A method for optimizing carbon efficiency in dense wireless networks with intelligent reflective surfaces

Abstract

This invention discloses a method for optimizing the carbon efficiency of dense wireless networks with intelligent reflector-assisted interference cancellation, relating to the field of wireless communication technology. The method includes establishing a system model for dense wireless networks with intelligent reflector-assisted interference cancellation. The system model includes a network model on the energy consumption side and an energy supply model on the energy supply side. The method optimizes the base station deployment density and the reflector unit ratio of the intelligent reflectors on the energy consumption side based on the interference set received by the user from the base station and the interference set to be eliminated from the intelligent reflector-assisted interference cancellation. The energy supply model on the energy supply side calculates the green energy supply factor and green energy coverage probability. With the goal of maximizing network carbon efficiency, the method jointly optimizes the green energy supply factor on the energy supply side and the ratio of intelligent reflectors to base station deployment density on the energy consumption side. This invention achieves efficient control of interference in dense wireless networks, maximizing the network carbon efficiency of dense wireless networks.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for optimizing the carbon efficiency of dense wireless networks with intelligent reflector-assisted methods. Background Technology

[0002] To meet users' demands for higher data rates and better communication quality, mobile services have spawned ultra-high data density and ultra-low power services for typical 5G Advanced-6G wireless communication systems. Simultaneously, countries worldwide have proposed dual-carbon goals (carbon peaking and carbon neutrality), placing low-carbon development requirements on communication infrastructure. Future wireless systems will see increasingly heterogeneous and denser communication infrastructure, expanding the signal and capacity coverage of existing base stations. Dense network deployment is the most effective way to improve network capacity and coverage, potentially increasing capacity by nearly 2700 times. Meanwhile, Intelligent Reflecting Surface (IRS) technology is a novel passive signal reflection technology for wireless communication networks. It intelligently reconstructs the wireless propagation environment using low-power reflective units. Due to its passive and low-cost characteristics, it has become a necessary choice for future low-energy communication systems. To achieve "green communication" and reduce carbon emissions from wireless communication networks, future wireless networks will evolve towards greater density and lower energy consumption. How to reduce and eliminate the complex interference caused by dense networks through intelligent reflective surfaces, while efficiently utilizing the existing resources of wireless communication networks and power supply networks to improve network carbon efficiency, has become a key issue to be addressed in current dense wireless networks.

[0003] Existing technical solutions:

[0004] 1. Performance optimization of dense wireless networks assisted by smart reflectors: In existing research, smart reflectors are usually used to enhance the coverage and expansion capabilities of different wireless communication networks. Optimizing the location deployment and phase of smart reflectors can improve network performance (network capacity or network energy efficiency). However, there is a lack of effective control over complex interference in dense wireless networks with limited interference.

[0005] 2. Current optimization of network performance focuses on high network energy efficiency or energy saving targets. It maximizes network energy efficiency by optimizing network resource allocation strategies and using methods such as alternating optimization. However, under the trend of energy conservation and emission reduction, it lacks consideration of green energy supply on the energy supply side and network carbon efficiency.

[0006] Hefei University of Technology, in its patent application "Method and System for Deploying Intelligent Reflectors" (Application No.: CN202210379879.X, Publication No.: CN114980132A), proposed a method, system, storage medium, and electronic device for deploying intelligent reflectors. The method first acquires relevant information about the transmitter and intelligent reflector to be deployed in the communication network. With the goal of maximizing the energy received by all user endpoints in the network, a joint optimization model is constructed regarding the transmitter deployment location, intelligent reflector deployment location, and intelligent reflector phase offset. Based on this joint optimization model, the transmitter deployment location, intelligent reflector deployment location, and intelligent reflector phase offset are obtained sequentially. After optimizing the deployment location of the intelligent reflector, the system energy is further improved. However, this method only considers system energy as the objective, without considering network performance or interference cancellation in interference-limited networks, making it unsuitable for performance optimization in dense wireless networks.

[0007] China Southern Power Grid Co., Ltd.'s Ultra-High Voltage Transmission Company disclosed a method for maximizing the energy efficiency of an IRS-assisted MISO wireless energy-carrying communication system in its patent application, "Energy Efficiency Maximization Method for IRS-Assisted MISO Wireless Energy-Carrying Communication System" (application number 202210634256.2, publication number: CN115173901A). This method constructs a smart reflector-assisted MISO wireless energy-carrying communication system, establishes a mathematical model for maximizing system energy efficiency, and designs and analyzes an alternating optimization algorithm that jointly optimizes the transmit beamforming vector, the reflected beamforming vector, and the power allocation factor to maximize system energy efficiency. While this paper utilizes a power allocation strategy to maximize system energy efficiency while meeting the minimum communication rate and minimum energy harvesting requirements of each user, it does not consider the randomness of service distribution and the power supply network in the actual system, nor does it address optimizing the deployment of smart reflectors to improve network capabilities. Therefore, it is not suitable for dense wireless network scenarios considering green energy supply.

[0008] The paper "Stochastic Geometry Analysis of IRS-Assisted Downlink Cellular Networks" provides approximate expressions for coverage probability, ergonomic capacity, and energy efficiency for IRS-assisted downlink cellular networks using stochastic geometry tools. However, this paper only verifies the derived non-closed-form expressions based on numerical results, only considers scenarios with enhanced coverage due to intelligent reflectors, lacks optimization for network performance, and is not applicable to interference-constrained dense wireless network scenarios. Furthermore, it only offers some useful insights and lacks guidance for network deployment optimization.

[0009] Therefore, to address the shortcomings of existing technologies, we propose a method for optimizing the carbon efficiency of dense wireless networks with intelligent reflective surfaces. Summary of the Invention

[0010] The purpose of this invention is to propose a carbon efficiency optimization method for dense wireless networks assisted by intelligent reflectors, so as to improve the carbon efficiency of dense wireless networks under interference-limited conditions. By designing the deployment density of intelligent reflectors, the number of intelligent reflector units, and the green energy supply factor of the green energy supply system, the invention achieves efficient control of interference in dense wireless networks, maximizes the network carbon efficiency of dense wireless networks, and solves the problems of maximizing network carbon efficiency in terms of green energy ratio, intelligent reflector deployment density, and intelligent reflector unit ratio.

[0011] The technical solution adopted in this invention is as follows:

[0012] This invention is a method for optimizing the carbon efficiency of dense wireless networks with intelligent reflector-assisted methods, comprising the following steps:

[0013] A dense wireless network system model with intelligent reflector-assisted interference cancellation is established. The system model includes a network model on the energy consumption side and an energy supply model on the energy supply side.

[0014] The network model on the energy consumption side includes base stations, smart reflectors, and users, with users receiving a set of interference from the base stations. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation

[0015] Calculate the network space throughput on the energy consumption side;

[0016] The deployment density of base stations on the energy consumption side and the ratio of reflective units on the smart reflective surface are optimized.

[0017] The energy supply model on the energy supply side converts green energy collection into electrical energy to supply the network model with energy, and calculates the green energy supply factor and green energy coverage probability on the energy supply side.

[0018] With the goal of maximizing network carbon efficiency, a joint optimization design is carried out on the green energy supply factor on the energy supply side and the ratio of intelligent reflective surface to base station deployment density on the energy consumption side.

[0019] Furthermore, the base station, smart reflector, and user in the network model on the energy consumption side follow a Poisson point process, respectively represented as Π BS ={BS i |BS i ∈R 2}、Π IRS ={IRS j |IRS j ∈R2} and Π U ={U u |U u ∈R 2}, where i,j,u∈N.

[0020] Furthermore, both the base station and the user are equipped with an omnidirectional antenna, and the height difference between the omnidirectional antennas of the base station and the user is h. BS The intelligent reflective surface consists of N reflective units, and the reflection coefficient matrix of the intelligent reflective surface is:

[0021]

[0022] Where β and The amplitude and phase reflection coefficients are represented respectively, and j is the imaginary unit. The intelligent reflector is deployed on a wall or column, and the height difference between the intelligent reflector and the user's omnidirectional antenna is h. IRS .

[0023] Furthermore, the user, base station, and smart reflector are associated with the nearest base station, i.e., the user is associated with the nearest base station, the user is associated with the nearest smart reflector, and the smart reflector is associated with the nearest base station.

[0024] Furthermore, the user receives a set of interference signals from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation Specifically, the following steps are included:

[0025] Step 11, considering the downlink with intelligent reflector assistance, the signal received by the user is the superposition of two signals: base station-intelligent reflector and base station-intelligent reflector-user. The cascaded channel from base station-intelligent reflector-user is:

[0026]

[0027] in, This represents the channel from the i-th base station to the j-th smart reflector. h represents the channel from the j-th smart reflector to the u-th user. i ∈C represents the channel from the i-th base station to the u-th user;

[0028] Step 12: Based on the cascaded channel from Step 11, the interference received by the user's omnidirectional antenna consists of two sets: one set of interference from the base station and the other set of interference from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The specific formula is as follows:

[0029]

[0030]

[0031] Where d, t, and r represent the link length from the base station to the user, the link length from the base station to the intelligent reflector, and the link length from the intelligent reflector to the user, respectively; N represents the number of reflector units in the intelligent reflector; and a q This represents the phase reflection coefficient of the smart reflector after the phase of the original signal is out of phase with the phase of the reflected beam signal. h represents the product of four Rayleigh random variables. Z Let h represent a gamma random variable. Z ~Γ(k) Z ,θ Z Its shape parameters and scale parameters are as follows:

[0032]

[0033]

[0034] In the above formula, α represents path loss.

[0035] Furthermore, the calculation of network space throughput on the energy consumption side specifically includes:

[0036] Step 21, calculate the user's signal-to-interference-plus-noise ratio (SIR), using the following formula:

[0037]

[0038] in, This indicates scattering interference from the intelligent reflector, which can be ignored when analyzing networks where interference is limited.

[0039] Step 22: Based on the signal-to-interference-plus-noise ratio (SIR) from Step 21, obtain the aggregated interference from the interfering base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The Laplace transforms are as follows:

[0040]

[0041]

[0042] in,

[0043] Step 23: Based on the nearest-neighbor principle between the user and the base station, the user coverage probability is the probability that the user is within the base station's coverage area and successfully completes the user's requested data transmission. Calculate the user coverage probability:

[0044]

[0045] Where τ represents the signal-to-interference-plus-noise ratio threshold at the user receiver, and λ and μ represent the deployment density of the base station and the deployment density of the smart reflector, respectively;

[0046] Step 24: Taking the user's location as the origin, and the base station and smart reflector closest to the user as the base station and smart reflector associated with the user, calculate the probability density function of the link distance between the user and the associated base station and smart reflector respectively. Then, the probability density function of the link distance between the user and the associated base station and smart reflector is:

[0047]

[0048]

[0049] Specifically, the link distance is calculated using 3D link distance, which requires replacing the two-dimensional link distance l in the probability to be solved with a three-dimensional distance l. 2 +h 2 ;

[0050] Step 25, calculate the network spatial throughput, specifically as follows:

[0051]

[0052] Where log2(1+τ) refers to the maximum link transmission rate per unit spectrum under error-free transmission.

[0053] Furthermore, the deployment density of base stations on the energy consumption side and the ratio of reflective units in the smart reflective surface are optimized, specifically as follows:

[0054] Step 31: Calculate the critical density λ for interference-limited dense wireless networks without intelligent reflector assistance. * :

[0055]

[0056] Step 32: Calculate the set of interferences to be eliminated while maximizing network capacity. Ideal upper bound optimal base station deployment density to eliminate all internal interference

[0057]

[0058] In interference-limited networks, the base station deployment density can be further increased after interference cancellation.

[0059] Step 33, calculate the semi-closed solution for network capacity:

[0060]

[0061] in,

[0062]

[0063]

[0064] Step 34: Calculate the optimal ratio of reflective elements for the intelligent reflective surface while maximizing network capacity.

[0065]

[0066] In the calculation of network spatial throughput, an approximate derivation was made, and only a semi-closed expression could be obtained. In order to obtain the optimal reflective unit ratio of the intelligent reflector, r 0,0 The above ratio is fixed only when interference is limited (μ>λ). * conditions are established.

[0067] Furthermore, the calculation of the green energy supply factor and green energy coverage probability on the energy supply side is as follows:

[0068] Step 41, Green Energy Supply Factor, is the green energy allocation ratio in the energy supply model of intelligent reflective surface dense wireless network. The specific formula is as follows:

[0069]

[0070] Where, γ G The green energy supply factor γ is used in the energy supply model to ensure that there is no energy interruption in the overall supply of green and non-green energy. G ∈[0,0.9], where E G E represents green energy (electricity). NG Non-green energy electricity

[0071] Similarly, the non-green energy allocation in the energy supply model is as follows:

[0072] γ NG =E NG / (E G +E NG );

[0073] Step 42, the green energy interruption probability is defined as follows: the energy output from the energy harvesting and storage device is insufficient to support the energy consumption of successful system data transmission and static power consumption. At this time, the base station and smart reflector on the energy consumption side are forced into an energy interruption state, that is:

[0074]

[0075] Among them, E H It is the energy output by green energy harvesting equipment, E τIt is the energy threshold for system data transmission and static power consumption, P g It is the output power of the energy harvesting device, P for green energy. g =P so l ar T is the collection time of the energy harvesting device;

[0076] The probability of green energy coverage is the probability of green energy non-interruption, meaning that the probability of green energy coverage and the probability of green energy interruption are complementary functions. The probability of green energy coverage is: G cov =P[E G >E τ ] = 1 - P[E G <E τ ].

[0077] Furthermore, with the goal of maximizing network carbon efficiency, a joint optimization design is carried out on the green energy supply factor on the energy supply side and the ratio of intelligent reflective surfaces to base station deployment density on the energy consumption side, specifically including:

[0078] Step 51: Calculate the probability density function and cumulative distribution function of the output power during green energy supply:

[0079]

[0080]

[0081] Among them, K c η is the radiation intensity threshold. c Photovoltaic efficiency, where S represents the area of ​​the photovoltaic photovoltaic system and I represents the solar radiation intensity, depends on the solar altitude angle and the attenuation caused by cloud cover. Its probability density function is:

[0082]

[0083] Among them, I=I d +ΔI, ΔI=II d It follows a standard normal distribution, I d (t) is a time-determined function:

[0084]

[0085] Among them, I max It is the maximum intensity of sunlight in a day;

[0086] Step 52, when calculating distributed supply, the green energy coverage probabilities of base stations with green energy and smart reflectors are respectively:

[0087]

[0088]

[0089] The probabilities of green energy outages for base stations with green energy and smart reflective surfaces are as follows:

[0090]

[0091]

[0092] in, The number of reflective elements and phase resolution of the smart reflective surface are related, where P c P represents the static power consumption of the base station. t ξ represents the base station antenna transmit power, which is expressed as v. -1 v represents the efficiency of the base station power amplifier;

[0093] Step 53, calculate the network carbon efficiency of distributed green energy supply:

[0094]

[0095] Where ρ=μ / λ,

[0096] Step 54: Calculate the optimal ratio ρ between the smart reflector and the base station deployment density to maximize network carbon efficiency. * , ρ * ∈[0.5,2], the user coverage probability is a semi-closed expression for the deployment density ratio, which is analyzed by the calculated numerical solution under interference-limited conditions, satisfying... Under these conditions, the network carbon efficiency reaches its maximum value;

[0097] Step 55: To maximize network carbon efficiency, a centralized green energy allocation method is used to approximate a distributed green energy allocation. The network carbon efficiency under changes in the green energy allocation is calculated. If green energy is interrupted, more non-green energy will participate in the supply. The approximate network carbon efficiency is:

[0098]

[0099] in, This indicates that at a threshold of γ G P total Probability of green energy interruption at / λ;

[0100] Step 56, under the condition of maximizing network carbon efficiency, the optimal green energy ratio is:

[0101]

[0102] in, This indicates a function that rounds down to the nearest integer. 0.9(·) indicates a function that takes the value of 0.9 if the condition within the parentheses is met.

[0103] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0104] 1. This invention is a carbon efficiency optimization method for dense wireless networks assisted by intelligent reflectors. It establishes a dense wireless network system model with intelligent reflector-assisted interference cancellation, and addresses the interference-constrained characteristics of dense wireless networks on the energy consumption side by suppressing interference and controlling it. The interference set is divided into two parts: the interference set from the base station and the interference set from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The Laplace transform is calculated separately, and the interference signal is suppressed or eliminated by utilizing the anti-phase working principle of the intelligent reflector. The ratio of reflective units of the intelligent reflector is optimized by approximation and fixed conditions using the obtained semi-closed expression to maximize the network capacity.

[0105] 2. This invention is a carbon efficiency optimization method for dense wireless networks assisted by intelligent reflectors. It proposes a green energy coverage probability and improves network carbon efficiency through a distributed green energy coverage method. It further optimizes the deployment density of intelligent reflectors and the green energy supply factor. The carbon efficiency obtained by the distributed supply method is approximated by the centralized supply method to obtain the carbon efficiency under the centralized supply method. Based on this, the deployment density ratio of intelligent reflectors and base stations and the green energy supply factor are optimized to ultimately maximize the carbon efficiency of the entire system. Attached Figure Description

[0106] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort, wherein:

[0107] Figure 1 This is a schematic diagram of the system model;

[0108] Figure 2 This is a schematic diagram of the optimal intelligent reflective surface reflective unit ratio and spatial throughput.

[0109] Figure 3 This is a schematic diagram showing the deployment density ratio of base stations with different intelligent reflective surfaces;

[0110] Figure 4 This is a schematic diagram illustrating the carbon efficiency of different green energy supply factors. Detailed Implementation

[0111] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0112] It should be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0113] This invention is a method for optimizing the carbon efficiency of dense wireless networks with intelligent reflector-assisted methods, comprising the following steps:

[0114] A model of a dense wireless network system with intelligent reflector-assisted interference cancellation is established. The system model includes a network model on the energy consumption side and an energy supply model on the energy supply side, such as... Figure 1 As shown;

[0115] The network model on the energy consumption side includes base stations, smart reflectors, and users, with users receiving a set of interference from the base stations. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation

[0116] In this embodiment, the system model's energy consumption is configured with a base station, a smart reflector, and users. The base station, smart reflector, and users follow a Poisson point process, denoted as Π. BS ={BS i |BS i ∈R 2}、

[0117] Π IRS ={IRS j |IRS j ∈R 2} and Π U ={U u |U u ∈R 2}, where i,j,u∈N. Both the base station and the user are equipped with an omnidirectional antenna, and the antenna height difference between the base station and the user is h. BS The intelligent reflective surface is composed of N reflective units, and the reflection coefficient matrix of the intelligent reflective surface is: Where, β and The amplitude and phase reflection coefficients are represented respectively, and j is the imaginary unit. The intelligent reflector is deployed on a wall or column, and the height difference between the intelligent reflector and the user's antenna is h. IRS The users, base stations, and smart reflectors are associated according to the nearest association principle, that is, users are associated with the nearest base station, users are associated with the nearest smart reflector, and smart reflectors are associated with the nearest base station.

[0118] The user receives a set of interference from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation Specifically, the following steps are included:

[0119] Step 11, considering the downlink with intelligent reflector assistance, the signal received by the user is the superposition of two signals: base station-intelligent reflector and base station-intelligent reflector-user. The cascaded channel from base station-intelligent reflector-user is:

[0120]

[0121] in, This represents the channel from the i-th base station to the j-th smart reflector. h represents the channel from the j-th smart reflector to the u-th user. i ∈C represents the channel from the i-th base station to the u-th user;

[0122] Step 12: Based on the cascaded channel from Step 11, the interference received by the user's omnidirectional antenna consists of two sets: one set of interference from the base station and the other set of interference from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The specific formula is as follows:

[0123]

[0124]

[0125] Where d, t, and r represent the link length from the base station to the user, the link length from the base station to the intelligent reflector, and the link length from the intelligent reflector to the user, respectively; N represents the number of reflector units in the intelligent reflector; and a q This represents the phase reflection coefficient of the smart reflector after the phase of the original signal is out of phase with the phase of the reflected beam signal. h represents the product of four Rayleigh random variables. Z Let h represent a gamma random variable. Z ~Γ(k) Z ,θ Z Its shape parameters and scale parameters are as follows:

[0126]

[0127]

[0128] In the above formula, α represents path loss.

[0129] The calculation of network space throughput on the energy consumption side specifically includes:

[0130] Step 21, calculate the user's signal-to-interference-plus-noise ratio (SIR), using the following formula:

[0131]

[0132] in, This indicates scattering interference from the intelligent reflector, which can be ignored when analyzing networks where interference is limited.

[0133] Step 22: Based on the signal-to-interference-plus-noise ratio (SIR) from Step 21, obtain the aggregated interference from the interfering base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The Laplace transforms are as follows:

[0134]

[0135]

[0136] in,

[0137] Step 23: Based on the nearest-neighbor principle between the user and the base station, the user coverage probability is the probability that the user is within the base station's coverage area and successfully completes the user's requested data transmission. Calculate the user coverage probability:

[0138]

[0139] Where τ represents the signal-to-interference-plus-noise ratio threshold at the user receiver, and λ and μ represent the deployment density of the base station and the deployment density of the smart reflector, respectively;

[0140] Step 24: Taking the user's location as the origin, and the base station and smart reflector closest to the user as the base station and smart reflector associated with the user, calculate the probability density function of the link distance between the user and the associated base station and smart reflector respectively. Then, the probability density function of the link distance between the user and the associated base station and smart reflector is:

[0141]

[0142]

[0143] Specifically, the link distance is calculated using 3D link distance, which requires replacing the two-dimensional link distance l in the probability to be solved with a three-dimensional distance l. 2 +h 2 ;

[0144] Step 25, calculate the network spatial throughput, specifically as follows:

[0145]

[0146] Where log2(1+τ) refers to the maximum link transmission rate per unit spectrum under error-free transmission.

[0147] Preferably, the base station deployment density and the reflective unit ratio of the smart reflector are optimized, specifically as follows:

[0148] Step 31: Calculate the critical density λ for interference-limited dense wireless networks without intelligent reflector assistance. * :

[0149]

[0150] Step 32: Calculate the set of interferences to be eliminated while maximizing network capacity. Ideal upper bound optimal base station deployment density to eliminate all internal interference

[0151]

[0152] In interference-limited networks, the base station deployment density can be further increased after interference cancellation.

[0153] Step 33, calculate the semi-closed solution for network capacity:

[0154]

[0155] in,

[0156]

[0157]

[0158] Step 34: Calculate the optimal ratio (number) of reflective units for the intelligent reflective surface while maximizing network capacity.

[0159]

[0160] In the calculation of network spatial throughput, an approximate derivation was made, and only a semi-closed expression could be obtained. In order to obtain the optimal reflective unit ratio of the intelligent reflector, r 0,0The above ratio is fixed only when interference is limited (μ>λ). * conditions are established.

[0161] The energy supply model on the energy supply side converts green energy collection into electrical energy to supply the network model with energy, and calculates the green energy supply factor and green energy coverage probability on the energy supply side.

[0162] Preferably, the calculation of the green energy supply factor and green energy coverage probability on the energy supply side is as follows:

[0163] Step 41, Green Energy Supply Factor, is the green energy allocation ratio in the energy supply model of intelligent reflective surface dense wireless network. The specific formula is as follows:

[0164]

[0165] Where, γ G The green energy supply factor γ is used in the energy supply model to ensure that there is no energy interruption in the overall supply of green and non-green energy. G ∈[0,0.9], where E G E represents green energy (electricity). NG Non-green energy electricity

[0166] Similarly, the non-green energy allocation in the energy supply model is as follows:

[0167] γ NG =E NG / (E G +E NG );

[0168] Step 42, the green energy interruption probability is defined as follows: the energy output from the energy harvesting and storage device is insufficient to support the energy consumption of successful system data transmission and static power consumption. At this time, the base station and smart reflector on the energy consumption side are forced into an energy interruption state, that is:

[0169]

[0170] Among them, E H It is the energy output by green energy harvesting equipment, E τ It is the energy threshold for system data transmission and static power consumption, P g It is the output power of the energy harvesting device, P for green energy. g =P solar T is the collection time of the energy harvesting device;

[0171] The probability of green energy coverage is the probability of green energy non-interruption, meaning that the probability of green energy coverage and the probability of green energy interruption are complementary functions. The probability of green energy coverage is: G cov =P[E G >E τ] = 1 - P[E G <E τ ].

[0172] Preferably, with the goal of maximizing network carbon efficiency, a joint optimization design is performed on the green energy supply factor on the energy supply side and the ratio of intelligent reflective surfaces to base station deployment density on the energy consumption side, specifically including:

[0173] Step 51: Calculate the probability density function and cumulative distribution function of the output power during green energy supply.

[0174]

[0175]

[0176] Among them, K c η is the radiation intensity threshold. c Photovoltaic efficiency, where S represents the area of ​​the photovoltaic photovoltaic system and I represents the solar radiation intensity, depends on the solar altitude angle and the attenuation caused by cloud cover. Its probability density function is:

[0177]

[0178] Among them, I=I d +ΔI, ΔI=II d It follows a standard normal distribution, I d (t) is a time-determined function:

[0179]

[0180] Among them, I max It is the maximum intensity of sunlight in a day;

[0181] Step 52, when calculating distributed supply, the green energy coverage probabilities of base stations with green energy and smart reflectors are respectively:

[0182]

[0183]

[0184] The probabilities of green energy outages for base stations with green energy and smart reflective surfaces are as follows:

[0185]

[0186]

[0187] in, The number of reflective elements and phase resolution of the smart reflective surface are related, where P c P represents the static power consumption of the base station. tξ represents the base station antenna transmit power, which is expressed as v. -1 v represents the efficiency of the base station power amplifier;

[0188] Step 53, calculate the network carbon efficiency of distributed green energy supply:

[0189]

[0190] Where ρ=μ / λ,

[0191] Step 54: Calculate the optimal ratio ρ between the smart reflector and the base station deployment density to maximize network carbon efficiency. * , ρ * ∈[0.5,2], the user coverage probability is a semi-closed expression for the deployment density ratio, which is analyzed by the calculated numerical solution under interference-limited conditions, satisfying... Under these conditions, the network carbon efficiency reaches its maximum value;

[0192] Step 55: To maximize network carbon efficiency, a centralized green energy allocation method is used to approximate a distributed green energy allocation. The network carbon efficiency under changes in the green energy allocation is calculated. If green energy is interrupted, more non-green energy will participate in the supply. The approximate network carbon efficiency is:

[0193]

[0194] in, This indicates that at a threshold of γ G P total Probability of green energy interruption at / λ;

[0195] Step 56, under the condition of maximizing network carbon efficiency, the optimal green energy ratio is:

[0196]

[0197] in, This indicates a function that rounds down to the nearest integer. 0.9(·) indicates a function that takes the value of 0.9 if the condition within the parentheses is met.

[0198] The network scene size of the simulation example of this invention is set to 1km × 1km, and the energy supply side has a solar radiation intensity threshold K. c =150, photovoltaic efficiency η c =0.02, maximum solar radiation intensity I max =2000; On the energy consumption side, the base station's transmission power P t =36dBm, amplifier efficiency ξ=v -1 =3.6, base station static power consumption P c =43dBm, height difference h between base station and user omnidirectional antenna BS=20m, receiver demodulation threshold τ=1dB, number of smart reflector elements N=550, power consumption of a single reflector element P r (b) = 10 dBm, the height difference h between the smart reflector and the user's antenna. IRS =10m, path loss factor α=4. Simultaneously, to simulate a real-world scenario, base station locations are randomly distributed, following a Poisson process with parameter λ, where λ is the base station deployment density. The total number of base stations in the network is determined by multiplying the base station deployment density by the scenario size. Similarly, intelligent reflector locations are randomly distributed, following a Poisson process with parameter μ, where μ is the intelligent reflector deployment density.

[0199] from Figure 2 As can be seen, by comparing the performance of the intelligent reflector unit and the one without intelligent reflector assistance using the optimization method of this invention, it can be concluded that when the base station deployment density is low, the application of this invention can only improve the network capacity to a limited extent. However, as the base station deployment density increases, the gain gradually increases. Compared with the wireless communication network without intelligent reflector assistance, the network capacity is effectively improved and approaches the ideal upper limit through interference suppression by the intelligent reflector.

[0200] Figure 3 A comparison of carbon efficiency in dense wireless networks with and without intelligent reflector-assisted interference cancellation reveals that network carbon efficiency continuously improves with increasing intelligent reflector base station deployment density. This is because increased intelligent reflector density enhances interference cancellation effectiveness and network capacity while introducing lower power consumption. However, further increasing intelligent reflector density limits the gain from the method of this invention due to the limited size of the interference set to be eliminated, leading to increased power consumption and decreased carbon efficiency in the network.

[0201] Figure 4 A comparison of carbon efficiency in dense wireless networks with and without intelligent reflector-assisted interference cancellation reveals that network carbon efficiency continuously improves with increasing green energy supply factor. However, setting the green energy supply factor too high can trigger green energy outages, as the green energy collected in the energy supply system becomes insufficient for network data transmission, leading to a sharp drop in carbon efficiency. The optimal green energy supply factor is a time-dependent function; the optimal green energy supply factor ratio at different times of the day maximizes carbon efficiency.

[0202] This invention addresses the problem of maximizing network carbon efficiency by considering dense wireless networks with green energy supply and the allocation of network resources (intelligent reflector density, intelligent reflector reflector units). It also solves the problem of difficulty in improving network spatial throughput and network carbon efficiency caused by the lack of interference control in dense wireless networks.

[0203] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be conceived by those skilled in the art within the technical scope disclosed in the present invention without creative effort should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for optimizing carbon efficiency in dense wireless networks assisted by intelligent reflective surfaces, characterized in that, Includes the following steps: A dense wireless network system model with intelligent reflector-assisted interference cancellation is established. The system model includes a network model on the energy consumption side and an energy supply model on the energy supply side. The network model on the energy consumption side includes base stations, smart reflectors, and users, with users receiving a set of interference from the base stations. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation Specifically, the following steps are included: Step 11, considering the downlink with intelligent reflector assistance, the signal received by the user is the superposition of two signals: base station-intelligent reflector and base station-intelligent reflector-user. The cascaded channel from base station-intelligent reflector-user is: in, This represents the channel from the i-th base station to the j-th smart reflector. h represents the channel from the j-th smart reflector to the u-th user. i ∈C represents the channel from the i-th base station to the u-th user; Step 12: Based on the cascaded channel from Step 11, the interference received by the user's omnidirectional antenna consists of two sets: one set of interference from the base station and the other set of interference from the base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The specific formula is as follows: Where d, t, and r represent the link length from the base station to the user, the link length from the base station to the intelligent reflector, and the link length from the intelligent reflector to the user, respectively; N represents the number of reflector units in the intelligent reflector; and a q This represents the phase reflection coefficient of the smart reflector after the phase of the original signal is out of phase with the phase of the reflected beam signal. Let r0 represent the product of four Rayleigh random variables, r0 represent the link length between the 0th IRS smart reflector and the user, and h0 represent the link length between the 0th IRS smart reflector and the user. Z Let h represent a gamma random variable. Z ~Γ(k) Z ,θ Z Its shape parameters and scale parameters are as follows: In the above formula, α represents path loss and K represents the number of interfering base stations; The calculation of network throughput on the energy consumption side includes the following steps: Step 21, calculate the user's signal-to-interference-plus-noise ratio (SIR), using the following formula: in, The scattering interference from the intelligent reflector can be ignored when analyzing interference-limited networks. d0 represents the link length between the 0th base station and the user, and h0 represents the channel from the 0th base station to the user. Step 22: Based on the signal-to-interference-plus-noise ratio (SIR) from Step 21, obtain the aggregated interference from the interfering base station. and the set of interferences to be eliminated from intelligent reflector-assisted interference cancellation The Laplace transforms are as follows: in, k Z θ is the gamma shape parameter. Z Since r is a gamma-scale parameter, 0，0 Let represent the link length between the 0th IRS smart reflector and the 0th user, and s represent the parameters in the Laplace transform; Step 23: Based on the nearest-neighbor principle between the user and the base station, the user coverage probability is the probability that the user is within the base station's coverage area and successfully completes the user's requested data transmission. Calculate the user coverage probability: Where τ represents the signal-to-interference-plus-noise ratio threshold at the user receiver, and λ and μ represent the deployment density of the base station and the deployment density of the smart reflector, respectively; Step 24: Taking the user's location as the origin, and the base station and smart reflector closest to the user as the base station and smart reflector associated with the user, calculate the probability density function of the link distance between the user and the associated base station and smart reflector respectively. Then, the probability density function of the link distance between the user and the associated base station and smart reflector is: Specifically, the link distance is calculated using 3D link distance, which requires replacing the two-dimensional link distance l in the probability to be solved with a three-dimensional distance l. 2 +h 2 ; Step 25, calculate the network spatial throughput, specifically as follows: Wherein, log2(1+τ) refers to the maximum link transmission rate per unit spectrum under error-free transmission; The optimization design of base station deployment density and reflective unit ratio of smart reflectors on the energy consumption side includes the following steps: Step 31: Calculate the critical density λ for interference-limited dense wireless networks without intelligent reflector assistance. * : Step 32: Calculate the set of interferences to be eliminated while maximizing network capacity. Ideal upper bound optimal base station deployment density to eliminate all internal interference In interference-limited networks, the base station deployment density can be further increased after interference cancellation. Step 33, calculate the semi-closed solution for network capacity: in, Step 34: Calculate the optimal ratio of reflective elements for the intelligent reflective surface while maximizing network capacity. The calculation of network spatial throughput was approximated and only a semi-closed expression could be obtained. To obtain the optimal reflective unit ratio of the intelligent reflector, r... 0,0 The above ratio is fixed only when interference is limited (μ>λ). * established under the conditions; The energy supply model on the energy supply side converts green energy harvesting into electricity to supply energy to the network model. It calculates the green energy supply factor and green energy coverage probability on the energy supply side, specifically including the following steps: Step 41, Green Energy Supply Factor, is the green energy allocation ratio in the energy supply model of intelligent reflective surface dense wireless network. The specific formula is as follows: Where, γ G The green energy supply factor γ is used in the energy supply model to ensure that there is no energy interruption in the overall supply of green and non-green energy. G ∈[0,0.9], where E G E represents green energy (electricity). NG Non-green energy electricity Similarly, the non-green energy allocation in the energy supply model is as follows: γ NG =And NG / (AND G +E NG ); Step 42, the green energy interruption probability is defined as follows: the energy output from the energy harvesting and storage device is insufficient to support the energy consumption of successful system data transmission and static power consumption. At this time, the base station and smart reflector on the energy consumption side are forced into an energy interruption state, that is: Among them, E H It is the energy output by green energy harvesting equipment, E τ It is the energy threshold for system data transmission and static power consumption, P g It is the output power of the energy harvesting device, P for green energy. g =P solar T is the collection time of the energy harvesting device; The probability of green energy coverage is the probability of green energy non-interruption, meaning that the probability of green energy coverage and the probability of green energy interruption are complementary functions. The probability of green energy coverage is: G cov =P[E G >E τ ] = 1 - P[E G <E τ ]; With the goal of maximizing network carbon efficiency, a joint optimization design is carried out on the green energy supply factor on the energy supply side and the ratio of intelligent reflective surfaces to base station deployment density on the energy consumption side. The specific steps include: Step 51: Calculate the probability density function and cumulative distribution function of the output power during green energy supply: Among them, K c η is the radiation intensity threshold. c Photovoltaic efficiency, where S represents the area of ​​the photovoltaic photovoltaic system and I represents the solar radiation intensity, depends on the solar altitude angle and the attenuation caused by cloud cover. Its probability density function is: Among them, I=I d +ΔI, ΔI=II d It follows a standard normal distribution, I d (t) is a time-determined function: Among them, I max It is the maximum intensity of sunlight in a day; Step 52, when calculating distributed supply, the green energy coverage probabilities of base stations with green energy and smart reflectors are respectively: The probabilities of green energy outages for base stations with green energy and smart reflective surfaces are as follows: in, Related to the number of reflective elements and phase resolution of the smart reflective surface, where P c P represents the static power consumption of the base station. t ξ represents the base station antenna transmit power, which is expressed as v. -1 v represents the efficiency of the base station power amplifier; Step 53, calculate the network carbon efficiency of distributed green energy supply: Where, p = m / l, Step 54: Calculate the optimal ratio ρ between the smart reflector and the base station deployment density to maximize network carbon efficiency. * , ρ * ∈[0.5,2], the user coverage probability is a semi-closed expression for the deployment density ratio, which is analyzed by the calculated numerical solution under interference-limited conditions, satisfying... Under these conditions, the network carbon efficiency reaches its maximum value; Step 55: To maximize network carbon efficiency, a centralized green energy allocation method is used to approximate a distributed green energy allocation. The network carbon efficiency under changes in the green energy allocation is calculated. If green energy is interrupted, more non-green energy will participate in the supply. The approximate network carbon efficiency is: in, This indicates that at a threshold of γ G P total / λ is the probability of green energy interruption, J / kg-CO2e represents the joule value corresponding to one kilogram of carbon dioxide equivalent; Step 56, under the condition of maximizing network carbon efficiency, the optimal green energy ratio is: in, This indicates a function that rounds down to the nearest integer; 0.9(·) indicates a function that takes the value of 0.9 if the condition within the parentheses is met. The base station, smart reflector, and user in the network model on the energy consumption side follow a Poisson point process, denoted as Π. BS ={BS i |BS i ∈R 2 }、Π IRS ={IRS j |IRS j ∈R 2 } and Π U ={U u |U u ∈R 2 }, where i,j,u∈N; Both the base station and the user are equipped with an omnidirectional antenna, and the height difference between the omnidirectional antennas of the base station and the user is h. BS The intelligent reflective surface consists of N reflective units, and the reflection coefficient matrix of the intelligent reflective surface is: Where β and The amplitude and phase reflection coefficients are represented respectively, and j is the imaginary unit. The intelligent reflector is deployed on a wall or column, and the height difference between the intelligent reflector and the user's omnidirectional antenna is h. IRS .

2. The method for optimizing carbon efficiency in dense wireless networks assisted by intelligent reflective surfaces according to claim 1, characterized in that: The user, base station, and smart reflector are associated according to the nearest association principle, that is, the user is associated with the nearest base station, the user is associated with the nearest smart reflector, and the smart reflector is associated with the nearest base station.

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