Joint optimization method of downlink spectrum efficiency and energy efficiency in decellularized massive MIMO systems

Abstract

The present invention discloses a method for jointly optimizing the downlink spectrum efficiency and energy efficiency of a decellularized large-scale MIMO system, which mainly includes: based on the decellularized large-scale MIMO system, obtaining the expressions of the downlink spectrum efficiency and energy efficiency of the system, and defining the expression of resource efficiency; establishing a resource efficiency maximization optimization problem, and then using the continuous convex approximation technology to convert the original problem into a standard geometric programming, and finally iteratively solving the obtained geometric programming problem based on the golden search method to obtain the optimal downlink transmission power allocation scheme. The present invention optimizes the downlink transmission power by using channel statistical information, while improving the spectrum efficiency and energy efficiency of the decellularized large-scale MIMO system, effectively achieving a compromise between spectrum efficiency and energy efficiency, and improving the utilization rate of wireless resources in the decellularized large-scale MIMO system.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized large-scale MIMO system. Background Art

[0002] Since entering the information age, with the further development of the Internet of Things, the number of mobile terminals and users has exploded. Although people use cell splitting technology to accommodate more users, it also causes more interference between cells, limiting the improvement of transmission rate. When the terminal is moving, it will frequently experience inter-cell switching, resulting in unstable or even interrupted communication connections, reducing user experience. Therefore, traditional cellular network systems are gradually unable to meet the communication requirements of high-speed and massive stable connections of the next generation of wireless communication systems.

[0003] In order to improve the above problems, two technologies, massive multiple-input multiple-output (MIMO) and decellularized network architecture, have been proposed and widely studied. For massive MIMO technology, its purpose is to deploy a large number of antennas or antenna arrays at the base station to achieve simultaneous transmission of multiple data streams, thereby improving system diversity and multiplexing gain, and greatly improving system capacity. Unlike the traditional cellular network architecture, the decellularized network architecture no longer divides cells and removes the central base station, avoiding inter-cell switching, and deploys a large number of access points (APs) around the user to serve all users at the same time, which not only shortens the communication distance and reduces the path loss of signal propagation, but also further improves macro-diversity and micro-diversity gains. In addition, the distributed deployment mode of the decellularized network effectively eliminates the boundary effect of the traditional cellular network and provides uniform and unified services to all users. In this context, decellularized massive MIMO technology has also become the most promising candidate technology for realizing the next generation of wireless communication systems.

[0004] However, it should be noted that, on the one hand, in a decellularized massive MIMO system, deploying more APs or increasing the number of antennas equipped on each AP will bring higher diversity gain and channel freedom, significantly improving the system spectrum efficiency; on the other hand, more APs or antennas will greatly increase the power consumed by the system, resulting in a decrease in the system energy efficiency. Most of the existing technologies for decellularized massive MIMO systems currently focus only on optimizing a single performance indicator, namely spectral efficiency or energy efficiency, resulting in low system resource utilization, which has hindered the further development of decellularized massive MIMO systems. Summary of the invention

[0005] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0006] One object of the present invention is to provide a method for jointly optimizing the downlink spectral efficiency and energy efficiency of a de-cellularized massive MIMO system. The method optimizes the downlink transmission power while improving the downlink spectral efficiency and energy efficiency of the system, so as to solve the conflict between spectral efficiency and energy efficiency in the de-cellularized massive MIMO system, achieve a balance between spectral efficiency and energy efficiency, and improve the resource utilization of the de-cellularized massive MIMO system.

[0007] In order to achieve the above-mentioned object, the present invention provides a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized massive MIMO system, comprising:

[0008] S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency;

[0009] S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the AP having the maximum transmit power as the constraint, a mathematical model for the downlink transmission power optimization problem is established;

[0010] S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem;

[0011] S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

[0012] A further preferred technical solution of the present invention is that in step S100, the downlink spectrum efficiency and energy efficiency The expressions are:

[0013]

[0014]

[0015] in, express quantity, Indicates the number of users, Indicates The signal-to-interference-noise ratio of each user is Indicates indivual With The power coefficient between users, Indicates indivual With The power coefficient between users, Indicates indivual With The channel sequence between users is Indicates indivual With The precoding sequence between users, Indicates indivual With The precoding sequence between users, represents the additive Gaussian noise power, represents the power amplification factor, Indicates the actual power consumed by the system. Indicates the inherent power consumption of the system circuit. represents the expectation operation, represents the Euclidean norm operation, Represents the conjugate transpose operation.

[0016] Preferably, in step S100, resource efficiency is defined as a weighted sum of downlink spectrum efficiency and energy efficiency, and its expression is:

[0017]

[0018] in, represents the weighting coefficient, Indicates the maximum power consumed by the system.

[0019] Preferably, the mathematical model of the downlink transmission power optimization problem established in step S200 is expressed as:

[0020]

[0021]

[0022] in, Indicates each Maximum power.

[0023] Preferably, the specific method of step S300 is:

[0024] S310, initialization of auxiliary variables and , and the threshold ; Iteration index , initialize the downlink power coefficient ,Will Substitute into the resource efficiency expression to calculate the initial resource efficiency ;

[0025] S320, Order , introduce auxiliary variables Replace Signal-to-interference-noise ratio , the original downlink transmission power optimization problem is equivalent to:

[0026]

[0027]

[0028]

[0029] in, ;

[0030] S330, for iterations, given Under the condition of The monomial of

[0031]

[0032] in, , ;

[0033] S340, for iterations, given Under the condition of The numerator is approximately The monomial of

[0034]

[0035] in, , , ;

[0036] S350, using the information obtained in step S330 and step S340 The monomials and about The equivalent optimization problem obtained in step S320 is transformed into a standard geometric programming problem, which is expressed as:

[0037]

[0038]

[0039]

[0040] Complete the conversion between the downlink transmission power optimization problem and the standard geometric programming problem.

[0041] Preferably, the specific method of step S400 is:

[0042] S410, use the mathematical tool CVX to solve the standard geometric programming problem and obtain the The initial solution of the iteration ;

[0043] S420, Substitute into the resource efficiency expression to calculate ,when season , , , ;when season , ;

[0044] S430, Order , loop through steps S320~S350 and S410~S420, and when the termination criteria are met End the loop when

[0045] S440, output optimal downlink power allocation solution , the optimal downlink power allocation scheme Substitute the downlink spectrum efficiency into and energy efficiency The expression of is used to calculate the downlink spectrum efficiency and energy efficiency of the system.

[0046] On the other hand, the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, which enable a computer to execute the above-mentioned method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized massive MIMO system.

[0047] Yet another aspect of the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the above-mentioned method for jointly optimizing the downlink spectrum efficiency and energy efficiency of the de-cellularized large-scale MIMO system.

[0048] On the other hand, the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the above-mentioned method for jointly optimizing the downlink spectrum efficiency and energy efficiency of the de-cellular large-scale MIMO system.

[0049] Beneficial effects: Different from the prior art which only focuses on a single performance indicator, the present invention considers the joint optimization of two performance indicators, spectrum efficiency and energy efficiency. By maximizing their weighted sum, i.e., resource efficiency, the spectrum efficiency and energy efficiency of the decellularized massive MIMO system are improved at the same time, effectively solving the contradiction between the two performance indicators, which has certain practical significance for actual system design.

[0050] The present invention uses continuous convex approximation and golden search method for the first time to solve the problem of joint optimization of spectral efficiency and energy efficiency in such systems, achieving a compromise between the two and improving the resource utilization of decellularized large-scale MIMO systems. Simulation experiments also verify the effectiveness and fast convergence of the method of the present invention.

[0051] The present invention utilizes channel state statistics that change slowly over time. The result obtained by performing one optimization can be used for power allocation within multiple consecutive coherent time intervals, reducing the signal processing overhead generated by frequently performing the optimization process, thereby making the method of the present invention highly feasible. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of the method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized massive MIMO system according to the present invention;

[0053] Figure 2 is a graph showing the relationship between downlink spectrum efficiency and the number of APs in the embodiment;

[0054] Figure 3 is a graph showing the relationship between system energy efficiency and the number of APs in the embodiment;

[0055] Figure 4 It is the convergence diagram of the joint optimization method in the embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0057] Combine the following Figure 1-Figure 4The present invention describes a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized large-scale MIMO system, a non-transitory computer-readable storage medium, an electronic device, and a computer program product.

[0058] Embodiment 1: This embodiment provides a method for jointly optimizing the downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system. By optimizing the downlink power, both the spectrum efficiency and energy efficiency of the system are improved, thereby improving the utilization rate of system resources. The main steps of the method are as follows: Figure 1 As shown, including:

[0059] S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency;

[0060] S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the AP having the maximum transmit power as the constraint, a mathematical model for the downlink transmission power optimization problem is established;

[0061] S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem;

[0062] S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

[0063] This embodiment describes the above steps in detail.

[0064] In step S100, for the decellularized large-scale MIMO system of this embodiment, APs and Users are randomly distributed in a given area, and all APs use the same time-frequency resources to serve all users at the same time. Therefore, each user will receive signals from all APs during the downlink transmission phase, and each user will receive data sent by all APs during the downlink data transmission phase. Data received by users for:

[0065]

[0066] in, express quantity, Indicates the number of users, Indicates indivual With The channel sequence between users is Indicates indivual The data sent, Indicates The additive Gaussian noise received by each user is represents the additive Gaussian noise power, Indicates AP and The power coefficient between users, Indicates indivual With The precoding sequence between users, Indicates The data signal of each user satisfies and , represents the expectation operation, represents the conjugate transpose operation, represents the conjugate operation, It indicates that it obeys the cyclic symmetric complex Gaussian distribution.

[0067] When the channel state statistics are known, the use-and-then-forget technique is used to rewrite the data received by the user as follows:

[0068]

[0069] in, Indicates AP and The power coefficient between users, Indicates indivual With The precoding sequence between users, Indicates The data signal of a user.

[0070] Based on The data expression received by the user is obtained Signal-to-interference-noise ratio for:

[0071]

[0072] in, represents the additive Gaussian noise power.

[0073] The system downlink spectrum efficiency is the sum of the spectrum efficiencies of all users, and the system energy efficiency is the ratio of the system spectrum efficiency to the power consumed by the system. and energy efficiency The expressions are:

[0074]

[0075]

[0076] in, express quantity, Indicates the number of users, Indicates The signal-to-interference-noise ratio of each user is Indicates indivual With The power coefficient between users, Indicates indivual With The power coefficient between users, Indicates indivual With The channel sequence between users is Indicates indivual With The precoding sequence between users, Indicates indivual With The precoding sequence between users, represents the additive Gaussian noise power, represents the power amplification factor, Indicates the actual power consumed by the system. Indicates the inherent power consumption of the system circuit. represents the expectation operation, represents the Euclidean norm operation, Represents the conjugate transpose operation.

[0077] Resource efficiency is defined as the weighted sum of downlink spectrum efficiency and energy efficiency, and its expression is:

[0078]

[0079] in, represents the weighting coefficient, Indicates the maximum power consumed by the system.

[0080] In step S200, the objective function is to maximize resource efficiency, and the AP has a maximum power constraint. The mathematical model of the downlink transmission power optimization problem is established, which is expressed as:

[0081]

[0082]

[0083] in, Indicates each Maximum power.

[0084] Step S300 uses a continuous convex approximation algorithm to convert the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem. The specific method is:

[0085] S310, initialization of auxiliary variables and , and the threshold ; Iteration index , initialize the downlink power coefficient ,Will Substitute into the resource efficiency expression to calculate the initial resource efficiency ;

[0086] S320, Order , introduce auxiliary variables Replace Signal-to-interference-noise ratio , the original downlink transmission power optimization problem is equivalent to:

[0087]

[0088]

[0089]

[0090] in, ;

[0091] S330, for iterations, given Under the condition of The monomial of

[0092]

[0093] in, , ;

[0094] S340, for iterations, given Under the condition of The numerator is approximately The monomial of

[0095]

[0096] in, , , ;

[0097] S350, using the information obtained in step S330 and step S340 The monomials and about The equivalent optimization problem obtained in step S320 is transformed into a standard geometric programming problem, which is expressed as:

[0098]

[0099]

[0100]

[0101] Complete the conversion between the downlink transmission power optimization problem and the standard geometric programming problem.

[0102] Step S400 iteratively solves the geometric programming problem based on the golden search method to obtain the optimal downlink transmission power allocation scheme, and uses the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system. The specific method is:

[0103] S410, use the mathematical tool CVX to solve the standard geometric programming problem and obtain the The initial solution of the iteration ;

[0104] S420, Substitute into the resource efficiency expression to calculate ,when season , , , ;when season , ;

[0105] S430, Order , loop through steps S320~S350 and S410~S420, and when the termination criteria are met End the loop when

[0106] S440, output optimal downlink power allocation solution , the optimal downlink power allocation scheme Substitute the downlink spectrum efficiency into and energy efficiency The expression of , the downlink spectrum efficiency and energy efficiency of the system are calculated as follows Figure 2 , Figure 3 shown.

[0107] It can be found that the downlink spectrum efficiency increases with the increase of the number of APs, and the energy efficiency increases first and then decreases with the increase of the number of APs. Among them, for the case of optimizing only a single performance indicator, the improvement of one indicator will cause the decline of another indicator. For example, the method that only optimizes the spectrum efficiency has the highest spectrum efficiency, but its corresponding energy efficiency is the lowest, and the method that only optimizes the energy efficiency has the opposite result. Since the method of the present invention optimizes two indicators at the same time, the spectrum efficiency and energy efficiency curves corresponding to the method of the present invention are both in the middle position. For example, when the number of APs M=40, the spectrum efficiency corresponding to the method of the present invention is 25.89 (bits / second), and the spectrum efficiencies of the method that only optimizes the spectrum efficiency and the method that only optimizes the energy efficiency are 26.82 (bits / second) and 22.74 (bits / second), respectively. For energy efficiency, the method of the present invention is 1.01 (bits / second / joule), and the method that only optimizes the spectrum efficiency and the method that only optimizes the energy efficiency are 0.71 (bits / second / joule) and 1.68 (bits / second / joule), respectively. Figure 2 , Figure 3 It is shown that the method of the present invention can simultaneously improve the spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system, achieve a compromise between the two performance indicators, and verify the effectiveness of the method of the present invention.

[0108] like Figure 4 As shown, it can be found that under any system configuration, with the increase of the number of iterations, the resource efficiency corresponding to the objective function of the method of the present invention gradually increases, and convergence is achieved after about 10 iterations. The simulation results effectively illustrate the rapid convergence and high feasibility of the method of the present invention.

[0109] Embodiment 2: This embodiment provides a non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions causing a computer to execute a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system, the method comprising the following steps:

[0110] S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency;

[0111] S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the AP having the maximum transmit power as the constraint, a mathematical model for the downlink transmission power optimization problem is established;

[0112] S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem;

[0113] S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

[0114] Embodiment 3: This embodiment provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized large-scale MIMO system, the method comprising the following steps:

[0115] S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency;

[0116] S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the AP having the maximum transmit power as the constraint, a mathematical model for the downlink transmission power optimization problem is established;

[0117] S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem;

[0118] S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

[0119] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0120] Embodiment 4: This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute a method for jointly optimizing downlink spectrum efficiency and energy efficiency of a de-cellularized massive MIMO system. The method includes the following steps:

[0121] S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency;

[0122] S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the AP having the maximum transmit power as the constraint, a mathematical model for the downlink transmission power optimization problem is established;

[0123] S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem;

[0124] S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

[0125] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system, characterized in that: include: S100, based on the decellularized massive MIMO system, obtaining expressions for downlink spectrum efficiency and energy efficiency of the system, and defining an expression for resource efficiency; Downlink spectrum efficiency η SE and energy efficiency η EE The expressions are: Among them, M represents the number of APs, K represents the number of users, and SINR k represents the signal-to-interference-noise ratio of the kth user, η mk represents the power coefficient between the mth AP and the kth user, η mk′ represents the power coefficient between the mth AP and the k′th user, g mk represents the channel sequence between the mth AP and the kth user, w mk represents the precoding sequence between the mth AP and the kth user, w mk′ represents the precoding sequence between the mth AP and the k′th user, represents the additive Gaussian noise power, α represents the power amplification factor, P sum Indicates the actual power consumed by the system, P fix represents the inherent power consumption of the system circuit, E{·} represents the expected operation, ∥·∥ represents the Euclidean norm operation, (·) $ represents the conjugate transpose operation; S200, according to the expression of resource efficiency, taking maximizing resource efficiency as the objective function and the maximum transmit power of the AP as the constraint, a mathematical model for downlink transmission power optimization problem is established; S300, using a continuous convex approximation algorithm, converting the objective function and inequality constraints of the mathematical model of the downlink transmission power optimization problem into a monomial form, thereby converting the downlink transmission power optimization problem into a standard geometric programming problem; S400, iteratively solving the geometric programming problem based on the golden search method to obtain an optimal downlink transmission power allocation scheme, and using the optimal downlink transmission power allocation scheme to respectively calculate the downlink spectrum efficiency and energy efficiency of the de-cellularized massive MIMO system.

2. The method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system according to claim 1, characterized in that: In step S100, resource efficiency is defined as the weighted sum of downlink spectrum efficiency and energy efficiency, and its expression is: Among them, δ represents the weighting coefficient, P tot Indicates the maximum power consumed by the system.

3. The method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system according to claim 2, characterized in that: The mathematical model of the downlink transmission power optimization problem established in step S200 is expressed as: Among them, P max Indicates the maximum power of each AP.

4. The method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system according to claim 3, characterized in that: The specific method of step S300 is: S310, initializing auxiliary variable ε max and ε min , and threshold ζ; iteration index i = 0, initialize the downlink power coefficient Will Substitute into the resource efficiency expression to calculate the initial resource efficiency S320, let ε mid =(ε max +ε min ) / 2, introduce auxiliary variable r k Replace the SINR of the kth user k , the original downlink transmission power optimization problem is equivalent to: in, S330, for the i-th iteration, given Under the condition of continuous convex approximation algorithm, the objective function is transformed into r k The monomial of in, S340, for the i-th iteration, given Under the condition of k The numerator is approximately mk The monomial of in, S350, using the information about r obtained in step S330 and step S340 k The monomials and about η mk The equivalent optimization problem obtained in step S320 is transformed into a standard geometric programming problem, which is expressed as: Complete the conversion between the downlink transmission power optimization problem and the standard geometric programming problem.

5. The method for jointly optimizing downlink spectrum efficiency and energy efficiency of a decellularized massive MIMO system according to claim 4, characterized in that: The specific method of step S400 is: S410, use the mathematical tool CVX to solve the standard geometric programming problem and obtain the initial solution for the (i+1)th iteration S420, Substitute into the resource efficiency expression to obtain when When ε pre =ε min , ε min =ε mid , when When ε min =ε pre , ε max =ε mid ; S430, let i=i+1, and execute steps S320-S350 and S410-S420 in a loop, when the termination criterion |ε is met max -ε min |<ζ ends the loop; S440, output optimal downlink power allocation solution The optimal downlink power allocation scheme Substitute the downlink spectrum efficiency η into SE and energy efficiency η EE The expression of is used to calculate the downlink spectrum efficiency and energy efficiency of the system.

6. A non-transitory computer-readable storage medium, characterized in that: Computer instructions are stored thereon, which enable the computer to execute the method for jointly optimizing the downlink spectrum efficiency and energy efficiency of the de-cellularized large-scale MIMO system as described in any one of claims 1-5.

7. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the method for jointly optimizing the downlink spectrum efficiency and energy efficiency of the de-cellularized large-scale MIMO system as described in any one of claims 1-5.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer executes the method for jointly optimizing the downlink spectrum efficiency and energy efficiency of a de-cellularized large-scale MIMO system as described in any one of claims 1 to 5.

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