A Deployment Method and System for DAC Heterogeneous Cell-Free Massive MIMO Access Points

Abstract

The present invention relates to the field of wireless communication technologies, and discloses a method and system for deploying a DAC heterogeneous cell-free massive MIMO access point. The method includes: constructing a cell-free massive MIMO system including two levels of access points, where the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs; obtaining the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distances between the access points and the users; aiming at maximizing the energy efficiency, obtaining the optimal access point density and the optimal access point quantization bits required for deploying the two levels of access points through an iterative alternating optimization algorithm. It can significantly reduce the cost required by the system and greatly improve the overall energy efficiency of the system without reducing the overall spectral efficiency of the system, making the final access point deployment more practical and having a very broad application prospect.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular, to a method and system for deploying a DAC heterogeneous cell-free massive MIMO access point. Background Art

[0002] In recent years, with the commercial deployment of 5G, the amount of user data has increased exponentially. However, the physical implementation problems encountered in increasing the antenna scale in a centralized system and the interference problems encountered in cell splitting have made the spectral efficiency of 5G systems unable to be continuously improved. To meet the more diverse future service applications and extreme performance requirements, it is necessary to break the traditional cellular architecture and the thinking mode of cell splitting, and adopt a new type of cell-free networking and corresponding large-scale cooperative multiple-input multiple-output transmission technology. This new type of networking technology, due to its flexible deployment characteristics and high-performance advantages, can be combined with various technologies and is widely applicable to various application scenarios, and has become an important trend in the development of sixth-generation mobile communication technologies.

[0003] However, when deploying a cell-free massive MIMO, although increasing the number of access points and antennas is more beneficial to performance improvement, it also requires an additional large number of wired backhaul links and backhaul data transmissions, resulting in a sharp increase in system energy consumption and cost. However, most of the current related research does not comprehensively consider system energy consumption and cost, so studying the access point deployment of cell-free massive MIMO should be the primary step.

[0004] Therefore, there is an urgent need for a deployment scheme with a reasonable line-of-sight propagation component that can reduce system energy consumption and cost. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for deploying a DAC heterogeneous cell-free massive MIMO access point to solve the problem that currently, an additional large number of wired backhaul links and backhaul data transmissions are required, resulting in a sharp increase in system energy consumption and cost.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for deploying a DAC heterogeneous cell-free massive MIMO access point, including:

[0009] Constructing a cell-free massive MIMO system including two levels of access points, where the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs;

[0010] Obtaining the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distances between the access points and the users;

[0011] With the goal of maximizing energy efficiency, the optimal access point density and the optimal number of access point quantization bits required for two levels of access point deployment are obtained through an iterative alternating optimization algorithm.

[0012] As a preferred solution of the DAC heterogeneous non-cellular massive MIMO access point deployment method of the present invention, wherein: the non-cellular massive MIMO system includes M access points with N antennas and K single-antenna users, wherein M1 access points are equipped with high-resolution DACs and M2 access points are equipped with low-resolution DACs;

[0013] M1 access points equipped with high-resolution DAC, M2 access points equipped with low-resolution DAC, and K users are respectively configured with densities λ1, λ2, and λ u The Poisson point process is randomly distributed in the plane.

[0014] As a preferred solution of the DAC heterogeneous non-cellular massive MIMO access point deployment method of the present invention, wherein: the sum of the transmission rates of all users obtained through line-of-sight propagation and non-line-of-sight propagation according to the distance between the access point and the user includes:

[0015] Calculate the path loss between the mth access point and the kth user. The path loss is a mixed LoS / NLoS multi-segment path loss and is expressed as:

[0016]

[0017] Among them, r mk is the distance between the mth access point and the kth user, α L and α NL Denote the path loss coefficients under LoS and NLoS respectively, p L (r mk ) and p NL (r mk ) are the probabilities of LoS and NLoS transmission, respectively, which can be expressed as:

[0018]

[0019] Among them, d L To define p L (r mk ) steepness parameter.

[0020] As a preferred solution of the DAC heterogeneous cell-free massive MIMO access point deployment method described in the present invention, where: all access points serve the k-th user, calculate the average achievable rate of the k-th user during the downlink transmission, and obtain the sum of the transmission rates of all users based on the average achievable rate.

[0021] As a preferred solution of the DAC heterogeneous cell-free massive MIMO access point deployment method described in the present invention, where: taking the maximum energy efficiency as the goal, which is expressed as:

[0022]

[0023] s.t. λ1≥0, λ2≥0

[0024]

[0025] where, the energy efficiency B represents the transmission bandwidth, is the total spectral efficiency of the downlink, where τ controls the length of the pilot sequence in each coherence interval T, P total is the total power consumption of the system downlink, b m is the DAC quantization accuracy of the m-th access point, Φ2 represents the set of access points equipped with low-resolution DACs, b TOT represents the DAC quantization bit number budget.

[0026] As a preferred solution of the DAC heterogeneous cell-free massive MIMO access point deployment method described in the present invention, where: decompose the maximum energy efficiency problem into two sub-problems, namely the access point density optimization and the quantization bit allocation optimization respectively. The sub-problems are solved by the gradient descent method, and the iterative alternating optimization algorithm is used to solve the access point density and the quantization bits.

[0027] As a preferred solution of the DAC heterogeneous cell-free massive MIMO access point deployment method described in the present invention, where: the sub-problems are solved by the gradient descent method, and the iterative alternating optimization algorithm is used to solve the access point density and the quantization bits, which specifically includes:

[0028] Step 1: Let the initial access point density vector be The initial access point quantization distortion factor is Search step sizes v1 = 0.1, v2 = 0.01, v3 = 1, maximum error ∈>0, maximum number of iterations γ = 200, and iteration variables t = 1, l = 1, e = 1;

[0029] Step 2: Calculate the gradient vector of the access point density through ;

[0030] Step 3: Through λt+1 = λ t + v1p t Calculate the access point density vector at the (t + 1)-th time and determine whether λ t+1 exceeds the constraint range. If it exceeds, calculate λ t+1 = λ t - v3p t ;

[0031] Step Four: Substitute λ t+1 into the calculation formula of energy efficiency to obtain f(λ t+1 );

[0032] Step Five: Let t = t + 1, λ e = λ t+1 , and repeat Step Two to Step Four until |f(λ t+1 ) - f(λ t )| < ∈ or the number of iterations t = γ;

[0033] Step Six: Calculate the gradient vector of the access point quantization damage factor through ;

[0034] Step Seven: + v2q l Calculate the access point quantization distortion factor vector at the (l + 1)-th time and determine whether α l+1 exceeds the constraint range. If it exceeds, calculate α l+1 = α l - v3q l ;

[0035] Step Eight: Substitute α l+1 into the calculation formula of energy efficiency to obtain f(α l+1 );

[0036] Step Nine: Let l = l + 1, α opt = α l+1 , and repeat Step Six to Step Eight until |f(α l+1 ) - f(α l )| < ∈ or the number of iterations l = γ;

[0037] Step Ten: Let e = e + 1, λ e = λ e-1 , α e = α opt , and repeat Step Two to Step Nine until |f(λ e , α e ) - f(λ e-1 , α e-1 )| < ∈ or the number of iterations e = γ;

[0038] Step Eleven: Finally, obtain the optimal access point density vector λ required for access point deploymentopt = λ e , and according to the optimal quantization loss factor α opt = α e map to obtain the optimal access point quantization bits;

[0039] In a second aspect, the present invention provides a DAC heterogeneous cell-free massive MIMO access point deployment system, including:

[0040] A construction module for constructing a cell-free massive MIMO system including two levels of access points, where the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs;

[0041] A first calculation module for obtaining the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distance between the access points and the users;

[0042] A second calculation module for obtaining the optimal access point density and the optimal access point quantization bits required for the deployment of the two levels of access points through an iterative alternating optimization algorithm with the goal of maximizing energy efficiency.

[0043] In a third aspect, the present invention provides a computing device, including:

[0044] A memory and a processor;

[0045] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the DAC heterogeneous cell-free massive MIMO access point deployment method are implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the DAC heterogeneous cell-free massive MIMO access point deployment method are implemented.

[0047] Compared with the prior art, the beneficial effects of the present invention: By configuring a heterogeneous cell-free network with two different types of APs coexisting and mixing high-resolution and low-resolution ADC / DACs, the present invention can significantly reduce the cost required by the system and greatly improve the overall energy efficiency of the system without reducing the overall spectral efficiency of the system. At the same time, the method of the present invention fully considers the randomness of the positions of the two levels of access points and users during design, and uses stochastic geometry and a multi-segment path loss model that simultaneously includes line-of-sight and non-line-of-sight propagation components for auxiliary modeling, making the final access point deployment more practical and capable of being effectively applied to actual communication scenarios; it provides a brand-new idea for related research and applications and has a very broad application prospect. Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic diagram of the overall process of the DAC heterogeneous cell-free massive MIMO access point deployment method described in the first embodiment of the present invention;

[0050] Figure 2 It is a curve graph showing the change of the average user downlink rate with the number of access point antennas in the DAC heterogeneous cell-free massive MIMO access point deployment method described in the first embodiment of the present invention;

[0051] Figure 3 It is a curve graph showing the change of energy efficiency with the access point density in the DAC heterogeneous cell-free massive MIMO access point deployment method described in the first embodiment of the present invention;

[0052] Figure 4 It is a curve graph showing the change of energy efficiency with the number of AP antennas under different optimization conditions in the DAC heterogeneous cell-free massive MIMO access point deployment method described in the first embodiment of the present invention. [[ID=__19]]Specific Embodiments

[0053] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will give a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0054] Embodiment 1

[0055] Refer to Figure 1 , which is an embodiment of the present invention, and provides a DAC heterogeneous cell-free massive MIMO access point deployment method, including,

[0056] S1: Construct a cell-free massive MIMO system including two levels of access points, and the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs;

[0057] In the embodiments of the present application, the cell-free massive MIMO system includes M access points with N antennas and K single-antenna users, where M1 access points are equipped with high-resolution DACs and M2 access points are equipped with low-resolution DACs;

[0058] M1 access points equipped with high - resolution DACs, M2 access points equipped with low - resolution DACs, and K users are randomly distributed in the plane according to Poisson point processes with densities λ1, λ2, and λ u respectively.

[0059] It should be noted that such a hierarchical design allows the system to adjust resources between different regions to adapt to different quality - of - service requirements and user distributions.

[0060] S2: Obtain the sum of the transmission rates of all users through line - of - sight (LoS) propagation and non - line - of - sight (NLoS) propagation respectively according to the distances between access points and users;

[0061] In the embodiment of the present application, obtaining the sum of the transmission rates of all users through line - of - sight (LoS) propagation and non - line - of - sight (NLoS) propagation respectively according to the distances between access points and users includes:

[0062] Calculate the path loss between the m - th access point and the k - th user. The path loss is a multi - segment path loss of LoS / NLoS mixture, expressed as:

[0063]

[0064] where r mk is the distance between the m - th access point and the k - th user, α L and α NL respectively represent the path - loss coefficients under LoS and NLoS, p L (r mk ) and p NL (r mk ) are the probabilities of LoS and NLoS transmissions respectively, specifically expressed as:

[0065]

[0066] where d L is the parameter defining the steepness of p L (r mk ).

[0067] In the embodiment of the present application, all access points provide services to the k - th user, calculate the average achievable rate of the k - th user in the downlink transmission process, and obtain the sum of the transmission rates of all users based on the average achievable rate.

[0068] Specifically, the average achievable rate of the k - th user is approximately:

[0069]

[0070] where ρ d is the average transmit power of the access point, α mis the quantization distortion factor of the low-resolution DAC at the access point end; D1, D2, D3, and D4 each represent a formula. To simplify the average achievable rate R of the k-th user k in its expression form.

[0071] It should be noted that in step S2, a stochastic geometry and a multi-segment path loss model that simultaneously includes line-of-sight and non-line-of-sight propagation components are used to assist in modeling, making the final access point deployment more practical, capable of being effectively applied to actual communication scenarios, and by considering the distance and propagation conditions between users and APs, resources can be more accurately allocated to avoid wasting resources on NLoS paths with poor signal quality.

[0072] S3: With the goal of maximizing energy efficiency, the optimal access point density and the optimal access point quantization bit number required for the two-level access point deployment are obtained through an iterative alternating optimization algorithm.

[0073] In the embodiment of this application, with the goal of maximizing energy efficiency, it is expressed as:

[0074]

[0075] s.t. λ1≥0, λ2≥0

[0076]

[0077] Among them, the energy efficiency B represents the transmission bandwidth, is the total downlink spectral efficiency, where τ controls the length of the pilot sequence in each coherence interval T, P total is the total power consumption of the system downlink, b m is the DAC quantization accuracy of the m-th access point, Φ2 represents the set of access points equipped with low-resolution DACs, b TOT represents the DAC quantization bit number budget.

[0078] In the embodiment of this application, the energy efficiency maximization problem is decomposed into two sub-problems, namely access point density optimization and quantization bit allocation optimization. The sub-problems are solved by the gradient descent method, and an iterative alternating optimization algorithm is used to solve the access point density and the quantization bit number.

[0079] In the embodiment of this application, the sub-problems are solved by the gradient descent method, and an iterative alternating optimization algorithm is used to solve the access point density and the quantization bit number, specifically including:

[0080] Step 1: Let the initial access point density vector be The initial access point quantization distortion factor is Search step sizes \(v1 = 0.1\), \(v2 = 0.01\), \(v3 = 1\), maximum error \(\epsilon>0\), maximum number of iterations \(\gamma = 200\), and iteration variables \(t = 1\), \(l = 1\), \(e = 1\);

[0081] Step two: Calculate the gradient vector of the access point density through ;

[0082] Step three: Calculate the \((t + 1)\)-th access point density vector through \(\lambda\) t+1 =\(\lambda\) t +v1p t , and determine whether \(\lambda\) t+1 exceeds the constraint range. If it does, calculate \(\lambda\) t+1 =\(\lambda\) t -v3p t ;

[0083] Step four: Substitute \(\lambda\) t+1 into the calculation formula of energy efficiency to obtain \(f(\lambda\) t+1 );

[0084] Step five: Let \(t = t + 1\), \(\lambda\) e =\(\lambda\) t+1 , and repeat steps two to four until \(|f(\lambda\) t+1 ) - f(\lambda\) t )| < \(\epsilon\) or the number of iterations \(t=\gamma\);

[0085] Step six: Calculate the gradient vector of the access point quantization damage factor through ;

[0086] Step seven: Calculate the \((l + 1)\)-th access point quantization distortion factor vector through \(\alpha\) l +v2q, and determine whether \(\alpha\) l+1 exceeds the constraint range. If it does, calculate \(\alpha\) l+1 =\(\alpha\) l -v3q l ;

[0087] Step eight: Substitute \(\alpha\) l+1 into the calculation formula of energy efficiency to obtain \(f(\alpha\) l+1 );

[0088] Step nine: Let \(l = l + 1\), \(\alpha\) opt =\(\alpha\) l+1 , and repeat steps six to eight until \(|f(\alpha\) l+1 ) - f(\alpha\) l )| < \(\epsilon\) or the number of iterations \(l=\gamma\);

[0089] Step ten: Let \(e = e + 1\), \(\lambda\) e =\(\lambda\) e-1 , \(\alpha\) e =\(\alpha\)opt , repeat steps 2 to 9 until |f(λ e ,α e )-f(λ e-1 ,α e-1 )|<∈ or number of iterations e=γ;

[0090] Step 11: Finally, the optimal access point density vector λ required for access point deployment is obtained opt =λ e , and according to the optimal quantization loss factor α opt =α e Mapping to obtain the optimal access point quantization bit number;

[0091] It should be noted that the optimized AP density and DAC quantization bit number can reduce unnecessary energy consumption and significantly lower operating costs for large-scale MIMO systems. Through optimization, the system can maximize the overall data transmission rate while ensuring a certain quality of service, thereby improving the efficiency and capacity of the entire network.

[0092] The above is a schematic scheme of a DAC heterogeneous cell-free massive MIMO access point deployment method of this embodiment. It should be noted that the technical solution of the DAC heterogeneous cell-free massive MIMO access point deployment system and the technical solution of the above-mentioned DAC heterogeneous cell-free massive MIMO access point deployment method are based on the same concept. For details not described in detail in the technical solution of the DAC heterogeneous cell-free massive MIMO access point deployment system in this embodiment, please refer to the description of the technical solution of the above-mentioned DAC heterogeneous cell-free massive MIMO access point deployment method.

[0093] The DAC heterogeneous non-cellular massive MIMO access point deployment system in this embodiment includes:

[0094] A building module for building a cell-free massive MIMO system including two levels of access points, wherein the two levels of access points are respectively equipped with a high-resolution DAC and a low-resolution DAC;

[0095] A first calculation module is configured to obtain the sum of transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation according to the distance between the access point and the user;

[0096] The second calculation module is used to obtain the optimal access point density and the optimal number of access point quantization bits required for two-level access point deployment through an iterative alternating optimization algorithm with the goal of maximizing energy efficiency.

[0097] This embodiment further provides a computing device applicable to deployment of DAC heterogeneous non-cellular massive MIMO access points, including:

[0098] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for deploying a DAC heterogeneous cell-free massive MIMO access point as proposed in the above embodiments.

[0099] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for deploying a DAC heterogeneous cell-free massive MIMO access point as proposed in the above embodiments.

[0100] The storage medium proposed in this embodiment and the method for deploying a DAC heterogeneous cell-free massive MIMO access point proposed in the above embodiments belong to the same inventive concept. For technical details not described in detail in this embodiment, reference can be made to the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0101] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present invention.

[0102] Embodiment 2

[0103] Refer to Figures 2 - 4 , based on the previous embodiment, this embodiment provides an application comparison case of a method for deploying a DAC heterogeneous cell-free massive MIMO access point to illustrate the feasibility and beneficial effects of our solution.

[0104] Figures 2 - 4 It is a graph of the average user downlink rate versus the number of access point antennas, a graph of the energy efficiency versus the access point density, and a graph of the energy efficiency versus the DAC quantization bit number for the access point deployment method in a DAC heterogeneous cell-free massive MIMO system.

[0105] Some of the parameters are: d L = 0.3 km, α L = 2, α NL = 3, ρ d= 1 W, B = 20 MHz.

[0106] In Figure 2 it is verified the accuracy of the analysis results of the present invention. The simulated achievable downlink rate is compared with the average downlink rate under different densities λ1, λ2 and the number of AP antennas N in the present invention. It can be seen from the figure that the simulation and the theory are highly close and follow a monotonically increasing trend, which proves the correctness of the average downlink rate in the present invention. In addition, it can also be found that the average downlink rate increases monotonically with the increase of the number of AP antennas or any density of the two sets of APs.

[0107] In Figure 3 in order to more clearly show the influence of the AP density on the energy efficiency, it is assumed that the densities of the two sets of APs are equal, i.e., λ1 = λ2. It can be seen that the energy efficiency curve is a concave function of the AP density. In particular, it can be seen from the figure that the energy efficiency increases with the increase of λ1 until a threshold is reached, after which the energy efficiency begins to decline at a stable rate. This is because when λ1 is small, the achievable downlink rate shows a continuous and stable increase, while the power consumption remains at a low level. However, when λ1 is large, too many APs will cause a sharp increase in the power consumption related to the APs. Therefore, there is an optimal AP density that can maximize the energy efficiency. Similarly, when the densities of the two sets of APs are different, there will also be an optimal combination of AP densities to maximize the energy efficiency.

[0108] In Figure 4 it gives the comparison of the energy efficiency after adopting the iterative alternating optimization algorithm of the present invention and the unoptimized energy efficiency. It can be seen from the figure that optimizing the AP density and the quantization bits can greatly improve the energy efficiency. In the case of only optimizing one of them, the effect of optimizing the AP density is better than that of optimizing the quantization bits.

[0109] In summary, a method for deploying access points in a DAC heterogeneous cell-free massive MIMO system proposed by the present invention can significantly reduce the cost required by the system and greatly improve the overall energy efficiency of the system without reducing the overall spectral efficiency of the system. At the same time, the method of the present invention fully considers the randomness of the two levels of access points and the user locations during the design, and uses random geometry and a multi-segment path loss model that simultaneously includes line-of-sight and non-line-of-sight propagation components to assist in modeling, making the final access point deployment more practical and capable of being effectively applied to actual communication scenarios.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A method for deploying a DAC heterogeneous cell-free massive MIMO access point, characterized in that Including: Construct a cell-free massive MIMO system including two levels of access points, where the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs; Obtain the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distance between the access points and the users; With the goal of maximizing energy efficiency, obtain the optimal access point density and the optimal access point quantization bits required for the deployment of the two levels of access points through an iterative alternating optimization algorithm; Among them, the goal of maximizing energy efficiency is expressed as: s.t.λ1≥0,λ2≥0 Among them, the energy efficiency B represents the transmission bandwidth, is the total downlink spectral efficiency, where τ controls the length of the pilot sequence in each coherence interval T, and P total is the total power consumption of the system's downlink, and b m is the DAC quantization accuracy of the m-th access point, Φ2 represents the set of access points equipped with low-resolution DACs, and b TOT represents the DAC quantization bit number budget; Decompose the energy efficiency maximization problem into two sub-problems, namely access point density optimization and quantization bit allocation optimization. The sub-problems are solved by the gradient descent method, and the iterative alternating optimization algorithm is used to solve the access point density and quantization bits; The sub-problems are solved by the gradient descent method, and the iterative alternating optimization algorithm is used to solve the access point density and quantization bits, specifically including: Step 1: Let the initial access point density vector be The initial access point quantization distortion factor is The search step sizes are v1 = 0.1, v2 = 0.01, v3 = 1, the maximum error ∈ > 0, the maximum number of iterations γ = 200, and the iteration variables are t = 1, l = 1, e = 1; Step 2: By calculate the gradient vector of the access point density; Step 3: Through λ t+1 = λ t + v1p t Calculate the access point density vector at t+1 times, and determine whether λ t+1 exceeds the constraint range. If it exceeds, calculate λ t+1 = λ t - v3p t ; Step 4: Substitute λ t+1 into the calculation formula of energy efficiency to obtain f(λ t+1 ); Step 5: Let t = t + 1, λ e = λ t+1 , repeat Steps 2 to 4 until |f(λ t+1 ) - f(λ t )| < ∈ or the number of iterations t = γ; Step 6: Through calculate the gradient vector of the access point quantization damage factor; Step Seven: +v2q l Calculate the quantization distortion factor vector of the access point l + 1 times, and judge α l+1 Whether it exceeds the constraint range. If it exceeds, calculate α l+1 = α l -v3q l ; Step Eight: Substitute α l+1 into the calculation formula of energy efficiency to obtain f(α l+1 ); Step Nine: Let l = l + 1, α opt = α l+1 , repeat Steps Six to Eight until |f(α l+1 ) - f(α l )| < ∈ or the iteration number l = γ; Step Ten: Let e = e + 1, λ e = λ e-1 , α e = α opt , repeat Steps Two to Nine until |f(λ e , α e ) - f(λ e-1 , α e-1 )| < ∈ or the number of iterations e = γ; Step Eleven: Finally, obtain the optimal access point density vector λ required for access point deployment opt = λ e , and map to obtain the optimal access point quantization bits according to the optimal quantization loss factor α opt = α e ​ 2. The method for deploying a DAC heterogeneous cell-free massive MIMO access point according to claim 1, characterized in that, The cell-free massive MIMO system includes M access points with N antennas and K single-antenna users, where M1 access points are equipped with high-resolution DACs and M2 access points are equipped with low-resolution DACs; M1 access points equipped with high-resolution DACs, M2 access points equipped with low-resolution DACs, and K users are randomly distributed in the plane according to Poisson point processes with densities λ1, λ2, and λ u , respectively.

3. The method for deploying a DAC heterogeneous cell-free massive MIMO access point according to claim 1 or 2, characterized in that The obtaining the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distance between the access points and the users includes: Calculate the path loss between the m-th access point and the k-th user, and the path loss is a multi-segment path loss of LoS / NLoS mixture, expressed as: where r mk is the distance between the m-th access point and the k-th user, α L and α NL represent the path loss coefficients in LoS and NLoS respectively, p L (r mk ) and p NL (r mk ) are the probabilities of LoS and NLoS transmissions respectively, and are specifically expressed as: Among them, d L is a parameter for defining the steepness of p L (r mk ).

4. The method for deploying a DAC heterogeneous cell-free massive MIMO access point according to claim 3, wherein, All access points serve the k-th user, calculate the average achievable rate of the k-th user during the downlink transmission process, and obtain the sum of the transmission rates of all users based on the average achievable rate.

5. A system applying the DAC heterogeneous cell-free massive MIMO access point deployment method as described in claim 1, characterized in that, Including: A construction module for constructing a cell-free massive MIMO system including two levels of access points, where the two levels of access points are respectively equipped with high-resolution DACs and low-resolution DACs; A first calculation module for obtaining the sum of the transmission rates of all users through line-of-sight propagation and non-line-of-sight propagation respectively according to the distance between the access points and the users; A second calculation module for obtaining the optimal access point density and the optimal access point quantization bits required for the deployment of the two levels of access points through an iterative alternating optimization algorithm with the goal of maximizing energy efficiency.

6. An electronic device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the DAC heterogeneous cell-free massive MIMO access point deployment method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium that stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the DAC heterogeneous cell-free massive MIMO access point deployment method described in any one of claims 1 to 4 are implemented.

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