Wireless power supply and communication network resource configuration method based on intelligent metasurface

Abstract

The invention discloses a wireless power supply and communication network resource allocation method based on an intelligent metasurface, and belongs to the technical field of wireless communication. The number of the intelligent metasurface reflection units and uplink and downlink time resource allocation are dynamically adjusted, on-demand configuration is achieved, the system resource utilization rate is improved, the complex optimization iteration process in a traditional method is avoided, hardware real-time implementation is facilitated, the method is suitable for a low-power-consumption terminal scene, and a calculation method for the minimum number of the intelligent metasurface reflection units is provided. The energy consumption overhead of the system is remarkably reduced under the condition that the signal to interference and noise ratio performance constraint is met, the overall deployment cost of the system is reduced, the system is adaptive to wireless power supply and communication network architecture, energy collection and information transmission are considered, and the design of a green intelligent communication system can be promoted.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication technology, and specifically relates to a wireless power supply and communication network resource configuration method based on intelligent metasurface. Background Art

[0002] The Internet of Everything (IoE) aims to seamlessly integrate people, devices, and data, building a borderless, intelligent, and collaborative ecosystem. However, due to cost and size constraints, most IoT devices typically rely on limited-capacity embedded batteries as their power source. Maintaining proper operation often requires frequent battery replacement, resulting in high maintenance costs.

[0003] To this end, RF downlink wireless energy transfer (WET) technology has garnered widespread attention in recent years due to its potential to significantly extend the battery life of IoT devices. Combining traditional uplink wireless information transmission (WIT) with downlink wireless energy transfer technology has resulted in a novel data and energy convergence network, offering a new approach to achieving sustainable green energy development in future 6G networks. In this system, IoT devices first harvest energy from RF signals emitted by base stations (BSs) and then use this harvested energy to transmit information. However, with the widespread adoption of millimeter-wave and higher frequency bands in wireless power and communication networks (WPCNs), RF energy signals have become increasingly sensitive to obstruction by obstacles such as buildings and trees, resulting in degraded system performance. New technical solutions are urgently needed to overcome this issue.

[0004] As an emerging technology, smart metasurfaces (RIS) can build virtual links, bypass obstructed paths, and effectively enhance the channel performance between transmitters and receivers. Therefore, they are considered a powerful solution to the above problems. Previous studies have shown that smart metasurfaces can significantly improve the system performance of wireless power supply and communication networks.

[0005] Despite this, most existing research on smart metasurface-assisted wireless power supply and communication networks mainly uses convex optimization or approximate conversion of non-convex problems into convex problems to solve and optimize parameters such as transmission power, precoding matrix and reflection phase. The lack of in-depth analysis of the system's essential performance leads to the problem of high deployment complexity of smart metasurfaces, especially in multi-cell network scenarios, without considering the joint impact of adjacent cells on users in a typical cell in terms of energy collection and data transmission. Summary of the Invention

[0006] In order to solve the problems raised in the above background technology, the present invention provides a wireless power supply and communication network resource configuration method based on intelligent metasurface, so as to solve the problems in the existing technology that the deployment of intelligent metasurface is highly complex and does not consider the joint impact of adjacent cells on users in typical cells in terms of energy collection and data transmission.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for configuring wireless power supply and communication network resources based on a smart metasurface comprises the following steps:

[0009] S1: Construct a smart metasurface-assisted wireless power supply and communication network system model. The system model includes M cells, where each cell includes a base station, a smart metasurface, and multiple user devices. Each smart metasurface is equipped with N reflective units. During the downlink wireless energy transmission phase, the edge user device collects energy from the RF signal transmitted by the base station through the smart metasurface. During the uplink wireless information transmission phase, the edge user device sends data to the base station through the smart metasurface and the collected energy.

[0010] S2: Based on the actual multi-cell network and Rician fading channel, establish the channel model and energy collection model for the downlink wireless energy transmission phase and the channel model and information rate model for the uplink wireless information transmission phase based on the smart metasurface;

[0011] S3: derive the average signal-to-interference-and-noise ratio of the user equipment at the edge of cell m using the parameters of each model in S2;

[0012] S4: Based on the threshold of the average signal-to-interference-and-noise ratio, obtain the minimum number of smart metasurface reflective units required for cell m;

[0013] S5: Define the traversal capacity model of edge user equipment in cell m during the uplink wireless information transmission phase by minimizing the number of intelligent metasurface reflection units, and optimize the time allocation coefficient of the traversal capacity model to maximize the traversal capacity of edge user equipment in cell m.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] The wireless power supply and communication network resource configuration method involved in this application can dynamically adjust the number of intelligent metasurface reflection units and uplink and downlink time resource allocation according to user equipment requirements and network conditions, realize on-demand configuration, improve system resource utilization, avoid the complex optimization iterative process in traditional methods, facilitate real-time hardware implementation, and is suitable for low-power terminal scenarios. It also proposes a calculation method for the minimum number of intelligent metasurface reflection units, significantly reduces system energy consumption overhead while meeting the signal-to-interference-noise ratio performance constraints, reduces the overall system deployment cost, is adapted to the wireless power supply and communication network architecture, takes into account the dual tasks of energy collection and information transmission, and can promote the design of green intelligent communication systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flowchart of the application;

[0017] Figure 2 This is a model diagram of the wireless power supply and communication network based on the smart metasurface in this application. DETAILED DESCRIPTION

[0018] To facilitate those skilled in the art to understand the technical content of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] like Figure 1 As shown, a method for configuring wireless power supply and communication network resources based on a smart metasurface includes the following steps:

[0020] S1: Construct a smart metasurface-assisted wireless power supply and communication network system model;

[0021] A wireless power and communication network (WPCN) system based on a smart metasurface (RIS) consists of M cells. In each cell, a base station (BS) uses a smart metasurface to serve a single-antenna user equipment (UE) within the cell. The present invention focuses on the UE at the cell edge to avoid the complex interference analysis between multiple UEs in the same cell. The purpose of this is to better evaluate the impact of interference from adjacent cells on energy harvesting and data transmission, and to derive actionable analysis results and insights.

[0022] Assume that the connection between the base station and the edge user device can only be established through the smart metasurface. The direct link may be blocked by obstacles such as trees or buildings. Each base station and smart metasurface is equipped with an antenna and N reflection units respectively. The present invention adopts a two-stage "energy collection-information transmission" protocol, namely the downlink wireless energy transmission (WET) and uplink wireless information transmission (WIT) stages. In the downlink wireless energy transmission stage, the edge user device first collects energy from the radio frequency signal transmitted by the base station. Then, in the uplink wireless information transmission stage, these edge user devices use the collected energy to send their data to the base station. It should be noted that, if Figure 2 As shown in the figure, edge user devices in typical cell m may receive energy signals from neighboring cells during the downlink wireless energy transmission phase. Similarly, edge user devices in neighboring cells may cause interference to edge user devices in typical cell m when sending data during the uplink wireless information transmission phase. In other words, while energy from neighboring cells enhances the uplink data transmission capability of edge user devices in typical cell m, co-channel interference from neighboring cells may reduce the transmission rate of edge user devices in typical cell m.

[0023] S2: Based on the actual multi-cell network and Ricean fading channel, establish the channel model and energy collection model for the downlink wireless energy transmission phase, and the channel model and information rate model for the uplink wireless information transmission phase based on the smart metasurface. Specifically, the following steps are included:

[0024] S21: Constructing a channel model for the downlink wireless energy transmission phase;

[0025] In the downlink wireless energy transmission phase, the complex channel from the base station in typical cell m to the smart metasurface in typical cell m, the complex channel from the smart metasurface in typical cell m to the user equipment at the edge of typical cell m, and the complex channel from the smart metasurface in adjacent cell i to the user equipment at the edge of typical cell m are expressed as , and Since the base station and the smart metasurface are usually deployed at a higher position, the present invention assumes that the channel between the base station and the smart metasurface is a line-of-sight (LoS) channel.

[0026] In addition, the channel between the reflective unit of the smart metasurface and the edge user device contains LoS and non-line-of-sight (NLoS) fading, that is, it experiences a Rician fading channel. Similarly, in the downlink wireless energy transmission stage, the channel after the energy of the adjacent cell i is reflected by the smart metasurface is also modeled as a Rician fading channel. Therefore, and Modeled as:

[0027] ;

[0028] ;

[0029] in, and denote the Ricean fading factors of the channel between the smart metasurface and the edge user equipment in the typical cell m, and the channel between the smart metasurface of the adjacent cell i and the edge user equipment in the typical cell m, respectively. and is the LoS component, and is the NLoS component and obeys a complex Gaussian distribution with mean 0 and variance 1.

[0030] Therefore, in the downlink wireless energy transmission stage, the channel model of the downlink wireless energy transmission stage is expressed as:

[0031] ;

[0032] in, represents the signal received by the edge user equipment in a typical cell m, Represents the conjugate transpose of a vector / matrix, P m,T is the transmission power of the base station in a typical cell m, Represents an information symbol and satisfies , is the additive white Gaussian noise of user equipment in a typical cell m, is the large-scale fading coefficient determined by the propagation distance, where d m,u Indicates the transmission distance, a m,u represents the path loss index, u∈{BR,RU}, BR is the relationship between the base station and the smart metasurface, RU is the relationship between the smart metasurface and the user equipment, represents the diagonal phase shift matrix of the smart metasurface in the downlink wireless energy transmission phase in a typical cell m, is the phase shift of the nth reflector element in a typical cell m.

[0033] S22: Construct an energy harvesting model for the downlink wireless energy transmission phase;

[0034] In order to maximize the energy received by the typical cell m-edge user device during the downlink wireless energy transmission phase, the phase shift of all reflective units of the smart metasurface will be adjusted to align with the edge user device, that is:

[0035] ;

[0036] The energy collected by edge user equipment in a typical cell m during the downlink wireless energy transmission phase can be expressed as:

[0037] ;

[0038] Where η∈(0,1) is the energy conversion efficiency of the user equipment, τ E It is the duration of each edge user equipment in the downlink wireless energy transmission phase.

[0039] S23: Establishing a channel model for uplink wireless information transmission;

[0040] In the uplink wireless information transmission phase, the complex channel from the typical cell m smart metasurface to the typical cell m base station, the complex channel from the typical cell m edge user equipment to the typical cell m smart metasurface, and the complex channel from the user equipment in the adjacent cell i to the typical cell m smart metasurface are expressed as , and , similar to the downlink wireless energy transmission channel model, with .therefore, and Expressed as:

[0041] ;

[0042] ;

[0043] in, and denote the Rice fading factors between the smart metasurface and the edge user equipment in the typical cell m, and the Rice fading factors between the smart metasurface of the typical cell m and the edge user equipment in the adjacent cell i, respectively. and They are the components of LoS, and Corresponding to NLoS components.

[0044] In the uplink wireless information transmission phase, the edge user equipment in each cell uses the energy collected in the downlink wireless energy transmission phase to transmit data in τ I Send data to the base station of the cell where it is located within the time and satisfy τ E +τ I =1, so the average transmit power of edge user equipment in a typical cell m can be expressed as:

[0045] ;

[0046] Therefore, the signal received during the uplink wireless information transmission phase can be expressed as:

[0047] ;

[0048] in, represents the signal received by the edge user equipment in a typical cell m during the uplink wireless information transmission phase, Indicates the data symbols transmitted by the edge user equipment during the uplink wireless information transmission phase, satisfying , Indicates that the mean at the base station in a typical cell m is 0 and the variance is Additive white Gaussian noise, represents the large-scale fading coefficient between the smart metasurface and the base station in the uplink phase, represents the large-scale fading coefficient between the user equipment and the smart metasurface in a typical cell m during the uplink phase, represents the large-scale fading coefficient from the user equipment in the neighboring cell k to the smart metasurface of the typical cell m, represents the diagonal phase shift matrix of the smart metasurface in a typical cell m during the uplink wireless information transmission phase, where and ;

[0049] S24: establishing an information rate model for the uplink wireless information transmission phase;

[0050] The instantaneous signal-to-interference-and-noise ratio (SINR) of the base station received signal in a typical cell m can be expressed as:

[0051] ;

[0052] Then, by defining , , , , γ m can be re-expressed as:

[0053] ;

[0054] The uplink achievable rate (in bit / s / Hz) of edge user equipment in a typical cell m is:

[0055] ;

[0056] S3: Derive the average signal-to-interference-and-noise ratio of the cell-edge user equipment;

[0057] In order to provide a resource adaptive configuration method for intelligent metasurface-assisted wireless power supply and communication networks, the present invention analyzes the average signal-to-interference-and-noise ratio of edge user equipment in a typical cell m. The average signal-to-interference-and-noise ratio of edge user equipment in a typical cell m is:

[0058] ;

[0059] in , , , , , Z=X+Y, Ω=ZL, and .

[0060] In order to solve the problem of difficulty in directly obtaining a closed-form expression for the average signal-to-interference-and-noise ratio, the present invention uses Jensen's inequality to derive an analytical lower bound, as shown below:

[0061] ;

[0062] Characterizing the expectations of random variables Ω and I, the expectations of Ω and I can be derived as follows:

[0063] ;

[0064] ;

[0065] make , , , , , , , , the average signal to interference and noise ratio of edge users in a typical cell m can be re-expressed as:

[0066] .

[0067] S4: Analyze the minimum number of smart metasurface reflective units required under a given signal-to-interference-noise ratio.

[0068] based on , the number of smart metasurface reflection units needs to satisfy the following inequality:

[0069] ;

[0070] make , , , , it is simplified to:

[0071] ;

[0072] Theorem 1: Given the signal-to-interference-and-noise ratio of a cell-edge user equipment, the minimum number of required smart metasurface reflective units satisfies the following conditions:

[0073] ;

[0074] where x1=λ4-λ5-λ 6a , x2=λ4-λ5+λ 6a , x3=λ4+λ5-λ 6a , x4=λ4+λ5+λ 6a , Indicates rounding x upwards. , , , , , , , , △1=C 2 +12AD, △2=2C 3 +27B 2 D-72ACD.

[0075] Proof: For The present invention converts the inequality into a single-variable quartic equation by equalizing the inequality. Subsequently, the four roots of the quartic equation can be obtained by adopting the Ferrari method. Since the number of reflection units is always a positive integer, the complex values ​​and negative values ​​of the four roots are discarded. In addition, the positive real roots need to be rounded up to obtain the required minimum number of smart metasurface reflection units.

[0076] S6. Optimize the uplink and downlink time allocation coefficients to maximize the traversal capacity of edge user devices;

[0077] According to the mechanism of the "energy collection-information transmission" protocol, there is a natural trade-off in the system's allocation of time resources: increasing the duration of the downlink wireless energy transmission phase can enable edge user devices to collect more energy, thereby having stronger transmission capabilities; however, the duration of the uplink wireless information transmission phase will be compressed, and the time available for data transmission will be reduced, which may reduce the overall traversal capacity of the system.

[0078] Based on gamma m The expression formula of R m The expression formula and The expression formula of , the ergodic capacity of edge user equipment in a typical cell m during the uplink wireless information transmission phase is defined as:

[0079] ;

[0080] where ε1=μ1N 2 +μ2N 3 +μ3N 4 +μ4N 2 +μ5N 3 ,ε2=μ6N 2 +μ7N 3 +μ8N 2 and ;

[0081] After observation, it can be found that there is an optimal uplink time allocation coefficient τ I , which can maximize the ergodic capacity of the user equipment at the edge of a typical cell m. The present invention provides an uplink and downlink time allocation coefficient using Taylor expansion in Theorem 2.

[0082] Theorem 2: When the number of reflective units of the smart metasurface reaches a certain value, an uplink time allocation coefficient can be derived to maximize the traversal capacity of edge user equipment in a typical cell m;

[0083] ;

[0084] in 、 ;

[0085] Proof: When the number of reflective units of the smart metasurface reaches a certain value, the Taylor expansion approximation can be used to obtain:

[0086] Formula 1: ;

[0087] in ;

[0088] Then, by substituting Formula 1 into the ergodic capacity of edge user equipment in a typical cell m during the defined uplink wireless information transmission phase, we can obtain:

[0089] ;

[0090] C m About U m Taking the derivative, we can get:

[0091] ;

[0092] make ,Right now , for the above single variable quadratic equation, it can be easily proved that its discriminant is , so we can get two solutions to this equation:

[0093] Formula 2: ;

[0094] Formula 3: ;

[0095] Because u m Need to meet u m >0, so discard formula 3. Finally, according to formula 2 and , we can get it by simple variable substitution . At this point, the proof is complete.

[0096] The method described in the present application is based on an edge user model that considers co-channel interference, combined with the "energy collection-information transmission" protocol, and jointly optimizes the number of reflective units of the intelligent metasurface and the ratio of uplink and downlink transmission time allocation to meet the optimality of system performance under the user signal-to-interference-noise ratio threshold. Specifically, in response to the problem of a large number of reflective units and high deployment complexity of intelligent metasurfaces, the present invention provides a method for calculating the minimum number of reflective units that can be analytically expressed; at the same time, combined with the approximate characteristics of large-scale reflective units, a closed-form solution for the suboptimal time allocation coefficient that does not require iteration is given to maximize the traversal capacity of network edge users. The method has low computational complexity and high stability, and is suitable for resource-constrained wireless power supply communication scenarios. This method can effectively improve the spectrum efficiency of wireless power supply and communication network systems based on intelligent reflective surfaces.

[0097] Those skilled in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the principles of the present invention, and it should be understood that the scope of the present invention is not limited to such specific descriptions and embodiments. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims.

Claims

1. A method for configuring wireless power supply and communication network resources based on smart metasurface, characterized in that: The following steps are involved: S1: Construct a smart metasurface-assisted wireless power supply and communication network system model. The system model includes M cells, where each cell includes a base station, a smart metasurface, and multiple user devices. Each smart metasurface is equipped with N reflective units. During the downlink wireless energy transmission phase, the edge user device collects energy from the RF signal transmitted by the base station through the smart metasurface. During the uplink wireless information transmission phase, the edge user device sends data to the base station through the smart metasurface and the collected energy. S2: Based on the actual multi-cell network and Rician fading channel, establish the channel model and energy collection model for the downlink wireless energy transmission phase and the channel model and information rate model for the uplink wireless information transmission phase based on the smart metasurface; S3: derive the average signal-to-interference-and-noise ratio of the user equipment at the edge of cell m using the parameters of each model in S2; S4: Based on the threshold of the average signal-to-interference-and-noise ratio, obtain the minimum number of smart metasurface reflective units required for cell m; S5: Define the traversal capacity model of edge user equipment in cell m during the uplink wireless information transmission phase by minimizing the number of intelligent metasurface reflection units, and optimize the time allocation coefficient of the traversal capacity model to maximize the traversal capacity of edge user equipment in cell m.

2. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 1, characterized in that: The channel model of the downlink wireless energy transmission stage is expressed as: ； in, represents the signal received by the edge user equipment in cell m, Represents the conjugate transpose of a vector / matrix, P m,T is the transmit power of the base station in cell m, Represents an information symbol and satisfies , is the additive white Gaussian noise of the user equipment in cell m, is the large-scale fading coefficient determined by the propagation distance, where d m,u Indicates the transmission distance, a m,u represents the path loss index, u∈{BR,RU}, BR is the relationship between the base station and the smart metasurface, RU is the relationship between the smart metasurface and the user equipment, represents the diagonal phase shift matrix of the smart metasurface in cell m during the downlink wireless energy transmission phase, is the phase shift of the nth reflector element in cell m, represents the complex channel from the base station in cell m to the smart metasurface, represents the complex channel from the smart metasurface in cell m to the edge user equipment, and represents the complex channel from the smart metasurface of the neighboring cell i of cell m to the edge user equipment of cell m; Assume that the channel between the base station and the smart metasurface is a line-of-sight channel LoS. In addition, the channel between the reflection unit of the smart metasurface and the edge user equipment includes the line-of-sight channel LoS and non-line-of-sight fading NLoS, that is, it experiences a Rician fading channel. Assume that in the downlink wireless energy transmission stage, the channel after the energy of the adjacent cell i is reflected by the smart metasurface is also modeled as a Rician fading channel. Therefore, and They are modeled as: ； ； in, and denote the Ricean fading factors of the channel between the smart metasurface and the edge user equipment in cell m, and the channel between the smart metasurface of the adjacent cell i and the edge user equipment in cell m, respectively. and is the LoS component, and is the NLoS component and obeys a complex Gaussian distribution with mean 0 and variance 1.

3. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 2, characterized in that: The phase shifts of all reflective units of the smart metasurface in cell m are adjusted to align with the edge user devices to maximize the energy received by the edge user devices during the downlink wireless energy transmission phase: ； The energy harvesting model in the downlink wireless energy transmission phase is expressed as: ； Among them, E m represents the energy collected by the edge user equipment in cell m during the downlink wireless energy transmission phase, η∈(0,1) is the energy conversion efficiency of the user equipment, τ E It is the duration of each edge user equipment in the downlink wireless energy transmission phase.

4. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 3, characterized in that: In the uplink wireless information transmission phase, the edge user equipment in each cell uses the energy collected in the downlink wireless energy transmission phase to transmit data in τ I Send data to the base station of the cell where it is located within the time and satisfy τ E +τ I =1, so the average transmit power P of edge user equipment in cell m m Specifically expressed as: ； The channel model of the uplink wireless information transmission stage is expressed as: ； in, Indicates the signal received by the edge user equipment in cell m during the uplink wireless information transmission phase, Indicates the data symbols transmitted by the edge user equipment during the uplink wireless information transmission phase, satisfying , The mean value at the base station in cell m is 0 and the variance is Additive white Gaussian noise, represents the large-scale fading coefficient between the smart metasurface and the base station in the uplink phase, represents the large-scale fading coefficient between the user equipment and the smart metasurface in cell m during the uplink phase, represents the large-scale fading coefficient from the user equipment in the neighboring cell k to the smart metasurface in cell m, represents the diagonal phase shift matrix of the smart metasurface in cell m during the uplink wireless information transmission phase, where and , represents the complex channel from the smart metasurface to the base station in cell m, represents the complex channel from the edge user equipment to the smart metasurface in cell m, represents the complex channel from the user equipment in the adjacent cell i to the smart metasurface in cell m, with ,therefore, and Expressed as: ； ； in, and denote the Rice fading factor between the smart metasurface and the edge user equipment in cell m, and the Rice fading factor between the smart metasurface in cell m and the adjacent edge user equipment in cell i, respectively. and They are the components of LoS, and Corresponding to NLoS components.

5. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 4, characterized in that: The information rate model of the uplink wireless information transmission stage is expressed as: ； Among them, R m is the uplink rate achievable by edge user equipment in cell m, in bit / s / Hz, γ m Expressed as: ； in , , , .

6. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 5, characterized in that: The average signal to interference and noise ratio of edge users in cell m is expressed as: ； in , , , , , Z=X+Y, Ω=ZL, and .

7. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 6, characterized in that: Using Jensen's inequality to derive The analyzable lower bound of is as follows: ； Characterizing the expectations of random variables Ω and I, the expectations of Ω and I can be derived as follows: ； ； make , , , , , , , , the average signal to interference and noise ratio of edge users in cell m is re-expressed as: 。 8. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 7, characterized in that: Analyze the minimum number of intelligent metasurface reflective units required under given signal-to-interference-noise ratio conditions based on , the following inequality needs to be satisfied: ； make , , , , it is simplified to: ； Given the signal-to-interference-to-noise ratio of a cell-edge user equipment, the minimum number of required smart metasurface reflective units meets the following conditions: ； where \(x1 = \lambda4-\lambda5-\lambda\) 6a , \(x2 = \lambda4-\lambda5+\lambda\) 6a , \(x3 = \lambda4+\lambda5-\lambda\) 6a , \(x4 = \lambda4+\lambda5+\lambda\) 6a , denotes the ceiling of \(x\), , , , , , , , , \(\triangle1 = C\) 2 + 12AD, \(\triangle2 = 2C\) 3 + 27B 2 D - 72ACD.

9. The method for configuring wireless power supply and communication network resources based on smart metasurface according to claim 8, characterized in that: During the uplink wireless information transmission phase, the ergodic capacity of edge user equipment in cell m is defined as: ； where ε1=μ1N 2 +μ2N 3 +μ3N 4 +μ4N 2 +μ5N 3 ,ε2=μ6N 2 +μ7N 3 +μ8N 2 and , for the uplink time allocation coefficient τ I Performing the optimal solution operation can maximize the traversal capacity of typical cell edge user equipment.

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