Communication network optimization decision method based on artificial intelligence

Abstract

The invention discloses a communication network optimization decision-making method based on artificial intelligence, which relates to the technical field of communication network optimization and comprises the following steps: S1, applying the communication network optimization decision-making method based on artificial intelligence to intelligent reflecting surface communication network optimization, and constructing a communication network based on the position information of the communication base station and the signal coverage area range, and modeling a wireless channel of the communication network through a ray tracing method. The invention provides a communication network optimization decision-making method based on artificial intelligence, and the method comprises the steps: building a communication network based on the position information of a communication base station and a signal coverage area range, carrying out the modeling of a wireless channel of the communication network, and achieving the purpose of optimizing the user coverage of the communication network. A wireless power supply relay communication model based on an intelligent reflecting surface is constructed, coverage expansion of edge users is realized, the basis of selecting an active transmission working mode or a back scattering working mode by a wireless power supply relay based on an energy collection level is determined, and two mode selection mechanisms are designed.

Description

Technical Field

[0001] The present invention relates to the technical field of communication network optimization, and in particular to a communication network optimization decision-making method based on artificial intelligence. Background Art

[0002] Smart services in modern society all rely on smartphones to access wireless communication networks, and then communicate with corresponding application servers through wireless communication networks. In the Chinese patent application number 202311793473.7, "a communication network optimization method and device are disclosed, the method includes receiving the network availability of the mobile communication network of each mobile terminal in a preset group and within a preset distance; determining the mobile terminal with the highest network availability in the preset group as a hotspot sharing terminal, and sending a sharing password request to the hotspot sharing terminal; receiving network sharing information sent by the hotspot sharing terminal; sending the network sharing information to each mobile terminal in the preset group and within a preset distance except the hotspot sharing terminal, selecting a hotspot sharing terminal with better signal from the preset group according to the network availability, requesting network sharing information from the hotspot sharing terminal, and sending the network sharing information to each mobile terminal in the preset group except the hotspot sharing terminal, the mobile terminal can connect to the hotspot sharing terminal through the network sharing information, and then access the Internet, and the user can obtain a better network signal in a remote area through the hotspot sharing terminal and smoothly access the Internet.";

[0003] This existing technology only solves the problem that in various complex environments, the signal coverage strengths of base stations of different operators vary, resulting in many users in remote areas being unable to obtain better network signals and unable to smoothly access the Internet. It does not take into account the need to determine the coverage of the communication network and improve the coverage expansion of marginal users, and it does not consider improving the service quality of the communication network under the existing energy, and it also takes into account the problem of co-frequency interference caused by insufficient time-frequency resources in the communication network and optimizes this. Summary of the invention

[0004] The purpose of the present invention is to provide a communication network optimization decision method based on artificial intelligence to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a communication network optimization decision method based on artificial intelligence, comprising the following steps:

[0006] S1: A communication network optimization decision method based on artificial intelligence is applied to the optimization of intelligent reflector communication network. The communication network is constructed based on the location information of communication base stations and the signal coverage area, and the wireless channel of the communication network is modeled by ray tracing method.

[0007] S2: To optimize the user coverage of the communication network, a wireless power relay communication model based on the intelligent reflective surface is constructed. Taking into account the energy collection level of the communication base station, a hard threshold working mode selection mechanism is designed to achieve coverage expansion of edge users. Furthermore, considering the user's final received signal rate level, a soft threshold working mode selection mechanism is designed.

[0008] S3: With the purpose of optimizing the balance between the service quality and energy consumption of the smart reflector communication network, a communication network model based on the wireless powered smart reflector was constructed, and the working mechanism of the wireless powered smart reflector was designed, thereby achieving the goal of meeting the communication needs of the communication network while improving the energy efficiency of the communication network without introducing additional energy;

[0009] S4: With the purpose of optimizing the co-channel interference of the communication network, a downlink layered communication network model based on the intelligent reflection surface is constructed, and a user grouping method based on channel differences is designed while considering the user channel similarity.

[0010] Preferably, step S1 also includes determining the location information of the communication base station through GPS technology, building a communication network in combination with the signal coverage of the communication base station, and modeling the wireless channel of the communication network through the reverse ray tracing method. The specific operation is to determine the position between the transmission source point and the field point, establish a virtual source tree to determine all the radio wave propagation paths from the transmission point to the receiving point, and the establishment of the virtual source tree first needs to search for the visible reflection surface and diffraction edge at the transmission source point, establish a root node and a child node of the root node, complete the generation of the first layer of virtual sources, and then search downward from the first layer of virtual sources for visible reflection surfaces and diffraction edges, and add them as child nodes to the virtual source tree, and search downward layer by layer until the receiving field point or the maximum number of reflection and diffraction times is reached. In this way, the establishment of the virtual source tree is completed, and the construction of the wireless channel of the communication network is realized. After determining the propagation path of the ray, the loss of the ray is calculated by the path loss algorithm, and the specific calculation formula is as follows:

[0011] L LOS =32.4+21log 10 (d)+20log 10 (f c )

[0012] In the formula, d represents the propagation distance, f c Indicates the frequency of electromagnetic waves.

[0013] Preferably, step S2 also includes constructing a wireless power relay communication model based on an intelligent reflective surface for the purpose of optimizing user coverage of the communication network, which is specifically composed of a communication base station, an intelligent reflective surface, an energy-limited relay and a user, wherein the communication base station and the intelligent reflective surface both obey a uniform rectangular array structure, the wireless power relay is equipped with two antennas, one for collecting radio frequency energy and receiving information, and the other for backscattering and active transmission, and the intelligent reflective surface and the energy-limited relay jointly assist the communication base station to assist the user, specifically by setting the wireless power relay and the intelligent reflective surface to cooperate and assist in enhancing the quality of the user's received signal, the radio frequency signal transmitted by the communication base station reaches the wireless power relay and the user through reflection by the intelligent reflective surface, and the wireless power relay selects an active transmission working mode or a backscattering working mode based on the energy collection level to help the radio frequency signal reach the user, and when the wireless power relay selects the active transmission working mode, two antennas are occupied at the same time, which are used for information reception and active transmission respectively, and when the wireless power relay selects the backscattering working mode, only one antenna is used for communication.

[0014] Preferably, step S2 also includes clarifying the basis for the wireless power supply relay to select the active transmission working mode or the backscattering working mode based on the energy collection level, and designing two mode selection mechanisms, namely a hard threshold mechanism and a soft threshold mechanism, wherein the threshold refers to the minimum circuit threshold required for the wireless power supply relay to select the active transmission working mode, wherein the hard threshold mechanism is that when the energy collected by the wireless power supply relay reaches the minimum circuit threshold required for the active transmission working mode, the wireless power supply relay can switch to the active transmission working mode, so that the wireless power supply relay can select the low circuit start threshold and low rate backscattering working mode or the high circuit start threshold and high rate active transmission working mode, and realize the coverage expansion of edge users without the need for an external power supply, wherein the value of the soft threshold in the soft threshold mechanism is determined by the received signal-to-noise ratio at the user, and by adjusting the value of the soft threshold, the wireless power supply relay will switch the working mode from backscattering to active transmission when the active transmission received signal-to-noise ratio is higher.

[0015] Preferably, step S3 also includes constructing a D2D communication network model based on a wireless powered intelligent reflective surface for the purpose of optimizing the balance between the service quality and energy consumption of the intelligent reflective surface communication network, which is specifically composed of D2D users, intelligent controllers, energy storage units, wireless power relays and intelligent reflective surfaces, wherein the intelligent reflective surface is equipped with a passive reflector, and the D2D communication pairs communicate with its assistance. A full-band multiplexing bottom-pad communication method is adopted between D2D transceiver users, and each D2D user is equipped with only one antenna. The passive reflector of the intelligent reflective surface is connected to an energy collection circuit, so that it can collect energy from the D2D radio frequency signal and store it in the energy storage unit, and the stored energy will be used for dynamic phase shift adjustment of the intelligent reflective surface.

[0016] Preferably, step S3 also includes designing a wireless powered intelligent reflective surface working mechanism, which includes two stages, specifically, the intelligent reflective surface collects energy from the D2D radio frequency signal to achieve self-sustainable operation, thereby reducing energy consumption, and the intelligent reflective surface only collects energy in the first stage, and uses the stored energy to perform discrete phase shift operations to achieve passive beamforming in the second stage. The switching of the intelligent reflective surface between the two stages is controlled by an intelligent controller. During the duration of the energy collection stage, all D2D radio frequency signals incident on the intelligent reflective surface are absorbed by the reflective elements of the intelligent reflective surface for energy collection, and the D2D receiving end can only receive D2D signals from the direct link. In the second stage, the intelligent reflective surface uses the energy collected in the first stage to dynamically adjust the phase shift, and uses passive beamforming to assist in enhancing D2D communication. In this stage, the D2D receiving end will simultaneously receive D2D signals from the direct link and the intelligent reflective surface reflection link, ensuring the reception of D2D communication signals, thereby achieving the reduction of energy loss of the communication network while meeting the communication needs of the communication network without introducing additional energy.

[0017] Preferably, step S4 also includes constructing a downlink layered communication network model based on an intelligent reflecting surface for the purpose of optimizing co-frequency interference in the communication network, including an intelligent reflecting surface, a communication base station, a user and a wireless power relay, and the intelligent reflecting surface is composed of reflecting elements and deployed between the communication base station and the user to assist downlink communication, and the antenna array of the communication base station and the reflecting element array of the intelligent reflecting surface both obey a uniform linear array.

[0018] Preferably, step S4 further includes designing a user grouping method based on channel differences while considering the user channel similarity, specifically designing a similarity measurement matrix to represent the similarity between different user channels, and the specific calculation formula is as follows:

[0019]

[0020] Where M is a symmetric matrix whose diagonal elements are all 1, and f i H represents the channel coefficient between the communication base station H and user i, f j The channel strength of user j is represented, the users are grouped based on the calculation results, and a hierarchical communication scheme based on user grouping is designed based on the RSMA protocol to reduce the problem of co-channel interference in communication.

[0021] Compared with the prior art, the beneficial effects of the present invention at least include: the present invention proposes a communication network optimization decision method based on artificial intelligence, constructs a communication network based on the location information of the communication base station and the signal coverage area, and models the wireless channel of the communication network. For the purpose of optimizing the user coverage of the communication network, a wireless power supply relay communication model based on an intelligent reflection surface is constructed to achieve coverage expansion of edge users, and the basis for the wireless power supply relay to select an active transmission working mode or a backscattering working mode based on the energy collection level is clarified, and two mode selection mechanisms are designed. For the purpose of optimizing the user coverage of the communication network, a wireless power supply relay communication model based on an intelligent reflection surface is constructed, so that the intelligent reflection surface can achieve self-sustainable operation by collecting energy, and achieves that the communication needs of the communication network are met without introducing additional energy. At the same time, the energy loss of the communication network is reduced, and for the purpose of optimizing the same-frequency interference of the communication network, a downlink layered communication network model based on the intelligent reflection surface is constructed, and a user grouping method based on channel differences is designed to reduce the problem of communication same-frequency interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a communication network optimization decision-making method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 The present invention provides a technical solution: a communication network optimization decision method based on artificial intelligence, comprising the following steps:

[0025] S1: A communication network optimization decision method based on artificial intelligence is applied to the optimization of intelligent reflector communication network. The communication network is constructed based on the location information of communication base stations and the signal coverage area, and the wireless channel of the communication network is modeled by ray tracing method.

[0026] S2: To optimize the user coverage of the communication network, a wireless power relay communication model based on the intelligent reflective surface is constructed. Taking into account the energy collection level of the communication base station, a hard threshold working mode selection mechanism is designed to achieve coverage expansion of edge users. Furthermore, considering the user's final received signal rate level, a soft threshold working mode selection mechanism is designed.

[0027] S3: With the purpose of optimizing the balance between the service quality and energy consumption of the smart reflector communication network, a communication network model based on the wireless powered smart reflector was constructed, and the working mechanism of the wireless powered smart reflector was designed, thereby achieving the goal of meeting the communication needs of the communication network while improving the energy efficiency of the communication network without introducing additional energy;

[0028] S4: With the purpose of optimizing the co-channel interference of the communication network, a downlink layered communication network model based on the intelligent reflection surface is constructed, and a user grouping method based on channel differences is designed while considering the user channel similarity.

[0029] Step S1 also includes determining the location information of the communication base station through GPS technology, building a communication network in combination with the signal coverage of the communication base station, and modeling the wireless channel of the communication network through the reverse ray tracing method. The specific operation is to determine the position between the transmission source point and the field point, establish a virtual source tree to determine all the radio wave propagation paths from the transmission point to the receiving point. The establishment of the virtual source tree first needs to search for the visible reflection surface and diffraction edge at the transmission source point, establish a root node and a child node of the root node, complete the generation of the first layer of virtual sources, and then search downward from the first layer of virtual sources for visible reflection surfaces and diffraction edges, and add them as child nodes to the virtual source tree, and search downward layer by layer until the receiving field point or the maximum number of reflection and diffraction times is reached. In this way, the establishment of the virtual source tree is completed, and the construction of the wireless channel of the communication network is realized. After determining the propagation path of the ray, the loss of the ray is calculated by the path loss algorithm. The specific calculation formula is as follows:

[0030] L LOS =32.4+21log 10 (d)+20log 10 (f c )

[0031] In the formula, d represents the propagation distance, f c Indicates the frequency of electromagnetic waves;

[0032] Step S2 also includes constructing a wireless power relay communication model based on an intelligent reflective surface for the purpose of optimizing the user coverage of the communication network, which is specifically composed of a communication base station, an intelligent reflective surface, an energy-limited relay and a user, wherein the communication base station and the intelligent reflective surface both obey a uniform rectangular array structure, and the wireless power relay is equipped with two antennas, one for collecting radio frequency energy and receiving information, and the other for backscattering and active transmission, and the intelligent reflective surface and the energy-limited relay jointly assist the communication base station to assist the user, specifically by setting the wireless power relay and the intelligent reflective surface to cooperate and assist in enhancing the quality of the user's received signal, the radio frequency signal transmitted by the communication base station reaches the wireless power relay and the user through reflection by the intelligent reflective surface, and the wireless power relay selects an active transmission working mode or a backscattering working mode based on the energy collection level to help the radio frequency signal reach the user, and when the wireless power relay selects the active transmission working mode, two antennas are occupied at the same time, which are used for information reception and active transmission respectively, and when the wireless power relay selects the backscattering working mode, only one antenna is used for communication;

[0033] Step S2 also includes clarifying the basis for the wireless power relay to select the active transmission working mode or the backscattering working mode based on the energy collection level, and designing two mode selection mechanisms, namely the hard threshold mechanism and the soft threshold mechanism, wherein the threshold refers to the minimum circuit threshold required for the wireless power relay to select the active transmission working mode, wherein the hard threshold mechanism is that when the energy collected by the wireless power relay reaches the minimum circuit threshold required for the active transmission working mode, the wireless power relay can switch to the active transmission working mode, so that the wireless power relay can select the low circuit start threshold and low rate backscattering working mode or the high circuit start threshold and high rate active transmission working mode, and realize the coverage expansion of edge users without the need for an external power supply, wherein the value of the soft threshold in the soft threshold mechanism is determined by the received signal-to-noise ratio at the user, and by adjusting the value of the soft threshold, the wireless power relay will switch the working mode from backscattering to active transmission when the active transmission received signal-to-noise ratio is higher;

[0034] Step S3 also includes constructing a D2D communication network model based on a wireless powered intelligent reflective surface for the purpose of optimizing the balance between the service quality and energy consumption of the intelligent reflective surface communication network, which is specifically composed of a D2D user, an intelligent controller, an energy storage unit, a wireless power relay and an intelligent reflective surface, wherein the intelligent reflective surface is equipped with a passive reflective element, and the D2D communication pair communicates with its assistance, and a full-band multiplexing bottom-pad communication method is adopted between D2D transceiver users, and each D2D user is equipped with only one antenna, and the passive reflective element of the intelligent reflective surface is connected to an energy collection circuit, so that it can collect energy from the D2D radio frequency signal and store it in the energy storage unit, and the stored energy will be used for dynamic phase shift adjustment of the intelligent reflective surface;

[0035] Step S3 also includes designing a wireless powered intelligent reflective surface working mechanism, which includes two stages, specifically, the intelligent reflective surface collects energy from the D2D radio frequency signal to achieve self-sustainable operation, thereby reducing energy consumption, and the intelligent reflective surface only collects energy in the first stage, and uses the stored energy to perform discrete phase shift operations to achieve passive beamforming in the second stage. The switching of the intelligent reflective surface between the two stages is controlled by an intelligent controller. During the duration of the energy collection stage, all D2D radio frequency signals incident on the intelligent reflective surface are absorbed by the reflective elements of the intelligent reflective surface for energy collection, and the D2D receiving end can only receive D2D signals from the direct link. In the second stage, the intelligent reflective surface uses the energy collected in the first stage to dynamically adjust the phase shift, and uses passive beamforming to assist in enhancing D2D communication. In this stage, the D2D receiving end will simultaneously receive D2D signals from the direct link and the intelligent reflective surface reflection link, ensuring the reception of D2D communication signals, and achieving the communication needs of the communication network without introducing additional energy while reducing the energy loss of the communication network;

[0036] Step S4 also includes constructing a downlink hierarchical communication network model based on an intelligent reflection surface for the purpose of optimizing the co-frequency interference of the communication network, including an intelligent reflection surface, a communication base station, a user and a wireless power relay, and the intelligent reflection surface is composed of reflection elements and is deployed between the communication base station and the user to assist downlink communication, and the antenna array of the communication base station and the reflection element array of the intelligent reflection surface both obey a uniform linear array;

[0037] Step S4 also includes designing a user grouping method based on channel differences in consideration of user channel similarity, specifically designing a similarity measurement matrix to represent the similarity between different user channels, and the specific calculation formula is as follows:

[0038]

[0039] Where M is a symmetric matrix whose diagonal elements are all 1, and f i H represents the channel coefficient between the communication base station H and user i, f j The channel strength of user j is represented, the users are grouped based on the calculation results, and a hierarchical communication scheme based on user grouping is designed based on the RSMA protocol to reduce the problem of co-channel interference in communication.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A communication network optimization decision-making method based on artificial intelligence, characterized in that The following steps are involved: S1: A communication network optimization decision method based on artificial intelligence is applied to the optimization of intelligent reflector communication network. The communication network is constructed based on the location information of communication base stations and the signal coverage area, and the wireless channel of the communication network is modeled by ray tracing method. S2: To optimize the user coverage of the communication network, a wireless power relay communication model based on the intelligent reflective surface is constructed. Taking into account the energy collection level of the communication base station, a hard threshold working mode selection mechanism is designed to achieve coverage expansion of edge users. Furthermore, considering the user's final received signal rate level, a soft threshold working mode selection mechanism is designed. S3: With the purpose of optimizing the balance between the service quality and energy consumption of the smart reflector communication network, a communication network model based on the wireless powered smart reflector was constructed, and the working mechanism of the wireless powered smart reflector was designed, thereby achieving the goal of meeting the communication needs of the communication network while improving the energy efficiency of the communication network without introducing additional energy; S4: With the purpose of optimizing the co-channel interference of the communication network, a downlink layered communication network model based on the intelligent reflection surface is constructed, and a user grouping method based on channel differences is designed while considering the user channel similarity.

2. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S1 also includes determining the location information of the communication base station through GPS technology, building a communication network in combination with the signal coverage of the communication base station, and modeling the wireless channel of the communication network through the reverse ray tracing method. The specific operation is to determine the position between the transmission source point and the field point, establish a virtual source tree to determine all the radio wave propagation paths from the transmission point to the receiving point. The establishment of the virtual source tree first requires searching for the visible reflection surface and diffraction edge at the transmission source point, establishing a root node and a child node of the root node, and completing the generation of the first layer of virtual sources. The first layer of virtual sources then searches downward for visible reflection surfaces and diffraction edges, and adds them as child nodes to the virtual source tree. The search is continued layer by layer until the receiving field point or the maximum number of reflection and diffraction times is reached. In this way, the establishment of the virtual source tree is completed, and the construction of the wireless channel of the communication network is realized. After determining the propagation path of the ray, the loss of the ray is calculated by a path loss algorithm.

3. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S2 also includes constructing a wireless power relay communication model based on an intelligent reflective surface for the purpose of optimizing the user coverage of the communication network, which is specifically composed of a communication base station, an intelligent reflective surface, an energy-limited relay and a user, wherein the communication base station and the intelligent reflective surface both obey a uniform rectangular array structure, the wireless power relay is equipped with two antennas, one for collecting radio frequency energy and receiving information, and the other for backscattering and active transmission, and the intelligent reflective surface and the energy-limited relay jointly assist the communication base station to assist the user, specifically by setting the wireless power relay and the intelligent reflective surface to cooperate and assist in enhancing the quality of the user's received signal, the radio frequency signal transmitted by the communication base station reaches the wireless power relay and the user through reflection by the intelligent reflective surface, and the wireless power relay selects an active transmission working mode or a backscattering working mode based on the energy collection level to help the radio frequency signal reach the user, and when the wireless power relay selects the active transmission working mode, two antennas are occupied at the same time, which are used for information reception and active transmission respectively, and when the wireless power relay selects the backscattering working mode, only one antenna is used for communication.

4. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S2 also includes clarifying the basis for the wireless power relay to select the active transmission working mode or the backscattering working mode based on the energy collection level, and designing two mode selection mechanisms, namely a hard threshold mechanism and a soft threshold mechanism, wherein the threshold refers to the minimum circuit threshold required for the wireless power relay to select the active transmission working mode, wherein the hard threshold mechanism is that when the energy collected by the wireless power relay reaches the minimum circuit threshold required for the active transmission working mode, the wireless power relay can switch to the active transmission working mode, so that the wireless power relay can select the backscattering working mode with a low circuit start threshold and a low rate or the active transmission working mode with a high circuit start threshold and a high rate, and realize the coverage expansion of edge users without the need for an external power supply, wherein the value of the soft threshold in the soft threshold mechanism is determined by the received signal-to-noise ratio at the user, and by adjusting the value of the soft threshold, the wireless power relay will switch the working mode from backscattering to active transmission when the active transmission received signal-to-noise ratio is higher.

5. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S3 also includes constructing a D2D communication network model based on a wireless powered intelligent reflective surface for the purpose of optimizing the balance between the service quality and energy consumption of the intelligent reflective surface communication network. The model is specifically composed of a D2D user, an intelligent controller, an energy storage unit, a wireless powered relay and an intelligent reflective surface, wherein the intelligent reflective surface is equipped with a passive reflective element, and the D2D communication pair communicates with its assistance. A full-band multiplexing bottom-pad communication method is adopted between D2D transceiver users, and each D2D user is only equipped with one antenna. The passive reflective element of the intelligent reflective surface is connected to an energy collection circuit, so that it can collect energy from the D2D radio frequency signal and store it in the energy storage unit. The stored energy will be used for dynamic phase shift adjustment of the intelligent reflective surface.

6. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S3 also includes designing a wireless powered intelligent reflective surface working mechanism, which includes two stages, specifically, the intelligent reflective surface collects energy from the D2D radio frequency signal to achieve self-sustainable operation, thereby reducing energy consumption, and the intelligent reflective surface only collects energy in the first stage, and uses the stored energy to perform discrete phase shift operations to achieve passive beamforming in the second stage. The switching of the intelligent reflective surface between the two stages is controlled by an intelligent controller. During the duration of the energy collection stage, all D2D radio frequency signals incident on the intelligent reflective surface are absorbed by the reflective elements of the intelligent reflective surface for energy collection, and the D2D receiving end can only receive D2D signals from the direct link. In the second stage, the intelligent reflective surface uses the energy collected in the first stage to dynamically adjust the phase shift, and uses passive beamforming to assist in enhancing D2D communication. In this stage, the D2D receiving end will simultaneously receive D2D signals from the direct link and the intelligent reflective surface reflection link, ensuring the reception of D2D communication signals, thereby achieving the reduction of energy loss of the communication network while meeting the communication needs of the communication network without introducing additional energy.

7. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S4 also includes constructing a downlink layered communication network model based on an intelligent reflection surface for the purpose of optimizing the co-frequency interference of the communication network, including an intelligent reflection surface, a communication base station, a user and a wireless power relay, and the intelligent reflection surface is composed of reflection elements and deployed between the communication base station and the user to assist downlink communication, and the antenna array of the communication base station and the reflection element array of the intelligent reflection surface both obey a uniform linear array.

8. The communication network optimization decision method based on artificial intelligence according to claim 1, characterized in that: The step S4 also includes designing a user grouping method based on channel differences while considering the similarity of user channels, specifically designing a similarity measurement matrix to represent the similarity between different user channels, grouping users based on the calculation results, and designing a hierarchical communication scheme based on user grouping on the basis of the RSMA protocol, thereby reducing the problem of co-channel interference in communication.

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