Parameter optimization method for passive six-dimensional movable antenna communication system and related device

Abstract

The parameter optimization method and related equipment of the passive six-dimensional movable antenna communication system provided by the embodiments of the present application, the method comprises: obtaining a first transmission parameter between each information sending end and the surface of the 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 antenna position parameters, antenna angle parameters and phase angle parameters of each passive intelligent reflecting surface, and obtaining beam forming parameters of the information receiving end; based on the antenna position parameters, the antenna angle parameters, the phase angle parameters, the beam forming parameters, the first transmission parameter and the second transmission parameter, a transmission rate optimization model is generated and solved to obtain optimized antenna positions, optimized antenna angles, optimized phase angles and optimized beam forming, so as to adjust the surface of the passive six-dimensional movable antenna and the information receiving end, thereby improving the data transmission rate between the information sending end and the information receiving end in the wireless communication system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a parameter optimization method of a passive six-dimensional movable antenna communication system and related equipment. BACKGROUND

[0002] In order to meet the growing number of Internet of Things devices in the upcoming sixth generation wireless network, the demand for data transmission rate between base stations and Internet of Things terminals is also increasing. In the related art, there is a signal reflection by setting an intelligent reflecting surface between an information receiving end and an information sending end to improve the data transmission rate between the information sending end and the information receiving end.

[0003] However, since the intelligent reflecting surface is fixed after being set, it is limited by position and angle when reflecting signals, so that its improvement of the data transmission rate between the information sending end and the information receiving end is limited. SUMMARY

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

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a parameter optimization method of a passive six-dimensional movable antenna communication system, the passive six-dimensional movable antenna communication system comprising 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 comprising at least one passive intelligent reflecting surface, the method comprising:

[0006] 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;

[0007] obtaining an antenna position parameter, an antenna angle parameter and a phase angle parameter of each passive intelligent reflecting surface, and obtaining a beamforming parameter of the information receiving end;

[0008] 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;

[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, the optimized antenna position, the optimized antenna angle, the optimized phase angle, and the optimized beamforming being used to improve a data transmission rate between the information sending end and the information receiving end;

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

[0011] In some embodiments, 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 comprises:

[0012] obtaining a concatenated channel gain between each information sending 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, the concatenated channel gain representing a channel gain from the information sending end to the information receiving end through the passive six-dimensional movable antenna surface;

[0013] taking each information sending end as a target information sending end one by one, the concatenated channel gain corresponding to the target information sending end being a target concatenated channel gain, and the beamforming parameter corresponding to the target information sending end being a target beamforming parameter;

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

[0015] obtaining a received 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 term and the second signal-to-noise ratio term;

[0016] obtaining a transmission rate optimization function by accumulating all the received signal-to-noise ratios, and 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.

[0017] In some embodiments, the first transmission parameter comprises a first channel complex gain between each of the information sending ends and each of the passive intelligent reflecting surfaces, the second transmission parameter comprises a second channel complex gain between each of the passive intelligent reflecting surfaces and the information receiving end, and the obtaining of the cascade channel gain between each information sending 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 comprises:

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

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

[0020] obtaining a unit cascade channel gain between each information sending end and the information receiving end based on the transmitting channel gain, the phase angle parameter, and the receiving channel gain, the cascade channel gain representing a channel gain between the information sending end and the information receiving end through the passive intelligent reflecting surface;

[0021] obtaining the cascade channel gain by accumulating all the unit cascade channel gains.

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

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

[0024] generating an angle of arrival parameter of the passive intelligent reflecting surface based on the antenna angle parameter, and obtaining a path direction vector between the information sending end and the passive intelligent reflecting surface based on a sine value and a cosine value of the angle of arrival parameter;

[0025] obtaining the first array response vector based on a product of the coordinate system position, the path direction vector, and an exponential parameter.

[0026] In some embodiments, 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:

[0027] obtaining a position parameter constraint and a non-overlapping constraint of the antenna position parameter;

[0028] generating a signal non-reflection constraint and a signal non-block 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 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, the beamforming parameter, the position parameter constraint, the non-overlapping constraint, the signal non-reflection constraint, the signal non-block constraint, and the phase parameter unit modulus constraint.

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

[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] solving the beamforming optimization model based on the antenna position parameter, the antenna angle parameter, and the phase angle parameter by using a mean square error method 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] solving the antenna position angle optimization model, the phase angle optimization model and the beamforming optimization model iteratively until the transmission rate value converges, and taking 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.

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

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

[0039] generating 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] generating the antenna position optimization model based on a product of the optimized feasible direction and the adaptive step size.

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

[0043] transforming the transmission rate optimization model into an equivalent transmission rate optimization model based on a multi-ratio fractional programming, a first relaxation variable and a second relaxation variable;

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

[0045] transforming the equivalent transmission rate optimization model into the phase angle optimization model based on the first optimized relaxation variable and the second optimized relaxation variable.

[0046] To achieve the above object, a second aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the parameter optimization method of the passive six-dimensional movable antenna communication system according to the first aspect when executing the computer program.

[0047] To achieve the above object, a third aspect of the embodiments of the present application provides a storage medium, which is a computer readable storage medium, and stores a computer program. The computer program is executed by a processor to implement the parameter optimization method of the passive six-dimensional movable antenna communication system according to the first aspect.

[0048] The parameter optimization method of the passive six-dimensional movable antenna communication system and the related device provided by the embodiments of the present application can effectively improve 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 set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

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

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

[0052] Figure 3 is a flowchart of step 203 in Figure 2

[0053] Figure 4 is a flowchart of step 301 in Figure 3

[0054] Figure 5 is a flowchart of step 401 in Figure 4

[0055] Figure 6 is a flowchart of step 305 in Figure 3

[0056] Figure 7 is a flowchart of step 204 in Figure 2

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

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

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

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

[0061] Figure 12 is a hardware structure schematic diagram of an electronic device provided by another embodiment of the present application. DETAILED DESCRIPTION

[0062] ​​​​​In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description will be given below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

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

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification herein is for the purpose of describing the embodiments of the present application only and is not intended to be limiting of the present application.

[0065] First, the meanings of several terms involved in the present application are analyzed:

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

[0067] In order to meet the growing number of Internet of Things devices in the upcoming sixth generation of wireless networks, the demand for data transmission rate between base stations and Internet of Things terminals is also increasing. In the related art, there is a signal reflection by setting an intelligent reflecting surface between an information receiving end and an information sending end to improve the data transmission rate between the information sending end and the information receiving end.

[0068] However, since the intelligent reflecting surface is fixed after being set, it is fixed in position and angle, which makes it susceptible to position and angle limitations when reflecting signals, making it limited in improving the data transmission rate between the information sending end and the information receiving end.

[0069] In order to improve the data transmission rate between the information sending end and the information receiving end in the wireless communication system, the embodiment of the application solves the transmission rate optimization model by setting a passive six-dimensional movable antenna surface between the information sending end and the information receiving end, and combining the first transmission parameter between the information sending 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, to obtain the optimized antenna position, the optimized antenna angle, the optimized phase angle and the optimized beam forming which can effectively improve the data transmission rate between the information sending end and the information receiving end, and adjusting 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 beam forming, so as to effectively improve 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 application, the passive six-dimensional movable antenna communication system applied to the parameter optimization method of the passive six-dimensional movable antenna communication system is described first as follows. Referring to Figure 1 , it is a structural schematic diagram of a passive six-dimensional movable antenna communication system provided by the embodiment of the application. As Figure 1 indicated, the passive six-dimensional movable antenna communication system includes a plurality of users (i.e. information sending end), a base station (i.e. information receiving end) and a passive six-dimensional movable antenna surface. The passive six-dimensional movable antenna surface includes a plurality of 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] The passive intelligent transmitting units in the passive six-dimensional movable antenna surface are different from the conventional intelligent reflecting surface in that the conventional intelligent reflecting surface can only adjust the phase and amplitude angle, while the passive six-dimensional movable antenna surface in the application can not only adjust the phase and amplitude angle, but also adjust the three-dimensional position and three-dimensional rotation angle in combination with the advantages of the six-dimensional movable antenna, so as to better improve the transmission efficiency of the data information.

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

[0073] Each passive intelligent reflecting surface is assumed to be a uniform planar array, composed of passive intelligent reflecting units, wherein and respectively represent the number of reflective surface units in the row and column. The passive smart reflective surfaces are connected to the central controller through extensible and rotatable poles embedded with flexible cables, so that their three-dimensional positions, three-dimensional rotations and reflection coefficients can be adjusted in coordination.

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

[0075] The parameter optimization method of the passive six-dimensional movable antenna communication system in the embodiments of the present application will be described in detail below. Referring to Figure 2 , an optional flowchart of the parameter optimization method of the passive six-dimensional movable antenna communication system provided by the embodiments of the present application, Figure 2 The method in the above embodiment can include but is not limited to including steps 201 to 205. It can be understood that the order of steps 201 to 205 in the above embodiment is not limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs. Figure 2 The order of steps 201 to 205 in the above embodiment is not limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0076] Step 201: Obtain the first transmission parameter between each information sending end and the surface of the passive six-dimensional movable antenna, and obtain the second transmission parameter between the surface of the passive six-dimensional movable antenna and the information receiving end.

[0077] Step 201 will be described in detail below.

[0078] In some embodiments, in response to at least one information sending end sending a data information sending request to the 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 adjust the phase, position and angle of the plurality of passive smart reflective surfaces in the surface of the passive six-dimensional movable antenna and the beamforming of the information receiving end based on the real-time related parameters of the passive six-dimensional movable antenna communication system.

[0079] Therefore, it is necessary to first obtain the real-time first transmission parameter between each information sending end and the surface of the passive six-dimensional movable antenna and the real-time second transmission parameter between the surface of the passive six-dimensional movable antenna and the information receiving end. The first transmission parameter includes the first channel complex gain between each information sending end and each passive smart reflective surface, and the transmission power of each smart reflective surface where k denotes an index parameter of the information transmitter, and l denotes a propagation path index parameter between the information transmitter and the passive smart reflecting surface. The second transmission parameter comprises a second channel complex gain between the passive six-dimensional movable antenna surface and the information receiver where p denotes a propagation path index parameter between the passive smart reflecting surface and the information receiver.

[0080] Step 202: Obtain the antenna position parameter, the antenna angle parameter, and the phase angle parameter of each passive smart reflecting surface, and obtain the beamforming parameter of the information receiver.

[0081] The following describes Step 202 in detail.

[0082] In some embodiments, let and denote the baseband equivalent channel from the k-th information transmitter to the b-th passive smart reflecting surface and from the b-th passive smart reflecting surface to the information receiver, respectively. Let and denote the antenna position parameter and the antenna angle parameter of the b-th passive smart reflecting surface, respectively. and where and denote the coordinate of the center point of the b-th passive smart reflecting surface in the global Cartesian coordinate system . , , and denote the rotation angle with respect to the x-axis, the y-axis, and the z-axis, respectively. Let denote the phase angle parameter of the b-th passive smart reflecting surface, where the reflection amplitude is set to 1 to maximize the signal reflection power, i.e. .

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

[0084] Step 203: 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.

[0085] The following describes Step 203 in detail.

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

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

[0088] Step 301: Based on the antenna position parameter, antenna angle parameter, phase angle parameter, first transmission parameter, and second transmission parameter, the cascade channel gain between each information sending end and 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 necessary to first generate the cascade channel gain from each information sending end to the information receiving end through the passive six-dimensional movable antenna surface based on the obtained antenna position parameter, antenna angle parameter, phase angle parameter, first transmission parameter, and second transmission parameter So that the cascade channel gain is used to construct a suitable transmission rate optimization model subsequently, as described below.

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

[0092] Step 401: Based on the antenna position parameter and antenna angle parameter, the first array response vector between each information sending end and each passive smart reflecting surface is obtained, and based on the antenna position parameter and antenna angle parameter, the second array response vector between each passive smart reflecting surface and information receiving end is obtained.

[0093] Step 401 is described in detail below.

[0094] In some embodiments, in order to construct a suitable cascade channel gain It is necessary to obtain a suitable first array response vector between each information sending end and each passive smart reflecting surface according to the antenna position parameter and antenna angle parameter ​ Correspondingly, based on antenna position parameters and antenna angle parameters The second array response vector between each passive intelligent reflector and the information receiver is obtained. The details are described below.

[0095] Reference Figure 5 The first array response vector between each information transmitter and each passive smart reflector is obtained based on the antenna position parameters and antenna angle parameters, including the following steps 501 to 503.

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

[0097] Step 502: Generate the angle of arrival parameters of the passive intelligent reflector based on the antenna angle parameters, and obtain the path direction vector between the information transmitter and the passive intelligent reflector based on the sine and cosine values ​​of the angle of arrival parameters.

[0098] Step 503: Based on the product of the coordinate system position, the path direction vector, and the exponential parameter, obtain the first array response vector.

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

[0100] In some embodiments, it is first necessary to obtain the antenna position parameters. Corresponding rotation matrix As shown in the following formula (1).

[0101] (1)

[0102] in and Then based on the rotation matrix and antenna angle parameters The coordinate system position for generating the passive intelligent reflective surface is: ,in Characterizing the first The position of the nth antenna of a passive smart reflector in the global Cartesian coordinate system.

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

[0104] (2)

[0105] path direction vector and denote the unit norm arrival direction vectors of the lth propagation path of the kth information transmitter and the pth path to the information receiver, which are defined with respect to the reference position O in Figure 1 and the wavelength of the carrier is λ.

[0106] Next, based on the coordinate system position , the path direction vector and and the index parameter, the first array response vector and the second array response vector are obtained as shown in the following equation (3).

[0107] (3)

[0108] The first array response vector and the second array response vector respectively denote the array response vectors of the bth passive smart reflecting surface to the lth transmission path of the kth information transmitter and to the pth reflection path.

[0109] Through the above steps 501 to 503, the first array response vector and the second array response vector are accurately generated by 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, so as to improve the accuracy of subsequent construction of the cascade channel gain between each information transmitter and the information receiver.

[0110] Step 402: based on the product of the first channel complex gain and the first array response vector, the transmission channel gain between each information transmitter and each passive smart reflecting surface is obtained, and based on the product of the second channel complex gain and the second array response vector, the reception channel gain between each passive smart reflecting surface and the information receiver is obtained.

[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 transmitter and the information receiver is obtained.

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

[0113] The steps 402 to 404 are described in detail as follows.

[0114] In some embodiments, after the first array response vector and the second array response vector Then, using the acquired first channel complex gain and the first array response vector The product of these terms yields the transmit channel gain between each information transmitter and each passive smart reflector. and based on the obtained second channel complex gain Second array response vector The product of these terms yields the receive channel gain between each passive smart reflector and the information receiver. As shown in the following formula (4).

[0115] (4)

[0116] in, P and P represent the number of propagation paths between the k-th information transmitter and the b-th passive intelligent reflector, and between the b-th passive intelligent reflector and the information receiver, respectively. and This represents the complex gain of the corresponding channel. and These represent the incident and reflected radiation patterns of each reflecting element on the surface, corresponding to the l-th path from user k and the p-th path to the base station, respectively. This pattern is determined by the angle of arrival (or departure angle) of the elevation and azimuth angles. and the rotation of passive surface b The decision is made. Specifically, paths l=1 and p=1 are typically line-of-sight paths, while the rest... Paths P and P-1 are non-line-of-sight paths. This represents the direction vector at the information receiving end.

[0117] Next, based on the obtained transmit channel gain Phase angle parameters and received channel gain The channel gain of the unit cascade between each information transmitter and information receiver is obtained as shown in the following formula (5).

[0118] (5)

[0119] cascaded channel gain The channel gain is represented from the k-th information transmitter to the b-th passive intelligent reflector and then to the information receiver.

[0120] Furthermore, the angle of arrival of the fixed uniform linear array with M antennas on the base station (information receiving end) and the path from the p-th path... The relevant array response vector, It can be defined in a similar way. Using , ,and respectively represent the position, rotation and reflection coefficient set of all passive intelligent reflecting surfaces, and the cascade channel gain is obtained by accumulating all unit cascade channel gains That is, the effective channel from the kth information sending end to the information receiving end through all passive intelligent reflecting surfaces can be expressed as the following formula (6).

[0121] (6)

[0122] wherein

[0123] (7)

[0124] Through the above steps 401 to 404, the unit cascade channel gain from each information sending end to the information receiving end through each passive intelligent reflecting surface can be accurately obtained, which corresponds to the real-time state, by using the first channel complex gain and the second channel complex gain in real time, and by using the first array response vector and the second array response vector obtained from the antenna position parameter and the antenna angle parameter, so as to further obtain the accurate cascade channel gain, thereby improving the reliability of subsequent construction of a suitable transmission rate optimization model by using the cascade channel gain.

[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 a second signal-to-noise ratio term is obtained by accumulating the cascade channel gains of other information sending ends and the product of the target beamforming parameter.

[0127] Step 304: A received signal-to-noise ratio between the information receiving end and the target information sending end is obtained based on the ratio of the first signal-to-noise ratio term and the second signal-to-noise ratio term.

[0128] Step 305: All received signal-to-noise ratios are accumulated to obtain a transmission rate optimization function, and a 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.

[0129] The steps 302 to 305 are described in detail as follows.

[0130] In some embodiments, after obtaining the cascade channel gain between each information sending end and the information receiving end Each information sending end is taken as a target information sending end one by one, the cascade channel gain corresponding to the target information sending end is a target cascade channel gain , and the beamforming parameter corresponding to the target information sending end is a target beamforming parameter .

[0131] Then, based on the target cascaded channel gain and the product of the target beamforming parameter , a first signal-to-noise ratio term is obtained, where is the transmission data symbol of the kth information sending end; and the cascaded channel gain and the target beamforming parameter product of other information sending ends are accumulated to obtain a second signal-to-noise ratio term Based on this, the information receiving end can apply a linear receiving beamforming vector to decode the signal from the kth information sending end , as shown in the following formula (8).

[0132] (8)

[0133] wherein is the additive white Gaussian noise at the information receiving end, wherein is the noise power. Further, based on the ratio of the first signal-to-noise ratio term and the second signal-to-noise ratio term , the received signal-to-noise ratio between the information receiving end and the target information sending end is obtained, that is, the signal-to-noise ratio expression of the information receiving end for decoding the target information sending end is shown in the following formula (9).

[0134] (9)

[0135] Therefore, based on the received signal-to-noise ratio, the per-Hertz achievable data rate (in bps / Hz) from the kth information sending end to the information receiving end is , and the sum of all achievable data rates can obtain the transmission rate optimization function .

[0136] In the following, the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter and the beamforming parameter will be used to generate a transmission rate optimization model.

[0137] Referring to Figure 6 , based on the transmission rate optimization function, the antenna position parameter , the antenna angle parameter , the phase angle parameter and the beamforming parameter , a transmission rate optimization model is generated, including the following steps 601 to 604.

[0138] Step 601: Obtain the position parameter constraint and non-overlapping constraint of the antenna position parameter.

[0139] ​Step 602: generating a signal non-reflection constraint and a signal non-block constraint based on the antenna position parameter and the antenna angle parameter.

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

[0141] Step 604: generating a transmission rate optimization model based on the transmission rate optimization function, the antenna position parameter, the antenna angle parameter, the phase angle parameter, the beamforming parameter, the position parameter constraint, the non-overlapping constraint, the signal non-reflection constraint, the signal non-block constraint, and the phase parameter unit modulus constraint.

[0142] The steps 601 to 604 are described in detail as follows.

[0143] In some embodiments, in order to generate a suitable and reliable transmission rate optimization model, it is also necessary to obtain constraint conditions matching the actual application scenario in advance, which specifically include a position parameter constraint of the antenna position parameter , ensuring that all movable regions of the passive intelligent reflecting surfaces are located within a given three-dimensional space (for example, a cube or a sphere); a non-overlapping constraint of the antenna position parameter , , , indicating a minimum distance, which is used to avoid mutual overlapping between the passive intelligent reflecting surfaces; a signal non-reflection constraint related to the antenna position parameter and the antenna angle parameter , which is used to avoid mutual signal reflection between any two passive intelligent reflecting surfaces; a signal non-block constraint related to the antenna position parameter and the antenna angle parameter , which is used to prevent the passive intelligent reflecting surfaces from facing the middle of the information receiving end; and a phase parameter unit modulus constraint of each transmission agent in the phase angle parameter .

[0144] Then further, the transmission rate optimization function is taken as an optimization function, the antenna position parameter , the antenna angle parameter , and the phase angle parameter of all the passive intelligent reflecting surfaces and the beamforming parameter of the information receiving end are taken as optimization variables, and the position parameter constraint, the non-overlapping constraint, the signal non-reflection constraint, the signal non-block constraint, and the phase parameter unit modulus constraint are taken as constraint conditions, so as to construct a transmission rate optimization model as shown in the following formula (10).

[0145] ​(10)

[0146] By the above steps 601 to 604, the transmission rate optimization model is constructed by using multiple constraint conditions corresponding to actual application and combining the required optimization variables (all passive smart reflector antenna position parameters, antenna angle parameters, phase angle parameters, and information receiving end beamforming parameters) to optimize all transmission rates, so that the subsequent optimization parameters 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: Solving the transmission rate optimization model to obtain the optimized antenna position, optimized antenna angle, optimized phase angle, and optimized beamforming.

[0148] The following describes step 204 in detail.

[0149] In some embodiments, after obtaining the transmission rate optimization model (10) corresponding to the transmission parameters of the passive six-dimensional movable antenna communication system in real time, the transmission rate optimization model (10) needs to be solved to obtain the optimized antenna position, optimized antenna angle, optimized phase angle, and optimized beamforming that can effectively improve the transmission rate between the information sending end and the information receiving end.

[0150] For the transmission rate optimization model (10), due to the coupling of variables, non-convex objective function, and non-convex constraint conditions in the transmission rate optimization model (10), the transmission rate optimization model (10) is a non-convex problem, and the alternating iteration method is used to solve it, which is described as follows.

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

[0152] Step 701: Dividing 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: Solving the beamforming optimization model based on the antenna position parameters, antenna angle parameters, and phase angle parameters by using the mean square error method to obtain updated beamforming.

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

[0155] Step 704: 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.

[0156] Step 705: iteratively solving the antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model with the updated beamforming as the new beamforming parameter, the updated antenna position as the new antenna position parameter, the updated antenna angle as the new antenna angle parameter, and the updated phase angle as the new phase angle parameter until the transmission rate value converges, and taking 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] The steps 701 to 705 will be described in detail below.

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

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

[0160] (11)

[0161] wherein, and and represent the effective channel matrix of the information sending end and the information receiving end and the diagonal transmission power matrix of the K information sending ends, respectively.

[0162] How to generate the antenna position angle optimization model will be further described below, wherein the antenna position angle optimization model includes an antenna position optimization model corresponding to the antenna position parameter and an antenna angle optimization model corresponding to the antenna angle parameter.

[0163] Referring to Figure 8 wherein the generation steps of the antenna position angle optimization model include the following steps 801 to 804.

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

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

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

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

[0168] The steps 801 to 804 are described in detail as follows.

[0169] In each iteration of the alternating optimization algorithm proposed in the embodiments of the present application, given the reflection coefficient and the beamforming matrix W, the antenna position parameter and the antenna angle parameter of each passive smart reflector are updated in turn, i.e. and while the antenna position parameter and the antenna angle parameter of the remaining passive smart reflectors are fixed, so as to optimize the antenna position parameter and the antenna angle parameter of all passive smart reflectors.

[0170] For the optimization of the antenna position parameter and the antenna angle of each smart reflector, first, for the optimization of the antenna position parameter, given the antenna angle parameter , the second non-convex constraint in the transmission rate optimization model (10) is approximated as a convex constraint shown in the following formula (12) using the first-order Taylor expansion at the antenna position parameter value in the last iteration (t-1).

[0171] (12)

[0172] Therefore, the feasible region of the antenna position parameter is a convex set. Then, the feasible gradient descent method is used to obtain the solution of the antenna position parameter in the current t-th iteration. That is, the adaptive step size and the feasible direction parameter corresponding to the antenna position parameter of the smart reflector need to be obtained first, where is the adaptive step size calculated by the Armijo rule.

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

[0174] (13)

[0175] wherein can be obtained by numerical solution as shown in the following formula (14).

[0176] (14)

[0177] in, It is a vector where the j-th element is 1 and the rest are 0. Finally, for... nonconvex problems It is transformed into a linear optimization problem It can be solved efficiently using linear programming.

[0178] Then, by solving the feasible direction optimization model (13), the optimized feasible direction can be obtained. Then, further optimization of feasible directions is conducted. and adaptive step size The product of these can generate an antenna position optimization model as shown in the following formula (15).

[0179] (15)

[0180] It is understandable that the antenna position optimization model (15) is obtained to solve for the antenna position parameters. Similarly, an antenna angle optimization model is obtained to solve for the antenna angle parameters. It can also be obtained through steps similar to steps 801 to 804 above.

[0181] The following section will further describe how to construct a phase angle optimization model corresponding to the phase angle parameters.

[0182] Reference Figure 9 The generation steps of the phase angle optimization model include the following steps 901 to 903.

[0183] Step 901: Based on multi-ratio fractional programming, the first slack variable, and the second slack variable, the transmission rate optimization model is transformed 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 the first optimization slack variable and the second optimization slack variable.

[0185] Step 903: Based on the first and second optimization slack variables, transform the equivalent transmission rate optimization model into a phase angle optimization model.

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

[0187] In some embodiments, for phase angle parameters The optimization solution, with fixed beamforming parameters Antenna position parameters and antenna angle parameters In this case, two auxiliary variables are introduced (including the first slack variable). Second relaxation variable Then, the objective function in the transmission rate optimization model (10) is transformed into the following equivalent transmission rate optimization model as shown in the following formula (16) by multi-ratio fractional programming.

[0188] (16)

[0189] Where * denotes conjugation. In phase angle parameters Solving equation (16) under fixed conditions yields the optimal first slack variable. Second optimization slack variable It is expressed as shown in the following formula (17).

[0190] , (17)

[0191] Therefore, the phase angle parameters can be iteratively optimized while keeping the reflection coefficients of other reflecting units constant. The j-th element, i.e. That is, by using the first optimization slack variable and the second optimization slack variable, the equivalent transmission rate optimization model is transformed into a phase angle optimization model. This problem is shown in the following formula (18).

[0192] (18)

[0193] in, It is a matrix The element in the j-th row and j-th column, ,in It is a vector The j-th element. Then, optimal It can be done Obtain, among which Other reflectance coefficients A similar method can be used to update.

[0194] After obtaining the antenna position angle optimization model, the phase angle optimization model and the beamforming optimization model, the alternate optimization algorithm is used to obtain the updated beamforming, the updated antenna position, the updated antenna angle and the updated phase angle in each iteration, and then the updated beamforming is taken as the beamforming parameter of the new iteration, the updated antenna position is taken as the antenna position parameter of the new iteration, the updated antenna angle is taken as the antenna angle parameter of the new iteration, and the updated phase angle is taken as the phase angle parameter of the new iteration, and then the antenna position angle optimization model (15), the phase angle optimization model (18) and the beamforming optimization model (11) are iteratively solved until the transmission rate value converges, and the updated beamforming is taken as the optimized beamforming, the updated antenna position is taken as the optimized antenna position, the updated antenna angle is taken as the optimized antenna angle, and the updated phase angle is taken as the optimized phase angle.

[0195] Through the above steps 701 to 705, steps 801 to 804, and steps 901 to 903, the alternate optimization algorithm is used to improve the accuracy of solving the transmission rate optimization model by converting the original transmission rate optimization model into sub-optimization models with respect to multiple optimization variables, and using convex optimization conversion method and least square method to obtain suitable sub-optimization models corresponding to each optimization variable during the conversion process, so as to obtain the optimal optimized antenna position, the optimal optimized antenna angle, the optimal optimized phase angle and the optimal optimized beamforming, thereby improving the transmission rate in the current passive six-dimensional movable antenna communication system.

[0196] Step 205: adjusting the three-dimensional position of the at least one passive smart reflector based on the optimized antenna position, adjusting the three-dimensional rotation angle of the at least one passive smart reflector based on the optimized antenna angle, adjusting the phase angle of the 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.

[0197] The step 205 is described in detail as follows.

[0198] In some embodiments, after obtaining the optimal optimized antenna position, the optimal optimized antenna angle, the optimal optimized phase angle and the optimal 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 smart reflector is adjusted based on the optimized antenna position, the three-dimensional rotation angle of the corresponding passive smart reflector is adjusted based on the optimized antenna angle, the phase angle of the corresponding passive smart reflector 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] To further verify the reliability of the parameter optimization method of the passive six-dimensional movable antenna communication system provided in the application, performance simulation is performed in this embodiment. The carrier wavelength is set to . The number of reflection units of each passive smart reflector is equal. The number of antennas of the information receiving end is , and the number of message sending ends is K=6. is set to . In the channel model, it is set that , where , and . and are randomly generated in the interval and respectively. and are randomly generated in the interval and respectively. The transmission power of each message sending end is set to be equal. The noise power is set to-80dMm.

[0200] To study the effectiveness of the parameter optimization method of the passive six-dimensional movable antenna communication system proposed in the application, the following schemes (with the same total number of reflection units) are considered for performance comparison.

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

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

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

[0204] The radiation pattern of the directional antenna is set according to the following expression:

[0205]

[0206] where A represents the area of each reflection unit, which is usually set to , The angle cosine between the arrival direction vector of the incident signal and the outer normal vector of the six-dimensional movable antenna surface b in the global coordinate system is calculated. In addition, the half-space isotropic radiation pattern of each reflection unit is also considered, and the reflection radiation pattern can be obtained by reflecting the signal the angle of arrival vector and the outer normal vector of the six-dimensional movable antenna surface b in the global coordinate system.

[0207] Referring to Figure 10 is a performance simulation schematic diagram of a parameter optimization method of a passive six-dimensional movable antenna communication system provided by an embodiment of the present application. As Figure 10 shown, the relationship between the total rate of users and the user transmit power in different schemes in the directional antenna radiation mode is shown. It can be observed that the passive 6DMA scheme (whether distributed or centralized) proposed in the present application is superior to the fixed position IRS scheme, and the performance advantage becomes more significant as the transmit 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 reflecting elements is the same, the passive six-dimensional movable antenna with a distributed surface is superior in performance to the passive six-dimensional movable antenna with a centralized surface. This is because the distributed surface has higher flexibility in position and rotation adjustment, and also helps to reduce the interference between users. However, this also increases the hardware cost and is more complex in controlling the movement of each six-dimensional movable antenna surface.

[0208] Referring to Figure 11 is still another performance simulation schematic diagram of a parameter optimization method of a passive six-dimensional movable antenna communication system provided by an embodiment of the present application. The relationship between the total rate of reflecting elements and the user transmit power in different schemes in the half-space isotropic radiation mode is shown. It can be observed that the performance gap between different schemes is smaller compared with the directional radiation mode of reflecting elements. For example, when the user transmit power is 15dBm, the total rate of the fixed IRS scheme using the half-space isotropic radiation mode is about 5bps / Hz higher than that of the fixed IRS scheme using 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 using the directional radiation mode. The above results show that when using the distributed passive 6DMA, the rate loss of the actual directional radiation mode compared with the ideal half-space isotropic radiation mode is smaller, because the 6DMA surface rotation can better utilize the directional radiation mode of the reflecting elements to suppress the interference between users.

[0209] Therefore, the passive six-dimensional movable antenna assisted wireless communication system proposed in the present application can provide an efficient communication scheme for a wireless communication system.

[0210] The parameter optimization method and related equipment of the passive six-dimensional movable antenna communication system provided in the embodiments of the present application, the passive six-dimensional movable antenna communication system comprising 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 comprising at least one passive intelligent reflecting surface, the method comprising the following steps: 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 an antenna position parameter, an antenna angle parameter and a phase angle parameter of each passive intelligent reflecting surface, and obtaining a beamforming parameter of the information receiving end;Secondly, a rotation matrix corresponding to the antenna position parameter is obtained, a coordinate system position of the passive smart reflector is generated based on the rotation matrix and the antenna angle parameter, an angle of arrival parameter of the passive smart reflector is generated based on the antenna angle parameter, a path direction vector between the information sending end and the passive smart reflector is obtained based on a sine value and a cosine value of the angle of arrival parameter, a first array response vector is obtained based on a product of the coordinate system position, the path direction vector and an exponential parameter, a second array response vector between each passive smart reflector and the information receiving end is obtained based on the antenna position parameter and the antenna angle parameter, a transmission channel gain between each information sending end and each passive smart reflector is obtained based on a product of the first channel complex gain and the first array response vector, a receiving channel gain between each passive smart reflector and the information receiving end is obtained based on a product of the second channel complex gain and the second array response vector, a unit cascade channel gain between each information sending end and the information receiving end is obtained based on the transmission channel gain, the phase angle parameter and the receiving channel gain, the cascade channel gain represents a channel gain between the information sending end and the information receiving end after passing through the passive smart reflector, a cascade channel gain is obtained by accumulating all the unit cascade channel gains, the cascade channel gain represents a channel gain between the information sending end and the information receiving end after passing through the passive smart reflector, each information sending end is taken as a target information sending end one by one, a target cascade channel gain corresponding to the target information sending end is obtained, a target beamforming parameter corresponding to the target information sending end is obtained, a first signal-to-noise ratio term is obtained based on a product of the target cascade channel gain and the target beamforming parameter, a second signal-to-noise ratio term is obtained by accumulating products of cascade channel gains of other information sending ends and the target beamforming parameter, a receiving signal-to-noise ratio between the information receiving end and the target information sending end is obtained based on a ratio of the first signal-to-noise ratio term and the second signal-to-noise ratio term, a transmission rate optimization function is obtained by accumulating all the receiving signal-to-noise ratios, a position parameter constraint and a non-overlapping constraint of the antenna position parameter are obtained, a signal non-reflection constraint and a signal non-blocking constraint are generated based on the antenna position parameter and the antenna angle parameter, a phase parameter unit modulus constraint of the phase angle parameter is obtained, and a 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, the beamforming parameter, the position parameter constraint, the non-overlapping constraint, the signal non-reflection constraint, the signal non-blocking constraint and the phase parameter unit modulus constraint.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 beamforming optimization model is solved based on the antenna position parameter, the antenna angle parameter, and the phase angle parameter using the mean square error method, to obtain an updated beamforming. The antenna position angle optimization model is solved based on the updated beamforming and the phase angle parameter, to obtain an updated antenna position and an updated antenna angle. The phase angle optimization model is solved based on the updated antenna position, the updated antenna angle, and the updated beamforming, to obtain an updated phase angle. The antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model are iteratively solved with 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, until the transmission rate value converges. The updated beamforming is taken as an optimized beamforming, the updated antenna position is taken as an optimized antenna position, the updated antenna angle is taken as an optimized antenna angle, and the updated phase angle is taken as an optimized phase angle. Finally, the three-dimensional position of the at least one passive intelligent reflecting surface is adjusted based on the optimized antenna position, the three-dimensional rotation angle of the at least one passive intelligent reflecting surface is adjusted based on the optimized antenna angle, the phase angle of the at least one 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.

[0211] The embodiments of the present application utilize the first channel complex gain and the second channel complex gain in real time, and utilize the first array response vector and the second array response vector obtained from the antenna position parameters and the antenna angle parameters, to accurately obtain the unit cascade channel gain from each information sending end to the information receiving end through each passive intelligent transmitting surface, which corresponds to the real-time state, to further obtain the accurate cascade channel gain, thereby improving the reliability of subsequent construction of a suitable transmission rate optimization model using the cascade channel gain; and, utilize 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, to correspond to the accurate generation of the first array response vector and the second array response vector, thereby improving the accuracy of subsequent construction of the cascade channel gain between each information sending end and the information receiving end; and, utilize the first channel complex gain and the second channel complex gain in real time, and utilize the first array response vector and the second array response vector obtained from the antenna position parameters and the antenna angle parameters, to accurately obtain the unit cascade channel gain from each information sending end to the information receiving end through each passive intelligent transmitting surface, which corresponds to the real-time state, to further obtain the accurate cascade channel gain, thereby improving the reliability of subsequent construction of a suitable transmission rate optimization model using the cascade channel gain; in addition, the transmission rate optimization model constructed by utilizing a plurality of constraint conditions corresponding to actual applications, and combining the required optimization variables (the antenna position parameters, the antenna angle parameters, and the phase angle parameters of all passive intelligent reflecting surfaces, and the beamforming parameters of the information receiving end) to optimize all transmission rates, can make the optimization parameters obtained by subsequent solving based on the transmission rate optimization model effectively improve the transmission rate between the information sending end and the information receiving end; secondly, by converting the original transmission rate optimization model into a sub-optimization model with respect to multiple optimization variables, and utilizing convex optimization conversion method and least square method in the conversion process to obtain suitable sub-optimization models corresponding to each optimization variable, the alternate optimization algorithm improves the accuracy of solving the transmission rate optimization model, to obtain the optimal optimized antenna position, the optimized antenna angle, the optimized phase angle, and the optimized beamforming, thereby improving the transmission rate in the current passive six-dimensional movable antenna communication system.In summary, by setting the passive six-dimensional movable antenna surface between the information sending end and the information receiving end, and combining the first transmission parameter between the information sending 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 to solve the transmission rate optimization model, the optimized antenna position, the optimized antenna angle, the optimized phase angle and the optimized beam forming which can effectively improve the data transmission rate between the information sending end and the information receiving end are obtained, and the passive six-dimensional movable antenna surface and the information receiving end are adjusted according to the optimized antenna position, the optimized antenna angle, the optimized phase angle and the optimized beam forming, so that the data transmission rate between the information sending end and the information receiving end in the wireless communication system is effectively improved.

[0212] The embodiment of the present application further provides an electronic device, including:

[0213] at least one memory;

[0214] at least one processor;

[0215] at least one program;

[0216] The program is stored in the memory, and the processor executes the at least one program to realize the parameter optimization method of the passive six-dimensional movable antenna communication system provided in the embodiment of the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer and the like.

[0217] Please refer to Figure 12 , Figure 12 The hardware structure of the electronic device of another embodiment is illustrated, and the electronic device includes:

[0218] The processor 1201 can be realized in the mode of a general CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit) or one or more integrated circuits, and is used to execute a related program to realize the technical solutions provided in the embodiment of the present application;

[0219] The memory 1202 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1202 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 1202 and are called and executed by the processor 1201 to perform the parameter optimization method of the passive six-dimensional movable antenna communication system according to the embodiments of the present application;

[0220] The input / output interface 1203 is configured to realize information input and output.

[0221] The communication interface 1204 is configured to realize the communication interaction between the device and other devices, and the communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0222] The bus 1205 is configured to transmit information between various components (for example, the processor 1201, the memory 1202, the input / output interface 1203, and the communication interface 1204) of the device.

[0223] The processor 1201, the memory 1202, the input / output interface 1203, and the communication interface 1204 are connected to each other through the bus 1205 to realize the communication connection between the device.

[0224] The embodiments of the present application also provide a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program. The computer program is executed by a processor to implement the parameter optimization method of the passive six-dimensional movable antenna communication system.

[0225] The memory is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0226] The embodiments described in the specification are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0227] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0228] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0229] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0230] The terms "first", "second", "third", "fourth" and the like (if any) in the specification of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0231] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c can be single or multiple.

[0232] In several embodiments provided in the 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 illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0233] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0234] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0235] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0236] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for parameter optimization of a passive six-dimensional movable antenna communication system, characterized in that, The passive six-dimensional movable antenna communication system comprises 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 comprises at least one passive smart reflecting surface, and the method comprises the following steps: obtaining first transmission parameters between each information sending end and the passive six-dimensional movable antenna surface, and obtaining second transmission parameters between the passive six-dimensional movable antenna surface and the information receiving end; obtaining antenna position parameters, antenna angle parameters and phase angle parameters of each passive smart reflecting surface, and obtaining beamforming parameters of the information receiving end; generating a transmission rate optimization model based on the antenna position parameters, the antenna angle parameters, the phase angle parameters, the beamforming parameters, the first transmission parameters and the second transmission parameters; solving the transmission rate optimization model to obtain optimized antenna positions, optimized antenna angles, optimized phase angles and optimized beamforming, which are used to improve the data transmission rate between the information sending end and the information receiving end; adjusting the three-dimensional positions of at least one passive smart reflecting surface based on the optimized antenna positions, adjusting the three-dimensional rotation angles of at least one passive smart reflecting surface based on the optimized antenna angles, adjusting the phase angles of at least one passive smart reflecting surface based on the optimized phase angles, and adjusting the beamforming of the information receiving end based on the optimized beamforming.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining the cascade channel gain between each information sending end and the information receiving end based on the antenna position parameters, the antenna angle parameters, the phase angle parameters, the first transmission parameters and the second transmission parameters, wherein the cascade channel gain represents the channel gain between the information sending end and the information receiving end after passing through the passive six-dimensional movable antenna surface; taking each information sending end as a target information sending end one by one, wherein the cascade channel gain corresponding to the target information sending end is a target cascade channel gain, and the beamforming parameter corresponding to the target information sending end is a target beamforming parameter; obtaining a first signal-to-noise ratio term based on the product of the target cascade channel gain and the target beamforming parameter, and obtaining a second signal-to-noise ratio term by accumulating the products of the cascade channel gains of other information sending ends and the target beamforming parameter; obtaining the received signal-to-noise ratio between the information receiving end and the target information sending end based on the ratio of the first signal-to-noise ratio term and the second signal-to-noise ratio term; obtaining a transmission rate optimization function by accumulating all the received signal-to-noise ratios, and generating the transmission rate optimization model based on the transmission rate optimization function, the antenna position parameters, the antenna angle parameters, the phase angle parameters and the beamforming parameters.

3. The method of claim 2, wherein, The first transmission parameter comprises a first channel complex gain between each of the information sending ends and each of the passive smart reflecting surfaces, the second transmission parameter comprises a second channel complex gain between each of the passive smart reflecting surfaces and the information receiving end, and the cascade channel gain between each of the information sending ends 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, and comprises: a first array response vector between each of the information sending ends and each of the passive smart reflecting surfaces is obtained based on the antenna position parameter and the antenna angle parameter, and a second array response vector between each of the passive smart reflecting surfaces and the information receiving end is obtained based on the antenna position parameter and the antenna angle parameter; a transmission channel gain between each of the information sending ends and each of the passive smart reflecting surfaces is obtained based on a product of the first channel complex gain and the first array response vector, and a receiving channel gain between each of the passive smart reflecting surfaces and the information receiving end is obtained based on a product of the second channel complex gain and the second array response vector; a unit cascade channel gain between each of the information sending ends and the information receiving end is obtained based on the transmission channel gain, the phase angle parameter, and the receiving channel gain, and the cascade channel gain represents a channel gain between the information sending end and the information receiving end through the passive smart reflecting surface; all the unit cascade channel gains are accumulated to obtain the cascade channel gain.

4. The method of claim 3, wherein, The first array response vector between each of the information sending ends and each of the passive smart reflecting surfaces is obtained based on the antenna position parameter and the antenna angle parameter, and comprises: a rotation matrix corresponding to the antenna position parameter is obtained, and a coordinate system position of the passive smart reflecting surface is generated based on the rotation matrix and the antenna angle parameter; an angle of arrival parameter of the passive smart reflecting surface is generated based on the antenna angle parameter, and a path direction vector between the information sending end and the passive smart reflecting surface is obtained based on a sine value and a cosine value of the angle of arrival parameter; the first array response vector is obtained based on a product of the coordinate system position, the path direction vector, and an exponential parameter.

5. The method for parameter optimization of a passive six-dimensional movable antenna communication system according to claim 2, characterized in that, 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, and comprises: a position parameter constraint and a non-overlapping constraint of the antenna position parameter are obtained; a signal non-reflection constraint and a signal non-blocking constraint are generated based on the antenna position parameter and the antenna angle parameter; a phase parameter unit modulus constraint of the phase angle parameter is obtained; 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, the beamforming parameter, the position parameter constraint, the non-overlapping constraint, the signal non-reflection constraint, the signal non-block constraint, and the phase parameter unit modulus constraint.

6. The method for parameter optimization of a 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, comprises: dividing the transmission rate optimization model into an antenna position angle optimization model, a phase angle optimization model, and a beamforming optimization model; solving the beamforming optimization model based on the antenna position parameter, the antenna angle parameter, and the phase angle parameter by using a mean square error method 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; iteratively solving the antenna position angle optimization model, the phase angle optimization model, and the beamforming optimization model by taking 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 until a transmission rate value converges, and taking 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.

7. The method of parameter optimization of a passive six-dimensional movable antenna communication system according to claim 6, characterized in that, the antenna position angle optimization model comprises an antenna position optimization model, and the generating step of the antenna position angle optimization model comprises: obtaining an adaptive step size and a feasible direction parameter corresponding to the antenna position parameter; generating 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; generating the antenna position optimization model based on a product of the optimized feasible direction and the adaptive step size.

8. The method of claim 6, wherein, the generating step of the phase angle optimization model comprises: transforming the transmission rate optimization model into an equivalent transmission rate optimization model based on a multi-ratio fractional programming, a first relaxation variable, and a second relaxation variable; solving the equivalent transmission rate optimization model based on the phase angle parameter to obtain a first optimized relaxation variable and a second optimized relaxation variable; transforming the equivalent transmission rate optimization model into the phase angle optimization model based on the first optimized relaxation variable and the second optimized relaxation variable. 9.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, the processor executes the computer program to implement the parameter optimization method of the passive six-dimensional movable antenna communication system according to any one of claims 1 to 8.

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

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