and the BS 0-IRS 0-UE 0 path 1 It can be expressed as According to the known r 0 and d 0 The first and second moments of the case are approximated by Gamma distribution. bf :

[0020]

[0021] For h 1 ,because For |h 1 | 2 , it is known that r 0 and d 0 The first-order moment in this case can be expressed as:

[0022]

[0023] where the beamforming gain is expressed as the coefficient is the sum of Gaussian random variables with expectation 0, so h 2 It follows a CSCG distribution with a mean of 0. The variance is So we know r 0 and d 0 In the case of |h 2 | 2 The first-order moment is expressed as:

[0024]

[0025] in because and Therefore, the second-order moment of the useful signal is expressed as

[0026]

[0027] Expressed as

[0028]

[0029] Expressed as

[0030]

[0031] Where E I3 (d 0 )=(E I1 (d 0 )) 2 +E I2 (d 0 ), The variance of the useful signal is calculated as Therefore, for the IRS beamforming case, bf The same first-order moment and second-order moment of the Gamma distribution Γ[k bf ,θ bf The shape parameter of ] is

[0032] When the distance between UE 0 and its serving IRS 0 satisfies D 1 ≤d 0 ≤D 2 hour, All IRSs within provide useful signal gain for UE 0 through random scattering. The first-order moment and second-order moment of the useful signal can be expressed as:

[0033]

[0034] in The Gamma distribution of the useful signal Γ[k bf ,θ bf The shape parameter of ] is

[0035] When the distance between UE 0 and its serving IRS 0 satisfies d 0 >D 2 When , the UE is only served by BS 0, and there is no IRS cascade link. Useful signal power S wo =|h d,0 | 2 The mean is g d,0 =g d (r 0 ) is the exponential distribution of the Gamma distribution k wo =1,θ wo =g d (r 0 ), that is, Swo ~Γ[k wo ,θ wo ].

[0036] The useful signal power can be expressed as

[0037]

[0038] Step 220, analyzing and deriving the interference signal power distribution of a typical user in the IRS beamforming, random scattering and intelligent reflector-assisted dynamic caching network closest to the user without a reflector.

[0039] Assumption l i,j ≈r i ,therefore From base station i∈Φ BS \{0}'s complex interference channel is the sum of independent CSCG random variables, so the interference channel has a mean of 0 and a variance of The CSCG distribution of the complex interference power I i It obeys exponential distribution, that is

[0040]

[0041] Among them G i ~exp(1), the average interference power of the user at a given base station and IRS position is

[0042]

[0043] in Represents all other links relative to the BS-UE direct link (including all The relative power of the scattering path of the IRS in the scattering path. η can be approximated by the following expression

[0044]

[0045] The Laplace transform of the interference signal power I is as follows:

[0046]

[0047] in 2 F 1 represents the Gaussian hypergeometric function, r nst is the distance between the user and the connected base station BS 0.

[0048] Step 230, derive a network coverage probability expression, and obtain a base station cache capacity C and smart reflective surface deployment parameter configuration scheme that satisfies the threshold requirements for network coverage probability based on the change relationship between the cache coverage probability index and the network deployment parameters.

[0049] The coverage probability in the IRS-assisted cache network is that the file requested by the user can be sent by the base station at the expected signal to interference plus noise ratio (SINR) threshold. The cache network coverage probability is expressed as:

[0050]

[0051] Among them, the SINR of the m file requested by the user m The expression is

[0052]

[0053] in Indicates that P t Normalized receiver noise, assumed to be power σ 2 Additive White Gaussian Noise (AWGN). For a Gamma-distributed random variable S~Γ[k S ,θ S ], the complementary cumulative distribution function (CCDF) is For independent random variables X:

[0054]

[0055] Where Y = X / θ S , for integer k S have Therefore, the conditional coverage probability expression is:

[0056]

[0057] Where Y = T(I + W) / θ S , T is the SINR threshold Y, and the conditional Laplace transform expression is:

[0058]

[0059] in For non-integer values ​​k, we use linear interpolation S Make an approximation:

[0060]

[0061] in and They represent the floor and ceiling functions respectively, and the weight w is written as:

[0062]

[0063] The network coverage probability expression is:

[0064]

[0065] in

[0066] The average network cache coverage probability under different IRS deployment densities and base station cache capacities is calculated respectively, and the IRS deployment density and base station cache capacity when the network coverage probability reaches the specified threshold are selected to optimize the configuration parameters of the intelligent reflective surface assisted cache network.

[0067] The intelligent reflective surface assisted dynamic caching network parameter configuration device comprises:

[0068] The first determination module is used to determine the strength distribution of useful signals received by typical users according to the initial fixed parameters in the target intelligent reflector-assisted caching network scenario;

[0069] The second determination module is used to determine the typical user receiving interference signal strength distribution according to the initial fixed parameters in the target intelligent reflector assisted caching network scenario;

[0070] The third determination module is used to determine the user coverage probability in the network and the base station cache capacity configuration and the intelligent reflective surface deployment density under the condition of maximizing the coverage probability according to the distribution of the useful signal strength and the interference signal strength received by the typical user;

[0071] The network side device includes a transceiver, a memory, a processor, a computer program stored in the memory and executable on the processor, and a user request file set of the base station cache capacity; when the processor executes the program, the steps of the method for configuring network parameters of the intelligent reflective surface-assisted dynamic cache are implemented as described above;

[0072] Intelligent reflective surface device: comprising a unit array, a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the intelligent reflective surface assisted dynamic cache network parameter configuration method as described above are implemented;

[0073] Terminal device: includes a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the method for configuring network parameters of the intelligent reflective surface-assisted dynamic caching are implemented as described above.

[0074] Beneficial Effects

[0075] The present invention statistically calculates the fixed parameters of the intelligent reflective surface assisted caching network, including the base station deployment density in the network, the number of typical IRS units N, the IRS service distance, the channel path loss index, the carrier frequency, the Zipf index, the user request file probability, the typical base station storage file probability, and the signal-to-interference-noise ratio threshold T, and uses them as input parameters of the average network coverage performance estimation algorithm; considering that when the network coverage performance reaches a given threshold, the IRS deployment enhances coverage to reduce the cost of increasing the base station cache capacity, and obtains the base station cache capacity and IRS deployment density configuration scheme in the network; the invention is oriented to the coverage performance of the intelligent reflective surface assisted caching network, with the coverage probability reaching the threshold as the optimization goal, and obtains the theoretical compact expression of the average cache coverage probability of the network by adopting a reasonable network model modeling, and obtains the network IRS deployment parameter design scheme by comparing the influence of IRS deployment and base station cache capacity parameters on coverage performance in different scenarios, thereby completing the IRS deployment density configuration of the intelligent reflective surface assisted caching network oriented to coverage performance, thereby ensuring the coverage performance of the network with limited base station cache capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to clearly explain the technical steps of the present invention, all the drawings used in the description of the present invention are briefly described below. It should be noted that the drawings described below are only some examples of the implementation of the present invention in this scenario, and other ordinary technicians in this field can still obtain other drawings in other different scenarios based on these drawings.

[0077] Figure 1 It is a model diagram of the intelligent reflective surface auxiliary cache network system of the present invention;

[0078] Figure 2 It is a flowchart of the algorithm implementation of the present invention;

[0079] Figure 3 The upper limit of the base station cache capacity of the present invention is 10 5 Schematic diagram of the relationship between the average network coverage probability and the base station cache capacity;

[0080] Figure 4 This is a graph showing the relationship between the average network coverage probability and the deployment density of the intelligent reflective surface when the base station cache capacity of the present invention is 40;

[0081] Figure 5It is a structural diagram of a communication system for intelligent reflective surface-assisted dynamic caching of network parameter configurations of the present invention;

[0082] Figure 6 A schematic diagram of a first determination module of a device for configuring network parameters of an intelligent reflective surface-assisted dynamic cache according to the present invention;

[0083] Figure 7 A schematic diagram of a second determination module of the intelligent reflective surface assisted dynamic caching network parameter configuration device of the present invention;

[0084] Figure 8 A schematic diagram of a third determination module of the intelligent reflective surface assisted dynamic caching network parameter configuration device of the present invention;

[0085] Fig. 9 A schematic diagram of a network-side device of a smart reflective surface-assisted dynamic caching network parameter configuration device of the present invention;

[0086] Fig.10 A schematic diagram of an intelligent reflective surface device of the intelligent reflective surface assisted dynamic caching network parameter configuration device of the present invention;

[0087] Fig.11 A schematic diagram of a terminal device of the intelligent reflective surface-assisted dynamic caching network parameter configuration device of the present invention; DETAILED DESCRIPTION

[0088] The steps and processes of the present invention are described completely and clearly below in conjunction with the drawings in the present application. It is obvious that the example described in the present application is only an example application scenario of the present invention, and other results based on the content of the present invention without making substantial changes all fall within the protection scope of the present invention.

[0089] Attached Figure 1 This is the intelligent reflector-assisted cache network system model of the present invention. Considering an IRS-assisted cache network architecture, when estimating the network cache coverage performance, the positions of the base stations and IRSs in the network are modeled as two-dimensional Poisson point processes, and each base station is configured with the same transmission power; each typical IRS is equipped with an average of N units, considering the limited IRS beamforming distance D 1 and random scattering distance D 2 , to jointly provide transmission for users with base stations, select representative typical users in the plane, and connect them to the nearest base station and IRS; according to the distance from the serving IRS to the UE, specifically analyze the situations where IRS provides beamforming gain, IRS provides random scattering gain, and IRS does not provide gain, and derive a compact analytical expression for the average cache coverage probability of the network.

[0090] The algorithm flow of this case is as follows Figure 2 As shown, the specific implementation steps are:

[0091] Step 200, taking the intelligent reflective surface assisted dynamic caching network coverage area as the research target area, obtaining the fixed deployment parameters in the target scenario, including the base station deployment density λ in the network BS , IRS typical unit number N, IRS service distance D 1 and D 2 , channel path loss index α, carrier frequency f c , Zipf index w, probability q(m) of a user requesting file m, probability p of a typical base station storing file m c (m), signal-to-interference-noise ratio threshold T.

[0092] Step 210, analyzing and deriving the useful signal power distribution of a typical user in the IRS beamforming, random scattering and intelligent reflector-assisted dynamic buffering network closest to the user without a reflector.

[0093] The useful signal power distribution can be expressed as formula (25):

[0094]

[0095] where Γ[k bf ,θ bf ] is the Gamma distribution function, k bf and θ bf is the shape parameter,

[0096] k wo =1,θ wo =g d (r 0 ); intermediate variables.

[0097] Step 220, analyze and derive the interference signal power distribution of a typical user in the IRS beamforming, random scattering and intelligent reflector-assisted dynamic caching network with no reflector closest to the user. The Laplace transform calculation formula of the interference signal power is formula (26):

[0098]

[0099] in 2 F 1 represents the Gaussian hypergeometric function,

[0100] Step 230, combining the above network parameters, derives the network coverage probability expression, calculates the typical user coverage probability under different base station buffer capacities and IRS deployment densities, and obtains the smart reflective surface deployment parameter coverage probability calculation formula under the maximum coverage probability as formula (27):

[0101]

[0102] Among them, the intermediate variable P c The calculation method of is as follows:

[0103]

[0104] Intermediate variables The calculation method of is as follows:

[0105]

[0106] Where Y = T(I + W) / θ S , the conditional Laplace transform expression of Y is:

[0107]

[0108] in

[0109] The network cache coverage probability reaching a certain threshold is used to characterize the coverage performance of the reflector-assisted cache network. The relationship between the network coverage probability and the different base station cache capacities and IRS deployment densities is analyzed. Then, the network IRS deployment density parameter configuration scheme is obtained according to the actual base station cache capacity limitations.

[0110] The simulation and estimation results are shown in the attached Figure 3 and attached Figure 4 As shown. Quantitative analysis of the impact of IRS deployment density and base station cache capacity on the average network coverage probability. Unless otherwise specified, the network parameters are set as follows: α = 3, f c =2GHz, D 1 =500m, D 2 =1000m,λ BS =10 / km 2 .

[0111] Attached Figure 3 The upper limit of the base station cache capacity is 10 5 The relationship between the average network coverage probability and the base station cache capacity; as the base station cache capacity and IRS deployment density increase, the network cache coverage probability increases accordingly; and the coverage probability threshold requires 0.6, w=1.4, and the IRS deployment density increases from 10 3 / km 2 Increase to 10 5 / km 2 , the base station cache file capacity can be reduced from 3798 to 22, so when the base station cache file capacity is less than 22, the IRS deployment density needs to be greater than 10 5 / km 2 When the threshold is 0.6, w = 0.6, the increase in IRS deployment density has little effect on improving the cache coverage probability. In this case, the IRS deployment density can be reduced.

[0112] Attached Figure 4 This is the relationship between the average network coverage probability and the deployment density of the intelligent reflective surface when the base station cache capacity is 40. The coverage probability increases first and then decreases with the deployment density of the intelligent reflective surface. Therefore, when the SINR threshold T = -3dB exists, the optimal IRS deployment density that maximizes the coverage probability is 10. 4.5 / km 2 ; When T = 3dB, the optimal IRS deployment density is 10 4.7 / km 2 In addition, the base station adopts the Most Popular Content (MPC) caching mechanism, which has a higher average network coverage probability than the Uniform Caching (UC) mechanism.

[0113] The intelligent reflective surface assists in dynamically caching network parameter configuration equipment, such as Figure 5 As shown, it includes: the first determination module and the second determination module are connected to the terminal device at the same time, the terminal device is connected to the third determination module, the third determination module sends cacheable file capacity information to the network side device, and the third module sends deployment density information to the intelligent reflective surface device.

[0114] The first determination module: Figure 6 As shown, it is used to determine the distribution of strength of useful signals received by typical users according to the initial fixed parameters in the target intelligent reflector-assisted caching network scenario;

[0115] The second determination module: Figure 7 As shown, it is used to determine the typical user receiving interference signal strength distribution according to the initial fixed parameters in the target intelligent reflector assisted caching network scenario;

[0116] The third determination module: Figure 8 As shown, it is used to determine the user coverage probability in the network and the base station cache capacity configuration and the intelligent reflective surface deployment density under the condition of maximizing the coverage probability according to the distribution of the useful signal strength and the interference signal strength received by the typical user;

[0117] Network side equipment: such as Fig. 9As shown, it includes a transceiver, a memory, a processor, a computer program stored in the memory and executable on the processor, and a user request file set of the base station cache capacity; when the processor executes the program, the steps of the method for configuring network parameters of the intelligent reflective surface-assisted dynamic cache are implemented as described above;

[0118] Intelligent reflective surface equipment: such as Fig.10 As shown, it includes a unit array, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the method for configuring parameters of a smart reflective surface-assisted dynamic caching network as described above are implemented;

[0119] Terminal equipment: such as Fig.11 As shown, it includes a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the method for configuring network parameters of the intelligent reflective surface-assisted dynamic cache are implemented as described above.

Claims

1. A method and device for configuring network parameters by using an intelligent reflective surface to assist dynamic caching, characterized in that: include: The initial fixed parameters in the network scenario assisted by the statistical target intelligent reflector mainly include the base station deployment density λ in the network BS , IRS typical unit number N, IRS service distance D1 and D2, channel path loss index α, carrier frequency f c , Zipf index w, probability q(m) of a user requesting file m, probability p of a typical base station storing file m c (m), signal-to-interference-noise ratio threshold T; calculate the useful signal strength distribution and interference signal power distribution of typical users in the intelligent reflector-assisted cache network when the serving IRS provides beamforming gain, random scattering gain and no gain to the UE; derive the network coverage probability expression, and according to the relationship between the cache coverage probability index and the network deployment parameters, obtain the base station cache capacity and intelligent reflector deployment parameter configuration scheme when the network coverage probability meets the threshold requirements.

2. The method according to claim 1, characterized in that Combined with the initial deployment parameters of the target intelligent reflector-assisted cache network, the useful signal strength of typical users is calculated. The distribution is calculated as: in variable k wo =1,θ wo =g d (r0); N is the number of typical IRS units, d j is the distance from the jth IRS closest to the UE to the UE, r0 is the distance from the serving base station to the UE, D1 and D2 are the IRS beamforming service distance and random scattering service distance, channel path loss index α, carrier frequency f c , g d,i =βr i -α .

3. The method according to claim 1, characterized in that Combined with the initial deployment parameters of the target intelligent reflector-assisted cache network, the average interference signal power of typical users is calculated. for The Laplace transform form is: in 2F1 represents Gaussian hypergeometric function, 4. The method according to claim 1, characterized in that: The initial deployment parameters of the intelligent reflector-assisted caching network are used as the input of the average network coverage probability estimation algorithm to derive the network coverage probability expression and calculate the typical user coverage probability under different base station cache capacities and IRS deployment densities: in Intermediate variable P c for Intermediate variables The calculation method is: Where Y = T(I + W) / θ S , the conditional Laplace transform expression of Y is: in 5. The method according to claim 1, characterized in that The network cache coverage probability reaching a certain threshold is used to characterize the coverage performance of the reflector-assisted cache network. The relationship between the typical user coverage probability and the changes in different base station cache capacities and IRS deployment densities is explored. Then, the network IRS deployment density parameter configuration scheme is obtained based on the actual base station cache capacity limitations.

6. A parameter configuration device using the intelligent reflective surface assisted dynamic cache network parameter configuration method according to claim 1, characterized in that: include: A first determining module, a second determining module, a third determining module, a network side device, an intelligent reflecting surface device, and a terminal device.

7. The intelligent reflective surface assisted dynamic caching network parameter configuration device according to claim 6, characterized in that: The first determination module is used to determine the strength distribution of useful signals received by typical users according to the initial fixed parameters in the target intelligent reflector-assisted caching network scenario; The second determination module is used to determine the typical user receiving interference signal strength distribution according to the initial fixed parameters in the target intelligent reflector assisted caching network scenario; The third determination module is used to determine the user coverage probability in the network and the base station cache capacity configuration and the intelligent reflective surface deployment density under the condition of maximizing the coverage probability according to the distribution of the useful signal strength and the interference signal strength received by the typical user; The network side device comprises a transceiver, a memory, a processor, a computer program stored in the memory and executable on the processor, and a user request file set of the base station cacheable capacity; when the processor executes the program, the steps of the method for configuring network parameters of the intelligent reflective surface assisted dynamic caching described in any one of claims 1 to 5 are implemented; Intelligent reflective surface device: comprising a unit array, a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the intelligent reflective surface auxiliary dynamic cache network parameter configuration method described in any one of claims 1 and 5 are implemented; Terminal device: comprising a transceiver, a memory, a processor and a computer program stored in the memory and executable on the processor; when the processor executes the program, the steps of the method for configuring network parameters of an intelligent reflective surface assisted dynamic caching network as described in any one of claims 1 and 2-4 are implemented.

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