A method for deploying an extended repeater network based on environment perception

Abstract

The present invention discloses an extended repeater network deployment method based on environment perception, comprising the following steps: step 1: building a mixed probability path loss model based on environment perception; step 2: establishing a 0-1 decision problem with the goal of maximizing average throughput; step 3: converting the decision problem into a convex optimization problem through fractional programming technology, and iteratively solving the optimal AP deployment strategy. The present invention combines the free space close-range reference distance model and the floating intercept model, models the path loss as a mixed probability path loss model, fully considers the impact of environmental factors on AP deployment, and then establishes the AP deployment problem as a 0-1 decision problem with the goal of maximizing average throughput, and finally converts the original problem into a convex optimization problem through fractional programming technology, and iteratively solves the optimal AP deployment strategy through an algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to an extended repeater network deployment method based on environment perception. Background Art

[0002] The extended repeater network separates the traditional base station baseband processing unit (BBU) and the radio frequency processing unit (RRU). The radio frequency processing unit is only responsible for the transmission and reception of wireless signals and is called the repeater access point (AP); the base station processing unit is responsible for processing the digital signals sent or received by all APs and is called the centralized processing unit (CPU). The two are connected through an optical fiber or microwave backhaul link. For traditional distributed base stations, different RRUs use different time or frequency resources for transmission. The extended repeater network deploys a large number of APs in the coverage area, using the same time and frequency resources to serve multiple users, and uses the backhaul link to connect to the CPU concentrated in the computer room to achieve collaborative transmission and flexible scheduling of multiple APs, thereby significantly improving performance.

[0003] In the existing research on extended repeater networking, it is usually assumed that APs in the coverage area are uniformly and randomly deployed. However, this assumption is too idealistic and does not conform to the actual deployment of the network. In the actual environment of extended repeater system application, due to the geographical distribution of APs and the influence of various obstacles (for example, buildings and trees in urban areas), the different distribution locations of APs may cause the channels between users to experience different path losses, which in turn leads to differences in the service quality of users in the coverage area, and the communication quality of some users is seriously reduced.

[0004] Therefore, an extended repeater network deployment method based on environment perception is proposed to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an extended repeater network deployment method based on environment perception to solve the above technical problems.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] An extended repeater network deployment method based on environment perception comprises the following steps:

[0008] Step 1: Build a hybrid probabilistic path loss model based on environment perception;

[0009] Step 2: Establish a 0-1 decision problem with the goal of maximizing the average throughput;

[0010] Step 3: Use fractional programming technology to transform the decision problem into a convex optimization problem and iteratively solve the optimal AP deployment strategy.

[0011] Furthermore, the specific steps of step 1 are as follows:

[0012] Step 1.1: Consider the impact of path loss when deploying APs. The large-scale fading coefficient is expressed as follows:

[0013]

[0014] Among them, PL mk represents the path loss between the mth AP and the kth user, Denote the standard deviation as σ sh The shadow of mk ~CN(0,1);

[0015] Step 1.2: In the actual propagation environment, the channel between the AP and the user may be LOS or NLOS. Considering the influence of geographical distribution parameters in the area, the probability of LOS link is as follows:

[0016]

[0017] in, γ represents the average height of the blockage, α is the fraction of the building area, μ represents the average number of blockages per unit area, and ε m and ε′ k represent the average height of AP and user respectively;

[0018] Step 1.3: Use the LOS link probability expressed in formula (2) to weight the LOS and NLOS path losses. The mixed probability path loss model is expressed as:

[0019] PL prob [dB](d)=P LOS ×PL LOS (d)+(1-P LOS )×PL NLOS (d), (3)

[0020] Among them, PL LOS (d) represents the path loss under LOS path, PL NLOS (d) represents the path loss under the NLOS path, and the two are expressed as follows;

[0021]

[0022]

[0023] Where λ is the carrier wavelength, d is the three-dimensional TR separation distance, and n LOS and n NLOSdenote the average path loss exponents under LOS path and NLOS path, respectively; α and ζ denote the intercept and slope of the floating intercept model parameters, respectively.

[0024] Furthermore, the specific steps of step 2 are as follows:

[0025] Step 2.1: Optimize the location of AP deployment based on the criterion of maximizing average throughput, and express the AP deployment decision vector as L = {l1,l2,...,l N} T , where l n is a binary decision vector, and the AP deployment on the grid point is expressed as follows:

[0026]

[0027] The vector product related to the AP position is expressed as follows:

[0028]

[0029] The AP deployment problem with the goal of maximizing the mean square error of average throughput is expressed as follows:

[0030]

[0031] Among them, C1 indicates that the power of a single user needs to be greater than the minimum power threshold constraint, and C2 indicates the AP deployment constraint. AP deployment is limited to a limited area.

[0032] Furthermore, the specific steps of step 3 are as follows:

[0033] Step 3.1: Use fractional programming techniques to transform the AP deployment problem into a convex optimization problem. The achievable rate of the kth user shown in formula (7) is equivalent to

[0034]

[0035] Among them, γ k and δ k is an auxiliary variable corresponding to each user k, and

[0036]

[0037] Assuming that each user satisfies the minimum power constraint C1 and relaxes the constraint C2, problem (8) can be rewritten as:

[0038]

[0039] The Lagrange multiplier method is used to optimize L;

[0040] Step 3.2: Given a variable L, Set it to 0 to get the optimal solution It is expressed as follows:

[0041]

[0042] By Set it to 0 to get the optimal solution It is expressed as follows:

[0043]

[0044] When the auxiliary variable γ k and δ k When fixed, the optimal AP deployment solution L can be obtained by formula (12), and the Lagrange multiplier method is used to relax the constraint {0≤l n ≤1}, define υ n is the Lagrangian multiplier associated with the constraint, and the Lagrangian function associated with the AP deployment scheme can be expressed as:

[0045]

[0046] By Set to 0, the optimal AP deployment solution L can be obtained by solving the linear equation system given by the following formula

[0047]

[0048] The optimization process (12)-(15) is completed through multiple iterations, and each iteration adjusts γ k , δ k and L for update;

[0049] In order to simulate a more realistic user distribution, a Gaussian mixture model is used to generate user locations, as shown in the following formula:

[0050]

[0051] Among them, the average position μ1=[0.45,0.55] T km, μ2 = [0.85, 0.55] T km, μ3 = [0.55, 0.25] T km, weight Variance σ k =200, k=1,2,3.

[0052] Furthermore, the optimal AP deployment strategy L is generated by iteration through Algorithm 1, and the process of Algorithm 1 is as follows:

[0053] Input: user location, number of APs m

[0054] Initialization: AP deployment strategy L

[0055] repeat

[0056] Update according to formula (13)

[0057] Update according to formula (14)

[0058] Fixed auxiliary variable γ k and δ k , calculate L according to formula (16);

[0059] Update υ using the subgradient method;

[0060] until and convergence

[0061] Set the first m maximum values ​​of L obtained by solving the problem to 1, and the others to 0;

[0062] Output: Optimal AP deployment strategy L opt .

[0063] The beneficial effects of the present invention are as follows:

[0064] The present invention combines the free space close-range reference distance model and the floating intercept model, models the path loss as a mixed probability path loss model, fully considers the impact of environmental factors on AP deployment, and then establishes the AP deployment problem as a 0-1 decision problem with the goal of maximizing average throughput. Finally, the original problem is transformed into a convex optimization problem through fractional programming technology, and the optimal AP deployment strategy is solved iteratively through the algorithm. Simulation results show that compared with traditional random deployment and existing AP deployment algorithms, the AP deployment strategy proposed in the present invention can meet uniform coverage and improve system performance, and can provide users with higher service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 Schematic diagram of the non-cellular massive MIMO system model of the present invention.

[0066] Figure 2 This is the cumulative distribution diagram of the single-user achievable rate when m=100 and k=30 according to the algorithm of the present invention. DETAILED DESCRIPTION

[0067] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0068] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0069] The examples are as follows:

[0070] See also Figure 1-2 , an extended repeater network deployment method based on environment perception, comprising the following steps:

[0071] Step 1: Build a hybrid probabilistic path loss model based on environment perception;

[0072] The specific steps of step 1 are as follows:

[0073] Step 1.1: APs at different locations will cause users to experience different path losses and shadow fading due to differences in surrounding scatterers and environmental shielding. Shadow fading is caused by changes in the environment when the user moves, and it changes slowly in logarithmic distribution. Path loss varies greatly depending on environmental differences and is the main reason affecting system performance. Consider the impact of path loss when deploying APs. The large-scale fading coefficient is expressed as follows:

[0074]

[0075] Among them, PL mk represents the path loss between the mth AP and the kth user (UE), Denote the standard deviation as σ sh The shadow of mk ~CN(0,1);

[0076] Step 1.2: In the actual propagation environment, the channel between the AP and the user may be line of sight (LOS) or non line of sight (NLOS), which mainly depends on whether there are obstacles between the two and the height of the obstacles. Assuming that in a specific area, the probability of the LOS link between the AP and the user depends on the height of the two and the geographical distribution parameters in the area, the ITU blocking model can give a complete model of the LOS link probability. The LOS link probability is as follows:

[0077]

[0078] in, γ represents the average height of the blockage, α is the fraction of the building area, μ represents the average number of blockages per unit area, and ε m and ε′ k represent the average height of AP and user respectively;

[0079] Step 1.3: The traditional long-range propagation path loss model is prone to path loss estimation bias outside the given measurement range. The LOS and NLOS models are combined to improve the path loss estimation. The free space close-range reference distance model (for the LOS component) and the floating intercept model (for the NLOS component) are used to combine LOS and NLOS propagation, respectively. The LOS link probability expressed in formula (2) is used to weight the LOS and NLOS path losses. The hybrid probability path loss model is expressed as:

[0080] PL prob [dB](d)=P LOS ×PL LOS (d)+(1-P LOS )×PL NLOS (d), (3)

[0081] Among them, PL LOS (d) represents the path loss under LOS path, PL NLOS (d) represents the path loss under the NLOS path, and the two are expressed as follows;

[0082]

[0083] Where λ is the carrier wavelength, d is the three-dimensional TR separation distance, and n LOS and n NLOS denote the average path loss index under LOS path and NLOS path, respectively, α and ζ denote the intercept and slope of the floating intercept model parameters, respectively;

[0084] Formula (3) shows that when the probability of LOS link between AP and user is greater, the path loss between AP and user is smaller, and vice versa. Therefore, when deploying AP, we should try to deploy AP in the position where the probability of LOS link between AP and user is greater, which can improve the communication performance between AP and user.

[0085] Step 2: Establish a 0-1 decision problem with the goal of maximizing the average throughput;

[0086] The specific steps of step 2 are as follows:

[0087] Step 2.1: Given the known user locations and the transmit power constraints of each user, in order to provide uniform coverage to users in the coverage area, it is proposed to optimize the location of AP deployment based on the criterion of maximizing average throughput. In order to facilitate the problem, the continuous two-dimensional space is discretized into grid points with a certain granularity, and it is assumed that APs can only be placed on preset grid points. Let N = {1,2,...N} represent the set of all grid points, and N >> M. The degree of refinement of the grid depends on the size of N, which in turn affects the accuracy of the optimal AP location deployed on the grid. The AP deployment decision vector is represented as L = {l1,l2,...,l N} T , where l n is a binary decision vector, and the AP deployment on the grid point is expressed as follows:

[0088]

[0089] The vector product related to the AP position is expressed as follows:

[0090]

[0091] The AP deployment problem with the goal of maximizing the mean square error of average throughput is expressed as follows:

[0092]

[0093] Among them, C1 indicates that the power of a single user needs to be greater than the minimum power threshold constraint, and C2 indicates the AP deployment constraint. AP deployment is limited to a limited area.

[0094] Step 3: Use fractional programming technology to transform the decision problem into a convex optimization problem and iteratively solve the optimal AP deployment strategy;

[0095] The specific steps of step 3 are as follows:

[0096] Step 3.1: According to formula (8), the AP deployment problem of the present invention belongs to a non-convex 0-1 integer programming problem. In order to solve this problem, the key idea is to use fractional programming technology to convert it into a convex optimization problem, and then optimize the AP deployment solution through an iterative convex optimization algorithm. The achievable rate of the kth user shown in formula (7) is equivalent to

[0097]

[0098] Among them, γ k and δ k is an auxiliary variable corresponding to each user k, and

[0099]

[0100] In the decellularized massive MIMO system, since a large number of APs provide services to users, assuming that each user satisfies the minimum power constraint C1 and the constraint C2 is relaxed, problem (8) can be rewritten as:

[0101]

[0102] The above problem is a convex optimization problem, and the Lagrange multiplier method is used to optimize L;

[0103] Step 3.2: Given a variable L, Set it to 0 to get the optimal solution It is expressed as follows:

[0104]

[0105] By Set it to 0 to get the optimal solution It is expressed as follows:

[0106]

[0107] When the auxiliary variable γ k and δ k When fixed, the optimal AP deployment solution L can be obtained by formula (12), and the Lagrange multiplier method is used to relax the constraint {0≤l n ≤1}, define υ n is the Lagrangian multiplier associated with the constraint, and the Lagrangian function associated with the AP deployment scheme can be expressed as:

[0108]

[0109] By Set to 0, the optimal AP deployment solution L can be obtained by solving the linear equation system given by the following formula

[0110]

[0111] The optimization process (12)-(15) is completed through multiple iterations, and each iteration adjusts γ k , δ k and L for update;

[0112] The present invention compares the proposed AP deployment scheme with a random AP deployment scheme, a Lloyd-based AP deployment scheme, and an AP deployment scheme based on maximization and rate;

[0113] The present invention considers a 1×1km 2 There are m APs and k users in the area. The area is divided into coverage units of the same size, and it is assumed that APs can only be placed in pre-set coverage units. In order to simulate a more realistic user distribution, a Gaussian mixture model is used to generate user locations, as shown in the following formula:

[0114]

[0115] Among them, the average position μ1=[0.45,0.55] T km, μ2 = [0.85, 0.55] T km, μ3 = [0.55, 0.25] T km, weight Variance σ k =200, k=1,2,3, some simulation parameter settings are shown in Table 1;

[0116] Table 1 Some simulation parameters

[0117]

[0118]

[0119] The optimal AP deployment strategy L is generated by iteratively using Algorithm 1. The process of Algorithm 1 is as follows:

[0120] Input: user location, number of APs m

[0121] Initialization: AP deployment strategy L

[0122] repeat

[0123] Update according to formula (13)

[0124] Update according to formula (14)

[0125] Fixed auxiliary variable γ k and δ k, calculate L according to formula (16);

[0126] Update υ using the subgradient method;

[0127] until and convergence

[0128] Set the first m maximum values ​​of L obtained by solving the problem to 1, and the others to 0;

[0129] Output: Optimal AP deployment strategy L opt .

[0130] In summary: the present invention combines the free space close-range reference distance model and the floating intercept model, models the path loss as a mixed probability path loss model, fully considers the impact of environmental factors on AP deployment, and then establishes the AP deployment problem as a 0-1 decision problem with the goal of maximizing average throughput. Finally, the original problem is transformed into a convex optimization problem through fractional programming technology, and the optimal AP deployment strategy is iteratively solved through Algorithm 1. The simulation results show that compared with traditional random deployment and existing AP deployment algorithms, the AP deployment strategy proposed in the present invention can meet uniform coverage and improve system performance, and can provide users with higher service quality.

[0131] The basic principles and main features of the present invention and the advantages of the present invention are shown and described above. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention, and any figure marks in the claims should not be regarded as limiting the claims involved.

[0132] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An extended repeater network deployment method based on environment perception, It is characterized in that The following steps are involved: Step 1: Build a hybrid probabilistic path loss model based on environment perception; Step 2: Establish a 0-1 decision problem with the goal of maximizing the average throughput; Step 3: Use fractional programming technology to transform the decision problem into a convex optimization problem and iteratively solve the optimal AP deployment strategy; The specific steps of step 1 are as follows: Step 1.1: Consider the impact of path loss when deploying APs. The large-scale fading coefficient is expressed as follows: Among them, PL mk represents the path loss between the mth AP and the kth user, Denote the standard deviation as σ sh The shadow of Step 1.2: In the actual propagation environment, the channel between the AP and the user may be LOS or NLOS. Considering the influence of geographical distribution parameters in the area, the probability of LOS link is as follows: in, γ represents the average height of the blockage, α is the fraction of the building area, μ represents the average number of blockages per unit area, and ε m and ε k ′ represents the average height of AP and user respectively; Step 1.3: Use the LOS link probability expressed in formula (2) to weight the LOS and NLOS path losses. The mixed probability path loss model is expressed as: PL prob [dB](d)=P LOS ×PL LOS (d)+(1-P LOS )×PL NLOS (d), (3) Among them, PL LOS (d) represents the path loss under LOS path, PL NLOS (d) represents the path loss under the NLOS path, and the two are expressed as follows; Where λ is the carrier wavelength, d is the three-dimensional TR separation distance, and n LOS and n NLOS denote the average path loss index under LOS path and NLOS path, respectively, α and ζ denote the intercept and slope of the floating intercept model parameters, respectively; The specific steps of step 2 are as follows: Step 2.1: Optimize the location of AP deployment based on the criterion of maximizing average throughput, and express the AP deployment decision vector as L = {l 1 , l 2 , ..., l N } T , where l n is a binary decision vector, and the AP deployment on the grid point is expressed as follows: The vector product related to the AP position is expressed as follows: The AP deployment problem with the goal of maximizing the mean square error of average throughput is expressed as follows: s.t.C1:R K ≥R min C2:I n ∈{0,1} N ,n=1,..,N, (8) Among them, C1 indicates that the power of a single user needs to be greater than the minimum power threshold constraint, and C2 indicates the AP deployment constraint. AP deployment is limited to a limited area.

2. According to the method for deploying an extended repeater network based on environment perception according to claim 1, It is characterized in that The specific steps of step 3 are as follows: Step 3.1: Use fractional programming techniques to transform the AP deployment problem into a convex optimization problem. The achievable rate of the kth user shown in formula (7) is equivalent to Among them, γ k and δ k is an auxiliary variable corresponding to each user k, and Assuming that each user satisfies the minimum power constraint C1 and relaxes the constraint C2, problem (8) can be rewritten as: s.t.0≤l n ≤1,n=1,...,N, (11) The Lagrange multiplier method is used to optimize L; Step 3.2: Given a variable L, Set to 0 to obtain the optimal solution It is expressed as follows: By Set to 0 to obtain the optimal solution It is expressed as follows: When the auxiliary variable γ k and δ k When fixed, the optimal AP deployment solution L is obtained by formula (12), and the Lagrange multiplier method is used to relax the constraint {0≤l n ≤1}, define υ n is the Lagrangian multiplier associated with the constraint, and the Lagrangian function associated with the AP deployment scheme is expressed as: By Set to 0, the optimal AP deployment solution L is obtained by solving the linear equation system given by the following formula: The above optimization process (12)-(15) is completed through multiple iterations, and each iteration is to adjust γ k , δ k and L for update; In order to simulate a more realistic user distribution, a Gaussian mixture model is used to generate user locations, as shown in the following formula: Among them, the average position μ 1 =[0.45,0.55] T km,μ 2 =[0.85,0.55] T km,μ 3 =[0.55,0.25] T km, weight Variance σ k =200, k=1,2,3.

3. According to the method for deploying an extended repeater network based on environment perception according to claim 2, It is characterized in that The optimal AP deployment strategy L is generated by iteration through Algorithm 1. The process of Algorithm 1 is as follows: Input: user location, number of APs m Initialization: AP deployment strategy L repeat Update according to formula (13) Update according to formula (14) Fixed auxiliary variable γ k and δ k , calculate L according to formula (16); Update υ using the subgradient method; until and convergence Set the first m maximum values ​​of L obtained by solving the problem to 1, and the others to 0; Output: Optimal AP deployment strategy L opt .

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