Intelligent reflecting surface assisted dynamic cache network parameter configuration method and device

Abstract

The application provides a kind of intelligent reflecting surface auxiliary dynamic cache network parameter configuration method and equipment.In the method, the initial fixed parameters of intelligent metasurface auxiliary cache network are counted as the input parameters of average network coverage performance estimation algorithm;Considering that the network coverage performance reaches a given threshold, I RS deployment enhances coverage to reduce the cost of base station cache capacity improvement, and the configuration scheme of base station cache capacity and I RS deployment density in the network is obtained;The application faces the coverage performance of intelligent metasurface auxiliary cache network, and takes the coverage probability reaching the threshold as the optimization target, obtains the theoretical expression of network average cache coverage probability by adopting reasonable network modeling, compares the influence of I RS deployment and base station cache capacity on coverage performance in different scenarios, obtains the network deployment parameter scheme, so as to complete the I RS deployment density configuration of intelligent metasurface auxiliary cache network facing coverage performance, so as to guarantee the coverage performance of base station cache capacity limited network.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method and device for configuring network parameters for intelligent reflector-assisted dynamic buffering in future fifth-generation (B5G) and sixth-generation (6G) mobile communications. Background Technology

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

[0003] Fortunately, recent breakthroughs in microelectromechanical systems (MEMS) and metamaterials have established Intelligent Reflecting Surfaces (IRS) as a promising technology for enhancing the propagation environment of wireless networks. Composed of numerous low-cost passive components, IRS can independently control the phase and amplitude of the incident signal, achieving three-dimensional (3D) passive beamforming. In IRS-assisted networks, densely deployed IRSs can coordinate with the Base Station (BS) to reconstruct the channel environment, bringing richer access opportunities and new dimensions of signal processing. By actively shaping the wireless propagation environment, IRS can be used to improve the quality of content delivery links, particularly in wireless edge or mobility management domains. However, existing caching research rarely incorporates IRS to improve caching performance through signal enhancement. Therefore, although caching-enabled IRS-assisted networks are promising, the theoretical analysis of the caching performance gains achieved through IRS-assisted communication remains an open research question. Summary of the Invention

[0004] This invention proposes a method and device for configuring parameters of an intelligent reflector-assisted dynamic buffer network. The method first requires statistical analysis of the initial deployment parameters of the intelligent reflector-assisted buffer network. Next, it analyzes and derives the useful signal strength distribution of a typical user in the intelligent reflector-assisted buffer network under conditions of IRS beamforming, random scattering, and the absence of a reflector. Then, it derives the interference signal power distribution of a typical user in the intelligent reflector-assisted buffer network. Finally, it obtains the network coverage probability expression and, based on the relationship between the buffer coverage probability index and different network deployment parameters, obtains the network deployment parameters with the maximum coverage probability.

[0005] The present invention provides a method for configuring parameters of a smart reflective surface-assisted dynamic buffer network, comprising the following steps:

[0006] Step 200: Obtain the initial fixed parameters in the target intelligent reflective surface-assisted dynamic caching network scenario, mainly including the base station deployment density λ in the network. BS Typical IRS unit number N, IRS service distances D1 and D2, channel path loss exponent α, carrier frequency f c Zipf exponent w, the probability q(m) of a user requesting file m, and the probability p of a typical base station storing file m c (m), signal-to-interference-plus-noise ratio threshold T.

[0007] Consider IRS-assisted network scenarios that support caching, as shown in the attached document. Figure 1 As shown, the location distributions of BS and IRS follow two independent homogeneous Poisson point processes (HPPP), i.e., their densities are λ and λ, respectively. BS and λ IRS of and Assume the BS and UE are equipped with a single antenna, and each IRS has N reflection elements. Assume only IRS within distance D1 provide O(N) antennas to the UE. 2 Beamforming of channel gain: Within a threshold distance D2 (D2 > D1), the IRS contributes signal and interference to the UE through random scattering of the 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 The h represents the transmit power of the BS. d,i Indicates the BS-UE direct connection channel, r i -α It is the path loss related to the distance r between the BS and the UE, G i This represents small-scale fading, assumed to be Rayleigh fading, i.e., G. i ~exp(1). Let f represent the average channel power gain, where c represents the speed of light, and f represents the average channel power gain. c It is the carrier frequency. Therefore, the amplitude |h d,i | Follows the scale parameter The Rayleigh distribution, and h d,i Following the principle that the mean is zero and the covariance is g d,i The circularly symmetric complex Gaussian (CSCG) distribution, in which

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

[0010]

[0011] in This 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 follows:

[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 of the BS-IRS channel and the BS-IRS distance, respectively, g r d represents the average channel power gain of the IRS-UE channel and the IRS-UE distance, and G represents the distance between the IRS-UE and the UE. b,n and G r,n This represents a unit-mean exponential random variable (RV) considering small-scale Rayleigh fading. Amplitude |hb,n | Follow scale parameters Rayleigh distribution, amplitude |h r,n | Follows the scale parameter Rayleigh distribution.

[0015] The cached file library consists of M files of the same size, and the content index set is represented as follows: q(m) represents the user's request for the m-th file f. m The probability, and satisfying The request probability is Where w (0 < w < 2) is the Zipf exponent. Each base station can store C (C < M) files, let p... c (m) indicates file f m The probability of caching at any BS, and the cache location of all content at the BS, are denoted as p. c =[p c (1),p c (2),…,p c (M)]. The cache at BS is constrained by the maximum cache capacity:

[0016] Step 210: Analyze and derive the useful signal strength distribution of a typical user in the intelligent reflector-assisted dynamic buffer network with IRS beamforming, random scattering, and no reflector, which is closest to the user.

[0017] When the distance between UE 0 and its serving IRS 0 satisfies d0 ≤ D1, IRS 0 provides beamforming gain for the BS-IRS signal. For other distances besides IRS 0 within the range of D2, the IRS signals are randomly scattered. The distance r0 between BS and UE 0, and the distance d between IRS j and UE 0 are also considered. j Distance l from BS-IRS j 0,r satisfy Relationship, among which It is the angle between the BS-UE 0-IRSj link projected onto the ground plane, assuming l 0,j ≈r0, therefore The useful signal strength S at this time bf The expression is:

[0018]

[0019] Where h d,0 For BS-UE direct connection channel, For BS-IRSj-UE concatenated channels, This represents the set of IRSs whose distance to the UE is less than D2. Assume the direct channel phase ∠h between BS 0 and UE 0d,0 It is known that IRS 0 can perform a general phase shift, thus and h d,0 They are in phase, therefore superimposed at UE 0. Other IRS Randomly scattered signals generate random phases The combined channel h1 of the BS 0-UE 0 direct path and the BS 0-IRS 0-UE 0 path can be represented as: Given the first and second moments with known r0 and d0, S is approximated using the Gamma distribution. bf :

[0020]

[0021] For h1, since For |h1| 2 Given r0 and d0, its first moment can be expressed as:

[0022]

[0023] Where beamforming gain is expressed as a coefficient h2 is the sum of Gaussian random variables with an expected value of 0, therefore h2 follows a CSCG distribution with a mean of 0. variance is Therefore, given r0 and d0, |h2| 2 The first moment is expressed as:

[0024]

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

[0026]

[0027] Represented as

[0028]

[0029] Represented as

[0030]

[0031] Where E I3 (d0)=(E I1 (d0)) 2 +E I2 (d0), The variance of the useful signal is calculated as follows: Therefore, in the case of IRS beamforming, it has the same characteristics as S bf The same first and second moments of the Gamma distribution Γ[k bf ,θ bf The shape parameters of ] are

[0032] When the distance between UE 0 and its serving IRS 0 satisfies D1≤d0≤D2, All IRS within the system provide useful signal gain to UE0 through random scattering. The first and second moments of the useful signal can be expressed as:

[0033]

[0034] in The Gamma distribution Γ[k] approximates the useful signal bf ,θ bf The shape parameters of ] are

[0035] When the distance between UE 0 and its serving IRS 0 satisfies d0 > D2, the UE is only served by BS 0, and there is no IRS cascading link. Useful signal power S wo =|h d,0 | 2 Follows the mean g d,0 =g d The exponential distribution of (r0) is a Gamma distribution k wo =1,θ wo =g d A special case of (r0), namely S wo ~Γ[k wo ,θ wo ].

[0036] Useful signal power can be expressed as

[0037]

[0038] Step 220: Analyze and derive the interference signal power distribution of a typical user in the intelligent reflector-assisted dynamic buffer network with IRS beamforming, random scattering, and no reflector, which is closest to the user.

[0039] Assume l i,j ≈r i ,therefore From base station i∈Φ BS Complex interference channel \{0} It is the sum of independent CSCG random variables, therefore the interference channel has a mean of 0 and a variance of . The CSCG distribution. Complex interference power I iIt follows an exponential distribution, that is...

[0040]

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

[0042]

[0043] in This refers to all other links relative to the BS-UE direct link (including all...). The relative power of the scattering path (IRS) in the middle. 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 2F1 represents the Gaussian hypergeometric function, r nst It is the distance between the user and the connecting base station BS0.

[0048] Step 230: The network coverage probability expression is derived. Based on the relationship between the cache coverage probability index and the network deployment parameters, the base station cache capacity C and the intelligent reflector deployment parameter configuration scheme that meet the threshold requirement for network coverage probability are obtained.

[0049] In an IRS-assisted caching network, the coverage probability is defined as the probability that a user-requested file can be transmitted by the base station at the desired Signal-to-Interference-plus-Noise Ratio (SINR) threshold. The caching network coverage probability is expressed as:

[0050]

[0051] Among them, the user requests the SINR of the m file. m The expression is

[0052]

[0053] in Indicates being P t Normalized receiver noise, assumed to be power σ 2Additive white Gaussian noise (AWGN). For a random variable S ~ Γ[k] distributed by Gamma. 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 expression for the conditional coverage probability is:

[0056]

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

[0058]

[0059] in Linear interpolation is used for non-integer values ​​k. S Approximation:

[0060]

[0061] in and Let w represent the floor function and the floor function respectively, with the weight w written as:

[0062]

[0063] The expression for network coverage probability is:

[0064]

[0065] in

[0066] Calculate the average network cache coverage probability under different IRS deployment densities and base station cache capacities, select the IRS deployment density and base station cache capacity when the network coverage probability reaches a specified threshold, and optimize the intelligent reflector-assisted cache network configuration parameters.

[0067] The intelligent reflective surface-assisted dynamic buffer network parameter configuration device includes:

[0068] The first determining module is used to determine the distribution of useful signal strength received by typical users based on the initial fixed parameters in the target intelligent reflector-assisted buffer network scenario.

[0069] The second determining module is used to determine the distribution of interference signal intensity received by typical users based on the initial fixed parameters in the target intelligent reflector-assisted buffer network scenario.

[0070] The third determining module is used to determine the user coverage probability in the network and the base station buffer capacity configuration and intelligent reflector deployment density under the condition of maximizing the coverage probability, based on the distribution of the useful signal strength and interference signal strength received by typical users.

[0071] Network-side equipment includes a transceiver, a memory, a processor, a computer program stored in the memory and executable on the processor, and a set of user request files for base station cache capacity; when the processor executes the program, it implements the steps of the intelligent reflector-assisted dynamic caching network parameter configuration method described above.

[0072] Intelligent reflective surface device: 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, it implements the steps of the intelligent reflective surface-assisted dynamic cache network parameter configuration method as described above;

[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, it implements the steps of the intelligent reflective surface-assisted dynamic buffer network parameter configuration method as described above.

[0074] Beneficial effects

[0075] This invention uses fixed parameters of a smart reflector-assisted caching network, including base station deployment density, typical IRS unit number N, IRS service distance, channel path loss index, carrier frequency, Zipf index, user request file probability, typical base station file storage probability, and signal-to-interference-plus-noise ratio (SINNR) threshold T, as input parameters for an average network coverage performance estimation algorithm. Considering that network coverage performance reaches a given threshold, IRS deployment enhances coverage to reduce the cost of increasing base station cache capacity, resulting in a configuration scheme for base station cache capacity and IRS deployment density. This invention focuses on the coverage performance of smart reflector-assisted caching networks, using the coverage probability reaching a threshold as the optimization objective. By employing a reasonable network model, a theoretically compact expression for the average cache coverage probability is obtained. By comparing the impact of IRS deployment and base station cache capacity parameters on coverage performance under different scenarios, a network IRS deployment parameter design scheme is obtained, thereby completing the IRS deployment density configuration for smart reflector-assisted caching networks oriented towards coverage performance, thus ensuring network coverage performance with limited base station cache capacity. Attached Figure Description

[0076] To clearly explain the technical steps of this invention, all the accompanying drawings used in this description will be briefly described below. It should be noted that the drawings described below are merely examples of this invention implemented in this scenario, and those skilled in the art can still obtain other drawings in other different scenarios based on these drawings.

[0077] Figure 1 This is a model diagram of the intelligent reflective surface-assisted caching network system of the present invention;

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

[0079] Figure 3 The maximum base station cache capacity of this invention is 10. 5 A schematic diagram illustrating 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 smart reflectors when the base station cache capacity is 40.

[0081] Figure 5 This is a structural diagram of a communication system for configuring network parameters with intelligent reflective surface-assisted dynamic buffering according to the present invention;

[0082] Figure 6 This is a schematic diagram of the first determining module of the intelligent reflective surface-assisted dynamic caching network parameter configuration device of the present invention;

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

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

[0085] Figure 9 This is a schematic diagram of the network side device of the intelligent reflective surface-assisted dynamic caching network parameter configuration device of the present invention;

[0086] Figure 10 A schematic diagram of the intelligent reflective surface device for the intelligent reflective surface-assisted dynamic caching network parameter configuration device of the present invention;

[0087] Figure 11 This is a schematic diagram of the terminal device of the intelligent reflective surface-assisted dynamic caching network parameter configuration device of the present invention; Detailed Implementation

[0088] The steps and processes of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the examples described in this application are merely one application scenario of the present invention, and other results based on the content of the present invention without making substantial changes are within the protection scope of the present invention.

[0089] Appendix Figure 1 This invention presents a model of an intelligent reflector-assisted caching network system. Considering an IRS-assisted caching network architecture, when estimating network cache coverage performance, the locations of base stations and IRSs in the network are modeled as two-dimensional Poisson point processes, with each base station configured with the same transmit power. Each typical IRS is equipped with an average of N units. Considering the finite IRS beamforming distance D1 and random scattering distance D2, the base stations work together to provide transmission for users. Representative typical users are selected in the plane and connected to the nearest base station and IRS. Based on the specific analysis of the distance from the serving IRS to the UE, considering the cases where the IRS provides beamforming gain, provides random scattering gain, and does not provide gain, a compact analytical expression for the average cache coverage probability of the network is derived.

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

[0091] Step 200: Taking the coverage area of ​​the intelligent reflective surface-assisted dynamic caching network as the research target area, obtain the fixed deployment parameters in the target scenario, including the base station deployment density λ in the network. BS Typical IRS unit number N, IRS service distances D1 and D2, channel path loss exponent α, carrier frequency f cZipf exponent w, the probability q(m) of a user requesting file m, and the probability p of a typical base station storing file m c (m), signal-to-interference-plus-noise ratio threshold T.

[0092] Step 210: Analyze and derive the useful signal power distribution of a typical user in the intelligent reflector-assisted dynamic buffer network with IRS beamforming, random scattering, and no reflector, which is closest to the user.

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

[0094]

[0095] Where Γ[k bf ,θ bf ] represents the Gamma distribution function, k bf and θ bf For shape parameters,

[0096] k wo =1, θ wo =g d (r0); intermediate variable.

[0097] Step 220: The interference signal power distribution of a typical user in the intelligent reflector-assisted dynamic buffer network under the conditions of IRS beamforming, random scattering, and absence of a reflector, closest to the user, is analyzed and derived. The Laplace transform formula for the interference signal power is Equation (26):

[0098]

[0099] in 2F1 represents the Gaussian hypergeometric function.

[0100] Step 230: Combining the above network parameters, the network coverage probability expression is derived, and the typical user coverage probability under different base station buffer capacities and IRS deployment densities is calculated. The calculation formula for the coverage probability of the intelligent reflector deployment parameters under the maximum coverage probability is Equation (27):

[0101]

[0102] Among them, the intermediate variable P c The calculation method is as shown in equation (28):

[0103]

[0104] intermediate variables The calculation method is as shown in equation (29):

[0105]

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

[0107]

[0108] in

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

[0110] Simulation and estimation results are attached. Figure 3 and attached Figure 4 As shown. A quantitative analysis of the impact of IRS deployment density and base station buffer capacity on the average network coverage probability is presented. Unless otherwise specified, the network parameters are set as follows: α = 3, f c =2GHz, D1=500m, D2=1000m, λ BS =10 / km 2 .

[0111] Appendix Figure 3 The base station's cache capacity is capped at 10. 5 The relationship between average network coverage probability and base station cache capacity is investigated; as base station cache capacity and IRS deployment density increase, network cache coverage probability also increases; and the coverage probability threshold requirements are 0.6, w=1.4, and IRS deployment density increases from 10... 3 / km 2 Increase to 10 5 / km 2 The base station cache file size can be reduced from 3798 to 22. Therefore, when the base station cache file size is less than 22, the IRS deployment density needs to be greater than 10. 5 / km 2 When the threshold is 0.6 and w = 0.6, increasing the IRS deployment density has no significant effect on improving the cache coverage probability. In this case, the IRS deployment density can be reduced.

[0112] Appendix Figure 4 This describes the relationship between the average network coverage probability and the deployment density of smart reflectors when the base station buffer capacity is 40. The coverage probability initially increases and then decreases with the deployment density of smart reflectors. Therefore, when the SINR threshold T = -3dB, the optimal IRS deployment density for maximizing coverage probability is 10. 4.5 / km2 When T = 3dB, the optimal IRS deployment density is 10. 4.7 / km 2 Furthermore, the most popular content (MPC) caching mechanism used by base stations results in a higher average network coverage probability compared to the uniform caching (UC) mechanism.

[0113] The intelligent reflective surface-assisted dynamic caching network parameter configuration device, such as Figure 5 As shown, it includes: a first determining module and a second determining module are simultaneously connected to a terminal device, the terminal device is connected to a third determining module, the third determining 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] First Determined Module: Such as Figure 6 As shown, this method is used to determine the distribution of useful signal strength received by a typical user based on initial fixed parameters in a target intelligent reflector-assisted buffer network scenario.

[0115] Second determination module: such as Figure 7 As shown, this method is used to determine the distribution of interference signal intensity received by typical users based on initial fixed parameters in a target intelligent reflector-assisted buffer network scenario.

[0116] The third determining module: such as Figure 8 As shown, this is used to determine the user coverage probability in the network and the base station buffer capacity configuration and smart reflector deployment density under the condition of maximizing the coverage probability, based on the distribution of the useful signal strength and interference signal strength received by typical users.

[0117] Network-side devices: such as Figure 9 As shown, it includes a transceiver, a memory, a processor, a computer program stored in the memory and executable on the processor, and a set of user request files for base station cache capacity; when the processor executes the program, it implements the steps of the intelligent reflector-assisted dynamic caching network parameter configuration method as described above;

[0118] Intelligent reflective surface devices: such as Figure 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, it implements the steps of the intelligent reflective surface-assisted dynamic buffer network parameter configuration method as described above;

[0119] Terminal equipment: such as Figure 11As 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, it implements the steps of the intelligent reflective surface-assisted dynamic buffer network parameter configuration method as described above.

Claims

1. An intelligent reflecting surface assisted dynamic cache network parameter configuration method, characterized in that, Comprise: The initial fixed parameters in a statistical target intelligent reflective surface-assisted caching network scenario mainly include the base station deployment density in the network. Typical number of IRS units IRS service distance and Channel path loss index carrier frequency Zipf index User request file probability Typical base station storage files probability Signal-to-interference-plus-noise ratio threshold ; In combination with the initial deployment parameters of the target smart reflector assisted cache network, the useful signal strength of a typical user is calculated The calculation formula of the distribution is: wherein , , , , , , variable , , , , , , , , , , , , ; , ; This represents the typical number of cells in an IRS. The closest to the UE Distance from IRS to UE To determine the distance from the service base station to the UE, and For IRS beamforming service range and random scattering service range, channel path loss index carrier frequency , , , , , For IRS distribution density, Indicates the average channel power gain; In combination with the initial deployment parameters of the target intelligent reflecting surface assisted cache network, the average interference signal power of a typical user is calculated For The Laplace transform form is: where , denotes the Gauss hypergeometric function, , denotes the set of BSs in the network, , is the path loss related to the BS -UE distance, is the average interference power of the user under the given base station, IRS location, , is the distance of the user and the connected base station BS 0, is the density of the BS distribution, ;​ The initial deployment parameters of the intelligent reflecting surface assisted cache network are input into the average network coverage probability estimation algorithm, and the network coverage probability expression is derived to calculate the typical user coverage probability under different base station cache capacities and IRS deployment densities: wherein , , , the intermediate variable is , , the intermediate variable is calculated as wherein , The conditional Laplace transform expression of wherein , denotes the probability that the user requests the file , denotes the SINR threshold, denotes the interference signal power, denotes the receiver noise normalized by , , ; According to the change relationship of the cache coverage probability index with the network deployment parameters, the base station cache capacity and the intelligent reflecting surface deployment parameter configuration scheme that meet the threshold requirement of the network coverage probability are obtained.

2. The method of claim 1, wherein, Using the network cache coverage probability reaching a certain threshold to represent the coverage performance of the reflecting surface assisted cache network, the change relationship of the typical user coverage probability with different base station cache capacities and IRS deployment densities is explored, and then the network IRS deployment density parameter configuration scheme is obtained according to the actual base station cache capacity limit.

3. A parameter configuration system for implementing the intelligent reflecting surface assisted dynamic cache network parameter configuration method of claim 1, comprising: Network side device, intelligent reflecting surface device and terminal device.

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