User downlink system optimization method and system of movable antenna in coordination with NOMA

Abstract

The application discloses a kind of movable antenna cooperation NOMA's user downlink system optimization method and system, belong to wireless communication technical field.Its method includes: obtaining the position of user movable antenna and the receiving path of user to base station link;Calculate the channel gain between user and base station;Calculate the signal-to-noise ratio of user, calculate the mobile energy consumption of user movable antenna;According to the mobile energy consumption of user movable antenna and the transmission power allocated to user, the objective function of system total energy consumption model is constructed, according to the signal-to-noise ratio of user, user mobile area, the channel gain order between user and base station, the transmission power order allocated to user, the constraint of system energy consumption model is constructed;System total energy consumption model is converted into convex optimization problem, and the transmission power allocated to user after optimization and the position of user movable antenna after optimization are obtained.The application can realize the performance improvement of system, and solve the complexity brought by optimization problem simultaneously.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to a method and system for optimizing user downlink systems with movable antennas and NOMA. Background Technology

[0002] Wireless communication is experiencing unprecedented development. With the emergence of diverse applications such as autonomous driving, augmented reality, and virtual reality, higher performance requirements are being placed on next-generation communication systems, particularly in terms of data transmission rates, massive connectivity, and ultra-low latency. To address these significant challenges, actively exploring and deploying innovative physical layer technologies is crucial.

[0003] Among numerous cutting-edge technologies, movable antenna technology, with its unique potential, is considered an effective way to reshape wireless channels. Unlike traditional fixed-position antennas, which are inherently limited by their static deployment and performance bottlenecks, movable antennas give the system the ability to dynamically adjust the physical position of the antenna, allowing it to move flexibly within a predetermined spatial range. This inherent flexibility enables movable antennas to actively optimize the coupling relationship between the antenna and the channel, effectively suppress interference, and fully utilize spatial diversity advantages, thereby significantly improving system performance.

[0004] Meanwhile, Non-Orthogonal Multiple Access (NOMA) technology, with its ability to allow multiple users to share the same time-frequency resources in the power domain, has become a key technology for improving spectrum efficiency. NOMA cleverly utilizes the channel differences between different users, making it particularly suitable for scenarios with diverse user channel conditions. By employing continuous interference cancellation technology at the receiver, NOMA can effectively serve users with different channel strengths.

[0005] Current research has extensively and independently explored movable antennas and NOMA. Fundamental research on movable antennas covers comprehensive channel modeling, single-user performance boundaries, and the basic performance gains brought about by antenna mobility. Recently, some preliminary work has begun to investigate the combination of movable antennas and NOMA. These pioneering studies have addressed challenges such as jointly optimizing movable antenna placement, power allocation, and precoding to improve the overall rate or energy efficiency of NOMA-assisted movable antenna systems. While these efforts highlight the significant performance improvements offered by NOMA-assisted movable antenna systems, they also underscore the inherent complexity of such optimization problems. Minimizing the total system power consumption in a movable antenna-assisted downlink NOMA system involves a complex joint optimization problem. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for optimizing user downlink systems with movable antennas and NOMA, which can achieve significant performance improvements brought about by NOMA-assisted movable antenna systems, while also solving the complexity caused by such optimization problems.

[0007] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0008] On one hand, the present invention provides a method for optimizing a user downlink system with a movable antenna-assisted NOMA, comprising:

[0009] Obtain the location of the user's movable antenna;

[0010] Calculate the signal propagation difference along the receiving path between the location of the user's movable antenna and the center of the user's mobile area;

[0011] Calculate the user's field response vector based on the signal propagation difference of the receiving path, and calculate the channel gain between the user and the base station based on the user's field response vector;

[0012] The signal-to-noise ratio of the user is calculated based on the channel gain, and the mobile energy consumption of the user's mobile antenna is calculated based on the location of the user's mobile antenna.

[0013] Based on the mobile energy consumption of the user's movable antenna and the transmit power allocated to the user, the objective function of the total system energy consumption model is constructed. Based on the user's signal-to-noise ratio, the user's mobile area, the channel gain order between the user and the base station, and the order of transmit power allocated to the user, the constraints of the system energy consumption model are constructed.

[0014] With the user's movable antenna at a preset position, the total system energy consumption model is transformed into a convex optimization problem to obtain the optimized transmit power allocated to the user;

[0015] Based on the optimized transmit power allocated to users, the total system energy consumption model is transformed into a convex approximation problem, and the optimized position of the user's movable antenna is obtained.

[0016] Optionally, the signal propagation difference of the receiving path between the location of the user's movable antenna and the center of the user's mobile area is expressed as:

[0017] ;

[0018] in, Indicates the first The location of the user's movable antenna and the center of the user's mobile area are the first... Signal propagation difference along the receiving path; , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation and azimuth angles of the receiving path; This indicates the number of users.

[0019] Optionally, the user's field response vector is represented as:

[0020] ;

[0021] in, Indicates the first Users in location The field response vector; Indicates the carrier wavelength; , , They represent the first The signal propagation difference of the first receiving path between the location of the user's movable antenna and the center of the user's mobile area, the signal propagation difference of the second receiving path, and the signal propagation difference of the third receiving path. Signal propagation difference along the receiving path; Represents the imaginary unit; Indicates matrix transpose; Indicates the number of users; π represents pi; e represents the natural constant.

[0022] Optionally, the channel gain between the user and the base station is expressed as:

[0023] ;

[0024] in, Indicates the first Channel gain between individual users and the base station; Indicates the first The path response vector of a user at the base station; Indicates the first Users in location The field response vector; Indicates the channel; This indicates the number of users.

[0025] Optionally, the user's signal-to-noise ratio is expressed as:

[0026] ;

[0027] in, Indicates the first Signal-to-noise ratio per user; , They respectively represent the assignments to the first The transmit power allocated to the user, Transmit power of each user; Indicates the first Channel gain between individual users and the base station; Indicates variance; This indicates the number of users.

[0028] Optionally, the mobile power consumption of the user's movable antenna can be expressed as follows:

[0029] ;

[0030] in, Indicates the first Mobile power consumption of a user's movable antenna; , These represent the horizontal and vertical movement energy consumption of the user's movable antenna, respectively. , These represent the horizontal and vertical velocities of the user's movable antenna, respectively. , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first Initial horizontal and initial vertical positions of each user's movable antenna; This indicates the number of users.

[0031] Optionally, the system energy consumption model is expressed as:

[0032] ;

[0033] in, This represents the set of locations where the user can move antennas; This represents the set of transmit power allocated to users; Indicates the first Mobile power consumption of a user's movable antenna; , , , These represent the transmission power allocated to the first user, the transmission power allocated to the second user, and the transmission power allocated to the third user, respectively. The transmit power allocated to the user, Transmit power of each user; Indicates the number of users; Indicates constraints; Indicates the first Signal-to-noise ratio per user; Indicates the minimum signal-to-noise ratio; , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first Initial horizontal and initial vertical positions of each user's movable antenna; , These represent the maximum horizontal and maximum vertical movement distances of the user's movable antenna, respectively. , , These represent the channel gain between the first user and the base station, the channel gain between the second user and the base station, and the channel gain between the third user and the base station, respectively. Channel gain between each user and the base station.

[0034] Optionally, at a preset location for the user's movable antenna, the total system energy consumption model is transformed into a convex optimization problem to obtain the optimized transmit power allocated to the user, including:

[0035] The convex optimization problem is expressed as:

[0036] ;

[0037] The convex optimization problem is solved using standard convex optimization tools to obtain the optimized transmit power allocated to the user. The optimized transmit power allocated to the user is expressed as follows:

[0038] ;

[0039] in, This indicates that the optimized allocation is assigned to the first... Transmit power of each user; Indicates the minimum signal-to-noise ratio; Indicates variance; Indicates assignment to the first Transmit power of each user; Indicates the first Channel gain between each user and the base station.

[0040] Optionally, based on the optimized transmit power allocated to the user, the total system energy consumption model is transformed into a convex approximation problem to obtain the optimized location of the user's movable antenna, including:

[0041] The convex approximation problem is expressed as:

[0042] ;

[0043] ;

[0044] The convex approximation problem is solved using standard convex optimization tools to obtain the optimized position of the user-movable antenna.

[0045] in, , , , The first-order Taylor expansions of the channel gain between the first user and the base station, the second-order Taylor expansions of the channel gain between the second user and the base station, and the third-order Taylor expansions of the channel gain between the first user and the base station are respectively represented by the first-order Taylor expansions of the channel gain between the first user and the base station. The first-order Taylor expansion of the channel gain between each user and the base station, the... First-order Taylor expansion of the channel gain between a user and a base station; Indicates the first At the nth iteration point The lower bound approximation of the first-order Taylor expansion of the channel gain between a user and a base station; express The complex conjugate; Indicates the first The location of the user's movable antenna and the center of the user's mobile area are the first... The path response vector of each receiving path; This indicates the number of receive paths in the link from the user to the base station; Indicates the carrier wavelength; , They represent the first The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation and azimuth angles of the receiving path; , They represent the first time. At the nth iteration point The horizontal and vertical positions of the user's movable antenna; Represents the imaginary unit; This indicates taking the real part of a complex number.

[0046] In a second aspect, the present invention provides a computer system, comprising:

[0047] Memory, used to store computer instructions;

[0048] A processor for executing the computer instructions to implement the steps of the user downlink system optimization method with movable antenna cooperative NOMA as described in the first aspect.

[0049] Beneficial effects

[0050] This invention minimizes the total power consumption of the system by jointly optimizing the positions of all users' mobile antennas and the power allocation coefficients of the base station, while considering fixed channel gain ordering and the mobile antenna movement range of users. To effectively solve the resulting non-convex and coupled optimization problems, the non-convex subproblem is transformed into a series of convex approximation problems that can be solved efficiently through continuous convex approximation techniques. Compared with traditional fixed-position antennas and orthogonal multiple access schemes, the proposed mobile antenna-assisted NOMA scheme shows significantly superior performance in terms of total power consumption, especially in scenarios with more users and larger mobile antenna movement areas, where its advantages are even more obvious. Attached Figure Description

[0051] Figure 1 The diagram shown is a flowchart of one embodiment of the user downlink system optimization method with movable antenna cooperative NOMA of the present invention.

[0052] Figure 2 The diagram shows a comparison of the total power consumption and the number of users in this invention and the benchmark scheme.

[0053] Figure 3 The diagram shows a comparison of the total power consumption and movement range between the present invention and the benchmark scheme. Detailed Implementation

[0054] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0055] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0056] Example 1

[0057] like Figure 1 As shown, this embodiment introduces a user downlink system optimization method based on movable antenna cooperative non-orthogonal multiple access (NOMA) technology. It considers a downlink communication system assisted by a movable antenna, consisting of a base station equipped with a single antenna and... The system consists of several users, each equipped with a single movable antenna. Each user's movable antenna is connected to the radio frequency chain via a flexible cable and can be flexibly adjusted within a defined square area.

[0058] The first user is set to be closest to the base station, the second user next, and so on, thus determining the NOMA decoding order. That is, users decode according to the channel gain between them and the base station from strong to weak. Strong channel users (closest to the base station) decode the signals of weak channel users (farthest from the base station) first and then cancel them.

[0059] The method includes the following steps:

[0060] Step 1: Obtain the location of the user's movable antenna, specifically:

[0061] No. The center of a user's mobile area is defined as Define a set Used to represent the location of all user-movable antennas, where, , , The first represents the location of the user's movable antenna, the second represents the location of the user's movable antenna, and the third represents the location of the user's movable antenna. The location of a user's movable antenna; Indicates matrix transpose; , , They represent the first The horizontal and vertical positions of the user's movable antenna; the... The total number of receive paths for each user-to-base station link is expressed as: .

[0062] For this mobile antenna-assisted communication system, the channel matrix is ​​determined by the signal propagation environment and the antenna position. A far-field wireless channel model is adopted, in which the size of the transmit / receive area is much smaller than the signal propagation distance. Under this condition, for each channel path component, the emission angle and the incident angle are assumed to not change significantly when the antenna position changes.

[0063] Step 2: Calculate the signal propagation difference along the receiving path, specifically:

[0064] No. The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation angle of the receiving path is expressed as Azimuth is expressed as Then the first Location of individual user movable antenna With user mobile area center Between Signal propagation difference of each receiving path Represented as:

[0065] .

[0066] Therefore, the first The receiving path is in the first Location of individual user movable antenna With user mobile area center The phase difference between them is ,in, Indicates the carrier wavelength.

[0067] Step 3: Calculate the channel gain between the user and the base station, specifically:

[0068] The user's field response vector is calculated based on the signal propagation difference along the receiving path. Users in location field response vector Represented as:

[0069] ;

[0070] in, , , They represent the first The signal propagation difference of the first receiving path between the location of the user's movable antenna and the center of the user's mobile area, the signal propagation difference of the second receiving path, and the signal propagation difference of the third receiving path. Signal propagation difference along the receiving path; Represents the imaginary unit; Pi represents the mathematical constant; e represents the natural constant.

[0071] Calculate the channel gain between the user and the base station based on the user's field response vector. Channel gain between individual users and base station Represented as:

[0072] ;

[0073] in, Indicates the first The path response vector of a user at the base station , They represent the first The path response vector of the first receiving path, the path response vector of the second receiving path, and the path response vector of the third receiving path between the location of the user's movable antenna and the center of the user's mobile area. The path response vector of each receiving path; Indicates the channel.

[0074] Step 4: Calculate the user's signal-to-noise ratio (SNR), specifically:

[0075] Assuming assigned to the first The transmit power of each user is and define a set To represent the set of transmit power for all users, , , These represent the transmission power allocated to the first user, the transmission power allocated to the second user, and the transmission power allocated to the third user, respectively. The transmit power of each user. Therefore, the first... Received signal for each user It can be represented as:

[0076] ;

[0077] in, Indicates sending to the Normalized signals for individual users; This represents additive white Gaussian noise at the receiver, which is assumed to have zero mean and variance. The cyclic symmetric complex Gaussian distribution.

[0078] Using the NOMA protocol, users need to be ranked according to channel gain. Signal-to-noise ratio per user Represented as:

[0079] ;

[0080] in, Indicates assignment to the first Transmit power of each user.

[0081] In addition, the achievable rate per user It can be represented as:

[0082] .

[0083] Step 5: Calculate the mobile power consumption of the user's movable antenna, specifically:

[0084] Regarding the energy consumption of the movable antenna movement, assuming the horizontal velocity of the user's movable antenna... Vertical velocity It is constant. Consider a slow fading channel and focus on a quasi-static fading block. Each time frame consists of two subframes: the first subframe is for the movement of the user's movable antenna, and the second subframe is for data transmission.

[0085] use Indicating the duration of the first subframe, the maximum horizontal movement distance of the user's movable antenna is: The maximum vertical distance is In the first subframe, the horizontal movement energy consumption of the user-movable antenna. Vertical movement energy consumption Assuming it is constant, therefore, the first Mobile power consumption per user movable antenna Represented as:

[0086] ;

[0087] in, , They represent the first The initial horizontal and initial vertical positions of each user's movable antenna.

[0088] Step Six: Construct the total system energy consumption model, specifically as follows:

[0089] Based on the mobile energy consumption of the user's movable antenna and the transmit power allocated to the user, the objective function of the total system energy consumption model is constructed. The constraints of the system energy consumption model are constructed based on the user's signal-to-noise ratio, the user's mobile area, the channel gain order between the user and the base station, and the order of transmit power allocated to the user.

[0090] The goal is to minimize the total power consumption of the system. The optimization variables of the system energy consumption model include the location of all user movable antennas and the transmit power allocated to users by the base station. Constraint 1 is the minimum rate requirement constraint for each user, constraint 2 is the range restriction constraint for the movement of user movable antennas, constraint 3 is the channel gain ranking constraint, and constraint 4 is the power allocation coefficient ranking constraint. This ensures that users with stronger channel conditions are allocated lower transmission power to comply with the continuous interference cancellation protocol.

[0091] The system energy consumption model is then expressed as:

[0092] ;

[0093] in, This represents the set of locations where the user can move antennas; This represents the set of transmit power allocated to users; Indicates constraints; Indicates the minimum signal-to-noise ratio; , , These represent the channel gain between the first user and the base station, the channel gain between the second user and the base station, and the channel gain between the third user and the base station, respectively. Channel gain between each user and the base station.

[0094] Step 7: Transform the total system energy consumption model into a convex optimization problem, specifically:

[0095] Given the preset location of a user's movable antenna, the power allocation problem becomes a convex optimization problem, which can be solved efficiently. The convex optimization problem is expressed as:

[0096] ;

[0097] This problem is convex, and can be solved efficiently using standard convex optimization tools to obtain the optimized transmit power allocated to the user, and the optimized transmit power allocated to the first user. The transmit power of an individual user is expressed as:

[0098] .

[0099] Step 8: Transform the total energy consumption model of the system into a convex approximation problem, specifically as follows:

[0100] Using the optimized transmit power allocated to users, the movable antenna locations for all users are further optimized. The total system energy consumption model is transformed into a convex approximation problem. The objective of this subproblem is to minimize the total power consumption, including mobility energy consumption. The convex approximation problem is expressed as:

[0101] ;

[0102] Because in the objective function The nonconvexity of this subproblem means that the problem itself is nonconvex. To solve this problem, we employ a continuous convex approximation technique. Defined as , Indicates the first The first-order Taylor expansion of the channel gain between a user and the base station is as follows:

[0103] ;

[0104] Through a first-order Taylor expansion, at its... iteration points We obtain a convex lower bound approximation, which transforms the convex approximation problem into:

[0105] ;

[0106] The convex approximation problem is solved using standard convex optimization tools to obtain the optimized position of the user-movable antenna.

[0107] in, , , The first-order Taylor expansions of the channel gain between the first user and the base station, the second-order Taylor expansions of the channel gain between the second user and the base station, and the third-order Taylor expansions of the channel gain between the first user and the base station are respectively represented by the first-order Taylor expansions of the channel gain between the first user and the base station. First-order Taylor expansion of the channel gain between a user and a base station; Indicates the first At the nth iteration point The lower bound approximation of the first-order Taylor expansion of the channel gain between a user and a base station; express The complex conjugate; Indicates the first The location of the user's movable antenna and the center of the user's mobile area are the first... The path response vector of each receiving path; , They represent the first The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation and azimuth angles of the receiving path; , They represent the first time. At the nth iteration point The horizontal and vertical positions of the user's movable antenna; This indicates taking the real part of a complex number.

[0108] This convex problem can be solved iteratively using standard convex optimization tools.

[0109] Step 9: Iterative convergence, specifically:

[0110] To minimize the total system power consumption, the transmit power allocated to the user optimized in step seven and the user's movable antenna position optimized in step eight are iteratively optimized alternately until the total system power consumption model converges to a preset accuracy, thus obtaining the optimized transmit power allocated to the user. And the optimized location of the user-movable antenna. .

[0111] This embodiment, considering fixed channel gain ranking and user mobile antenna mobility constraints, minimizes the total system power consumption by jointly optimizing the mobile antenna position and base station power allocation coefficient. The complex joint optimization problem is decomposed into two manageable sub-problems: First, the base station power allocation is optimized under a given preset position of the user mobile antenna to effectively utilize power domain multiplexing; second, the position of the user mobile antenna is optimized under a fixed power allocation condition to achieve excellent system performance.

[0112] Example 2

[0113] Based on Example 1, this example introduces an experimental example of a user downlink system optimization method with movable antenna cooperative NOMA, including:

[0114] This embodiment presents a numerical simulation of a mobile antenna-assisted downlink NOMA system to evaluate its performance. The simulation scenario is set as a base station service. Each user is equipped with a movable antenna, and the first user is set to be closest to the base station (15-25m), the second user next (25-35m), and so on.

[0115] System parameters include: number of paths carrier wavelength noise power The channel adopts a Rayleigh distribution, and the path loss factor is... Maximum movable distance Drive power consumption Movement speed To verify performance, it was compared with benchmark solutions such as NOMA-assisted fixed-position antenna, orthogonal multiple access-assisted movable antenna, and orthogonal multiple access-assisted fixed-position antenna.

[0116] Figure 2 It depicts the total power consumption and the number of users. This embodiment compares the relationships between the following technologies: Non-Orthogonal Multiple Access-Movable Antenna (NOMA-MA), Non-Orthogonal Multiple Access-Fixed Antenna (NOMA-FPA), Orthogonal Multiple Access-Movable Antenna (OMA-MA), and Orthogonal Multiple Access-Fixed Antenna (OMA-FPA). Four communication schemes were proposed. The total energy consumption of the four schemes showed different trends as the number of users K increased (from 6 to 10). The NOMA-MA scheme had the lowest energy consumption with a gradual increase, rising from approximately 22 dBm at K=6 to approximately 50 dBm at K=10. The NOMA-FPA and OMA-MA schemes had moderate energy consumption with an almost linear increase, with the former slightly higher than the latter but both significantly lower than the traditional OMA-FPA scheme. The OMA-FPA scheme had the highest energy consumption, rising sharply from approximately 50 dBm at K=6 to approximately 90 dBm at K=10. Overall, NOMA-MA showed the best energy efficiency, OMA-FPA the worst, and NOMA-FPA and OMA-MA falling in between.

[0117] Figure 3The relationship between total power consumption and the maximum mobile area D of the movable antenna is shown. The total power consumption of the four communication schemes exhibits different characteristics as the maximum mobile distance changes. The NOMA-MA scheme has the lowest power consumption, which decreases slightly with increasing distance, from about 45.5 dBm to about 44.5 dBm and then remains stable. The NOMA-FPA scheme's power consumption remains stable at a low level of about 45.5 dBm and is almost unaffected by distance. The OMA-MA scheme has moderate power consumption, starting from about 61 dBm and decreasing slightly with increasing distance to about 58 dBm before stabilizing. The OMA-FPA scheme consistently has the highest power consumption, remaining stable at about 64.5 dBm and completely unaffected by distance changes. Overall, NOMA-MA has the best power efficiency and the lowest sensitivity to distance changes, NOMA-FPA has the second best power efficiency and stable performance, OMA-MA has moderate performance, and OMA-FPA has the worst power efficiency and lacks adaptability.

[0118] Example 3

[0119] This embodiment describes a computer system, including:

[0120] Memory, used to store computer instructions;

[0121] A processor for executing the computer instructions to implement the steps of the user downlink system optimization method with movable antenna cooperative NOMA as described in Embodiment 1 or 2.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing a user downlink system using a movable antenna-coordinated NOMA, characterized in that, include: Obtain the location of the user's movable antenna; Calculate the signal propagation difference along the receiving path between the location of the user's movable antenna and the center of the user's mobile area; Calculate the user's field response vector based on the signal propagation difference of the receiving path, and calculate the channel gain between the user and the base station based on the user's field response vector; The signal-to-noise ratio of the user is calculated based on the channel gain, and the mobile energy consumption of the user's mobile antenna is calculated based on the location of the user's mobile antenna. Based on the mobile energy consumption of the user's movable antenna and the transmit power allocated to the user, the objective function of the total system energy consumption model is constructed. Based on the user's signal-to-noise ratio, the user's mobile area, the channel gain order between the user and the base station, and the order of transmit power allocated to the user, the constraints of the system energy consumption model are constructed. With the user's movable antenna at a preset position, the total system energy consumption model is transformed into a convex optimization problem to obtain the optimized transmit power allocated to the user; Based on the optimized transmit power allocated to users, the total system energy consumption model is transformed into a convex approximation problem, and the optimized position of the user's movable antenna is obtained.

2. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The signal propagation difference of the receiving path between the location of the user's movable antenna and the center of the user's mobile area is expressed as: ； in, Indicates the first The location of the user's movable antenna and the center of the user's mobile area are the first... Signal propagation difference along the receiving path; , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation and azimuth angles of the receiving path; This indicates the number of users.

3. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The user's field response vector is represented as: ； in, Indicates the first Users in location The field response vector; Indicates the carrier wavelength; , , They represent the first The signal propagation difference of the first receiving path between the location of the user's movable antenna and the center of the user's mobile area, the signal propagation difference of the second receiving path, and the signal propagation difference of the third receiving path. Signal propagation difference along the receiving path; Represents the imaginary unit; Indicates matrix transpose; Indicates the number of users; π represents pi; e represents the natural constant.

4. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The channel gain between the user and the base station is expressed as: ； in, Indicates the first Channel gain between individual users and the base station; Indicates the first The path response vector of a user at the base station; Indicates the first Users in location The field response vector; Indicates the channel; This indicates the number of users.

5. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The user's signal-to-noise ratio is expressed as: ； in, Indicates the first Signal-to-noise ratio per user; , They respectively represent the assignments to the first The transmit power allocated to the user, Transmit power of each user; Indicates the first Channel gain between individual users and the base station; Indicates variance; This indicates the number of users.

6. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The mobile energy consumption of a user's movable antenna is expressed as follows: ； in, Indicates the first Mobile power consumption of a user's movable antenna; , These represent the horizontal and vertical movement energy consumption of the user's movable antenna, respectively. , These represent the horizontal and vertical velocities of the user's movable antenna, respectively. , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first Initial horizontal and initial vertical positions of each user's movable antenna; This indicates the number of users.

7. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 1, characterized in that, The system energy consumption model is expressed as follows: ； in, This represents the set of locations where the user can move antennas; This represents the set of transmit power allocated to users; Indicates the first Mobile power consumption of a user's movable antenna; , , , These represent the transmission power allocated to the first user, the transmission power allocated to the second user, and the transmission power allocated to the third user, respectively. The transmit power allocated to the user, Transmit power of each user; Indicates the number of users; Indicates constraints; Indicates the first Signal-to-noise ratio per user; Indicates the minimum signal-to-noise ratio; , They represent the first The horizontal and vertical positions of the user's movable antenna; , They represent the first Initial horizontal and initial vertical positions of each user's movable antenna; , These represent the maximum horizontal and maximum vertical movement distances of the user's movable antenna, respectively. , , These represent the channel gain between the first user and the base station, the channel gain between the second user and the base station, and the channel gain between the third user and the base station, respectively. Channel gain between each user and the base station.

8. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 7, characterized in that, With the user's movable antenna at a preset location, the total system energy consumption model is transformed into a convex optimization problem to obtain the optimized transmit power allocated to the user, including: The convex optimization problem is expressed as: ； The convex optimization problem is solved using standard convex optimization tools to obtain the optimized transmit power allocated to the user. The optimized transmit power allocated to the user is expressed as follows: ； in, This indicates that the optimized allocation is assigned to the first... Transmit power of each user; Indicates the minimum signal-to-noise ratio; Indicates variance; Indicates assignment to the first Transmit power of each user; Indicates the first Channel gain between each user and the base station.

9. The user downlink system optimization method with movable antenna cooperative NOMA according to claim 8, characterized in that, Based on the optimized transmit power allocated to users, the total system energy consumption model is transformed into a convex approximation problem, yielding the optimized locations of the user's movable antennas, including: The convex approximation problem is expressed as: ； ； The convex approximation problem is solved using standard convex optimization tools to obtain the optimized position of the user-movable antenna. in, , , , The first-order Taylor expansions of the channel gain between the first user and the base station, the second-order Taylor expansions of the channel gain between the second user and the base station, and the third-order Taylor expansions of the channel gain between the first user and the base station are respectively represented by the first-order Taylor expansions of the channel gain between the first user and the base station. The first-order Taylor expansion of the channel gain between each user and the base station, the... First-order Taylor expansion of the channel gain between a user and a base station; Indicates the first At the nth iteration point The lower bound approximation of the first-order Taylor expansion of the channel gain between a user and a base station; express The complex conjugate; Indicates the first The location of the user's movable antenna and the center of the user's mobile area are the first... The path response vector of each receiving path; This indicates the number of receive paths in the link from the user to the base station; Indicates the carrier wavelength; , They represent the first The location of the user's movable antenna and the center of the user's mobile area are the first... The elevation and azimuth angles of the receiving path; , They represent the first time. At the nth iteration point The horizontal and vertical positions of the user's movable antenna; Represents the imaginary unit; This indicates taking the real part of a complex number.

10. A computer system, characterized in that, include: Memory, used to store computer instructions; A processor for executing the computer instructions to implement the steps of the user downlink system optimization method with movable antenna cooperative NOMA as described in any one of claims 1-9.

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