Large-scale MIMO wireless backhaul network transmission method, apparatus and device, and storage medium

Abstract

Disclosed are a large-scale MIMO wireless backhaul network transmission method, apparatus and device, and a storage medium, relating to the technical field of communications, comprising: determining an initial transmission efficiency between a target macro base station and a target micro base station by using a first downlink transmission rate corresponding to the target macro base station and a second downlink transmission rate corresponding to the target micro base station, constructing a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency, and converting the multi-objective optimization function and the preset constraint condition into a beam and power allocation sub-problem of signal access transmission and a bandwidth allocation sub-problem of signal backhaul transmission; and determining a first optimal solution corresponding to the beam and power allocation sub-problem and a second optimal solution corresponding to the bandwidth allocation sub-problem to obtain target transmission efficiency corresponding to the multi-target optimization function, so as to carry out large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency. Therefore, the network adaptability can be improved, and the network transmission efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a large-scale MIMO wireless backhaul network transmission method, apparatus, device, and storage medium. Background Art

[0002] Wireless backhaul brings additional challenges to massive MIMO (Multiple Input Multiple Output) link transmission. First, wireless backhaul uses limited spectrum resources, which inevitably limits link capacity and thus the transmission capacity of wireless backhaul. Second, existing research and methods for massive MIMO wireless backhaul treat transmission frequency efficiency and energy efficiency as two independent optimization objectives, lacking dynamic adaptability based on different optimization objectives. The present invention aims to address the problem of increasing network adaptability while considering the limited capacity of massive MIMO wireless backhaul. Summary of the Invention

[0003] In view of this, the present invention aims to provide a large-scale MIMO wireless backhaul network transmission method, apparatus, device, and storage medium that can increase network adaptability and improve network transmission efficiency. The specific solution is as follows:

[0004] In a first aspect, the present application discloses a massive MIMO wireless backhaul network transmission method, comprising:

[0005] Determining a first downlink transmission rate of the target macro base station within a corresponding first coverage area and a second downlink transmission rate of the target micro base station within a corresponding second coverage area based on a transmission rate calculation formula; transmitting signals between the target macro base station and the target micro base station using a preset reverse time division duplex mode;

[0006] Determine an initial transmission efficiency between the target macro base station and the target micro base station using the first downlink transmission rate and the second downlink transmission rate, and construct a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency;

[0007] Based on the scalarization method and alternating iterative optimization theory, the multi-objective optimization function and the preset constraints are converted into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission;

[0008] Based on a preset solution algorithm and an iterative optimization method, a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem are determined, and based on the first optimal solution and the second optimal solution, a target transmission efficiency corresponding to the multi-objective optimization function is determined, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

[0009] Optionally, the determining, based on a transmission rate calculation formula, a first downlink transmission rate of a target macro base station within a corresponding first coverage area and a second downlink transmission rate of a target micro base station within a corresponding second coverage area includes:

[0010] Determine a precoding for transmission by the target macro base station within the corresponding first coverage range based on a preset regularized zero-breaking precoding formula, and define a MIMO beamforming formula for the target macro base station based on the preset regularized zero-breaking precoding formula and the precoding;

[0011] Determining, based on the MIMO beamforming formula and the channel gain used by the target macro base station for signal transmission, a first signal to interference and noise ratio value for access signal transmission and a second signal to interference and noise ratio value for return signal transmission of the target macro base station, respectively, and substituting the first signal to interference and noise ratio value and the second signal to interference and noise ratio value into corresponding transmission rate calculation formulas, respectively, to obtain a first signal access downlink transmission rate and a signal return downlink transmission rate corresponding to the target macro base station;

[0012] Obtain the transmission power of the target micro base station within the corresponding second coverage range, and determine the second signal access downlink transmission rate corresponding to the target micro base station based on the transmission power and the channel gain used by the target micro base station for signal transmission.

[0013] Optionally, the determining the initial transmission efficiency between the target macro base station and the target micro base station by using the first downlink transmission rate and the second downlink transmission rate, and constructing a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency, includes:

[0014] Inputting the first signal access downlink transmission rate, the signal return downlink transmission rate, and the second signal access downlink transmission rate into a preset transmission frequency efficiency model to obtain an initial transmission frequency efficiency between the target macro base station and the target micro base station, and determining a corresponding initial energy efficiency based on the initial transmission frequency efficiency and the total network transmission power consumption;

[0015] Constructing preset constraints and a multi-objective optimization function corresponding to the initial transmission frequency efficiency and the initial energy efficiency.

[0016] Optionally, converting the multi-objective optimization function and the preset constraints into a beam and power allocation subproblem for signal access transmission and a bandwidth allocation subproblem for signal return transmission based on a scalar quantization method and alternating iterative optimization theory includes:

[0017] Setting a target unitless parameter to characterize the importance values ​​of the initial transmission frequency efficiency and the total transmission power consumption of the network, and converting the multi-objective optimization function into a single-objective optimization function based on the target unitless parameter;

[0018] The alternating iterative optimization theory is used to split the single-objective optimization function and the preset constraints into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission.

[0019] Optionally, the determining, based on a preset solution algorithm and an iterative optimization method, a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem includes:

[0020] Determining a current bandwidth allocation ratio for signal backhaul transmission in the beam and power allocation subproblem, and determining a first current solution corresponding to the beam and power allocation subproblem using a preset continuous approximate convex optimization method;

[0021] Determining the first current solution as the current beam and current power of signal access transmission in the bandwidth allocation subproblem, and solving the second current solution corresponding to the bandwidth allocation subproblem using a Lagrangian dual method;

[0022] Substituting the first current solution and the second current solution into the single-objective optimization function, and then determining whether the single-objective optimization function can converge to the target optimal value while satisfying the preset constraints;

[0023] If the single-objective optimization function can converge to the target optimal value under the preset constraint condition, then determining the first current solution as the first optimal solution corresponding to the beam and power allocation subproblem, and determining the second current solution as the second optimal solution corresponding to the bandwidth allocation subproblem;

[0024] If the single-objective optimization function cannot converge to the target optimal value while satisfying the preset constraints, the second current solution is determined as the new current bandwidth allocation rate for signal return transmission in the beam and power allocation sub-problem, and the process jumps to the step of determining the first current solution corresponding to the beam and power allocation sub-problem using the preset continuous approximate convex optimization method.

[0025] Optionally, the determining a first current solution corresponding to the beam and power allocation subproblem by using a preset continuous approximate convex optimization method includes:

[0026] The constraints in the beam and power allocation subproblem are converted into concave functions using a preset additional slack variable and a first-order Taylor approximation method to obtain a converted allocation subproblem, and an upper bound expression and a lower bound expression of the preset downlink transmission rate are determined using a preset continuous approximate convex optimization method;

[0027] Substitute the upper bound expression and the lower bound expression into the transformed allocation subproblem to transform the transformed allocation subproblem into a convex optimization function, and determine the optimal solution of the convex optimization function as the first current solution corresponding to the beam and power allocation subproblem.

[0028] Optionally, the using the Lagrangian dual method to solve the second current solution corresponding to the bandwidth allocation subproblem includes:

[0029] A bandwidth allocation ratio expression for the bandwidth allocation subproblem is determined based on a Lagrangian update formula and Lagrangian multipliers corresponding to the constraints of the bandwidth allocation subproblem, and an optimal solution to the bandwidth allocation ratio expression is determined to obtain a second current solution corresponding to the bandwidth allocation subproblem.

[0030] In a second aspect, the present application discloses a massive MIMO wireless backhaul network transmission device, comprising:

[0031] A transmission rate determination module is configured to determine, based on a transmission rate calculation formula, a first downlink transmission rate of a target macro base station within a corresponding first coverage area and a second downlink transmission rate of a target micro base station within a corresponding second coverage area; the target macro base station and the target micro base station transmit signals via a preset reverse time division duplex mode;

[0032] an optimization function determination module, configured to determine an initial transmission efficiency between the target macro base station and the target micro base station using the first downlink transmission rate and the second downlink transmission rate, and to construct a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency;

[0033] A function splitting module is used to convert the multi-objective optimization function and the preset constraints into a beam and power allocation sub-problem for signal access transmission and a bandwidth allocation sub-problem for signal return transmission based on a scalar quantization method and alternating iterative optimization theory;

[0034] A network transmission module is used to determine a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem based on a preset solution algorithm and an iterative optimization method, and to determine a target transmission efficiency corresponding to the multi-objective optimization function based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

[0035] In a third aspect, the present application discloses an electronic device, comprising:

[0036] Memory, used to store computer programs;

[0037] A processor is configured to execute the computer program to implement the aforementioned massive MIMO wireless backhaul network transmission method.

[0038] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the aforementioned large-scale MIMO wireless backhaul network transmission method.

[0039] It can be seen that in the present application, first, based on the transmission rate calculation formula, the first downlink transmission rate of the target macro base station within the corresponding first coverage range and the second downlink transmission rate of the target micro base station within the corresponding second coverage range are determined; the target macro base station and the target micro base station transmit signals through a preset reverse time division duplex mode; the first downlink transmission rate and the second downlink transmission rate are used to determine the initial transmission efficiency between the target macro base station and the target micro base station, and the preset constraints and multi-objective optimization function corresponding to the initial transmission efficiency are constructed; based on the scalarization method and alternating iterative optimization theory, the multi-objective optimization function and the preset constraints are converted into beam and power allocation sub-problems for signal access transmission and bandwidth allocation sub-problems for signal backhaul transmission; based on the preset solution algorithm and iterative optimization method, the first optimal solution corresponding to the beam and power allocation sub-problem and the second optimal solution corresponding to the bandwidth allocation sub-problem are determined, and the target transmission efficiency corresponding to the multi-objective optimization function is determined based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency. In this way, by utilizing scalarization and iterative optimization methods, the multi-objective optimization problem is equivalently converted into two sub-optimization problems. Through iterative training of the two sub-optimization problems, the single-objective optimization problem converges to the optimal value, thereby achieving the optimal target transmission efficiency. In this way, while considering the limited capacity of massive MIMO wireless backhaul, using transmission frequency efficiency and energy efficiency as multiple optimization objectives can increase network adaptability and improve network transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of a large-scale MIMO wireless backhaul network transmission method disclosed in this application;

[0042] Figure 2 A schematic diagram of a specific MIMO wireless backhaul network model disclosed in this application;

[0043] Figure 3 This is a flow chart of a specific large-scale MIMO wireless backhaul network transmission method disclosed in this application;

[0044] Figure 4 This is a schematic diagram of the structure of a large-scale MIMO wireless backhaul network transmission device disclosed in this application;

[0045] Figure 5 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] Existing research and methods for massive MIMO wireless backhaul treat transmission frequency efficiency and energy efficiency as two independent optimization objectives, lacking dynamic adaptability based on different optimization objectives. This application specifically introduces a massive MIMO wireless backhaul network transmission method that can increase network adaptability while limiting MIMO wireless backhaul capacity.

[0048] See also Figure 1 As shown, the embodiment of the present application discloses a large-scale MIMO wireless backhaul network transmission method, including:

[0049] Step S11: Determine the first downlink transmission rate of the target macro base station within the corresponding first coverage range and the second downlink transmission rate of the target micro base station within the corresponding second coverage range based on the transmission rate calculation formula; the target macro base station and the target micro base station transmit signals through a preset reverse time division duplex mode.

[0050] In this embodiment, first Figure 2 As shown in the figure, a large-scale MIMO wireless backhaul downlink transmission model is established, including a macro base station with N≥100 antennas, A micro base station with a single antenna. All base stations in the network use the set Indicates that It is represented as a micro base station set, and the index number '0' represents the macro base station. The macro base station is within its coverage area. The first phase of access transmission service is provided to macro base station terminal users (hereinafter referred to as "macro users"). Each of the micro base stations has a coverage area of The first phase of the access transmission service is provided to micro base station terminal users (referred to as "micro users"). The network adopts reverse time division duplex mode. When the macro base station is transmitting data in the downlink, the micro base station is transmitting data in the uplink. Conversely, when the macro base station is transmitting data in the uplink, the micro base station is transmitting data in the downlink. This can avoid the interference of the macro base station on the micro user in the downlink. At the same time, the macro base station provides the micro base station with the second phase of backhaul transmission service. The first phase of access transmission and the second phase of backhaul transmission share network frequency resources. is the frequency resource allocation ratio for the second phase of backhaul transmission, and the remaining Frequency resources are used for the first phase of access transmission.

[0051] It should be noted that, based on the traversal reachability and rate maximization criteria, the target macro base station and the target micro base station adopt an uplink / downlink reverse time configuration mode in the first phase of access transmission and the second phase of backhaul transmission, and split the transmission frequency into two parts to accommodate the communication transmission in the two phases. Specifically, during the downlink transmission period of the first phase of access transmission and the second phase of backhaul transmission performed by the target macro base station, the target micro base station simultaneously performs uplink transmission of the first phase of access transmission; during the uplink transmission period of the first phase of access transmission and the second phase of backhaul transmission performed by the target macro base station, the target micro base station simultaneously performs downlink transmission of the first phase of access transmission. By adopting the above-mentioned reverse time division duplex mode, the first phase downlink transmission of the micro base station can avoid cross-layer interference from the first phase access and second phase backhaul of the macro base station.

[0052] In this embodiment, the determining of the first downlink transmission rate of the target macro base station within the corresponding first coverage range and the second downlink transmission rate of the target micro base station within the corresponding second coverage range based on the transmission rate calculation formula includes: determining the precoding transmitted by the target macro base station within the corresponding first coverage range based on a preset regularized zero-breaking precoding formula, and defining the MIMO beamforming formula of the target macro base station based on the preset regularized zero-breaking precoding formula and the precoding; determining the first signal-to-interference-and-noise ratio value of the target macro base station during access signal transmission and the second signal-to-interference-and-noise ratio value during return signal transmission based on the MIMO beamforming formula and the channel gain used by the target macro base station for signal transmission, and substituting the first signal-to-interference-and-noise ratio value and the second signal-to-interference-and-noise ratio value into the corresponding transmission rate calculation formula to obtain the first signal access downlink transmission rate and the signal return downlink transmission rate corresponding to the target macro base station; obtaining the transmission power of the target micro base station within the corresponding second coverage range, and determining the second signal access downlink transmission rate corresponding to the target micro base station based on the transmission power and the channel gain used by the target micro base station for signal transmission. That is, the target macro base station provides the first-stage access communication service information and the second-stage backhaul service communication service information within its coverage area, and the target micro base station provides the first-stage access communication service information within its coverage area. The macro base station that deploys large-scale MIMO adopts regularized zero-forcing precoding, and calculates the signal-to-interference-and-noise ratio value of the first-stage access transmission according to the large-scale MIMO beamforming formula, and substitutes it into the first-stage access transmission rate calculation formula to obtain the first-stage access downlink transmission rate; calculates the signal-to-interference-and-noise ratio value of the second-stage backhaul transmission, and substitutes it into the second-stage transmission rate calculation formula to obtain the second-stage backhaul downlink transmission rate. The target micro base station deploys a single antenna, and based on the first-stage access transmission signal-to-interference-and-noise ratio formula, calculates the access signal-to-interference-and-noise ratio value of the terminal user within its coverage area, and substitutes it into the first-stage access transmission rate calculation formula to obtain the first access downlink transmission rate of the micro base station. Specifically, the downlink transmission objects of the target macro base station include providing the first-stage access transmission service for macro users and providing the second-stage backhaul transmission service for the micro base station, wherein the definition is the downlink transmission object set of the macro base station, then the received signal of the kth transmission object of the target macro base station is expressed as:

[0053] ;

[0054] in, is the beam vector transmitted by the target macro base station to object k, is the channel vector transmitted by the target macro base station to object k, is the data information transmitted by the target macro base station to the object k, is the Gaussian noise during the transmission from the target macro base station to object k. Object k includes macro users and micro base stations. Here, k and k' are used to distinguish them. Since the target macro base station uses regularized zero-breaking precoding, the precoding of the macro base station's transmission to object k is calculated according to the preset regularized zero-breaking precoding formula as follows:

[0055] ;

[0056] Among them, Represented as the channel matrix of a collection of macro base station objects, Power conditions must be met According to the regularized zero-breaking precoding calculation formula, the beam formula transmitted by the macro base station to the object k can be defined as Among them, the signal-to-interference-and-noise ratio formula of the macro base station transmitting to object k can be defined as ,in It is represented as the channel gain of the macro base station transmitting to the object k, is the noise power. The formula for the achievable rate of transmission from the macro base station to the object k is defined as:

[0057] ;

[0058] The achievable rate includes the first signal to interference and noise ratio value of the target macro base station when transmitting the access signal and the second signal to interference and noise ratio value of the return signal; that is, the target macro base station is within its coverage range. The first signal to interference and noise ratio value of the first phase access transmission service provided by the macro base station terminal user and the second transmission signal to interference and noise ratio value of the second phase backhaul transmission service provided by the target macro base station to the target micro base station. The transmission power of the target micro base station j is expressed as , the transmission channel from micro base station j to micro user k is expressed as The formula for the transmission rate of micro base station j to micro user k is defined as:

[0059] ;

[0060] in, is the number of micro users accessing the transmission service of micro base station j in the first phase, is the noise power.

[0061] Step S12: Determine the initial transmission efficiency between the target macro base station and the target micro base station using the first downlink transmission rate and the second downlink transmission rate, and construct preset constraints and a multi-objective optimization function corresponding to the initial transmission efficiency.

[0062] In this embodiment, the first downlink transmission rate and the second downlink transmission rate are used to determine the initial transmission efficiency between the target macro base station and the target micro base station, and to construct preset constraints and multi-objective optimization functions corresponding to the initial transmission efficiency, including: inputting the first signal access downlink transmission rate, the signal return downlink transmission rate, and the second signal access downlink transmission rate into a preset transmission frequency efficiency model to obtain the initial transmission frequency efficiency between the target macro base station and the target micro base station, and determining the corresponding initial energy efficiency based on the initial transmission frequency efficiency and the total network transmission power consumption; constructing preset constraints and multi-objective optimization functions corresponding to the initial transmission frequency efficiency and the initial energy efficiency. That is, the transmission frequency efficiency and energy efficiency between the macro base station and the micro base station are determined based on the first signal access downlink transmission rate, the signal return downlink transmission rate, and the second signal access downlink transmission rate, and the multi-objective optimization function and the preset constraints are determined based on the transmission frequency efficiency and the energy efficiency. Specifically, the energy efficiency model is:

[0063] ;

[0064] in, Defined as the total transmission power consumption of the network, Expressed as the amplification factor at the transmission end, Defined as the transmission frequency efficiency model, is the beam. While maximizing the above multiple objectives, three constraints should be met: (1) the access transmission rate received by the terminal user in the first phase should not be less than the minimum preset transmission rate requirement; (2) the downlink transmission capacity of the micro base station in the second phase should not be less than the downlink capacity of its own access in the first phase; (3) the transmission power of the macro base station and the micro base station should not be greater than the maximum preset power limit requirement. Therefore, the corresponding preset constraints and multi-objective optimization function are constructed as follows:

[0065] ;

[0066] Among them, the objective function C0 is a multi-objective optimization, which is expressed as maximizing the transmission frequency efficiency and energy efficiency of the network at the same time; the constraint condition C1 is expressed as the downlink achievable rate of the macro base station to the object k in the first phase of access transmission is not less than the minimum preset rate value ; Constraint C2 is expressed as the downlink achievable rate of the micro base station j to the micro user k in the first phase of access transmission is not less than the minimum preset rate value Constraint C3 indicates that the downlink capacity of the micro base station j in the first phase of access transmission is not greater than the downlink capacity of the macro base station in the second phase of backhaul transmission to ensure normal execution of transmission; Constraint C4 indicates that the downlink beam transmitted by the macro base station to the object k is not greater than the maximum preset power value , the downlink transmission power of micro base station j in the first phase of access transmission is not greater than the maximum preset power value Constraint C5 represents the value range of the frequency resource allocation ratio of the second-stage backhaul transmission.

[0067] Step S13: Based on the scalar quantization method and alternating iterative optimization theory, the multi-objective optimization function and the preset constraints are converted into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission.

[0068] In this embodiment, the multi-objective optimization function and the preset constraints are converted into the beam and power allocation sub-problems of signal access transmission and the bandwidth allocation sub-problems of signal return transmission based on the scalarization method and alternating iterative optimization theory, including: setting a target unitless parameter to characterize the importance value of the initial transmission frequency efficiency and the total transmission power consumption of the network, and converting the multi-objective optimization function into a single-objective optimization function based on the target unitless parameter; using the alternating iterative optimization theory to split the single-objective optimization function and the preset constraints into the beam and power allocation sub-problems of signal access transmission and the bandwidth allocation sub-problems of signal return transmission. First, the multi-objective optimization function C0 is equivalently converted into the following conflicting multi-objective optimization function:

[0069] ;

[0070] Among them, the conflicting multi-objective optimization function Defined as maximizing the transmission frequency efficiency While minimizing the total network transmission power consumption . Since maximizing the transmission frequency efficiency and energy efficiency of the network at the same time is a multi-objective optimization problem, the multi-objective optimization problem is equivalently converted into a single-objective optimization problem through the scalarization method, including: introducing unitless parameters according to the scalarization method, dynamically adjusting the importance between transmission frequency efficiency and energy efficiency, that is, during the peak of transmission service, increasing transmission frequency efficiency is more important than energy efficiency to meet the terminal user's demand for transmission rate. Conversely, reducing total power consumption during non-peak hours is more important than transmission frequency efficiency, which can improve the energy efficiency of the network. Specifically, the variable is introduced. It can dynamically adjust the transmission frequency efficiency according to the network status and the total transmission power consumption of the network Specifically, when the network is at the peak of transmission service, increase the transmission frequency efficiency Reduce the total transmission power consumption of the network More importantly, to meet the high demand of end users for transmission rate, conversely, when the network is in non-peak hours, reduce the total transmission power consumption of the network Than increase the transmission frequency efficiency More important, because it can improve the energy efficiency. Equivalently converted to the following single-objective optimization function :

[0071] ;

[0072] in, Expressed as the transmission frequency efficiency The normalization factor of Expressed as the total transmission power consumption of the network Then, based on the alternating optimization principle, the single-objective optimization function and the constraints are split into two sub-optimization problems, namely (1) the beam and power allocation sub-problem of the first-stage access transmission; and (2) the bandwidth allocation sub-problem of the second-stage backhaul transmission.

[0073] Step S14: Determine a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem based on a preset solution algorithm and an iterative optimization method, and determine a target transmission efficiency corresponding to the multi-objective optimization function based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

[0074] In this embodiment, the method of determining the first optimal solution corresponding to the beam and power allocation subproblem and the second optimal solution corresponding to the bandwidth allocation subproblem based on a preset solution algorithm and an iterative optimization method includes: determining the current bandwidth allocation rate of the signal return transmission in the beam and power allocation subproblem, and using a preset continuous approximate convex optimization method to determine the first current solution corresponding to the beam and power allocation subproblem; determining the first current solution as the current beam and current power of the signal access transmission in the bandwidth allocation subproblem, and using the Lagrangian dual method to solve the second current solution corresponding to the bandwidth allocation subproblem; substituting the first current solution and the second current solution into the single-objective optimization function, and then judging the single-objective optimization function. whether the single-objective optimization function can converge to the target optimal value while satisfying the preset constraints; if the single-objective optimization function can converge to the target optimal value while satisfying the preset constraints, then the first current solution is determined as the first optimal solution corresponding to the beam and power allocation subproblem, and the second current solution is determined as the second optimal solution corresponding to the bandwidth allocation subproblem; if the single-objective optimization function cannot converge to the target optimal value while satisfying the preset constraints, then the second current solution is determined as the new current bandwidth allocation rate for signal return transmission in the beam and power allocation subproblem, and the process jumps to the step of determining the first current solution corresponding to the beam and power allocation subproblem using the preset continuous approximate convex optimization method. Specifically, independent solution algorithms are designed for the two sub-optimization problems respectively. The two sub-optimization problems are iterated alternately to ultimately prove that the single-objective optimization function can converge to the optimal value while satisfying the constraints, including: using a continuous approximate convex optimization method to equivalently convert the beam and power allocation sub-problem of the first-stage access transmission into an approximate convex problem to obtain the approximate optimal values ​​of the beam and power; and using a Lagrangian dual method to solve the bandwidth allocation sub-problem of the second-stage backhaul transmission to obtain the optimal value of the backhaul bandwidth allocation.

[0075] In this embodiment, the use of a preset continuous approximate convex optimization method to determine the first current solution corresponding to the beam and power allocation subproblem includes: using preset additional slack variables and a first-order Taylor approximation method to convert the constraints in the beam and power allocation subproblem into a concave function to obtain a converted allocation subproblem, and using a preset continuous approximate convex optimization method to determine the upper bound expression and the lower bound expression of the preset downlink transmission rate; substituting the upper bound expression and the lower bound expression into the converted allocation subproblem to convert the converted allocation subproblem into a convex optimization function, and determining the optimal solution of the convex optimization function as the first current solution corresponding to the beam and power allocation subproblem. That is, a continuous convex approximation method is used to solve the beamforming and power sub-problems of the first-stage access transmission, the backhaul bandwidth allocation rate of the micro base station is fixed, and additional slack variables are introduced to equivalently replace the downlink transmission capacity constraints of the second-stage backhaul transmission; a first-order Taylor approximation method is used to approximate and optimize the same-layer interference of the downlink transmission rate of the second-stage backhaul transmission; a continuous convex approximation method is used to derive the upper and lower bounds of the downlink transmission rate of the first-stage access transmission, and it is proved that the upper and lower bounds will eventually converge to the Carlo-Kuhn-Tucker point, that is, the beamforming and power allocation sub-problems of the first-stage access transmission are convex problems and can obtain the optimal value. Specifically, the backhaul bandwidth allocation rate of the micro base station is fixed. ,The bandwidth allocation sub-problem of the first phase backhaul transmission is defined as:

[0076] ;

[0077] Introducing additional slack variables By equivalently replacing the downlink transmission capacity constraint C3 of the second-stage backhaul transmission, the following equivalent replacement expression is obtained:

[0078] ;

[0079] Equivalent replacement constraints It has the same non-convex structure as the constraints C1 and C2. Furthermore, the equivalent replacement of the constraints It can be replaced with the following equivalent expression:

[0080] ;

[0081] The equivalent replacement constraint is a concave function. Further, the first-order Taylor approximation method is used at point Nearby approximate optimization of the equivalent replacement constraints The right half of is:

[0082] ;

[0083] Among them, the equivalent replacement constraint The superscript t represents the tth iteration of the iterative process. Similarly, the constraints C1 and C2 can also be equivalently replaced by the constraints Finally, the continuous convex approximation method is used to derive the downlink transmission rate of the first stage access transmission The upper and lower bounds of , the lower bound expression is defined as:

[0084] ;

[0085] The upper bound expression is defined as:

[0086] .

[0087] Finally, by increasing the first phase access transmission downlink transmission rate The upper bound expression of and the lower bound expression Substituting this into the bandwidth allocation subproblem for the first-stage backhaul transmission, we obtain the following convex optimization approximation problem. The optimal solution of the convex optimization function is determined as the first current solution corresponding to the beam and power allocation subproblem:

[0088] .

[0089] In this embodiment, using the Lagrangian dual method to solve the second current solution corresponding to the bandwidth allocation subproblem includes: determining a bandwidth allocation ratio expression for the bandwidth allocation subproblem based on the Lagrangian update formula and Lagrangian multipliers corresponding to the constraints of the bandwidth allocation subproblem, and determining the optimal solution to the bandwidth allocation ratio expression to obtain the second current solution corresponding to the bandwidth allocation subproblem. Specifically, using a continuous approximate convex optimization method to equivalently transform the beam and power allocation subproblem of the first-stage access transmission into an approximate convex problem to obtain approximate optimal values ​​for the beam and power; and using the Lagrangian dual method to solve the bandwidth allocation subproblem of the second-stage backhaul transmission to obtain the optimal value for the backhaul bandwidth allocation. That is, the Lagrangian dual method is used to solve the backhaul bandwidth allocation subproblem, the beam and power of the first-stage access transmission are fixed, three Lagrangian multipliers are defined to correspond to the constraints C1, C2, and C3 in the multi-objective optimization problem, and the Lagrangian function of the bandwidth allocation subproblem of the second-stage backhaul transmission is derived. After the Lagrangian multiplier satisfies the iteration conditions through iteration, the optimal backhaul bandwidth allocation ratio is obtained. Specifically, the beam of the first-stage access transmission is fixed. and power ,The bandwidth allocation sub-problem of the second-stage backhaul transmission is defined as:

[0090] .

[0091] Furthermore, the Lagrange multiplier of the constraint condition C1 is expressed as , the Lagrange multiplier of the constraint condition C2 is expressed as , the Lagrange multiplier of the constraint condition C3 is expressed as The Lagrangian function of the objective function C0_2 is defined as:

[0092] .

[0093] Therefore, based on the Lagrangian function of the objective function C0_2, the Lagrangian dual equation of the bandwidth allocation subproblem of the second-stage backhaul transmission is defined as:

[0094] .

[0095] Furthermore, the Lagrange multiplier update formula is used to obtain the Lagrange multiplier of the constraint condition C1 from the first iteration: , the Lagrange multiplier of the constraint condition C2 and the Lagrange multiplier of the constraint C3 :

[0096] .

[0097] Furthermore, based on the Lagrange multiplier update formula, the bandwidth allocation ratio is obtained The expression is:

[0098] .

[0099] Finally, an optimal solution of the bandwidth allocation ratio expression is determined to obtain a second current solution corresponding to the bandwidth allocation subproblem.

[0100] It can be seen that in this embodiment, Figure 3As shown, first, based on the transmission rate calculation formula, the first downlink transmission rate of the target macro base station in the corresponding first coverage area and the second downlink transmission rate of the target micro base station in the corresponding second coverage area are determined; the target macro base station and the target micro base station transmit signals through a preset reverse time division duplex mode; the first downlink transmission rate and the second downlink transmission rate are used to determine the initial transmission efficiency between the target macro base station and the target micro base station, and the preset constraints and multi-objective optimization function corresponding to the initial transmission efficiency are constructed; based on the scalarization method and alternating iterative optimization theory, the multi-objective optimization function and the preset constraints are converted into beam and power allocation sub-problems for signal access transmission and bandwidth allocation sub-problems for signal backhaul transmission; based on the preset solution algorithm and iterative optimization method, the first optimal solution corresponding to the beam and power allocation sub-problem and the second optimal solution corresponding to the bandwidth allocation sub-problem are determined, and the target transmission efficiency corresponding to the multi-objective optimization function is determined based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency. In this way, by utilizing scalarization and iterative optimization methods, the multi-objective optimization problem is equivalently converted into two sub-optimization problems. Through iterative training of the two sub-optimization problems, the single-objective optimization problem converges to the optimal value, thereby achieving the optimal target transmission efficiency. In this way, while considering the limited capacity of massive MIMO wireless backhaul, using transmission frequency efficiency and energy efficiency as multiple optimization objectives can increase network adaptability and improve network transmission efficiency.

[0101] refer to Figure 4 The embodiment of the present application further discloses a massive MIMO wireless backhaul network transmission device, including:

[0102] A transmission rate determination module 11 is configured to determine, based on a transmission rate calculation formula, a first downlink transmission rate of a target macro base station within a corresponding first coverage area and a second downlink transmission rate of a target micro base station within a corresponding second coverage area; the target macro base station and the target micro base station transmit signals via a preset reverse time division duplex mode;

[0103] an optimization function determination module 12, configured to determine an initial transmission efficiency between the target macro base station and the target micro base station using the first downlink transmission rate and the second downlink transmission rate, and to construct a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency;

[0104] A function splitting module 13 is used to convert the multi-objective optimization function and the preset constraints into a beam and power allocation sub-problem for signal access transmission and a bandwidth allocation sub-problem for signal return transmission based on a scalar quantization method and alternating iterative optimization theory;

[0105] The network transmission module 14 is used to determine the first optimal solution corresponding to the beam and power allocation subproblem and the second optimal solution corresponding to the bandwidth allocation subproblem based on a preset solution algorithm and an iterative optimization method, and determine the target transmission efficiency corresponding to the multi-objective optimization function based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

[0106] As can be seen, this embodiment utilizes scalarization and iterative optimization methods to convert the multi-objective optimization problem into two sub-optimization problems. Iterative training of these two sub-optimization problems allows the single-objective optimization problem to converge to its optimal value, thereby achieving the optimal target transmission efficiency. This approach, while considering the limited capacity of massive MIMO wireless backhaul, incorporates transmission frequency efficiency and energy efficiency as multiple optimization objectives, increasing network adaptability and improving network transmission efficiency.

[0107] In some specific embodiments, the transmission rate determination module 11 may specifically include:

[0108] A beamforming formula definition unit is configured to determine a precoding for transmission by a target macro base station within a corresponding first coverage range based on a preset regularized zero-breaking precoding formula, and define a MIMO beamforming formula for the target macro base station based on the preset regularized zero-breaking precoding formula and the precoding;

[0109] a first transmission rate determining unit, configured to determine, based on the MIMO beamforming formula and the channel gain used by the target macro base station for signal transmission, a first signal to interference and noise ratio value for access signal transmission and a second signal to interference and noise ratio value for return signal transmission of the target macro base station, respectively, and substitute the first signal to interference and noise ratio value and the second signal to interference and noise ratio value into corresponding transmission rate calculation formulas, respectively, to obtain a first signal access downlink transmission rate and a signal return downlink transmission rate corresponding to the target macro base station;

[0110] The second transmission rate determination unit is used to obtain the transmission power of the target micro base station within the corresponding second coverage range, and determine the second signal access downlink transmission rate corresponding to the target micro base station based on the transmission power and the channel gain used by the target micro base station for signal transmission.

[0111] In some specific embodiments, the optimization function determination module 12 may specifically include:

[0112] an energy efficiency calculation unit, configured to input the first signal access downlink transmission rate, the signal return downlink transmission rate, and the second signal access downlink transmission rate into a preset transmission frequency efficiency model to obtain an initial transmission frequency efficiency between the target macro base station and the target micro base station, and determine a corresponding initial energy efficiency based on the initial transmission frequency efficiency and the total network transmission power consumption;

[0113] A function construction unit is used to construct preset constraint conditions and a multi-objective optimization function corresponding to the initial transmission frequency efficiency and the initial energy efficiency.

[0114] In some specific embodiments, the function splitting module 13 may specifically include:

[0115] a function conversion unit, configured to set a target unitless parameter for characterizing the importance values ​​of the initial transmission frequency efficiency and the total transmission power consumption of the network, and convert the multi-objective optimization function into a single-objective optimization function based on the target unitless parameter;

[0116] A function splitting unit is used to split the single-objective optimization function and the preset constraint conditions into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission by using alternating iterative optimization theory.

[0117] In some specific embodiments, the network transmission module 14 may specifically include:

[0118] a first current solution determination submodule, configured to determine a current bandwidth allocation ratio for signal return transmission in the beam and power allocation subproblem, and to determine a first current solution corresponding to the beam and power allocation subproblem using a preset continuous approximate convex optimization method;

[0119] a second current solution determination submodule for the bundle and current power, and solving the second current solution corresponding to the bandwidth allocation subproblem using a Lagrangian dual method;

[0120] a function judgment unit, configured to substitute the first current solution and the second current solution into the single-objective optimization function, and then judge whether the single-objective optimization function can converge to a target optimal value while satisfying the preset constraint condition;

[0121] an optimal solution determining unit, configured to determine, if the single-objective optimization function can converge to a target optimal value under the preset constraint condition, the first current solution as a first optimal solution corresponding to the beam and power allocation subproblem, and the second current solution as a second optimal solution corresponding to the bandwidth allocation subproblem;

[0122] A step jump unit is used to determine the second current solution as the new current bandwidth allocation rate for signal return transmission in the beam and power allocation subproblem if the single-objective optimization function cannot converge to the target optimal value while satisfying the preset constraints, and jump to the step of determining the first current solution corresponding to the beam and power allocation subproblem using a preset continuous approximate convex optimization method.

[0123] In some specific embodiments, the first current solution determination submodule may specifically include:

[0124] an expression determination unit, configured to transform the constraints in the beam and power allocation subproblem into a concave function using a preset additional slack variable and a first-order Taylor approximation method to obtain a transformed allocation subproblem, and to determine an upper bound expression and a lower bound expression of the preset downlink transmission rate using a preset continuous approximate convex optimization method;

[0125] A current solution determination unit is used to substitute the upper bound expression and the lower bound expression into the transformed allocation subproblem to convert the transformed allocation subproblem into a convex optimization function, and determine the optimal solution of the convex optimization function as the first current solution corresponding to the beam and power allocation subproblem.

[0126] In some specific embodiments, the second current solution determination submodule can be specifically used to determine the bandwidth allocation ratio expression of the bandwidth allocation subproblem based on the Lagrangian update formula and the Lagrangian multipliers corresponding to the constraints of the bandwidth allocation subproblem, and determine the optimal solution of the bandwidth allocation ratio expression to obtain the second current solution corresponding to the bandwidth allocation subproblem.

[0127] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram should not be considered as any limitation to the scope of application of the present application.

[0128] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the massive MIMO wireless backhaul network transmission method disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0129] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0130] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0131] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20. The operating system 221 can be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs capable of implementing the massive MIMO wireless backhaul network transmission method performed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs capable of performing other specific tasks.

[0132] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned Massive MIMO wireless backhaul network transmission method. The specific steps of this method can be referred to the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0133] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0134] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0135] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0136] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0137] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A large-scale MIMO wireless backhaul network transmission method, characterized in that: include: Determine a first downlink transmission rate of the target macro base station within a corresponding first coverage area and a second downlink transmission rate of the target micro base station within a corresponding second coverage area based on a transmission rate calculation formula; transmit signals between the target macro base station and the target micro base station through a preset reverse time division duplex mode; Determine the initial transmission efficiency between the target macro base station and the target micro base station by using the first downlink transmission rate and the second downlink transmission rate, and construct a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency; Based on the scalarization method and the alternating iterative optimization theory, the multi-objective optimization function and the preset constraint conditions are converted into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission; Based on a preset solution algorithm and an iterative optimization method, a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem are determined, and based on the first optimal solution and the second optimal solution, a target transmission efficiency corresponding to the multi-objective optimization function is determined, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

2. The large-scale MIMO wireless backhaul network transmission method according to claim 1, characterized in that: The determining, based on the transmission rate calculation formula, a first downlink transmission rate of the target macro base station within a corresponding first coverage range and a second downlink transmission rate of the target micro base station within a corresponding second coverage range includes: Determine the precoding for transmission of the target macro base station within the corresponding first coverage range based on a preset regularized zero-breaking precoding formula, and define a MIMO beamforming formula for the target macro base station based on the preset regularized zero-breaking precoding formula and the precoding; Based on the MIMO beamforming formula and the channel gain used by the target macro base station for signal transmission, respectively determine a first signal to interference and noise ratio value when transmitting an access signal and a second signal to interference and noise ratio value when transmitting a return signal of the target macro base station, and substitute the first signal to interference and noise ratio value and the second signal to interference and noise ratio value into corresponding transmission rate calculation formulas, so as to obtain a first signal access downlink transmission rate and a signal return downlink transmission rate corresponding to the target macro base station; Acquire the transmission power of the target micro base station within the corresponding second coverage range, and determine the second signal access downlink transmission rate corresponding to the target micro base station based on the transmission power and the channel gain used by the target micro base station for signal transmission.

3. The massive MIMO wireless backhaul network transmission method according to claim 2, characterized in that: The determining of the initial transmission efficiency between the target macro base station and the target micro base station by using the first downlink transmission rate and the second downlink transmission rate, and constructing a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency, includes: Inputting the first signal access downlink transmission rate, the signal return downlink transmission rate and the second signal access downlink transmission rate into a preset transmission frequency efficiency model to obtain an initial transmission frequency efficiency between the target macro base station and the target micro base station, and determining a corresponding initial energy efficiency based on the initial transmission frequency efficiency and the total network transmission power consumption; Constructing preset constraints and a multi-objective optimization function corresponding to the initial transmission frequency efficiency and the initial energy efficiency.

4. The large-scale MIMO wireless backhaul network transmission method according to claim 3, characterized in that: The method of converting the multi-objective optimization function and the preset constraint conditions into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission based on the scalarization method and the alternating iterative optimization theory includes: Setting a target unitless parameter to characterize the importance values ​​of the initial transmission frequency efficiency and the total transmission power consumption of the network, and converting the multi-objective optimization function into a single-objective optimization function based on the target unitless parameter; The single-objective optimization function and the preset constraint conditions are split into the beam and power allocation sub-problem of signal access transmission and the bandwidth allocation sub-problem of signal return transmission by using the alternating iterative optimization theory.

5. The massive MIMO wireless backhaul network transmission method according to claim 4, characterized in that: The determining, based on a preset solution algorithm and an iterative optimization method, a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem comprises: Determine a current bandwidth allocation rate for signal backhaul transmission in the beam and power allocation subproblem, and determine a first current solution corresponding to the beam and power allocation subproblem using a preset continuous approximate convex optimization method; Determine the first current solution as the current beam and current power of the signal access transmission in the bandwidth allocation subproblem, and solve the second current solution corresponding to the bandwidth allocation subproblem by using the Lagrangian dual method; Substituting the first current solution and the second current solution into the single-objective optimization function, and then determining whether the single-objective optimization function can converge to a target optimal value under the preset constraint condition; If the single-objective optimization function can converge to the target optimal value under the preset constraint condition, the first current solution is determined as the first optimal solution corresponding to the beam and power allocation subproblem, and the second current solution is determined as the second optimal solution corresponding to the bandwidth allocation subproblem; If the single-objective optimization function cannot converge to the target optimal value while satisfying the preset constraints, the second current solution is determined as the new current bandwidth allocation rate for signal feedback transmission in the beam and power allocation sub-problem, and the process jumps to the step of determining the first current solution corresponding to the beam and power allocation sub-problem using the preset continuous approximate convex optimization method.

6. The massive MIMO wireless backhaul network transmission method according to claim 5, characterized in that: The determining a first current solution corresponding to the beam and power allocation subproblem by using a preset continuous approximate convex optimization method includes: The constraint conditions in the beam and power allocation subproblem are converted into a concave function using a preset additional slack variable and a first-order Taylor approximation method to obtain a converted allocation subproblem, and an upper bound expression and a lower bound expression of the preset downlink transmission rate are determined using a preset continuous approximate convex optimization method; Substitute the upper bound expression and the lower bound expression into the transformed allocation subproblem to transform the transformed allocation subproblem into a convex optimization function, and determine the optimal solution of the convex optimization function as the first current solution corresponding to the beam and power allocation subproblem.

7. The massive MIMO wireless backhaul network transmission method according to claim 5, characterized in that: The method of using the Lagrangian dual method to solve the second current solution corresponding to the bandwidth allocation subproblem includes: A bandwidth allocation ratio expression for the bandwidth allocation subproblem is determined based on a Lagrangian update formula and Lagrangian multipliers corresponding to the constraints of the bandwidth allocation subproblem, and an optimal solution to the bandwidth allocation ratio expression is determined to obtain a second current solution corresponding to the bandwidth allocation subproblem.

8. A large-scale MIMO wireless backhaul network transmission device, characterized in that: include: A transmission rate determination module, used to determine a first downlink transmission rate of a target macro base station within a corresponding first coverage range and a second downlink transmission rate of a target micro base station within a corresponding second coverage range based on a transmission rate calculation formula; the target macro base station and the target micro base station transmit signals through a preset reverse time division duplex mode; An optimization function determination module, used to determine the initial transmission efficiency between the target macro base station and the target micro base station using the first downlink transmission rate and the second downlink transmission rate, and to construct a preset constraint condition and a multi-objective optimization function corresponding to the initial transmission efficiency; A function splitting module, used for converting the multi-objective optimization function and the preset constraint conditions into a beam and power allocation sub-problem of signal access transmission and a bandwidth allocation sub-problem of signal return transmission based on a scalar quantization method and an alternating iterative optimization theory; A network transmission module is used to determine a first optimal solution corresponding to the beam and power allocation subproblem and a second optimal solution corresponding to the bandwidth allocation subproblem based on a preset solution algorithm and an iterative optimization method, and to determine a target transmission efficiency corresponding to the multi-objective optimization function based on the first optimal solution and the second optimal solution, so that the target macro base station and the target micro base station perform large-scale MIMO wireless backhaul network transmission based on the target transmission efficiency.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the large-scale MIMO wireless backhaul network transmission method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the large-scale MIMO wireless backhaul network transmission method as described in any one of claims 1 to 7.

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