Energy consumption resource collaborative optimization method, apparatus and equipment based on rate segmentation multiple access

Abstract

This application discloses a method, apparatus, and device for coordinated optimization of energy consumption resources based on rate division multiple access (RDA), belonging to the field of wireless communication technology. The method includes: obtaining a first channel vector from a serving device to each user device; grouping the user devices according to the first channel vector to obtain multiple user groups, wherein each user group includes one near-end user device and one far-end user device; calculating the beamforming vector corresponding to each user group based on a second channel vector from the serving device to each near-end user device; establishing a capacity and power model based on each first channel vector and each beamforming vector; the capacity and power model characterizes the transmitted signal power of the serving device and the decoding efficiency of the user devices; and solving the transmitted signal matrix of the serving device based on the capacity and power model, with the goal of maximizing the energy efficiency of the serving device. This application can improve the resource utilization and anti-interference capability of the serving device, thereby improving the operating efficiency of the network system.

Description

Technical Field

[0001] This application belongs to the field of wireless communication technology, and in particular relates to a method, apparatus and device for coordinated optimization of energy consumption resources based on rate division multiple access. Background Technology

[0002] In wireless communication systems, wireless communication resources such as time, frequency, and power are limited, while the number of user equipment is constantly increasing. In order to make full use of limited resources and adapt to the ever-changing communication environment, it is necessary to continuously adjust the signal transmission strategies of service equipment to provide users with high-quality communication services.

[0003] In related technologies, the current channel state is typically estimated based on historical statistical data of the channel state, and signal transmission strategies are designed based on fixed interference assessment systems and anti-interference strategies. This approach is difficult to adapt to dynamic changes in the channel and fluctuations in network load, resulting in low network resource utilization and overall anti-interference resilience, which seriously affects the operating efficiency of the network system. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, apparatus, and device for coordinated optimization of energy consumption resources based on rate segmentation multiple access (RSMA) to improve the operating efficiency of network systems.

[0005] Firstly, this application provides a method for coordinated optimization of energy consumption resources based on rate segmentation multiple access, including: Obtain the first channel vector from the service device to each user device, and group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device; Calculate the beamforming vector corresponding to each user group based on the second channel vector from the service device to each of the near-end user devices; A capacity and power model is established based on each of the first channel vectors and each of the beamforming vectors; the capacity and power model characterizes the signal transmission power of the serving equipment and the information decoding efficiency of the user equipment. With the goal of maximizing the energy efficiency of the service equipment, the transmission signal matrix of the service equipment is solved based on the capacity and power model.

[0006] The energy consumption resource collaborative optimization method based on rate division multiple access provided in this application can dynamically adjust user grouping and signal transmission methods according to the real-time channel state by grouping users, calculating beamforming vectors and modeling based on the channel vector representing the current channel state, thereby reducing the impact of dynamic channel changes. By aiming to maximize the energy efficiency of the service equipment and solving the transmission signal matrix based on the capacity and power model, it can reduce power redundancy consumption and improve the resource utilization efficiency of the service equipment. Thus, it improves the operating efficiency of the network system.

[0007] According to one embodiment of this application, the step of grouping each user equipment according to the first channel vector to obtain multiple user groups includes: Calculate the corresponding channel strength and channel direction based on the first channel vector; Based on the channel strength, a timer is set for each of the user equipment, wherein the timer timeout rate is positively correlated with the signal strength. Record the timeout order of each timer, identify the half of the user equipment that timed out first as near-end user equipment, and identify the remaining user equipment as far-end user equipment; Calculate the similarity of the channel direction between each near-end user equipment and each far-end user equipment, and add the near-end user equipment and far-end user equipment with matching similarity into the same user group to obtain multiple user groups.

[0008] In this embodiment, by setting a timer based on channel strength and dividing near-end and far-end user equipment according to the timer timeout order, the signaling overhead in the user grouping process can be reduced. By adding near-end and far-end user equipment with similar channel directions into the same user group, when designing the beamforming vectors corresponding to each user group, only one beamforming vector can be designed with the near-end user equipment as the channel representative, thus reducing computational complexity.

[0009] According to one embodiment of this application, the step of grouping each user equipment according to the first channel vector to obtain multiple user groups includes: Identify the user equipment served by different service devices; the different service devices include ground service devices and non-ground service devices. Based on the first channel vector, user equipment serving the same service device is grouped into multiple user groups.

[0010] In this embodiment, by first identifying the user equipment served by the ground service equipment and the user equipment served by the non-ground service equipment respectively, and then grouping the user equipment served by the same service equipment, the grouping conflict can be reduced.

[0011] According to one embodiment of this application, determining the user equipment served by different service devices includes: Determine the service status parameters from the ground service equipment to each user equipment; User equipment whose service status parameters are within the service range and within the service capacity is identified as user equipment serving the ground service equipment. User equipment whose service status parameters are "out of service range" or "out of service capacity" is identified as user equipment serving the non-terrestrial service equipment.

[0012] In this embodiment, by determining the service status parameters from the ground service equipment to each user equipment, and determining the type of service equipment corresponding to the user equipment based on the service status parameters, the allocation of service equipment can be dynamically adjusted according to the actual situation, thereby improving the stability of the network system.

[0013] According to one embodiment of this application, the step of calculating the beamforming vector corresponding to each user group based on the second channel vector from the serving device to each of the near-end user devices includes: Construct a channel matrix based on each of the second channel vectors; If the channel matrix is ​​invertible, then the first beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; If the channel matrix is ​​not invertible, a second beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; the array gain of the second beamforming algorithm is higher than that of the first beamforming algorithm.

[0014] In this embodiment, by employing a beamforming algorithm with higher array gain to calculate the beamforming vector when the channel matrix is ​​irreversible, the system capacity reduction caused by the complexity of the channel environment can be reduced, thereby improving the stability of the network system.

[0015] According to one embodiment of this application, if the channel matrix is ​​invertible, according to the formula:

[0016] The beamforming vector corresponding to each user group is calculated; Where W is the beamforming matrix of the service device. Let H be the beamforming vector corresponding to the k-th user group, and let H be the channel matrix. Let H be the normalized matrix. , Represents the conjugate transpose of the channel matrix H. Representation matrix The i-th main diagonal, where N is the number of user groups.

[0017] According to one embodiment of this application, if the channel matrix is ​​not invertible, according to the formula:

[0018] The beamforming vector corresponding to each user group is calculated; in, This is the beamforming vector corresponding to the k-th user group. The k-th column of the channel matrix It represents the second norm of a vector.

[0019] According to one embodiment of this application, the capacity and power model includes a total capacity model and a total power model; according to the formula:

[0020]

[0021] Establish capacity and power models; in, For the total system capacity, Where N is the total system power and N is the number of user groups. This refers to the common information beam vector of the service device for the remote user equipment. This refers to the private information beam vector of the service device for the remote user device. Let the system capacity be the remote user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding public information for remote user equipment in the k-th user group. ;in, This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference with the private portion of the signal from the service equipment; Let the system capacity be the near-end user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding common information for near-end user equipment in the k-th user group. , The signal-to-interference-plus-noise ratio (SIR) for decoding private information for near-end user equipment in the k-th user group. , This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference in the common part of the signal of the service equipment.

[0022] According to one embodiment of this application, the step of solving the transmission signal matrix of the service device's transmitted signals based on the capacity and interference model, with the goal of maximizing the energy efficiency of the service device, includes: With the goal of maximizing the energy efficiency of the service equipment, an objective function and objective constraints are constructed; the objective function is the ratio of the total system capacity to the total system power, and the objective constraints include total power constraints and power allocation coefficient constraints. Based on the objective function, define a first auxiliary function; The first auxiliary function is iterated with the total system power and power allocation coefficient as variables to obtain the target total system power and target power allocation coefficient when the iteration reaches convergence; Based on the total power of the target system and the target power allocation coefficient, a second auxiliary function is defined; The second auxiliary function is iterated using the transmitted signal matrix as a variable to obtain the target transmitted signal matrix when the iteration converges, and the target transmitted signal matrix is ​​used as the transmitted signal matrix of the service device.

[0023] In this embodiment, by first solving for the total power of the target system and the target power allocation coefficient, and then solving for the transmitted signal matrix based on the obtained total power of the target system and the target power allocation coefficient, the non-convex optimization problem can be transformed into a convex problem, thereby reducing the algorithm complexity.

[0024] According to one embodiment of this application, the transmitted signal matrix is:

[0025] Where N is the number of user groups. The vector of the transmitted signal of the service equipment for the k-th user group. ; For public information beam vectors, For public information modulation symbols, ; For private information beam vectors, Modulation symbols for private information, .

[0026] According to one embodiment of this application, the target constraint is:

[0027]

[0028] in, Let k be the transmitted signal power of the k-th user group. For the maximum power of the service equipment, This is the power constraint coefficient. ,in Let be the public information power of the k-th user group.

[0029] According to one embodiment of this application, the first auxiliary function is:

[0030] Where C represents the total system capacity and P represents the total system power. Energy efficiency of service equipment.

[0031] According to one embodiment of this application, the second auxiliary function is:

[0032] Where X is the transmitted signal matrix, Z is the global auxiliary variable, and X = Z is constrained; g(Z) is an indicator function, when When g(Z) = 0, otherwise ; For the maximum power of the service equipment, As dual variables, This is the penalty coefficient.

[0033] Secondly, this application provides an energy consumption resource collaborative optimization device based on rate segmentation multiple access, comprising: The grouping module is used to obtain the first channel vector from the service device to each user device, and to group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device. A beamforming module is used to calculate the beamforming vector corresponding to each user group based on the second channel vector from the service equipment to each of the near-end user equipments. A module is established to build a capacity and power model based on each of the first channel vectors and each of the beamforming vectors; the capacity and power model characterizes the signal power transmitted by the serving equipment and the information decoding efficiency of the user equipment. The solution module is used to solve the transmission signal matrix of the service device based on the capacity and power model, with the goal of maximizing the energy efficiency of the service device.

[0034] According to the energy consumption resource collaborative optimization device based on rate division multiple access of this application, by grouping users, calculating beamforming vectors and modeling according to the channel vector representing the current channel state, it can dynamically adjust user grouping and signal transmission mode according to the real-time channel state, reducing the impact of dynamic channel changes; by taking the maximization of energy efficiency of the service equipment as the objective and solving the transmission signal matrix according to the capacity and power model, it can reduce power redundancy consumption and improve the resource utilization efficiency of the service equipment; thereby improving the operating efficiency of the network system.

[0035] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in the first aspect above.

[0036] Fourthly, this application provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in the first aspect above.

[0037] Fifthly, this application provides a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in the first aspect above.

[0038] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in the first aspect above.

[0039] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects: According to the energy consumption resource collaborative optimization method based on rate division multiple access in this application, by grouping users, calculating beamforming vectors and modeling according to the channel vector representing the current channel state, it is possible to dynamically adjust user grouping and signal transmission mode according to the real-time channel state, thereby reducing the impact of dynamic channel changes; by taking the maximization of the energy efficiency of the service equipment as the objective and solving the transmission signal matrix according to the capacity and power model, it is possible to reduce power redundancy consumption and improve the resource utilization efficiency of the service equipment; thus, the operating efficiency of the network system is improved.

[0040] In some embodiments, by setting a timer based on channel strength and dividing near-end and far-end user equipment according to the timer timeout order, the signaling overhead in the user grouping process can be reduced. By adding near-end and far-end user equipment with similar channel directions into the same user group, when designing the beamforming vector corresponding to each user group, only one beamforming vector can be designed with the near-end user equipment as the channel representative, thus reducing computational complexity.

[0041] In some embodiments, by first identifying user equipment served by ground service equipment and user equipment served by non-ground service equipment respectively, and then grouping user equipment served by the same service equipment, grouping conflicts can be reduced.

[0042] In some embodiments, by determining the service status parameters from the ground service equipment to each user equipment, and determining the type of service equipment corresponding to the user equipment based on the service status parameters, the allocation of service equipment can be dynamically adjusted according to the actual situation, thereby improving the stability of the network system.

[0043] In some embodiments, by employing a beamforming algorithm with higher array gain to calculate the beamforming vector when the channel matrix is ​​irreversible, the system capacity reduction caused by the complexity of the channel environment can be reduced, thereby improving the stability of the network system.

[0044] In some embodiments, by first solving for the total power of the target system and the target power allocation coefficient, and then solving for the transmitted signal matrix based on the obtained total power of the target system and the target power allocation coefficient, the non-convex optimization problem can be transformed into a convex problem, thereby reducing the algorithm complexity.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the energy consumption resource collaborative optimization method based on rate segmentation multiple access provided in the embodiments of this application; Figure 2 This is a schematic diagram of the energy consumption resource collaborative optimization process based on rate segmentation multiple access provided in the embodiments of this application; Figure 3This is a schematic diagram of the energy consumption resource collaborative optimization device based on rate division multiple access provided in the embodiments of this application; Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0049] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0050] The following description, in conjunction with the accompanying drawings, details the energy consumption resource collaborative optimization method, apparatus, and equipment based on rate partitioning multiple access provided in this application through specific embodiments and application scenarios.

[0051] Among them, the energy consumption resource collaborative optimization method based on rate partitioning multiple access can be applied to the terminal, specifically executed by the hardware or software in the terminal.

[0052] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that, in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).

[0053] The following embodiments describe a terminal including a display and a touch-sensitive surface. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.

[0054] The energy consumption resource collaborative optimization method based on rate partitioned multiple access provided in this application embodiment can be executed by an electronic device or a functional module or functional entity in an electronic device that can implement the energy consumption resource collaborative optimization method based on rate partitioned multiple access. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablet computers, computers, etc. The following uses an electronic device as the execution subject to illustrate the energy consumption resource collaborative optimization method based on rate partitioned multiple access provided in this application embodiment.

[0055] like Figure 1 As shown, the energy consumption resource collaborative optimization method based on rate partitioned multiple access includes steps 110, 120, 130 and 140.

[0056] Step 110: Obtain the first channel vector from the service device to each user device, and group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device.

[0057] In this embodiment, the service device is a device that provides wireless network services, such as a base station, communication satellite, or communication drone. The user equipment is a device that uses the wireless network services, such as a mobile phone, in-vehicle computer, or IoT sensor. The service device can establish a connection with the user equipment, receive downlink data streams from the core network, encode and modulate them into electromagnetic wave signals, and send them to the corresponding user equipment, thereby enabling the user equipment to obtain data from the network by receiving and decoding the electromagnetic wave signals.

[0058] A channel vector is a complex vector that describes the wireless communication links between the serving device's transmit antennas and the user equipment. For example, the channel vector between the serving device and user equipment u is... ,in, The number of transmitting antennas for the service equipment, in complex numbers This represents the phase attenuation and amplitude shift information of the electromagnetic wave signal during its propagation from the i-th transmitting antenna of the service equipment to the user equipment u.

[0059] The service equipment can broadcast a pilot signal x to each user equipment. After receiving the pilot signal x, each user equipment u returns a signal to the service equipment. Then we can compare The channel vector is obtained by combining x and x. ,Will As the first channel vector from the service device to the user device u.

[0060] After obtaining the first channel vectors of 2N user equipments, each user equipment is divided into multiple user groups based on the first channel vectors. Each user group contains one near-end user equipment and one far-end user equipment. For each user equipment, the cosine similarity between its first channel vector and the first channel vectors of other user equipments is calculated. The user equipment with the highest cosine similarity is assigned to the same user group. The channel quality is then evaluated based on the first channel vectors, and the user equipment with better channel quality is identified as the near-end user equipment, while the other is identified as the far-end user equipment. Thus, a total of N user groups are obtained, each containing one near-end user equipment and one far-end user equipment.

[0061] Step 120: Calculate the beamforming vector corresponding to each user group based on the second channel vector from the service equipment to each near-end user equipment.

[0062] In this embodiment, beamforming is a signal processing technique based on the principle of electromagnetic wave interference. By adjusting the phase and amplitude of the sub-transmitted signals from each transmitting antenna, the sub-transmitted signals are superimposed in the target direction and canceled out in other directions, thereby causing the direction of the synthesized total transmitted signal to point towards the target receiving end. The beamforming vector is a complex vector describing the beamforming strategy of the serving device, and its length is equal to the number of transmitting antennas of the serving device.

[0063] Since electromagnetic wave signals are affected by the channel during propagation, beamforming vectors need to be designed based on the channel vector describing the channel environment. In this embodiment, the channel directions of two user equipments in the same user group are approximately the same; therefore, each user group k uses only the channel vector of the near-end user equipment. To represent this, design a beamforming vector. .in, Includes public information beam vector and private information beam vector The channel conditions at the remote user equipment are poor, therefore only decoding is required. Partially encoded data; the channel conditions at the near-end user equipment are better, therefore decoding is possible. and Partial data.

[0064] Beamforming algorithms such as maximum transmission ratio beamforming, zero-forcing beamforming, and minimum mean square error beamforming can be used to calculate the beamforming vector corresponding to each user group; of course, adaptive beamforming algorithms based on machine learning can also be used, and this application does not limit this.

[0065] Step 130: Establish a capacity and power model based on each first channel vector and each beamforming vector; the capacity and power model characterizes the signal power transmitted by the service equipment and the information decoding efficiency of the user equipment.

[0066] In this embodiment, the capacity and power model is a mathematical description model characterizing the power of the signal transmitted by the serving device and the efficiency of the user equipment in decoding information. Based on the beamforming vector, the power of the signal transmitted by the serving device to each user equipment can be calculated, and thus the total power used by the serving device to provide the service can be calculated. Based on the first channel vector and the beamforming vector, the signal-to-interference-plus-noise ratio (SIR) of each user equipment when receiving the signal can be calculated, and then the system capacity of each user equipment receiving the signal can be calculated according to Shannon's theorem, thereby calculating the total capacity of the wireless network system.

[0067] In some embodiments, the capacity and power model includes a total capacity model and a total power model; according to the formula:

[0068]

[0069] Establish capacity and power models; in, For the total system capacity, Where N is the total system power and N is the number of user groups. This refers to the common information beam vector of the service device for the remote user equipment. This refers to the private information beam vector of the service device for the remote user device. Let the system capacity be the remote user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding public information for remote user equipment in the k-th user group. ;in, This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference with the private portion of the signal from the service equipment; Let the system capacity be the near-end user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding common information for near-end user equipment in the k-th user group. , The signal-to-interference-plus-noise ratio (SIR) for decoding private information for near-end user equipment in the k-th user group. , This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference in the common part of the signal of the service equipment.

[0070] Step 140: With the goal of maximizing the energy efficiency of the service equipment, solve for the transmission signal matrix of the service equipment based on the capacity and power model.

[0071] In this embodiment, energy efficiency refers to the efficiency with which the energy consumed by the service equipment in transmitting signals is converted into effective transmitted signals. The transmitted signal matrix is ​​a matrix that describes the signals actually transmitted by each transmitting antenna of the service equipment to each user equipment.

[0072] In some embodiments, the transmitted signal matrix is:

[0073] Where N is the number of user groups. The vector of the transmitted signal of the service equipment for the k-th user group. ; For public information beam vectors, For public information modulation symbols, ; For private information beam vectors, Modulation symbols for private information, .

[0074] The ratio of total system capacity to total system power can be used as the energy efficiency of the service equipment and as the objective function for solving the problem. The transmitted signal matrix is ​​used as the objective variable to be solved. Constraints are set based on the power limit of the service equipment. At the start of the solution process, the user can pre-set the initial value of the transmitted signal matrix. In each iteration, the energy efficiency is calculated based on the transmitted signal matrix at that time, and the preset optimization target is evaluated to determine if the energy efficiency exceeds a preset threshold. If the optimization target is not met, the transmitted signal matrix is ​​updated according to the user-preset parameter update strategy, and the next iteration begins. This process is repeated until the preset optimization target or the preset maximum number of iterations is reached.

[0075] After the calculation is completed, the transmission signal matrix obtained from the last iteration is used as the transmission signal matrix of the service device.

[0076] The energy consumption resource collaborative optimization method based on rate division multiple access provided in this application can dynamically adjust user grouping and signal transmission methods according to the real-time channel state by grouping users, calculating beamforming vectors and modeling based on the channel vector representing the current channel state, thereby reducing the impact of dynamic channel changes. By aiming to maximize the energy efficiency of the service equipment and solving the transmission signal matrix based on the capacity and power model, power redundancy consumption can be reduced and the resource utilization efficiency of the service equipment can be improved. Thus, the operating efficiency of the network system is improved.

[0077] In some embodiments, user equipments are grouped according to a first channel vector to obtain multiple user groups, including: Calculate the corresponding channel strength and channel direction based on the first channel vector; Based on the channel strength, a timer is set for each user equipment, where the timer timeout rate is positively correlated with the signal strength. Record the timeout order of each timer, identify the half of the user equipment that timed out first as near-end user equipment, and identify the remaining user equipment as far-end user equipment; Calculate the channel direction similarity between each near-end user equipment and each far-end user equipment, and add near-end user equipment and far-end user equipment with matching similarity into the same user group to obtain multiple user groups.

[0078] In this embodiment, channel strength is a numerical value describing the amplitude gain of the electromagnetic wave signal from the transmitter to the receiver; a larger value indicates better channel conditions. The square of the magnitude of the first channel vector can be calculated as its corresponding channel strength. Channel direction is a unit vector describing the proportional relationship between the signal strengths of each transmitting antenna and the receiver; the unit vector of the first channel vector can be calculated as its corresponding channel direction. For example, the first channel vector of user equipment i is... Then its corresponding channel strength is The channel direction is .

[0079] After obtaining the channel strength for each user equipment, a timer is set for each user equipment based on the channel strength, where the timer timeout rate is positively correlated with the signal strength. For example, it can be done according to the formula... Set the length of the timer for user equipment i. ,in It is a value greater than 0. For a time value, k and The value can be preset by the user. Therefore, the higher the channel strength value and the shorter the timer length, the faster the timer times out.

[0080] Of course, the length of each timer can also be determined according to other algorithms, such as making the timer length inversely proportional to the channel strength, etc., but this application does not limit this.

[0081] After setting up timers for a total of 2N user devices, start timing simultaneously and record the timeout order of each timer. The user devices corresponding to the N timers that time out first are identified as the near-end user devices. The remaining user equipment is identified as remote user equipment. .

[0082] After identifying the near-end user equipment and the far-end user equipment, the specific user group is determined based on the channel direction.

[0083] For near-end user equipment n and far-end user equipment f, the formula can be used. Calculate the similarity of their channel directions. Calculated It is a numerical value between 0 and 1, with a larger value indicating a higher similarity in channel direction. An optimal weighted bipartite graph matching algorithm can be used for matching to construct a... The similarity matrix S, where The optimal remote user equipment (RPA) for each near-end user equipment is determined using the Hungarian algorithm, and these RPAs are then added to the same user group. This results in N user groups, each containing one near-end user equipment and one remote user equipment.

[0084] Of course, other algorithms can also be used to calculate the optimal matching pair, such as a greedy algorithm based on nearest neighbor search, etc., and this application does not limit this.

[0085] In this embodiment, by setting a timer based on channel strength and dividing near-end and far-end user equipment according to the timer timeout order, the signaling overhead in the user grouping process can be reduced. By adding near-end and far-end user equipment with similar channel directions into the same user group, when designing the beamforming vectors corresponding to each user group, only one beamforming vector can be designed with the near-end user equipment as the channel representative, thus reducing computational complexity.

[0086] In some embodiments, user equipments are grouped according to a first channel vector to obtain multiple user groups, including: Identify the user equipment served by different service equipment; these different service equipment include ground service equipment and non-ground service equipment. User equipment serving the same service device is grouped according to the first channel vector to obtain multiple user groups.

[0087] In this application embodiment, ground service equipment refers to communication service equipment deployed on the Earth's surface or in the near-Earth atmosphere, such as ground base stations and low-altitude communication drones. Non-ground service equipment refers to communication service equipment deployed in space outside the Earth's atmosphere or on high-altitude platforms, such as geostationary satellites and high-altitude platform stations.

[0088] Each service device can broadcast its own location, configuration parameters, and other information. After receiving the broadcast information from each service device, each user device determines the optimal target device based on the location relationship and configuration parameters, and sends a response signal containing its own information and access request to its corresponding optimal target device. Each service device receives the response signal and identifies the user device that is the target of its response signal as the user device it is serving.

[0089] After determining the user equipment served by different service devices, the user equipment served by the same service device is then grouped according to the first channel vector. For example, there are R service devices in total. The i-th service device The set of user equipment that provides the service is When grouping, according to and The first channel vector of each user equipment in the middle, for The user equipment in the system is grouped.

[0090] In this embodiment, by first identifying the user equipment served by the ground service equipment and the user equipment served by the non-ground service equipment respectively, and then grouping the user equipment served by the same service equipment, the grouping conflict can be reduced.

[0091] In some embodiments, determining the user equipment served by different service equipment includes: Determine the service status parameters from ground service equipment to each user's equipment; User equipment whose service status parameters are within the service range and within the service capacity is identified as user equipment serving ground service equipment. User equipment whose service status parameters are "out of service range" or "out of service capacity" is identified as user equipment serving non-terrestrial service equipment.

[0092] In this application embodiment, the service range refers to the geographical area covered by the electromagnetic wave signals that the service device can provide and that meet the service quality requirements; the service capacity describes the scale of communication services that the service device can provide, and can be defined as the number of user devices that the service device can serve simultaneously. Service status parameters are a set of data describing the operating status of a service device, and may include data such as current load, availability status, and current performance.

[0093] For ground service equipment b, after receiving response signals from each user equipment, it can analyze the location information of the user equipment contained in the response signals and compare it with its own service range. User equipment outside its service range is recorded as "out of service range" in its service status parameters. The remaining user equipment is then sorted from nearest to farthest, and the first... The service status parameter for one user device is recorded as "accessible", and the service status parameter for the remaining user devices is recorded as "exceeding service capacity".

[0094] After the analysis is completed, user equipment with the service status parameter "accessible" is identified as user equipment served by ground service equipment b, and user equipment with the service status parameter "out of service range" or "out of service capacity" is identified as user equipment served by non-ground service equipment s.

[0095] In this embodiment, by determining the service status parameters from the ground service equipment to each user equipment, and determining the type of service equipment corresponding to the user equipment based on the service status parameters, the allocation of service equipment can be dynamically adjusted according to the actual situation, thereby improving the stability of the network system.

[0096] In some embodiments, calculating the beamforming vector corresponding to each user group based on the second channel vector from the serving device to each near-end user equipment includes: Construct a channel matrix based on each second channel vector; If the channel matrix is ​​invertible, the first beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix. If the channel matrix is ​​not invertible, the second beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; the array gain of the second beamforming algorithm is higher than that of the first beamforming algorithm.

[0097] In this embodiment, the channel matrix is ​​a matrix composed of multiple channel vectors. For example, there are N user groups, and the channel vector from the serving device to the near-end user equipment in the k-th user group is... Then a channel matrix can be constructed. Each row of H corresponds to a channel vector, and each column corresponds to the channel information between a transmitting antenna of the serving device and each user device.

[0098] The characteristics of the channel matrix can be analyzed to assess the channel environment between each transmitting antenna of the service equipment and each user equipment, and then a beamforming algorithm can be selected.

[0099] If the channel matrix H is invertible, it means that the columns of the channel matrix H are linearly independent, the signal interference between the service equipment and the user equipment is relatively mild, and the signal power loss caused by interference elimination is small. If the channel matrix H is not invertible, it means that the columns of the channel matrix H are linearly dependent, the signal interference between the service equipment and the user equipment will affect each other, and the signal power loss caused by interference elimination is severe.

[0100] Therefore, when the channel matrix is ​​invertible, the first beamforming algorithm is used; when the channel matrix is ​​not invertible, the second beamforming algorithm with higher array gain is used to calculate the beamforming vector. Here, array gain refers to the degree to which the signal power received by the target user is increased after the signals transmitted by each transmitting antenna are coherently superimposed in the target direction.

[0101] In one embodiment, if the channel matrix is ​​invertible, according to the formula:

[0102] The beamforming vectors corresponding to each user group are calculated. Where W is the beamforming matrix of the service device. Let H be the beamforming vector corresponding to the k-th user group, and let H be the channel matrix. Let H be the normalized matrix. , Represents the conjugate transpose of the channel matrix H. Representation matrix The i-th main diagonal, where N is the number of user groups.

[0103] The beamforming vector can be calculated in this way to reduce mutual interference between user signals.

[0104] In one embodiment, if the channel matrix is ​​non-invertible, according to the formula:

[0105] The beamforming vectors corresponding to each user group are calculated. in, This is the beamforming vector corresponding to the k-th user group. The k-th column of the channel matrix It represents the second norm of a vector.

[0106] The beamforming vector calculated in this way has the same direction as the original channel vector. The sub-transmitted signals are superimposed along the target direction, which can improve the power of the total transmitted signal received by the user.

[0107] In this embodiment, by employing a beamforming algorithm with higher array gain to calculate the beamforming vector when the channel matrix is ​​irreversible, the system capacity reduction caused by the complexity of the channel environment can be reduced, thereby improving the stability of the network system.

[0108] In some embodiments, with the goal of maximizing the energy efficiency of the service equipment, the transmission signal matrix of the service equipment's transmitted signals is solved according to a capacity and interference model, including: With the goal of maximizing the energy efficiency of service equipment, an objective function and objective constraints are constructed; the objective function is the ratio of the total system capacity to the total system power, and the objective constraints include total power constraints and power allocation coefficient constraints. Based on the objective function, define the first auxiliary function; The first auxiliary function is iterated with the total system power and power allocation coefficient as variables to obtain the target total system power and target power allocation coefficient when the iteration reaches convergence. Based on the total power of the target system and the target power allocation coefficient, a second auxiliary function is defined; The second auxiliary function is iterated with the transmitted signal matrix as a variable to obtain the target transmitted signal matrix when the iteration converges. The target transmitted signal matrix is ​​then used as the transmitted signal matrix of the service device.

[0109] In the embodiments of this application, the solution process is as follows: Figure 2 As shown.

[0110] First, with the goal of maximizing the energy efficiency of service equipment, an objective function and objective constraints are constructed.

[0111] The objective function is the ratio of the total system capacity to the total system power, for example:

[0112] The target constraints include total power constraints and power allocation coefficient constraints. All calculation results obtained in subsequent calculations should satisfy these target constraints. For example, the target constraints might be:

[0113]

[0114] in, Let k be the transmitted signal power of the k-th user group. For the maximum power of the service equipment, This is the power constraint coefficient. ,in Let be the public information power of the k-th user group.

[0115] Since the objective function is in the form of a ratio, directly finding the maximum value of a function in the form of a ratio is a non-convex optimization problem. This problem has high computational complexity and is prone to getting trapped in local optima, making it difficult to find the global optimum. Therefore, based on the objective function, a first auxiliary function is defined:

[0116] Where C represents the total system capacity and P represents the total system power. Energy efficiency of service equipment.

[0117] Therefore, the non-convex optimization problem of maximizing the ratio can be transformed into a convex optimization problem of minimizing the difference, thereby reducing the algorithm complexity and improving the solution speed.

[0118] After constructing the first auxiliary function, the first auxiliary function is iterated using the total system power and power distribution coefficient as variables.

[0119] Before starting the iteration, set the initial values ​​for the system's total power and power distribution coefficient. , And calculate the initial energy efficiency. After completing the calculation, iteration begins. In each iteration, algorithms such as gradient descent and Lagrange duality are used to solve the problem. The smallest value and Subsequently, according to and calculate Repeat the above process until the convergence condition is met. .

[0120] When the function converges This refers to the maximum energy efficiency of the service equipment. Then at this time and As the total power of the target system and target power allocation coefficient .

[0121] After obtaining the total power of the target system and the target power allocation coefficients, a second auxiliary function is defined based on these parameters. For example, the second auxiliary function has the following form:

[0122] Where X is the transmitted signal matrix, Z is the global auxiliary variable, and X = Z is constrained; g(Z) is an indicator function, when When g(Z) = 0, otherwise ; For the maximum power of the service equipment, As dual variables, This is the penalty coefficient.

[0123] Will The value is fixed at the maximum energy efficiency obtained from the previous stage. and will Fixed as the total power of the target system .

[0124] Subsequently, the second auxiliary function is iterated using the transmitted signal matrix as a variable.

[0125] In each iteration, the solution makes The smallest value And then according to Solve ;Will Project to The interval is updated, and the dual variable is updated. Repeat the above process until the convergence condition is met. .

[0126] When the function converges That is, the target transmitted signal matrix .

[0127] Will As a signal transmission matrix for the service device, it can be distributed to the transmission unit of the service device, enabling the service device to transmit signals according to... The transmission power of each signal group is adjusted in real time to maximize system energy efficiency.

[0128] In this embodiment, by first solving for the total power of the target system and the target power allocation coefficient, and then solving for the transmitted signal matrix based on the obtained total power of the target system and the target power allocation coefficient, the non-convex optimization problem can be transformed into a convex problem, thereby reducing the algorithm complexity.

[0129] The energy consumption resource collaborative optimization method based on rate segmented multiple access (RSMIA) provided in this application can be executed by an energy consumption resource collaborative optimization device based on rate segmented multiple access. This application uses the execution of the energy consumption resource collaborative optimization method based on rate segmented multiple access by the energy consumption resource collaborative optimization device based on rate segmented multiple access as an example to illustrate the energy consumption resource collaborative optimization device based on rate segmented multiple access provided in this application.

[0130] This application also provides an energy consumption resource collaborative optimization device based on rate segmentation multiple access.

[0131] like Figure 3 As shown, the energy consumption resource collaborative optimization device based on rate partitioning multiple access includes: The grouping module 310 is used to obtain the first channel vector from the service device to each user device, and to group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device. Beamforming module 320 is used to calculate the beamforming vector corresponding to each user group based on the second channel vector from the service equipment to each near-end user equipment; Module 330 is used to establish a capacity and power model based on each first channel vector and each beamforming vector; the capacity and power model characterizes the signal power transmitted by the serving equipment and the information decoding efficiency of the user equipment. The solver module 340 is used to solve the transmission signal matrix of the service equipment based on the capacity and power model, with the goal of maximizing the energy efficiency of the service equipment.

[0132] According to the energy consumption resource collaborative optimization device based on rate division multiple access of this application, by grouping users, calculating beamforming vectors and modeling according to the channel vector representing the current channel state, it can dynamically adjust user grouping and signal transmission mode according to the real-time channel state, reducing the impact of dynamic channel changes; by taking the maximization of energy efficiency of the service equipment as the objective and solving the transmission signal matrix according to the capacity and power model, it can reduce power redundancy consumption and improve the resource utilization efficiency of the service equipment; thereby improving the operating efficiency of the network system.

[0133] In some embodiments, the grouping module 310 is further configured to: Calculate the corresponding channel strength and channel direction based on the first channel vector; Based on the channel strength, a timer is set for each user equipment, where the timer timeout rate is positively correlated with the signal strength. Record the timeout order of each timer, identify the half of the user equipment that timed out first as near-end user equipment, and identify the remaining user equipment as far-end user equipment; Calculate the channel direction similarity between each near-end user equipment and each far-end user equipment, and add near-end user equipment and far-end user equipment with matching similarity into the same user group to obtain multiple user groups.

[0134] In some embodiments, the grouping module 310 is further configured to: Identify the user equipment served by different service equipment; these different service equipment include ground service equipment and non-ground service equipment. User equipment serving the same service device is grouped according to the first channel vector to obtain multiple user groups.

[0135] In some embodiments, the grouping module 310 is further configured to: Determine the service status parameters from ground service equipment to each user's equipment; User equipment whose service status parameters are within the service range and within the service capacity is identified as user equipment serving ground service equipment. User equipment whose service status parameters are "out of service range" or "out of service capacity" is identified as user equipment serving non-terrestrial service equipment.

[0136] In some embodiments, the beamforming module 320 is further configured to: Construct a channel matrix based on each second channel vector; If the channel matrix is ​​invertible, the first beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix. If the channel matrix is ​​not invertible, the second beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; the array gain of the second beamforming algorithm is higher than that of the first beamforming algorithm.

[0137] In some embodiments, the solver module 340 is further configured to: With the goal of maximizing the energy efficiency of service equipment, an objective function and objective constraints are constructed; the objective function is the ratio of the total system capacity to the total system power, and the objective constraints include total power constraints and power allocation coefficient constraints. Based on the objective function, define the first auxiliary function; The first auxiliary function is iterated with the total system power and power allocation coefficient as variables to obtain the target total system power and target power allocation coefficient when the iteration reaches convergence. Based on the total power of the target system and the target power allocation coefficient, a second auxiliary function is defined; The second auxiliary function is iterated with the transmitted signal matrix as a variable to obtain the target transmitted signal matrix when the iteration converges. The target transmitted signal matrix is ​​then used as the transmitted signal matrix of the service device.

[0138] The energy consumption resource collaborative optimization device based on rate division multiple access in this application embodiment can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM or self-service machine, etc. The embodiments of this application do not specifically limit it.

[0139] The energy consumption resource collaborative optimization device based on rate division multiple access in this application embodiment can be a device with an operating system. This operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems; this application embodiment does not specifically limit it.

[0140] In some embodiments, such as Figure 4 As shown, this application embodiment also provides an electronic device 400, including a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. When the program is executed by the processor 401, it implements the various processes of the above-described embodiment of the energy consumption resource collaborative optimization method based on rate partitioning multiple access, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0141] It should be noted that the electronic devices in the embodiments of this application include the aforementioned mobile electronic devices and non-mobile electronic devices.

[0142] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described embodiment of the energy consumption resource collaborative optimization method based on rate partitioning multiple access, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0143] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0144] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described energy consumption resource collaborative optimization method based on rate partitioning multiple access.

[0145] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0146] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the energy consumption resource collaborative optimization method based on rate partitioning multiple access, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0147] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0148] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0149] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0150] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application 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 this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0151] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0152] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for coordinated optimization of energy consumption resources based on rate partitioning multiple access, characterized in that, include: Obtain the first channel vector from the service device to each user device, and group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device; Calculate the beamforming vector corresponding to each user group based on the second channel vector from the service device to each of the near-end user devices; A capacity and power model is established based on each of the first channel vectors and each of the beamforming vectors; the capacity and power model characterizes the signal transmission power of the serving equipment and the information decoding efficiency of the user equipment. With the goal of maximizing the energy efficiency of the service equipment, the transmission signal matrix of the service equipment is solved based on the capacity and power model.

2. The method according to claim 1, characterized in that, The step of grouping user equipment according to the first channel vector to obtain multiple user groups includes: Calculate the corresponding channel strength and channel direction based on the first channel vector; Based on the channel strength, a timer is set for each of the user equipment, wherein the timer timeout rate is positively correlated with the signal strength. Record the timeout order of each timer, identify the half of the user equipment that timed out first as near-end user equipment, and identify the remaining user equipment as far-end user equipment; Calculate the similarity of the channel direction between each near-end user equipment and each far-end user equipment, and add the near-end user equipment and far-end user equipment with matching similarity into the same user group to obtain multiple user groups.

3. The method according to claim 1, characterized in that, The step of grouping user equipment according to the first channel vector to obtain multiple user groups includes: Identify the user equipment served by different service devices; the different service devices include ground service devices and non-ground service devices. Based on the first channel vector, user equipment serving the same service device is grouped into multiple user groups.

4. The method according to claim 3, characterized in that, The user equipment for which different service devices are determined includes: Determine the service status parameters from the ground service equipment to each user equipment; User equipment whose service status parameters are within the service range and within the service capacity is identified as user equipment serving the ground service equipment. User equipment whose service status parameters are "out of service range" or "out of service capacity" is identified as user equipment serving the non-terrestrial service equipment.

5. The method according to claim 1, characterized in that, The step of calculating the beamforming vector corresponding to each user group based on the second channel vector from the serving device to each of the near-end user devices includes: Construct a channel matrix based on each of the second channel vectors; If the channel matrix is ​​invertible, then the first beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; If the channel matrix is ​​not invertible, a second beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; the array gain of the second beamforming algorithm is higher than that of the first beamforming algorithm.

6. The method according to claim 5, characterized in that, If the channel matrix is ​​invertible, according to the formula: The beamforming vector corresponding to each user group is calculated; Where W is the beamforming matrix of the service device. Let H be the beamforming vector corresponding to the k-th user group, and H be the channel matrix. Let H be the normalized matrix. , Represents the conjugate transpose of the channel matrix H. Representation matrix The i-th main diagonal, where N is the number of user groups.

7. The method according to claim 5, characterized in that, If the channel matrix is ​​not invertible, according to the formula: The beamforming vector corresponding to each user group is calculated; in, This is the beamforming vector corresponding to the k-th user group. The k-th column of the channel matrix It represents the second norm of a vector.

8. The method according to claim 1, characterized in that, The capacity and power model includes a total capacity model and a total power model; according to the formula: Establish capacity and power models; in, For the total system capacity, Where N is the total system power and N is the number of user groups. This refers to the common information beam vector of the service device for the remote user equipment. This refers to the private information beam vector of the service device for the remote user device. Let the system capacity be the remote user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding public information for remote user equipment in the k-th user group. ;in, This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference with the private portion of the signal from the service equipment; Let the system capacity be the near-end user equipment in the k-th user group. ;in, The signal-to-interference-plus-noise ratio (SIR) for decoding common information for near-end user equipment in the k-th user group. , The signal-to-interference-plus-noise ratio (SIR) for decoding private information for near-end user equipment in the k-th user group. , This is the first channel vector of the remote user equipment. It is Gaussian white noise. Interference in the common part of the signal of the service equipment.

9. The method according to claim 1, characterized in that, The step of maximizing the energy efficiency of the service equipment and solving for the transmission signal matrix of the service equipment based on the capacity and interference model includes: With the goal of maximizing the energy efficiency of the service equipment, an objective function and objective constraints are constructed; the objective function is the ratio of the total system capacity to the total system power, and the objective constraints include total power constraints and power allocation coefficient constraints. Based on the objective function, define a first auxiliary function; The first auxiliary function is iterated with the total system power and power allocation coefficient as variables to obtain the target total system power and target power allocation coefficient when the iteration reaches convergence; Based on the total power of the target system and the target power allocation coefficient, a second auxiliary function is defined; The second auxiliary function is iterated using the transmitted signal matrix as a variable to obtain the target transmitted signal matrix when the iteration converges, and the target transmitted signal matrix is ​​used as the transmitted signal matrix of the service device.

10. The method according to claim 9, characterized in that, The transmitted signal matrix is: Where N is the number of user groups. The vector of the transmitted signal of the service equipment for the k-th user group. ; For public information beam vectors, For public information modulation symbols, ; For private information beam vectors, Modulation symbols for private information, .

11. The method according to claim 9, characterized in that, The target constraint is as follows: in, Let the transmitted signal power of the k-th user group be _____. For the maximum power of the service equipment, This is the power constraint coefficient. ,in Let be the public information power of the k-th user group.

12. The method according to claim 9, characterized in that, The first auxiliary function is: Where C represents the total system capacity and P represents the total system power. Energy efficiency of service equipment.

13. The method according to claim 9, characterized in that, The second auxiliary function is: Where X is the transmitted signal matrix, Z is the global auxiliary variable, and X = Z is constrained; g(Z) is an indicator function, when When g(Z) = 0, otherwise ; For the maximum power of the service equipment, As dual variables, This is the penalty coefficient.

14. A device for coordinated optimization of energy consumption resources based on rate partitioning multiple access, characterized in that, include: The grouping module is used to obtain the first channel vector from the service device to each user device, and to group each user device according to the first channel vector to obtain multiple user groups, wherein each user group includes a near-end user device and a far-end user device. A beamforming module is used to calculate the beamforming vector corresponding to each user group based on the second channel vector from the service equipment to each of the near-end user equipments. A module is established to build a capacity and power model based on each of the first channel vectors and each of the beamforming vectors; the capacity and power model characterizes the signal power transmitted by the serving equipment and the information decoding efficiency of the user equipment. The solution module is used to solve the transmission signal matrix of the service device based on the capacity and power model, with the goal of maximizing the energy efficiency of the service device.

15. The apparatus according to claim 14, characterized in that, The grouping module is also used for: Calculate the corresponding channel strength and channel direction based on the first channel vector; Based on the channel strength, a timer is set for each of the user equipment, wherein the timer timeout rate is positively correlated with the signal strength. Record the timeout order of each timer, identify the half of the user equipment that timed out first as near-end user equipment, and identify the remaining user equipment as far-end user equipment; Calculate the similarity of the channel direction between each near-end user equipment and each far-end user equipment, and add the near-end user equipment and far-end user equipment with matching similarity into the same user group to obtain multiple user groups.

16. The apparatus according to claim 14, characterized in that, The grouping module is also used for: Identify the user equipment served by different service devices; the different service devices include ground service devices and non-ground service devices. Based on the first channel vector, user equipment serving the same service device is grouped into multiple user groups.

17. The apparatus according to claim 16, characterized in that, The grouping module is also used for: Determine the service status parameters from the ground service equipment to each user equipment; User equipment whose service status parameters are within the service range and within the service capacity is identified as user equipment serving the ground service equipment. User equipment whose service status parameters are "out of service range" or "out of service capacity" is identified as user equipment serving the non-terrestrial service equipment.

18. The apparatus according to claim 14, characterized in that, The beamforming module is also used for: Construct a channel matrix based on each of the second channel vectors; If the channel matrix is ​​invertible, then the first beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; If the channel matrix is ​​not invertible, a second beamforming algorithm is used to calculate the beamforming vector corresponding to each user group based on the channel matrix; the array gain of the second beamforming algorithm is higher than that of the first beamforming algorithm.

19. The apparatus according to claim 14, characterized in that, The solution module is also used for: With the goal of maximizing the energy efficiency of the service equipment, an objective function and objective constraints are constructed; the objective function is the ratio of the total system capacity to the total system power, and the objective constraints include total power constraints and power allocation coefficient constraints. Based on the objective function, define a first auxiliary function; The first auxiliary function is iterated with the total system power and power allocation coefficient as variables to obtain the target total system power and target power allocation coefficient when the iteration reaches convergence; Based on the total power of the target system and the target power allocation coefficient, a second auxiliary function is defined; The second auxiliary function is iterated using the transmitted signal matrix as a variable to obtain the target transmitted signal matrix when the iteration converges, and the target transmitted signal matrix is ​​used as the transmitted signal matrix of the service device.

20. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in any one of claims 1-13.

21. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the energy consumption resource collaborative optimization method based on rate partitioning multiple access as described in any one of claims 1-13.

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