Parameter optimization method of passive six-dimensional mobile antenna communication system and related equipment

Abstract

The embodiment of the invention provides a parameter optimization method of a passive six-dimensional movable antenna communication system and related equipment, and the method comprises the steps: obtaining a first transmission parameter between each information transmitting end and the surface of a passive six-dimensional movable antenna, and obtaining a second transmission parameter between the surface of the passive six-dimensional movable antenna and an information receiving end; obtaining an antenna position parameter, an antenna angle parameter and a phase angle parameter of each passive intelligent reflecting surface, and obtaining a beam forming parameter of an information receiving end; based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beam forming parameter, the first transmission parameter and the second transmission parameter, a transmission rate optimization model is generated and solved, and an optimized antenna position, an optimized antenna angle, an optimized phase angle and optimized beam forming are obtained. Therefore, the surface of the passive six-dimensional movable antenna and the information receiving end are adjusted, so that the data transmission rate between the information sending end and the information receiving end in the wireless communication system is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a parameter optimization method and related equipment for a passive six-dimensional movable antenna communication system. Background Art

[0002] In order to meet the growing number of IoT devices in the upcoming sixth-generation wireless network, the demand for data transmission rate between base stations and IoT terminals has also increased. In the related art, there is a method of setting a smart reflection surface between the information receiving end and the information sending end to reflect the signal so as to improve the data transmission rate between the information sending end and the information receiving end.

[0003] However, since the position and angle of the smart reflective surface are fixed after being fixed, it is easily restricted by the position and angle when reflecting signals, which limits its ability to improve the data transmission rate between the information sending end and the information receiving end. Summary of the invention

[0004] The embodiments of the present application provide a parameter optimization method and related equipment for a passive six-dimensional movable antenna communication system, which can improve the data transmission rate between an information sending end and an information receiving end in a wireless communication system.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a parameter optimization method for a passive six-dimensional movable antenna communication system, wherein the passive six-dimensional movable antenna communication system includes at least one information transmitting end, an information receiving end, and a passive six-dimensional movable antenna surface, wherein the passive six-dimensional movable antenna surface includes at least one passive intelligent reflective surface, and the method includes:

[0006] Acquire a first transmission parameter between each of the information transmitting ends and the passive six-dimensional movable antenna surface, and acquire a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end;

[0007] Obtaining antenna position parameters, antenna angle parameters and phase angle parameters of each of the passive intelligent reflective surfaces, and obtaining beamforming parameters of the information receiving end;

[0008] Generate a transmission rate optimization model based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beamforming parameter, the first transmission parameter, and the second transmission parameter;

[0009] Solving the transmission rate optimization model to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle, and an optimized beamforming, wherein the optimized antenna position, the optimized antenna angle, the optimized phase angle, and the optimized beamforming are used to improve the data transmission rate between the information transmitting end and the information receiving end;

[0010] The three-dimensional position of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized antenna position, the three-dimensional rotation angle of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized antenna angle, the phase angle of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized phase angle, and the beamforming of the information receiving end is adjusted based on the optimized beamforming.

[0011] In some embodiments, generating a transmission rate optimization model based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beamforming parameter, the first transmission parameter, and the second transmission parameter includes:

[0012] Based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the first transmission parameter, and the second transmission parameter, a cascade channel gain between each information transmitting end and the information receiving end is obtained, wherein the cascade channel gain represents the channel gain from the information transmitting end through the passive six-dimensional movable antenna surface to the information receiving end;

[0013] Taking each information sending end as a target information sending end one by one, the cascade channel gain corresponding to the target information sending end is the target cascade channel gain, and the beamforming parameter corresponding to the target information sending end is the target beamforming parameter;

[0014] A first signal-to-noise ratio term is obtained based on the product of the target cascade channel gain and the target beamforming parameter, and a second signal-to-noise ratio term is obtained by accumulating the products of the cascade channel gains of other information transmitting ends and the target beamforming parameter;

[0015] Obtaining a reception signal-to-noise ratio between the information receiving end and the target information sending end based on a ratio of the first signal-to-noise ratio item to the second signal-to-noise ratio item;

[0016] All the received signal-to-noise ratios are accumulated to obtain a transmission rate optimization function, and the transmission rate optimization model is generated based on the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter, and the beamforming parameter.

[0017] In some embodiments, the first transmission parameter includes a first channel complex gain between each of the information transmitting ends and each of the passive intelligent reflecting surfaces, the second transmission parameter includes a second channel complex gain between each of the passive intelligent reflecting surfaces and the information receiving end, and obtaining the cascade channel gain between each information transmitting end and the information receiving end based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the first transmission parameter, and the second transmission parameter includes:

[0018] Obtaining a first array response vector between each of the information transmitting ends and each of the passive intelligent reflection surfaces based on the antenna position parameters and the antenna angle parameters, and obtaining a second array response vector between each of the passive intelligent reflection surfaces and the information receiving end based on the antenna position parameters and the antenna angle parameters;

[0019] Obtaining a transmission channel gain between each of the information transmitting ends and each of the passive intelligent reflection surfaces based on the product of the first channel complex gain and the first array response vector, and obtaining a receiving channel gain between each of the passive intelligent reflection surfaces and the information receiving end based on the product of the second channel complex gain and the second array response vector;

[0020] Based on the transmission channel gain, the phase angle parameter and the receiving channel gain, a unit cascade channel gain between each information transmitting end and the information receiving end is obtained, wherein the cascade channel gain represents the channel gain from the information transmitting end through the passive intelligent reflective surface to the information receiving end;

[0021] The cascade channel gain is obtained by accumulating all the unit cascade channel gains.

[0022] In some embodiments, obtaining a first array response vector between each of the information transmitting ends and each of the passive intelligent reflective surfaces based on the antenna position parameter and the antenna angle parameter includes:

[0023] Obtaining a rotation matrix corresponding to the antenna position parameter, and generating a coordinate system position of the passive intelligent reflector based on the rotation matrix and the antenna angle parameter;

[0024] Generate an arrival angle parameter of the passive intelligent reflector based on the antenna angle parameter, and obtain a path direction vector between the information transmitting end and the passive intelligent reflector based on the sine value and cosine value of the arrival angle parameter;

[0025] The first array response vector is obtained based on the product of the coordinate system position, the path direction vector and an index parameter.

[0026] In some embodiments, generating the transmission rate optimization model based on the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter, and the beamforming parameter includes:

[0027] Obtaining position parameter constraints and non-overlap constraints of the antenna position parameters;

[0028] Generate a signal non-reflection constraint and a signal non-blocking constraint based on the antenna position parameter and the antenna angle parameter;

[0029] Obtaining a phase parameter unit modulus constraint of the phase angle parameter;

[0030] The transmission rate optimization model is generated based on the transmission rate optimization function, the antenna position parameters, the antenna angle parameters, the phase angle parameters, the beamforming parameters, the position parameter constraints, the non-overlap constraints, the signal non-reflection constraints, the signal non-blocking constraints and the phase parameter unit mode constraints.

[0031] In some embodiments, solving the transmission rate optimization model to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle, and an optimized beamforming includes:

[0032] Dividing the transmission rate optimization model into an antenna position angle optimization model, a phase angle optimization model and a beamforming optimization model;

[0033] Using a mean square error method, the beamforming optimization model is solved based on the antenna position parameter, the antenna angle parameter, and the phase angle parameter to obtain an updated beamforming;

[0034] Solving the antenna position angle optimization model based on the updated beamforming and the phase angle parameter to obtain an updated antenna position and an updated antenna angle;

[0035] Solving the phase angle optimization model based on the updated antenna position, the updated antenna angle, and the updated beamforming to obtain an updated phase angle;

[0036] The updated beamforming is used as the new beamforming parameter, the updated antenna position is used as the new antenna position parameter, the updated antenna angle is used as the new antenna angle parameter, and the updated phase angle is used as the new phase angle parameter. The antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model are iteratively solved until the transmission rate value converges, and the updated beamforming is used as the optimized beamforming, the updated antenna position is used as the optimized antenna position, the updated antenna angle is used as the optimized antenna angle, and the updated phase angle is used as the optimized phase angle.

[0037] In some embodiments, the antenna position angle optimization model includes an antenna position optimization model, and the step of generating the antenna position angle optimization model includes:

[0038] Acquire an adaptive step size and a feasible direction parameter corresponding to the antenna position parameter;

[0039] Generate a feasible direction optimization model based on the feasible direction parameter and the transmission rate optimization model;

[0040] Solving the feasible direction optimization model to obtain an optimized feasible direction;

[0041] The antenna position optimization model is generated based on the product of the optimization feasible direction and the adaptive step size.

[0042] In some embodiments, the step of generating the phase angle optimization model includes:

[0043] Based on multi-ratio fractional programming, a first slack variable and a second slack variable, the transmission rate optimization model is converted into an equivalent transmission rate optimization model;

[0044] Solving the equivalent transmission rate optimization model based on the phase angle parameter to obtain a first optimized slack variable and a second optimized slack variable;

[0045] The equivalent transmission rate optimization model is converted into the phase angle optimization model based on the first optimization slack variable and the second optimization slack variable.

[0046] To achieve the above-mentioned purpose, the second aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the parameter optimization method of the passive six-dimensional movable antenna communication system as described in the first aspect.

[0047] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, it implements the parameter optimization method of the passive six-dimensional movable antenna communication system described in the first aspect above.

[0048] The parameter optimization method and related equipment of the passive six-dimensional movable antenna communication system proposed in the embodiment of the present application, the passive six-dimensional movable antenna communication system includes at least one information sending end, an information receiving end and a passive six-dimensional movable antenna surface, the passive six-dimensional movable antenna surface includes at least one passive intelligent reflection surface, the method includes: first, obtaining a first transmission parameter between each information sending end and the passive six-dimensional movable antenna surface, and obtaining a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end; then, obtaining the antenna position parameter, antenna angle parameter and phase angle parameter of each passive intelligent reflection surface, and obtaining the beamforming parameter of the information receiving end; secondly, based on the antenna position parameter, antenna angle parameter, phase angle parameter, beam forming parameters, a first transmission parameter and a second transmission parameter to generate a transmission rate optimization model; next, solving the transmission rate optimization model to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle and an optimized beamforming, wherein the optimized antenna position, the optimized antenna angle, the optimized phase angle and the optimized beamforming are used to improve the data transmission rate between the information sending end and the information receiving end; finally, adjusting the three-dimensional position of at least one passive intelligent reflective surface based on the optimized antenna position, adjusting the three-dimensional rotation angle of at least one passive intelligent reflective surface based on the optimized antenna angle, adjusting the phase angle of at least one passive intelligent reflective surface based on the optimized phase angle, and adjusting the beamforming of the information receiving end based on the optimized beamforming. The embodiment of the present application sets a passive six-dimensional movable antenna surface between the information sending end and the information receiving end, and solves the transmission rate optimization model in combination with the real-time first transmission parameters between the information sending end and the passive six-dimensional movable antenna surface and the second transmission parameters between the passive six-dimensional movable antenna surface and the information receiving end, so as to obtain the optimized antenna position, optimized antenna angle, optimized phase angle and optimized beam forming that can effectively improve the data transmission rate between the information sending end and the information receiving end, and adjusts the parameters of the passive six-dimensional movable antenna surface and the information receiving end according to the optimized antenna position, optimized antenna angle, optimized phase angle and optimized beam forming, thereby effectively improving the data transmission rate between the information sending end and the information receiving end in the wireless communication system.

[0049] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a structural schematic diagram of a passive six-dimensional movable antenna communication system provided in one embodiment of the present application.

[0051] Figure 2 This is a flowchart of a parameter optimization method for a passive six-dimensional movable antenna communication system provided by another embodiment of the present application.

[0052] Figure 3 yes Figure 2 Flow chart of step 203 in FIG.

[0053] Figure 4 yes Figure 3 Flow chart of step 301 in FIG.

[0054] Figure 5 yes Figure 4 Flow chart of step 401 in FIG.

[0055] Figure 6 yes Figure 3 Flow chart of step 305 in FIG.

[0056] Figure 7 yes Figure 2 Flow chart of step 204 in FIG.

[0057] Figure 8 This is a flowchart of generating an antenna position angle optimization model provided by another embodiment of the present application.

[0058] Fig. 9 This is a flowchart of generating a phase angle optimization model provided by another embodiment of the present application.

[0059] Fig.10 It is a performance simulation diagram of a parameter optimization method for a passive six-dimensional movable antenna communication system provided by another embodiment of the present application.

[0060] Fig.11 This is another performance simulation diagram of a parameter optimization method for a passive six-dimensional movable antenna communication system provided by another embodiment of the present application.

[0061] Fig.12 This is a schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0065] First, some nouns involved in this application are analyzed:

[0066] Passive Intelligent Reflecting Surface (IRS) is an emerging wireless communication technology that integrates a large number of low-cost passive reflective elements on a plane to intelligently reconfigure the wireless propagation environment, thereby significantly improving the performance of wireless communication networks. Different elements of IRS can independently reflect the incident signal by controlling its amplitude and / or phase, achieving sophisticated three-dimensional (3D) passive beamforming for directional signal enhancement or nulling. This technology is in stark contrast to existing transmitter / receiver wireless link adaptation technologies. IRS actively modifies the wireless channel between them through highly controllable and intelligent signal reflection, providing new degrees of freedom to further improve the performance of wireless links.

[0067] In order to meet the growing number of IoT devices in the upcoming sixth-generation wireless network, the demand for data transmission rate between base stations and IoT terminals has also increased. In the related art, there is a method of setting a smart reflection surface between the information receiving end and the information sending end to reflect the signal so as to improve the data transmission rate between the information sending end and the information receiving end.

[0068] However, since the position and angle of the smart reflective surface are fixed after being fixed, it is easily restricted by the position and angle when reflecting signals, which limits its ability to improve the data transmission rate between the information sending end and the information receiving end.

[0069] In order to improve the data transmission rate between an information sending end and an information receiving end in a wireless communication system, an embodiment of the present application sets a passive six-dimensional movable antenna surface between the information sending end and the information receiving end, and solves a transmission rate optimization model in combination with a real-time first transmission parameter between the information sending end and the passive six-dimensional movable antenna surface and a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end, so as to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle and an optimized beamforming that can effectively improve the data transmission rate between the information sending end and the information receiving end, and adjusts the parameters of the passive six-dimensional movable antenna surface and the information receiving end according to the optimized antenna position, the optimized antenna angle, the optimized phase angle and the optimized beamforming, thereby effectively improving the data transmission rate between the information sending end and the information receiving end in the wireless communication system.

[0070] In order to better describe the parameter optimization method of the passive six-dimensional movable antenna communication system provided by the present application, the following first describes the passive six-dimensional movable antenna communication system applied to the parameter optimization method of the passive six-dimensional movable antenna communication system. Figure 1 , is a schematic diagram of the structure of a passive six-dimensional movable antenna communication system provided in an embodiment of the present application. Figure 1 As shown, the passive six-dimensional movable antenna communication system includes multiple users (i.e., information sending ends), base stations (i.e., information receiving ends), and a passive six-dimensional movable antenna surface. The passive six-dimensional movable antenna surface includes multiple passive intelligent transmitting units, the information sending end sends data information to the information receiving end, and the passive six-dimensional movable antenna surface transmits the data information to the information receiving end through the passive intelligent transmitting units.

[0071] Among them, the passive intelligent transmitting unit in the passive six-dimensional movable antenna surface is different from the traditional intelligent reflecting surface. The traditional intelligent reflecting surface can only adjust the phase and amplitude angle, while the passive six-dimensional movable antenna surface in the present application combines the advantages of the six-dimensional movable antenna, which can not only adjust the phase and amplitude angle, but also adjust the three-dimensional position and three-dimensional rotation angle, thereby better improving the transmission efficiency of data information.

[0072] like Figure 1 As shown in , in the uplink transmission of a multi-user communication system, B passive intelligent reflectors are deployed in the passive six-dimensional movable antenna to enhance the communication from K single-antenna users (i.e., information transmitters) to the base station BS equipped with M antennas (i.e., information receiver).

[0073] Each passive intelligent reflector is assumed to be a uniform plane array, where N = N x N y Passive intelligent reflection units, N x and Ny denote the number of reflector units in rows and columns, respectively. The passive smart reflector is connected to a central controller via an extendable and rotatable rod embedded with flexible cables, so its 3D position, 3D rotation, and reflection coefficient can be adjusted collaboratively.

[0074] Based on the above passive six-dimensional movable antenna communication system, the parameter optimization method and related equipment of the passive six-dimensional movable antenna communication system provided in the embodiment of the present application will be further described below. The parameter optimization method of the passive six-dimensional movable antenna communication system provided in the embodiment of the present application can be applied to the control server in the passive six-dimensional movable antenna communication system, etc.

[0075] The following is a detailed description of the parameter optimization method of the passive six-dimensional movable antenna communication system in the embodiment of the present application. Figure 2 , which is an optional flow chart of a parameter optimization method for a passive six-dimensional movable antenna communication system provided in an embodiment of the present application, Figure 2 The method in the embodiment may include but is not limited to steps 201 to 205. Figure 2 The order of step 201 to step 205 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0076] Step 201: Acquire a first transmission parameter between each information transmitting end and the passive six-dimensional movable antenna surface, and acquire a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end.

[0077] The following is a detailed description of step 201.

[0078] In some embodiments, in response to at least one information sending end making a data information sending request to an information receiving end, in order to further improve the transmission rate between the information sending end and the information receiving end, it is necessary to appropriately adjust the phase, position and angle of multiple passive intelligent reflective surfaces in the surface of the passive six-dimensional movable antenna and the beamforming of the information receiving end based on relevant parameters of the real-time passive six-dimensional movable antenna communication system.

[0079] Therefore, it is necessary to first obtain the real-time first transmission parameters between each information transmitting end and the passive six-dimensional movable antenna surface and the real-time second transmission parameters between the passive six-dimensional movable antenna surface and the information receiving end. Among them, the first transmission parameters include the first channel complex gain a between each information transmitting end and each passive intelligent reflective surface k,l , and the transmission power P of each smart reflector k, where k represents the index parameter of the information sending end, and l represents the propagation path index parameter between the information sending end and the passive intelligent reflective surface. The second transmission parameter includes the second channel complex gain v between the passive six-dimensional movable antenna surface and the information receiving end p , where p represents the propagation path index parameter between the passive intelligent reflective surface and the information receiving end.

[0080] Step 202: Acquire antenna position parameters, antenna angle parameters, and phase angle parameters of each passive intelligent reflector, and acquire beamforming parameters of the information receiving end.

[0081] Step 202 is described in detail below.

[0082] In some embodiments, and Denote the baseband equivalent channels from the kth information transmitter to the bth passive intelligent reflector and from the bth passive intelligent reflector to the information receiver. Denote the antenna position parameter and antenna angle parameter of the bth passive intelligent reflector as q b and u b , and in and x b ,y b , z b They represent the coordinates of the center point of the bth passive intelligent reflector in the global Cartesian coordinate system (CCS) O-xyz. x ∈[0,2n),ζ y ∈[0, 2π), and ζ z ∈[0, 2π) represent the rotation angles relative to the x-axis, y-axis, and z-axis, respectively. represents the phase angle parameter of the bth passive smart reflector, where the reflection amplitude is set to 1 to maximize the signal reflection power, that is,

[0083] In addition, for uplink transmission, the information receiving end applies a linear receive beamforming vector (i.e., beamforming parameter) To decode the signal x corresponding to the data information sent from the kth information transmitter k .

[0084] Step 203: Generate a transmission rate optimization model based on the antenna position parameters, antenna angle parameters, phase angle parameters, beamforming parameters, first transmission parameters, and second transmission parameters.

[0085] The following is a detailed description of step 203.

[0086] In some embodiments, based on the obtained real-time first transmission parameters and second transmission parameters, as well as the constructed antenna position parameters, antenna angle parameters, phase angle parameters, and beamforming parameters, a transmission rate optimization model can be further generated to improve the data transmission rate from the information transmitting end through the passive six-dimensional movable antenna surface and reaching the information receiving end, as described below.

[0087] Reference Figure 3 , based on antenna position parameters, antenna angle parameters, phase angle parameters, beamforming parameters, first transmission parameters and second transmission parameters, a transmission rate optimization model is generated, including the following steps 301 to 305.

[0088] Step 301: Based on antenna position parameters, antenna angle parameters, phase angle parameters, first transmission parameters, and second transmission parameters, a cascade channel gain between each information transmitting end and each information receiving end is obtained.

[0089] Step 301 is described in detail below.

[0090] In some embodiments, in order to generate a suitable transmission rate optimization model, it is first necessary to generate a cascade channel gain h starting from each information transmitting end through the passive six-dimensional movable antenna surface and reaching the information receiving end based on the obtained antenna position parameters, antenna angle parameters, phase angle parameters, first transmission parameters and second transmission parameters. k (q b ,u b ), so as to subsequently use the cascade channel gain to construct a suitable transmission rate optimization model, as described below.

[0091] Reference Figure 4 , based on antenna position parameters, antenna angle parameters, phase angle parameters, first transmission parameters and second transmission parameters, the cascade channel gain between each information sending end and the information receiving end is obtained, including the following steps 401 to 404.

[0092] Step 401: obtaining a first array response vector between each information transmitting end and each passive intelligent reflection surface based on antenna position parameters and antenna angle parameters, and obtaining a second array response vector between each passive intelligent reflection surface and the information receiving end based on antenna position parameters and antenna angle parameters.

[0093] Step 401 is described in detail below.

[0094] In some embodiments, to construct a suitable cascade channel gain h k (q b ,u b ), it is necessary to calculate the antenna position parameter q in advanceb and antenna angle parameter u b Get the appropriate first array response vector t between each information transmitting end and each passive intelligent reflector k,l (q b ,u b ); accordingly, based on the antenna position parameter q b and antenna angle parameter u b Get the second array response vector between each passive intelligent reflector and the information receiving end The details are described as follows.

[0095] Reference Figure 5 , obtaining a first array response vector between each information transmitting end and each passive intelligent reflective surface based on antenna position parameters and antenna angle parameters, including the following steps 501 to 503.

[0096] Step 501: Obtain a rotation matrix corresponding to the antenna position parameters, and generate a coordinate system position of the passive intelligent reflector based on the rotation matrix and the antenna angle parameters.

[0097] Step 502: Generate an arrival angle parameter of the passive intelligent reflective surface based on the antenna angle parameter, and obtain a path direction vector between the information transmitting end and the passive intelligent reflective surface based on the sine value and cosine value of the arrival angle parameter.

[0098] Step 503: Obtain a first array response vector based on the product of the coordinate system position, the path direction vector and the index parameter.

[0099] Steps 501 to 503 are described in detail below.

[0100] In some embodiments, it is first necessary to obtain the antenna position parameter u b The corresponding rotation matrix As shown in the following formula ().

[0101]

[0102] where c x = cos(x) and s x = sin(x). Then based on the rotation matrix R(u b ) and antenna angle parameter q b The coordinate system position of the passive intelligent reflector is generated as in Characterizes the position of the nth antenna of the bth passive smart reflector in the global Cartesian coordinate system.

[0103] Furthermore, based on the antenna angle parameter u b Generate the arrival angle parameters of the passive smart reflector (i.e. ), and based on the sine and cosine values ​​of the arrival angle parameter, the path direction vector f between the information transmitter and the passive intelligent reflector is obtained. k，l As shown in the following formula (2).

[0104]

[0105] Path direction vector f k，l and p Represents the unit norm arrival direction vector of the first propagation path of the kth information sender and the pth path to the information receiver, both of which are relative to Figure 1 is defined as the reference position O in , and λ is the carrier wavelength.

[0106] Next, based on the coordinate system position r b，n (q b ,u b ), path direction vector f k，l 、s p And the product of the exponential parameter, the first array response vector t is obtained k,l (q b ,u b ) and the second array response vector e p (q b ,u b ) is shown in the following formula (3).

[0107]

[0108] First array response vector and the second array response vector They respectively represent the array response vector of the bth passive intelligent reflector surface to the lth transmission path of the kth information transmitter and toward the pth reflection path.

[0109] Through the above, steps 501 to 503, the rotation matrix corresponding to the antenna position parameters and the coordinate system position accurately determined by the antenna angle parameters and the rotation matrix are used to accurately generate the first array response vector and the second array response vector, thereby improving the accuracy of the subsequent construction of the cascade channel gain between each information transmitting end and the information receiving end.

[0110] Step 402: Obtain a transmission channel gain between each information transmitting end and each passive intelligent reflection surface based on the product of the first channel complex gain and the first array response vector, and obtain a receiving channel gain between each passive intelligent reflection surface and the information receiving end based on the product of the second channel complex gain and the second array response vector.

[0111] Step 403: Based on the transmission channel gain, the phase angle parameter and the reception channel gain, the unit cascade channel gain between each information transmitting end and the information receiving end is obtained.

[0112] Step 404: Accumulate all unit cascade channel gains to obtain the cascade channel gain.

[0113] Steps 402 to 404 are described in detail below.

[0114] In some embodiments, after obtaining the first array response vector t k，l (q b ,u b ) and the second array response vector e p (q b ,u b ) and then, using the first channel complex gain α k，l and the first array response vector t k，l (q b ,u b ) to obtain the transmission channel gain g between each information transmitter and each passive intelligent reflector k (q b ,u b ), and based on the obtained second channel complex gain v p and the second array response vector e p (q b ,u b ) to obtain the receiving channel gain V(q b ,u b ) is shown in the following formula (4).

[0115]

[0116] Among them, L k and P represent the number of propagation paths between the kth information transmitter and the bth passive intelligent reflector and between the bth passive intelligent reflector and the information receiver, respectively. k，l and v p represents the complex gain of the corresponding channel, and denote the incident and reflected radiation patterns of each reflective element on the surface, corresponding to the lth path from user k and the pth path to the base station, respectively, which are determined by the angle of arrival (or angle of departure) in elevation and azimuth. and the rotation u of the passive surface b b Specifically, usually the l=1 and p=1 paths are line-of-sight paths, and the remaining L k The -1 and P-1 paths are non-line-of-sight paths.p Represents the direction vector of the information receiving end.

[0117] Next, based on the obtained transmit channel gain g k (q b ,u b ), phase angle parameter θ b and the receiving channel gain g k (q b ,u b ), the unit cascade channel gain between each information sending end and information receiving end is obtained as shown in the following formula (5).

[0118] h k (q b ,u b )=V(q b ,u b )diag(θ b ) k (q b ,u b ) (5)

[0119] The cascade channel gain h k (q b ,u b ) represents the channel gain from the kth information sending end to the information receiving end after passing through the bth passive intelligent reflector.

[0120] In addition, the arrival angle ψ of the fixed uniform linear array with M antennas at the base station (information receiving end) and the pth path p The associated array response vector, can be defined in a similar way. and They represent the position, rotation and reflection coefficient set of all passive intelligent reflectors respectively, and the cascade channel gain h is obtained by accumulating all unit cascade channel gains. k (q, u), that is, the effective channel from the kth information sending end to the information receiving end through all passive intelligent reflection surfaces can be expressed as shown in the following formula (6).

[0121] h k (q,u)=V(q,u)diag(θ)g k (q,u), (6)

[0122] in

[0123]

[0124] Through the above steps 401 to 404, by using the real-time first channel complex gain and second channel complex gain, and the first array response vector and the second array response vector obtained by the antenna position parameters and the antenna angle parameters, it is possible to accurately obtain the unit cascade channel gain that is accurate and corresponds to the real-time state from each information transmitting end through each passive intelligent transmitting surface to the information receiving end, so as to further obtain the accurate cascade channel gain, thereby improving the reliability of the subsequent use of the cascade channel gain to construct a suitable transmission rate optimization model.

[0125] Step 302: Each information sending end is taken as a target information sending end one by one.

[0126] Step 303: A first signal-to-noise ratio term is obtained based on the product of the target cascade channel gain and the target beamforming parameter, and the product of the cascade channel gain and the target beamforming parameter of other information transmitting ends is accumulated to obtain a second signal-to-noise ratio term.

[0127] Step 304: Based on the ratio of the first signal-to-noise ratio item and the second signal-to-noise ratio item, a receiving signal-to-noise ratio between the information receiving end and the target information sending end is obtained.

[0128] Step 305: Accumulate all received signal-to-noise ratios to obtain a transmission rate optimization function, and generate a transmission rate optimization model based on the transmission rate optimization function, antenna position parameters, antenna angle parameters, phase angle parameters, and beamforming parameters.

[0129] Steps 302 to 305 are described in detail below.

[0130] In some embodiments, after obtaining the cascade channel gain h between each information transmitting end and the information receiving end k (q, u) After that, each information sending end is taken as the target information sending end one by one, and the cascade channel gain corresponding to the target information sending end is the target cascade channel gain h k (q, u), the beamforming parameter corresponding to the target information transmitter is the target beamforming parameter

[0131] Then, based on the target cascade channel gain h k (q,u) and target beamforming parameters The first signal-to-noise ratio term is obtained by multiplying in is the transmission data symbol of the kth information transmitter; and the product of the cascade channel gain of other information transmitters and the target beamforming parameter is accumulated to obtain the second signal-to-noise ratio term Based on this, the linear receiving beam forming vector can be obtained by the information receiving end. To decode the signal x from the kth information sender k, as shown in the following formula (8).

[0132]

[0133] in, is the additive white Gaussian noise at the information receiving end, where σ 2 is the noise power. Further, based on the first signal-to-noise ratio term and the second SNR term The ratio of is used to obtain the received signal-to-noise ratio between the information receiving end and the target information sending end, that is, the signal-to-noise ratio expression used by the information receiving end to decode the target information sending end is shown in the following formula (9).

[0134]

[0135] Therefore, based on the received signal-to-noise ratio, the achievable data rate per Hz from the kth information transmitter to the information receiver (in bps / Hz) is log2(1+Y k ), accumulating all achievable data rates can obtain the transmission rate optimization function

[0136] The following will further generate a transmission rate optimization model based on the transmission rate optimization function, antenna position parameters, antenna angle parameters, phase angle parameters and beamforming parameters.

[0137] Reference Figure 6 , based on the transmission rate optimization function and antenna position parameter q b , antenna angle parameter u b , phase angle parameter θ b and beamforming parameters Generating a transmission rate optimization model includes the following steps 601 to 604.

[0138] Step 601: Obtain position parameter constraints and non-overlap constraints of antenna position parameters.

[0139] Step 602: Generate a signal non-reflection constraint and a signal non-blocking constraint based on the antenna position parameters and the antenna angle parameters.

[0140] Step 603: Obtain a phase parameter unit modulus constraint of a phase angle parameter.

[0141] Step 604: Generate a transmission rate optimization model based on the transmission rate optimization function, antenna position parameters, antenna angle parameters, phase angle parameters, beamforming parameters, position parameter constraints, non-overlap constraints, signal non-reflection constraints, signal non-blocking constraints, and phase parameter unit mode constraints.

[0142] Steps 601 to 604 are described in detail below.

[0143] In some embodiments, in order to generate a suitable and reliable transmission rate optimization model, it is also necessary to obtain constraints matching the actual application scenario in advance, specifically including the position parameter constraints of the antenna position parameter qb Ensure that the movable areas of all passive smart reflective surfaces are located in a given three-dimensional space inside (e.g., a cube or a sphere); antenna location parameters The non-overlapping constraint d min Represents the minimum distance. This constraint is used to avoid overlap between passive smart reflectors. It is related to the antenna position parameter and antenna angle parameters Related signal non-reflection constraints Used to avoid mutual reflection of signals between any two passive intelligent reflectors; related to antenna position parameters and antenna angle parameters The associated signal does not block the constraint Used to prevent the passive intelligent reflector from facing the middle of the information receiving end; and the phase parameter unit mode constraint of each transmitting representative in the phase angle parameter θ

[0144] Then further, the transmission rate optimization function As an optimization function, the antenna position parameters of all passive smart reflectors are Antenna angle parameters and phase angle parameter θ and beamforming parameter at the information receiving end As optimization variables, position parameter constraints, non-overlap constraints, signal non-reflection constraints, signal non-blocking constraints and phase parameter unit mode constraints are used as constraints to construct a transmission rate optimization model as shown in the following formula (10).

[0145]

[0146] Through the above steps 601 to 604, a transmission rate optimization model is constructed with the goal of optimizing all transmission rates by utilizing multiple constraints corresponding to actual applications and combining the required optimization variables (antenna position parameters, antenna angle parameters and phase angle parameters of all passive intelligent reflective surfaces and beamforming parameters of the information receiving end). The optimization parameters subsequently obtained by solving the transmission rate optimization model can effectively improve the transmission rate between the information sending end and the information receiving end.

[0147] Step 204: Solve the transmission rate optimization model to obtain the optimized antenna position, optimized antenna angle, optimized phase angle, and optimized beamforming.

[0148] Step 204 is described in detail below.

[0149] In some embodiments, after obtaining a transmission rate optimization model (10) corresponding to relevant transmission parameters of a real-time passive six-dimensional movable antenna communication system, it is necessary to solve the transmission rate optimization model (10) to obtain an optimized antenna position, optimized antenna angle, optimized phase angle, and optimized beamforming that can effectively improve the transmission rate between an information transmitter and an information receiver.

[0150] With respect to the transmission rate optimization model (10), due to the coupling of variables, non-concave objective function and non-convex constraints, the transmission rate optimization model (10) is a non-convex problem. The embodiment of the present application adopts an alternating iterative method to solve it, as described below.

[0151] Reference Figure 7 , solving the transmission rate optimization model to obtain the optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming, including the following steps 701 to 705.

[0152] Step 701: Divide the transmission rate optimization model into an antenna position angle optimization model, a phase angle optimization model, and a beamforming optimization model.

[0153] Step 702: Using a mean square error method, the beamforming optimization model is solved based on antenna position parameters, antenna angle parameters, and phase angle parameters to obtain updated beamforming.

[0154] Step 703: Solve the antenna position angle optimization model based on the updated beamforming and phase angle parameters to obtain an updated antenna position and an updated antenna angle.

[0155] Step 704: Solve the phase angle optimization model based on the updated antenna position, updated antenna angle, and updated beamforming to obtain an updated phase angle.

[0156] Step 705: Use the updated beamforming as a new beamforming parameter, the updated antenna position as a new antenna position parameter, the updated antenna angle as a new antenna angle parameter, and the updated phase angle as a new phase angle parameter, iteratively solve the antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model until the transmission rate value converges, and use the updated beamforming as the optimized beamforming, the updated antenna position as the optimized antenna position, the updated antenna angle as the optimized antenna angle, and the updated phase angle as the optimized phase angle.

[0157] Steps 701 to 705 are described in detail below.

[0158] In some embodiments, the transmission rate optimization model is divided into an antenna position angle optimization model corresponding to antenna position parameters and antenna angle parameters, a phase angle optimization model corresponding to phase angle parameters, and a beamforming optimization model corresponding to beamforming parameters based on multiple optimization variables. The details are described as follows.

[0159] For the beamforming optimization model, for any given position parameter q, rotation angle u, and reflection coefficient θ of the passive intelligent reflector, the receiving beamforming of the information receiving end is designed based on the minimum mean square error (MMSE) method, that is, the beamforming optimization model is shown in the following formula (11).

[0160]

[0161] Where H(q,u)=[h1(q,u),…,h K (q,u)] and They respectively represent the channel matrix of the effective information sending end and the information receiving end and the diagonal transmission power matrix of K information sending ends.

[0162] The following will further describe how to generate an antenna position angle optimization model, wherein the antenna position angle optimization model includes an antenna position optimization model corresponding to antenna position parameters and an antenna angle optimization model corresponding to antenna angle parameters.

[0163] Reference Figure 8 , wherein the step of generating the antenna position angle optimization model includes the following steps 801 to 804.

[0164] Step 801: Acquire the adaptive step size and feasible direction parameters corresponding to the antenna position parameters.

[0165] Step 802: Generate a feasible direction optimization model based on the feasible direction parameters and the transmission rate optimization model.

[0166] Step 803: Solve the feasible direction optimization model to obtain the optimized feasible direction.

[0167] Step 804: Generate an antenna position optimization model based on the product of the optimized feasible direction and the adaptive step size.

[0168] Steps 801 to 804 are described in detail below.

[0169] In each iteration of the alternating optimization algorithm proposed in the embodiment of the present application, given the reflection coefficient θ and the beamforming matrix W, by sequentially updating the antenna position parameters and antenna angle parameters of each passive smart reflector, that is, q b and u b, while the antenna position parameters and antenna angle parameters of the remaining passive intelligent reflectors are fixed, thereby optimizing the antenna position parameters and antenna angle parameters of all passive intelligent reflectors.

[0170] For each smart reflector, the antenna position parameter optimization and antenna angle optimization are firstly carried out. b In the case of , firstly, the antenna position parameter q at the last iteration (t-1) is b Using the first-order Taylor expansion at the value, the second non-convex constraint in the transmission rate optimization model (10) is approximated as a convex constraint as shown in the following formula (12).

[0171]

[0172] Therefore, relative to the antenna position parameter q b The feasible region of is a convex set. Then, the feasible gradient descent method is used to obtain the antenna position parameter q in the current tth iteration. b That is, we first need to obtain the adaptive step length κ corresponding to the antenna position parameters of the smart reflector t-t ∈(0,1] and feasible direction parameter d t-1 , where κ t-1 ∈(0,1] is the adaptive step size calculated by the Armijo rule.

[0173] Next, based on the feasible direction parameters, the transmission rate optimization model can be transformed into a feasible direction optimization model as shown in the following formula (13).

[0174]

[0175] in, It can be obtained through numerical solution, as expressed in the following formula (14).

[0176]

[0177] Among them, a j is a vector whose jth element is 1 and the rest are 0. Finally, for q b The non-convex problem is converted into a linear optimization problem It can be solved efficiently by linear programming.

[0178] Then, by solving the feasible direction optimization model (13), the optimized feasible direction d can be obtained. t-1 Then further optimize the feasible direction d t-1 and adaptive step size κ t-1 The product of can generate the antenna position optimization model as shown in the following formula (13).

[0179]

[0180] It can be understood that the antenna position optimization model (14) is obtained to solve the antenna position parameter q b Similarly, the antenna angle optimization model is obtained to solve the antenna angle parameter u b It can also be obtained through related steps similar to the above steps 801 to 804.

[0181] How to construct a phase angle optimization model corresponding to the phase angle parameters will be further described below.

[0182] Reference Fig. 9 , wherein the step of generating the phase angle optimization model includes the following steps 901 to 903.

[0183] Step 901: Based on multi-ratio fractional programming, a first slack variable and a second slack variable, a transmission rate optimization model is converted into an equivalent transmission rate optimization model.

[0184] Step 902: Solve the equivalent transmission rate optimization model based on the phase angle parameter to obtain a first optimized slack variable and a second optimized slack variable.

[0185] Step 903: Convert the equivalent transmission rate optimization model into a phase angle optimization model based on the first optimized slack variable and the second optimized slack variable.

[0186] Steps 901 to 903 are described in detail below.

[0187] In some embodiments, for the optimization solution of the phase angle parameter θ, under the condition of fixed beamforming parameter W, antenna position parameter q and antenna angle parameter u, two auxiliary variables (including the first slack variable α=[α1,…,α K ] and the second slack variable ∈=[∈1,…,∈ K ]), and then the objective function in the transmission rate optimization model (10) is converted into the following equivalent transmission rate optimization model through multi-ratio fractional programming as shown in the following formula (15).

[0188]

[0189] Where * indicates conjugation, A k =V(q,u)diag(g k (q,u)). When the phase angle parameter θ is fixed, the formula (15) is solved to obtain the optimal first optimization relaxation variable α k and the second optimized slack variable ∈ k It is expressed as shown in the following formula (16).

[0190]

[0191] Therefore, the jth element of the phase angle parameter θ, i.e., θ j That is, the equivalent transmission rate optimization model is transformed into a phase angle optimization model using the first optimized slack variable and the second optimized slack variable. This problem is shown in the following formula (17).

[0192]

[0193] Among them, u j,j is the element in the jth row and jth column of the matrix U, where v j is the jth element of the vector v. Then, the optimal θ j Can be Obtain, among which Other reflection coefficients θ i , i≠j can be updated in a similar way.

[0194] After obtaining the antenna position angle optimization model, the phase angle optimization model and the beamforming optimization model, an alternating optimization algorithm is used to obtain updated beamforming, updated antenna position, updated antenna angle and updated phase angle in each iteration, and the updated beamforming is used as the beamforming parameter of a new iteration, the updated antenna position is used as the antenna position parameter of a new iteration, the updated antenna angle is used as the antenna angle parameter of a new iteration, and the updated phase angle is used as the phase angle parameter of a new iteration. Then, the antenna position angle optimization model (14), the phase angle optimization model (17) and the beamforming optimization model (11) are iteratively solved until the transmission rate value converges, and the updated beamforming is used as the optimized beamforming, the updated antenna position is used as the optimized antenna position, the updated antenna angle is used as the optimized antenna angle, and the updated phase angle is used as the optimized phase angle.

[0195] Through the above steps 701 to 705, steps 801 to 804, and steps 901 to 903, the alternating optimization algorithm is used to transform the original transmission rate optimization model into a sub-optimization model for multiple optimization variables, and in the transformation process, the convex optimization transformation method and the least squares method are used to obtain a suitable sub-optimization model corresponding to each optimization variable, thereby improving the accuracy of solving the transmission rate optimization model, so as to obtain the optimal optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming, thereby improving the transmission rate in the current passive six-dimensional movable antenna communication system.

[0196] Step 205: Adjust the three-dimensional position of at least one passive intelligent reflective surface based on the optimized antenna position, adjust the three-dimensional rotation angle of at least one passive intelligent reflective surface based on the optimized antenna angle, adjust the phase angle of at least one passive intelligent reflective surface based on the optimized phase angle, and adjust the beamforming of the information receiving end based on the optimized beamforming.

[0197] The following is a detailed description of step 205.

[0198] In some embodiments, after obtaining the optimal optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming corresponding to the relevant communication parameters in the current passive six-dimensional movable antenna communication system, the three-dimensional position of the corresponding passive intelligent reflecting surface is adjusted based on the optimized antenna position, the three-dimensional rotation angle of the corresponding passive intelligent reflecting surface is adjusted based on the optimized antenna angle, the phase angle of the corresponding passive intelligent reflecting surface is adjusted based on the optimized phase angle, and the beamforming of the information receiving end is adjusted based on the optimized beamforming, thereby improving the transmission rate between the information sending end and the information receiving end in the current passive six-dimensional movable antenna communication system.

[0199] In order to further verify the reliability of the parameter optimization method of the passive six-dimensional movable antenna communication system provided by the present application, the present embodiment performs performance simulation. The carrier wavelength is set to λ = 0.125. The number of reflection units of each passive intelligent reflection surface is equal. The number of antennas at the information receiving end is M = 6, and the number of antennas at the message sending end is K = 6. d min Set as In the channel model, set L k =2, where and and Randomly generated in the interval [0,π / 2] and [π,3π / 2] respectively. ψ p , and They are randomly generated in the intervals [0,π / 2], [π / 2,π] and [-π / 2,0] respectively. The transmission power of each message sender are set equal. Noise power σ 2 Set to -80dBm.

[0200] In order to study the effectiveness of the proposed parameter optimization method for the passive six-dimensional movable antenna communication system, it seems necessary to consider the following schemes (with the same total number of reflection units) for performance comparison.

[0201] 1. Distributed passive 6DMA (six-dimensional movable antenna), with multiple surfaces, i.e. B = 4, N = 4;

[0202] 2. Centralized passive 6DMA (six-dimensional movable antenna) with a single surface, i.e. B = 1, N = 16;

[0203] 3. Traditional fixed IRS (intelligent reflecting surface), N=16.

[0204] Radiation Pattern of a Directional Antenna Set it according to the following expression:

[0205]

[0206]

[0207] Where A represents the area of ​​each reflector unit, usually set to (λ / 2) 2 , <n(u b ),-f k,l >Calculate the arrival direction vector f of the incident signal k,l The cosine of the angle between the external normal vector of the six-dimensional movable antenna surface b in the global coordinate system. In addition, the half-space isotropic radiation pattern of each reflector unit is considered for comparison. The reflected radiation pattern The reflected signal s p The angle between the arrival angle vector and the external normal vector of the six-dimensional movable antenna surface b in the global coordinate system is defined by the cosine of the angle.

[0208] Reference Fig.10 , is a performance simulation diagram of a parameter optimization method for a passive six-dimensional movable antenna communication system provided in an embodiment of the present application. Fig.10 The figure shows the relationship between the total rate of users and the user transmission power in different schemes under the directional antenna radiation mode. It can be observed that the passive 6DMA scheme proposed in this application (whether distributed or centralized) is better than the fixed-position IRS scheme, and the performance advantage becomes more significant as the transmission power increases. This is because the passive six-dimensional movable antenna introduces additional degrees of freedom through position and rotation adjustment. Although the total number of reflection units is the same, the passive six-dimensional movable antenna on the distributed surface is better than the centralized passive six-dimensional movable antenna in performance. This is because the distributed surface has higher flexibility in position and rotation adjustment, and also helps to reduce interference between users. However, this also increases the hardware cost and is more complicated in controlling the movement of each six-dimensional movable antenna surface.

[0209] Reference Fig.11, is another performance simulation diagram of a parameter optimization method for a passive six-dimensional movable antenna communication system provided in an embodiment of the present application. It shows the relationship between the total rate of the reflection unit and the user transmission power under different schemes in the half-space isotropic radiation mode. It can be observed that the performance gap between different schemes is smaller than that of the directional radiation mode of the reflection unit. For example, when the user transmission power is 15dBm, the total rate of the fixed IRS scheme using the half-space isotropic radiation mode is increased by about 5bps / Hz compared with the fixed IRS scheme of the directional radiation mode. In contrast, the total rate of the proposed distributed / centralized passive 6DMA scheme using the half-space isotropic radiation mode is only increased by 2bps / Hz and 3bps / Hz respectively compared with the distributed / centralized passive 6DMA scheme of the directional radiation mode. The above results show that when using distributed passive 6DMA, the actual directional radiation mode has a smaller rate loss than the ideal half-space isotropic radiation mode, because the 6DMA surface rotation can better utilize the directional radiation mode of the reflection unit to suppress interference between users.

[0210] Therefore, the passive six-dimensional mobile antenna-assisted wireless communication system proposed in the present application can provide an efficient communication solution for the wireless communication system.

[0211] The parameter optimization method and related equipment of the passive six-dimensional movable antenna communication system proposed in the embodiment of the present application, the passive six-dimensional movable antenna communication system includes at least one information sending end, an information receiving end and a passive six-dimensional movable antenna surface, the passive six-dimensional movable antenna surface includes at least one passive intelligent reflection surface, and the method includes: first, obtaining a first transmission parameter between each information sending end and the passive six-dimensional movable antenna surface, and obtaining a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end; then, obtaining antenna position parameters, antenna angle parameters and phase angle parameters of each passive intelligent reflection surface, and obtaining beamforming parameters of the information receiving end;Secondly, a rotation matrix corresponding to the antenna position parameters is obtained, and a coordinate system position of the passive intelligent reflector is generated based on the rotation matrix and the antenna angle parameters, an arrival angle parameter of the passive intelligent reflector is generated based on the antenna angle parameters, and a path direction vector between the information sending end and the passive intelligent reflector is obtained based on the sine value and cosine value of the arrival angle parameter, a first array response vector is obtained based on the product of the coordinate system position, the path direction vector and the exponential parameter, and a second array response vector between each passive intelligent reflector and the information receiving end is obtained based on the antenna position parameters and the antenna angle parameters, and a second array response vector between each passive intelligent reflector and the information receiving end is obtained based on the complex gain of the first channel and the complex gain of the second channel. The transmission channel gain between each information transmitting end and each passive intelligent reflection surface is obtained based on the product of the first and second array response vectors, and the receiving channel gain between each passive intelligent reflection surface and the information receiving end is obtained based on the product of the second channel complex gain and the second array response vector. Based on the transmission channel gain, the phase angle parameter and the receiving channel gain, the unit cascade channel gain between each information transmitting end and the information receiving end is obtained. The cascade channel gain represents the channel gain from the information transmitting end through the passive intelligent reflection surface to the information receiving end. The cascade channel gain is obtained by accumulating all the unit cascade channel gains. The channel gain characterizes the channel gain from the information transmitting end through the passive six-dimensional movable antenna surface to the information receiving end. Each information transmitting end is taken as the target information transmitting end one by one. The cascade channel gain corresponding to the target information transmitting end is the target cascade channel gain. The beamforming parameter corresponding to the target information transmitting end is the target beamforming parameter. The first signal-to-noise ratio item is obtained based on the product of the target cascade channel gain and the target beamforming parameter. The product of the cascade channel gains of other information transmitting ends and the target beamforming parameter is accumulated to obtain the second signal-to-noise ratio item. Based on the ratio of the first signal-to-noise ratio item to the second signal-to-noise ratio item, the information The receiving signal-to-noise ratio between the receiving end and the target information sending end is accumulated to obtain the transmission rate optimization function, and the position parameter constraint and non-overlapping constraint of the antenna position parameter are obtained, and the signal non-reflection constraint and the signal non-blocking constraint are generated based on the antenna position parameter and the antenna angle parameter, and the phase parameter unit mode constraint of the phase angle parameter is obtained, and the transmission rate optimization model is generated based on the transmission rate optimization function, antenna position parameters, antenna angle parameters, phase angle parameters, beamforming parameters, position parameter constraints, non-overlapping constraints, signal non-reflection constraints, signal non-blocking constraints and phase parameter unit mode constraints;Next, the transmission rate optimization model is divided into an antenna position angle optimization model, a phase angle optimization model and a beamforming optimization model. The mean square error method is used to solve the beamforming optimization model based on the antenna position parameters, antenna angle parameters and phase angle parameters to obtain an updated beamforming. The antenna position angle optimization model is solved based on the updated beamforming and phase angle parameters to obtain an updated antenna position and an updated antenna angle. The phase angle optimization model is solved based on the updated antenna position, updated antenna angle and updated beamforming to obtain an updated phase angle. The updated beamforming is used as a new beamforming parameter, the updated antenna position is used as a new antenna position parameter, and the updated antenna angle is used as a new antenna. angle parameter, taking the updated phase angle as a new phase angle parameter, iteratively solving the antenna position angle optimization model, the phase angle optimization model and the beamforming optimization model until the transmission rate value converges, and taking the updated beamforming as the optimized beamforming, taking the updated antenna position as the optimized antenna position, taking the updated antenna angle as the optimized antenna angle, and taking the updated phase angle as the optimized phase angle; finally, adjusting the three-dimensional position of at least one passive smart reflector based on the optimized antenna position, adjusting the three-dimensional rotation angle of at least one passive smart reflector based on the optimized antenna angle, adjusting the phase angle of at least one passive smart reflector based on the optimized phase angle, and adjusting the beamforming of the information receiving end based on the optimized beamforming. ;

[0212] The embodiment of the present application utilizes the real-time first channel complex gain and the second channel complex gain, and the first array response vector and the second array response vector obtained by the antenna position parameter and the antenna angle parameter, to accurately obtain the unit cascade channel gain starting from each information sending end through each passive intelligent transmitting surface to reach the information receiving end, which is accurate and corresponds to the real-time state, so as to further obtain the accurate cascade channel gain, thereby improving the reliability of the subsequent use of the cascade channel gain to construct a suitable transmission rate optimization model; and, using the rotation matrix corresponding to the antenna position parameter, and the coordinate system position accurately determined by the antenna angle parameter and the rotation matrix, to correspondingly and accurately generate the first array response vector and the second array response vector, thereby improving the accuracy of the subsequent construction of the cascade channel gain between each information sending end and the information receiving end; and, using the real-time first channel complex gain and the second channel complex gain, and the first array response vector and the second array response vector obtained by the antenna position parameter and the antenna angle parameter, it is possible to accurately obtain the unit cascade channel gain starting from each information sending end through each passive intelligent transmitting surface to reach the information receiving end. The unit cascade channel gain in real-time state can be further obtained to obtain accurate cascade channel gain, thereby improving the reliability of constructing a suitable transmission rate optimization model using cascade channel gain in the future; in addition, a transmission rate optimization model constructed with the goal of optimizing all transmission rates is constructed by using multiple constraints corresponding to actual applications and combining the required optimization variables (antenna position parameters, antenna angle parameters and phase angle parameters of all passive intelligent reflectors and beamforming parameters of the information receiving end), so that the optimization parameters obtained by solving the transmission rate optimization model in the future can effectively improve the transmission rate between the information sending end and the information receiving end; secondly, by using the alternating optimization algorithm, the original transmission rate optimization model is transformed into a sub-optimization model for multiple optimization variables, and the convex optimization transformation method and the least squares method are used in the transformation process to obtain a suitable sub-optimization model corresponding to each optimization variable, thereby improving the accuracy of solving the transmission rate optimization model, so as to obtain the optimal optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming, thereby improving the transmission rate in the current passive six-dimensional movable antenna communication system;In summary, by setting a passive six-dimensional movable antenna surface between the information transmitting end and the information receiving end, and combining the first transmission parameter between the real-time information transmitting end and the passive six-dimensional movable antenna surface and the second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end, the transmission rate optimization model is solved to obtain the optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming that can effectively improve the data transmission rate between the information transmitting end and the information receiving end, and according to the optimized antenna position, optimized antenna angle, optimized phase angle and optimized beamforming, the parameters of the passive six-dimensional movable antenna surface and the information receiving end are adjusted, thereby effectively improving the data transmission rate between the information transmitting end and the information receiving end in the wireless communication system. ;

[0213] The present application also provides an electronic device, including:

[0214] at least one memory;

[0215] at least one processor;

[0216] at least one program;

[0217] The program is stored in the memory, and the processor executes the at least one program to implement the parameter optimization method of the passive six-dimensional movable antenna communication system implemented in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0218] See also Fig.12 , Fig.12 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0219] The processor 1201 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0220] The memory 1202 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 1202 can store operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1202, and the processor 1201 calls and executes the parameter optimization method of the passive six-dimensional movable antenna communication system of the embodiment of the present application;

[0221] Input / output interface 1203, used to implement information input and output;

[0222] The communication interface 1204 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0223] A bus 1205 that transmits information between various components of the device (e.g., the processor 1201, the memory 1202, the input / output interface 1203, and the communication interface 1204);

[0224] The processor 1201 , the memory 1202 , the input / output interface 1203 and the communication interface 1204 are connected to each other in communication within the device via the bus 1205 .

[0225] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the parameter optimization method of the above-mentioned passive six-dimensional movable antenna communication system is implemented.

[0226] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0227] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0228] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0229] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0230] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0231] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0232] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0233] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0234] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0235] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0236] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0237] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A parameter optimization method for a passive six-dimensional movable antenna communication system, characterized in that: The passive six-dimensional movable antenna communication system comprises at least one information transmitting end, an information receiving end and a passive six-dimensional movable antenna surface, wherein the passive six-dimensional movable antenna surface comprises at least one passive intelligent reflective surface, and the method comprises: Acquire a first transmission parameter between each of the information transmitting ends and the passive six-dimensional movable antenna surface, and acquire a second transmission parameter between the passive six-dimensional movable antenna surface and the information receiving end; Acquire antenna position parameters, antenna angle parameters and phase angle parameters of each of the passive intelligent reflective surfaces, and acquire beamforming parameters of the information receiving end; Generate a transmission rate optimization model based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beamforming parameter, the first transmission parameter, and the second transmission parameter; Solving the transmission rate optimization model to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle, and an optimized beamforming, wherein the optimized antenna position, the optimized antenna angle, the optimized phase angle, and the optimized beamforming are used to improve the data transmission rate between the information transmitting end and the information receiving end; The three-dimensional position of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized antenna position, the three-dimensional rotation angle of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized antenna angle, the phase angle of at least one of the passive intelligent reflecting surfaces is adjusted based on the optimized phase angle, and the beamforming of the information receiving end is adjusted based on the optimized beamforming.

2. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 1, characterized in that: The generating a transmission rate optimization model based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beamforming parameter, the first transmission parameter, and the second transmission parameter includes: Based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the first transmission parameter, and the second transmission parameter, a cascade channel gain between each information transmitting end and the information receiving end is obtained, wherein the cascade channel gain represents the channel gain from the information transmitting end through the passive six-dimensional movable antenna surface to the information receiving end; Taking each information sending end as a target information sending end one by one, the cascade channel gain corresponding to the target information sending end is the target cascade channel gain, and the beamforming parameter corresponding to the target information sending end is the target beamforming parameter; A first signal-to-noise ratio term is obtained based on the product of the target cascade channel gain and the target beamforming parameter, and a second signal-to-noise ratio term is obtained by accumulating the products of the cascade channel gains of other information transmitting ends and the target beamforming parameter; Obtaining a reception signal-to-noise ratio between the information receiving end and the target information sending end based on a ratio of the first signal-to-noise ratio item to the second signal-to-noise ratio item; All the received signal-to-noise ratios are accumulated to obtain a transmission rate optimization function, and the transmission rate optimization model is generated based on the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter, and the beamforming parameter.

3. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 2 is characterized in that: The first transmission parameter includes a first channel complex gain between each of the information transmitting ends and each of the passive intelligent reflecting surfaces, the second transmission parameter includes a second channel complex gain between each of the passive intelligent reflecting surfaces and the information receiving end, and the cascade channel gain between each information transmitting end and the information receiving end is obtained based on the antenna position parameter, the antenna angle parameter, the phase angle parameter, the first transmission parameter, and the second transmission parameter, including: Obtaining a first array response vector between each of the information transmitting ends and each of the passive intelligent reflection surfaces based on the antenna position parameters and the antenna angle parameters, and obtaining a second array response vector between each of the passive intelligent reflection surfaces and the information receiving end based on the antenna position parameters and the antenna angle parameters; Obtaining a transmission channel gain between each of the information transmitting ends and each of the passive intelligent reflection surfaces based on the product of the first channel complex gain and the first array response vector, and obtaining a receiving channel gain between each of the passive intelligent reflection surfaces and the information receiving end based on the product of the second channel complex gain and the second array response vector; Based on the transmission channel gain, the phase angle parameter and the receiving channel gain, a unit cascade channel gain between each information transmitting end and the information receiving end is obtained, wherein the cascade channel gain represents the channel gain from the information transmitting end through the passive intelligent reflective surface to the information receiving end; The cascade channel gain is obtained by accumulating all the unit cascade channel gains.

4. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 3 is characterized in that: The obtaining, based on the antenna position parameter and the antenna angle parameter, a first array response vector between each of the information transmitting ends and each of the passive intelligent reflective surfaces comprises: Obtaining a rotation matrix corresponding to the antenna position parameter, and generating a coordinate system position of the passive intelligent reflector based on the rotation matrix and the antenna angle parameter; Generate an arrival angle parameter of the passive intelligent reflector based on the antenna angle parameter, and obtain a path direction vector between the information transmitting end and the passive intelligent reflector based on the sine value and cosine value of the arrival angle parameter; The first array response vector is obtained based on the product of the coordinate system position, the path direction vector and an index parameter.

5. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 2, characterized in that: The generating the transmission rate optimization model based on the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter and the beamforming parameter comprises: Obtaining position parameter constraints and non-overlap constraints of the antenna position parameters; Generate a signal non-reflection constraint and a signal non-blocking constraint based on the antenna position parameter and the antenna angle parameter; Obtaining a phase parameter unit modulus constraint of the phase angle parameter; The transmission rate optimization model is generated based on the transmission rate optimization function, the antenna position parameters, the antenna angle parameters, the phase angle parameters, the beamforming parameters, the position parameter constraints, the non-overlap constraints, the signal non-reflection constraints, the signal non-blocking constraints and the phase parameter unit mode constraints.

6. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 1, characterized in that: Solving the transmission rate optimization model to obtain an optimized antenna position, an optimized antenna angle, an optimized phase angle, and an optimized beamforming includes: Dividing the transmission rate optimization model into an antenna position angle optimization model, a phase angle optimization model and a beamforming optimization model; Using a mean square error method, the beamforming optimization model is solved based on the antenna position parameter, the antenna angle parameter, and the phase angle parameter to obtain an updated beamforming; Solving the antenna position angle optimization model based on the updated beamforming and the phase angle parameter to obtain an updated antenna position and an updated antenna angle; Solving the phase angle optimization model based on the updated antenna position, the updated antenna angle, and the updated beamforming to obtain an updated phase angle; The updated beamforming is used as the new beamforming parameter, the updated antenna position is used as the new antenna position parameter, the updated antenna angle is used as the new antenna angle parameter, and the updated phase angle is used as the new phase angle parameter. The antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model are iteratively solved until the transmission rate value converges, and the updated beamforming is used as the optimized beamforming, the updated antenna position is used as the optimized antenna position, the updated antenna angle is used as the optimized antenna angle, and the updated phase angle is used as the optimized phase angle.

7. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 6, characterized in that: The antenna position angle optimization model includes an antenna position optimization model, and the generation step of the antenna position angle optimization model includes: Acquire an adaptive step size and a feasible direction parameter corresponding to the antenna position parameter; Generate a feasible direction optimization model based on the feasible direction parameter and the transmission rate optimization model; Solving the feasible direction optimization model to obtain an optimized feasible direction; The antenna position optimization model is generated based on the product of the optimization feasible direction and the adaptive step size.

8. The parameter optimization method of the passive six-dimensional movable antenna communication system according to claim 6, characterized in that: The generation step of the phase angle optimization model includes: Based on multi-ratio fractional programming, a first slack variable and a second slack variable, the transmission rate optimization model is converted into an equivalent transmission rate optimization model; Solving the equivalent transmission rate optimization model based on the phase angle parameter to obtain a first optimized slack variable and a second optimized slack variable; The equivalent transmission rate optimization model is converted into the phase angle optimization model based on the first optimization slack variable and the second optimization slack variable.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the parameter optimization method of the passive six-dimensional movable antenna communication system according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the parameter optimization method of the passive six-dimensional movable antenna communication system according to any one of claims 1 to 8 is implemented.

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