CN116961710A - Method and system for controlling and quantifying power of honeycomb-free large-scale MIMO - Google Patents
Method and system for controlling and quantifying power of honeycomb-free large-scale MIMO Download PDFInfo
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Abstract
The application discloses a power control and quantization method and a system of honeycomb-free large-scale MIMO, wherein the method comprises the steps of carrying out channel estimation on each access point, and gathering information of channel estimation through a backhaul link after signal quantization; performing power distribution and CB precoding for all access points, and transmitting data to the access points after signal quantization; calculating the spectrum efficiency and the total spectrum efficiency by receiving signals to obtain the optimal data transmitting power and signal quantization error variance; the method combines the quantization error variance optimization and the transmission power control of the CB precoding technology, and improves the total spectrum efficiency of the MIMO system with limited backhaul link capacity; reducing backhaul capacity using quantized compressed uplink pilot training and downlink data transmitted signals; and each return stroke is set to have different quantization precision, and the access point quantizes the channel estimation of different users with different precision during the uplink pilot training period, so that the balance between the accuracy of the channel estimation and the calculation and communication cost is realized.
Description
Technical Field
The present application relates to the field of wireless communications technologies, and in particular, to a method and a system for power control and quantization of non-cellular massive MIMO.
Background
As a new generation wireless communication technology, the non-cellular large-scale MIMO system combines the characteristics of large-scale MIMO and distributed antennas, and can effectively solve the contradiction between the coverage range and the speed of the traditional cellular network, thereby remarkably promoting the breakthrough of the future mobile communication in the aspect of key network performance. Although non-cellular massive MIMO has many excellent characteristics, in practical grounding, the capacity of the backhaul link between AP (Access Point) and CPU (Central Processing Unit) is very limited in consideration of hardware cost, and as known from related researches, the total spectral efficiency of the system is limited by the capacity of the backhaul link. Therefore, there is a need to find a way to fully utilize the limited backhaul link capacity.
Unlike the general method of maximizing the overall spectral efficiency of the system by controlling only the power of the transmitted signal, the method combines quantization error variance optimization with transmit power control, thus better utilizing the limited backhaul link capacity, further optimizing the downlink data transmission performance of the non-cellular massive MIMO system, and providing an important reference value for facilitating the implementation of the technology on-ground.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art. Therefore, the application provides a power control and quantization method for honeycomb-free large-scale MIMO, which is used for solving the problem that the total spectrum efficiency of a system is limited by the capacity of a backhaul link in practical problems.
In order to solve the technical problems, the application provides the following technical scheme:
in a first aspect, the present application provides a method for power control and quantization for cellular-free massive MIMO, comprising:
channel estimation is carried out on each access point, and after signal quantization, information of channel estimation is collected through a backhaul link;
in the downlink data transmission stage, power distribution and CB precoding are carried out for all access points, and data are transmitted to the access points after signal quantization;
calculating spectral efficiency and total spectral efficiency by receiving the signal;
and (3) taking the maximization of the total spectrum efficiency as a target, and under the constraint of the total transmission power and the capacity of a backhaul link, obtaining the optimal data transmission power and the signal quantization error variance.
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: the channel estimation is performed on each access point, and after signal quantization, information of the channel estimation is collected through a backhaul link, including:
setting a honeycomb-free large-scale MIMO system, wherein the system comprises 1 central processing unit, M access points and K single-antenna users; wherein each access point and each user are uniformly distributed in a square area with the side length of D and are mutually independent; simultaneously, each access point is provided with a single antenna, and the large-scale fading coefficient of the channel between the mth access point and the kth user is beta mk Meaning that all access points are connected to the central processor through a limited capacity backhaul link.
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: further comprises:
all users in the system use the same time-frequency resource, randomly select one orthogonal pilot sequence from a predefined pilot set and send the orthogonal pilot sequence to all access points, wherein each pilot sequence consists of tau symbols;
each access point detects the received pilot sequence and performs MMSE channel estimation at the same time, and then the access point willIs aggregated to a central processor over a limited capacity backhaul link;
wherein ,for channelsStatus information matrix->The mth row and the kth column elements of (a).
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: in the downlink data transmission stage, power distribution and CB precoding are performed for all access points, and after signal quantization, data is transmitted to the access points, including:
when transmitting user data vector s downwards, the CPU is based onGenerating a corresponding CB precoding matrix W; and meanwhile, performing power control on the precoded downlink data signals, transmitting the processed data signals to each access point through a backhaul link with limited capacity, and performing power amplification on the signals by the mth access point and transmitting the signals to all users in the area.
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: calculating spectral efficiency and total spectral efficiency from the received signal, comprising:
normalizing the additive Gaussian white noise of the wireless channel, and calculating the independent spectrum efficiency of the kth user in the downlink transmission system adopting CB precoding;
and adding the individual spectrum efficiencies of all the users to obtain the total spectrum efficiency.
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: with the objective of maximizing the total spectrum efficiency, under the constraint of the total transmitting power and the capacity of the backhaul link, the method for obtaining the optimal data transmitting power and the signal quantization error variance comprises the following steps:
and solving an optimization problem through an iterative optimization algorithm, and constructing a deep learning model to obtain an optimal solution so as to obtain optimal transmitting power and quantization error variance of each user data.
As a preferred scheme of the method for controlling and quantifying power of the non-cellular massive MIMO according to the present application, the method comprises: the constructing the deep learning model to obtain the optimal solution comprises the following steps:
obtaining different channel state matrixes through traversalQ under maximized total spectral efficiency of time system u And the N matrix is integrated and converted into a matrix with two rows of M-K columns, and the matrix is used as a deep learning label; wherein the first line of data is the original Q u The matrix, the second row data is the original N matrix;
constructing a deep learning model, setting an input layer as a matrix of M x K columns, setting an output layer as a matrix of M x K columns, wherein a hidden layer comprises 2M x K neurons, selecting a sigmoid function by an activation function, and setting relevant learning parameters of the model;
each channel state information matrix corresponding to the deep learning labelConverting the matrix into M-by-K columns as sample data, dividing the sample data, and setting the model verification accuracy rule;
matrix channel state informationInputting into the model to obtain a first row vector and a second row vector of an output matrix, and sorting to obtain +.> and N* 。
In a second aspect, the present application provides a cellular-free massive MIMO power control and quantization system comprising:
the channel estimation processing module is used for carrying out channel estimation on each access point and gathering information of channel estimation through a backhaul link after signal quantization;
the downlink data transmission module is used for carrying out power distribution and CB precoding on all access points in a downlink data transmission stage, and transmitting data to the access points after signal quantization;
the frequency spectrum efficiency calculation module is used for calculating frequency spectrum efficiency and total frequency spectrum efficiency through receiving signals;
and the data optimization module is used for obtaining the optimal data transmission power and signal quantization error variance under the constraint of limited total transmission power and backhaul link capacity with the aim of maximizing the total spectrum efficiency.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, wherein: the processor, when executing the computer program, implements any of the steps of the method described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program, wherein: which when executed by a processor performs any of the steps of the method described above.
Compared with the prior art, the application has the beneficial effects that: the application carries out channel estimation on each access point, and gathers the information of channel estimation through a backhaul link after signal quantization; in the downlink data transmission stage, power distribution and CB precoding are carried out for all access points, and data are transmitted to the access points after signal quantization; calculating spectral efficiency and total spectral efficiency by receiving the signal; with the aim of maximizing the total spectrum efficiency, under the constraint of the total transmitting power and the capacity limitation of a return link, the optimal data transmitting power and the signal quantization error variance are obtained; the application combines the quantization error variance optimization and the transmitting power control of the CB pre-coding technology, and improves the total spectrum efficiency of the honeycomb-free large-scale MIMO system with limited backhaul link capacity; and compressing signals for uplink pilot training and downlink data transmission using quantization to reduce backhaul capacity; while assuming that each backhaul can have different quantization accuracy, the access point quantizes the channel estimates for different users with different accuracy during uplink pilot training to achieve a balance between accuracy of channel estimates and computational, communication costs.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a general flow chart of a method for power control and quantization for cell-free massive MIMO according to an embodiment of the present application;
fig. 2 is a comparison diagram of parameter solutions of a method for power control and quantization for non-cellular massive MIMO according to an embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present application have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present application. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, a first embodiment of the present application provides a method for power control and quantization for honeycomb-free massive MIMO, including:
s1, carrying out channel estimation on each access point, and gathering information of channel estimation through a backhaul link after signal quantization;
furthermore, a honeycomb-free large-scale MIMO system is arranged, wherein the system comprises 1 central processing unit, M access points and K single-antenna users; wherein each access point and each user are uniformly distributed in a square area with the side length of D and are mutually independent; simultaneously, each access point is provided with a single antenna, and the large-scale fading coefficient of the channel between the mth access point and the kth user is beta mk Indicating that all access points are passed throughThe backhaul link with limited capacity is connected to the central processing unit;
furthermore, all users in the system use the same time-frequency resource, and randomly select one orthogonal pilot sequence from a predefined pilot set to send to all access points, wherein each pilot sequence consists of tau symbols; each access point detects the received pilot sequence and performs MMSE channel estimation at the same time, and then the access point willIs aggregated to a central processor over a limited capacity backhaul link;
wherein ,is a channel state information matrix->The mth row and the kth column elements of (a);
it should be noted that the channel estimation formula received by the central processor is expressed as:
wherein ,qu,mk Mean value 0, variance Q p,mk Is a complex gaussian variable representing access point versus uplink dataError generated after quantization; />An estimate representing channel state information between the mth access point and the kth user;
s2, in the downlink data transmission stage, power distribution and CB precoding are carried out for all access points, and data are transmitted to the access points after signal quantization;
further, when transmitting user data vector downstreams time, the central processing unit is based onGenerating a corresponding CB precoding matrix W; meanwhile, the power control is carried out on the pre-coded downlink data signals, the processed data signals are sent to all access points through a backhaul link with limited capacity, and the mth access point in the data signals carries out power amplification on the signals and sends the signals to all users in the area;
still further, the precoding matrix W is expressed as:
wherein ,αCB Precoding a power limiting factor for CB;
it should be noted that the factor is such that the precoding matrix W is transposed with its conjugate W H After multiplication, the sum of the diagonal elements of the obtained matrix is expected to be 1; if the sum of the diagonal elements of the matrix is represented by tr (), it can be expressed as
Wherein E {. Cndot. } is expressed as the expectation of the data in brackets;
further, the processed data signal is sent to each access point through the backhaul link with limited capacity, and the mth access point receives the signal x m Expressed as:
wherein ,qd,m Mean value 0, variance Q d,m And (2) complex Gaussian variable representing error generated when CPU quantizes downlink data sent to mth access point, W mk S is the m-th row and k-th column element in matrix W k Is the k-th line element in the vector s; η (eta) mk Power control for user k signal is sent for the mth access point;
further, the mth access point is required to amplify the power of the signal to obtain
wherein ,Pd η mk The transmission power of the kth user signal for the mth access point;
s3, calculating the spectrum efficiency and the total spectrum efficiency through receiving signals;
further, normalization processing is carried out on the additive Gaussian white noise of the received wireless channel, and the independent spectrum efficiency of the kth user in the downlink transmission system adopting CB precoding is calculatedThe formula is:
wherein ψ represents inter-user interference, T is the symbol length of the coherence time, γ mk Representing channel estimatesIs a variance of (2); τ is denoted as pilot signal;
further, the individual spectral efficiencies of all users are added to obtain a total spectral efficiency S, expressed as:
s4, with the aim of maximizing the total spectrum efficiency, under the constraint of the total transmitting power and the capacity of a return link, obtaining the optimal data transmitting power and the signal quantization error variance;
further, solving an optimization problem through an iterative optimization algorithm, and constructing a deep learning model to obtain an optimal solution at the same time so as to obtain optimal transmitting power and quantization error variance of each user data, wherein the formula of the optimization problem is expressed as follows:
wherein s.t. represents constraint conditions, C m Is the capacity of the backhaul link between the mth access point and the central processor,is a quantization error variance matrix in uplink transmission, Q * u Is the optimal quantization error variance matrix, +.>Is a power control matrix, N * Is an optimal power control matrix; e { |·| } represents the expectation of absolute values of the data in brackets;
further, the steps for obtaining the optimal solution are as follows:
by traversing 10-4 different channel state matricesQ under maximized total spectral efficiency of time system u And the N matrix is integrated and converted into a matrix with two rows of M-K columns, and the matrix is used as a deep learning label; wherein,the first line of data is the original Q u The matrix, the second row data is the original N matrix;
constructing a five-layer deep learning model, wherein an input layer is set to be a matrix of one row M x K column, an output layer is set to be a matrix of two rows M x K column, a hidden layer comprises 2M x K neurons, an activation function selects a sigmoid function, and relevant learning parameters of the model are set;
it should be noted that, setting the model-related learning parameters includes, momentum of 0.9, regularization coefficient of 0.001 for preventing overfitting; the initial learning rate is 0.01, the learning rate variation is 0.1, and the optimized parameter data takes 30 samples as a group;
matrix each channel state information corresponding to the deep learning labelConverting the matrix into a row of M-K columns as sample data, dividing the sample data, and setting the model verification accuracy rule;
it should be noted that 80%7 of the sample data is input into the training deep learning model, and the other 20% of the sample data is used to verify the accuracy of the model;
further, the model verification accuracy rule includes that the error between the deep learning output obtained by the probability of 0.1% and the corresponding label value is 10%, and the verification frequency is set to be verified once every 5 iteration times;
matrix channel state informationInputting into the model to obtain a first row vector and a second row vector of an output matrix, and sorting to obtain +.> and N* 。
Further, the present embodiment also provides a system for controlling and quantifying power of a cell-free massive MIMO, including:
the channel estimation processing module is used for carrying out channel estimation on each access point and gathering information of channel estimation through a backhaul link after signal quantization;
the downlink data transmission module is used for carrying out power distribution and CB precoding on all access points in a downlink data transmission stage, and transmitting data to the access points after signal quantization;
the frequency spectrum efficiency calculation module is used for calculating frequency spectrum efficiency and total frequency spectrum efficiency through receiving signals;
and the data optimization module is used for obtaining the optimal data transmission power and signal quantization error variance under the constraint of limited total transmission power and backhaul link capacity with the aim of maximizing the total spectrum efficiency.
The embodiment also provides a computer device, which is suitable for the situation of a power control and quantization method of a cell-free massive MIMO, and includes:
a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the method for power control and quantization for cellular-free massive MIMO as proposed in the above embodiments.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method for power control and quantization for cell-free massive MIMO as proposed in the above embodiments.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
Example 2
Referring to fig. 2, a second embodiment of the present application provides a method for power control and quantization for honeycomb-free massive MIMO, comprising:
the optimal power control matrix N obtained by the method of the application * As shown in table 1:
TABLE 1
The optimal uplink quantization error variance matrix obtained by the applicationAs shown in table 2:
TABLE 2
By combining tables 1 and 2, and combining the obtained optimal N * 、After the parameters, the resulting rate is compared with the initial N, Q used u The resulting rates were compared and the resultsAs shown in fig. 2;
it can be seen that N is obtained using the process of the application * 、The parameters can be compared with those of N, Q obtained by the prior art u The signal-to-noise ratio and the downlink average rate of the parameters are higher, so that the identification and the data transmission rate of the signals are improved; the method has stronger practical applicability and provides reference value for fully utilizing limited channel resources.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (10)
1. A method for power control and quantization for cell-free massive MIMO, comprising:
channel estimation is carried out on each access point, and after signal quantization, information of channel estimation is collected through a backhaul link;
in the downlink data transmission stage, power distribution and CB precoding are carried out for all access points, and data are transmitted to the access points after signal quantization;
calculating spectral efficiency and total spectral efficiency by receiving the signal;
and (3) taking the maximization of the total spectrum efficiency as a target, and under the constraint of the total transmission power and the capacity of a backhaul link, obtaining the optimal data transmission power and the signal quantization error variance.
2. The method for power control and quantization of non-cellular massive MIMO according to claim 1, wherein the channel estimation is performed for each access point, and information of the channel estimation is collected through a backhaul link after signal quantization, comprising:
setting honeycomb-free massive MIMO systemThe system comprises 1 central processing unit, M access points and K single-antenna users; wherein each access point and each user are uniformly distributed in a square area with the side length of D and are mutually independent; simultaneously, each access point is provided with a single antenna, and the large-scale fading coefficient of the channel between the mth access point and the kth user is beta mk Meaning that all access points are connected to the central processor through a limited capacity backhaul link.
3. The method for power control and quantization for non-cellular massive MIMO according to claim 2, further comprising:
all users in the system use the same time-frequency resource, randomly select one orthogonal pilot sequence from a predefined pilot set and send the orthogonal pilot sequence to all access points, wherein each pilot sequence consists of tau symbols;
each access point detects the received pilot sequence and performs MMSE channel estimation at the same time, and then the access point willIs aggregated to a central processor over a limited capacity backhaul link;
wherein ,is a channel state information matrix->The mth row and the kth column elements of (a).
4. A method for power control and quantization of non-cellular massive MIMO according to claim 2 or 3, characterized in that, in the downlink data transmission stage, power allocation and CB precoding are performed for all access points, and after signal quantization, data are transmitted to the access points, comprising:
when transmitting user data vector s downwards, the CPU is based onGenerating a corresponding CB precoding matrix W; and meanwhile, performing power control on the precoded downlink data signals, transmitting the processed data signals to each access point through a backhaul link with limited capacity, and performing power amplification on the signals by the mth access point and transmitting the signals to all users in the area.
5. The method for power control and quantization for non-cellular massive MIMO according to claim 4, wherein calculating spectral efficiency and total spectral efficiency from the received signal comprises:
normalizing the additive Gaussian white noise of the wireless channel, and calculating the independent spectrum efficiency of the kth user in the downlink transmission system adopting CB precoding;
and adding the individual spectrum efficiencies of all the users to obtain the total spectrum efficiency.
6. The method for power control and quantization for non-cellular massive MIMO according to claim 5, wherein the obtaining of the optimum data transmit power and signal quantization error variance under the constraint of the total transmit power and the capacity of the backhaul link with the goal of maximizing the total spectral efficiency comprises:
and solving an optimization problem through an iterative optimization algorithm, and constructing a deep learning model to obtain an optimal solution so as to obtain optimal transmitting power and quantization error variance of each user data.
7. The method for power control and quantization for non-cellular massive MIMO according to claim 6, wherein said constructing a deep learning model to obtain an optimal solution comprises:
obtaining different channel state matrixes through traversalQ under maximized total spectral efficiency of time system u And N matrix, and integrating and converting it into two rows M-K columns matrix as deepA degree learning label; wherein the first line of data is the original Q u The matrix, the second row data is the original N matrix;
constructing a deep learning model, setting an input layer as a matrix of M x K columns, setting an output layer as a matrix of M x K columns, wherein a hidden layer comprises 2M x K neurons, selecting a sigmoid function by an activation function, and setting relevant learning parameters of the model;
each channel state information matrix corresponding to the deep learning labelConverting the matrix into M-by-K columns as sample data, dividing the sample data, and setting the model verification accuracy rule;
matrix channel state informationInputting into the model to obtain a first row vector and a second row vector of an output matrix, and sorting to obtain +.> and N* 。
8. A system for power control and quantization for non-cellular massive MIMO based on the method for power control and quantization for non-cellular massive MIMO according to any one of claims 1 to 7, comprising:
the channel estimation processing module is used for carrying out channel estimation on each access point and gathering information of channel estimation through a backhaul link after signal quantization;
the downlink data transmission module is used for carrying out power distribution and CB precoding on all access points in a downlink data transmission stage, and transmitting data to the access points after signal quantization;
the frequency spectrum efficiency calculation module is used for calculating frequency spectrum efficiency and total frequency spectrum efficiency through receiving signals;
and the data optimization module is used for obtaining the optimal data transmission power and signal quantization error variance under the constraint of limited total transmission power and backhaul link capacity with the aim of maximizing the total spectrum efficiency.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the processor, when executing the computer program, implements the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the method of any of claims 1 to 7.
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