CN116405946A

CN116405946A - Network deployment optimization method, device, electronic equipment and storage medium

Info

Publication number: CN116405946A
Application number: CN202310396225.2A
Authority: CN
Inventors: 古强; 荣志超
Original assignee: Shanghai Wuqi Microelectronics Co Ltd
Current assignee: Shanghai Wuqi Microelectronics Co Ltd
Priority date: 2023-04-13
Filing date: 2023-04-13
Publication date: 2023-07-07
Anticipated expiration: 2043-04-13
Also published as: CN116405946B

Abstract

The present invention provides a network deployment optimization method, device, electronic equipment, and storage medium. When grid deployment is performed, the basic network deployment information corresponding to the target scene is obtained first; then based on the basic network deployment information and preset constraints, the The Viterbi algorithm determines the target deployment result corresponding to the target scene that reaches the preset throughput threshold; where the preset constraints include that the K optimal AP positions selected from multiple candidate AP positions should meet the preset distance constraints Conditions, while making K the smallest, the highest coverage of the entire network and the largest throughput of the entire network. In this way, according to the characteristics of the 2B scene, the Viterbi algorithm is used to reduce the complexity of optimization. On the premise of ensuring network coverage, it maximizes the throughput of the entire network and minimizes the number of APs used, thereby ensuring the reliability and fluency of network coverage. At the same time, the economy of network deployment is guaranteed.

Description

Network deployment optimization method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a network deployment optimization method, a device, an electronic device, and a storage medium.

Background

The wireless communication technology plays an increasingly important role in a 2C scene, and more industrial applications depend on the wireless communication technology in various scenes, such as unmanned control of ports, real-time monitoring of factories, video feedback and machine tool control, robot control, full automation of large warehouses, large office areas, large live broadcast platform sites and the like, and the reliability, full coverage and high-speed connection capability of wireless networks are required. Therefore, wi-Fi and other wireless technologies are increasingly widely applied to 2B scenes of large wireless local area networks. Wherein, the 2C scene refers to a scene using a 2C network, and the 2C network refers to a network which is oriented to mass users and mainly uses personal applications; the 2B scenario refers to a scenario using a 2B network, and the 2B network refers to a network mainly for non-personal applications, such as industry, organization, enterprise, industry, etc.

However, the space is larger in the 2B scene, the environment is more complex, and how to ensure the reliability and fluency of network coverage and the economical efficiency of network deployment is a problem to be solved urgently.

Disclosure of Invention

The invention aims to provide a network deployment optimization method, a network deployment optimization device, electronic equipment and a storage medium, so that the reliability and fluency of network coverage are ensured, and the economical efficiency of network deployment is ensured.

In a first aspect, an embodiment of the present invention provides a network deployment optimization method, including:

acquiring network deployment basic information corresponding to a target scene; the network deployment basic information comprises a three-dimensional model and a plurality of alternative AP positions;

based on the network deployment basic information and a preset constraint condition, determining a target deployment result reaching a preset throughput threshold corresponding to the target scene by adopting a Viterbi algorithm; the preset constraint conditions comprise that K optimal AP positions selected from the multiple candidate AP positions are required to meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is highest.

Further, the determining, by using a viterbi algorithm, a target deployment result that reaches a preset throughput threshold corresponding to the target scene based on the network deployment basic information and a preset constraint condition includes:

acquiring the current value of the initialized AP quantity;

determining a current deployment result corresponding to the current value based on the network deployment basic information and a Viterbi algorithm; the current deployment result comprises an optimal AP position combination and an optimal parameter combination of each target AP position in the optimal AP position combination, and the overall network coverage rate and the overall network throughput corresponding to the optimal AP position combination under the corresponding optimal parameter combination are the highest;

When the throughput of the whole network corresponding to the current deployment result does not reach the preset throughput threshold, updating the current value according to a preset step length;

when the whole network throughput corresponding to the current deployment result reaches the preset throughput threshold, determining the current deployment result as a target deployment result corresponding to the target scene.

Further, the determining, based on the network deployment basic information and the viterbi algorithm, a current deployment result corresponding to the current value includes:

determining a plurality of candidate AP positions corresponding to each to-be-deployed AP in the current value to-be-deployed APs based on the plurality of candidate AP positions and the distance constraint condition;

based on the three-dimensional model and a ray tracing technology, determining an optimal parameter combination corresponding to each candidate AP position of each AP to be deployed; the total channel capacity corresponding to the candidate AP position under the corresponding optimal parameter combination is the largest;

determining a metric value corresponding to each AP to be deployed; the measurement value is composed of total channel capacity corresponding to each candidate AP position under the corresponding optimal parameter combination;

and determining an optimal AP position combination and a corresponding optimal parameter combination corresponding to each AP to be deployed through a Viterbi algorithm based on the metric value corresponding to each AP to be deployed.

Further, the determining, based on the plurality of candidate AP positions and the distance constraint condition, a plurality of candidate AP positions corresponding to each AP to be deployed in the current value of APs to be deployed includes:

based on the plurality of alternative AP positions and the distance constraint condition, determining a plurality of AP position combinations corresponding to the current value to-be-deployed APs;

and determining a plurality of candidate AP positions corresponding to each AP to be deployed based on the plurality of AP position combinations.

Further, the determining, based on the three-dimensional model and the ray tracing technology, an optimal parameter combination corresponding to each candidate AP position of each AP to be deployed includes:

for each candidate AP position of each AP to be deployed, calculating to obtain the corresponding total channel capacity of the AP to be deployed under each parameter combination of the candidate AP position based on the three-dimensional model and the ray tracing technology;

and determining the parameter combination with the maximum total channel capacity as the optimal parameter combination corresponding to the candidate AP position of the AP to be deployed.

Further, the network deployment basic information further comprises a gridding result, wherein the gridding result comprises a plurality of grids obtained by carrying out networking processing on the plane graph of the target scene; the calculating, based on the three-dimensional model and the ray tracing technology, a total channel capacity corresponding to the AP to be deployed under each parameter combination of the candidate AP position includes:

For each parameter combination, calculating to obtain corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed by utilizing a ray tracing technology based on the three-dimensional model; the ray parameter information comprises the number of rays, the amplitude of each ray and time delay information;

according to the corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed, calculating to obtain the channel capacity from the AP to be deployed to each grid under the parameter combination of the candidate AP position;

and calculating the total channel capacity of the to-be-deployed AP under the parameter combination of the candidate AP position according to the channel capacity of the to-be-deployed AP to each grid under the parameter combination of the candidate AP position.

Further, the determining, by a viterbi algorithm, an optimal AP position combination and a corresponding optimal parameter combination corresponding to each AP to be deployed based on the metric value corresponding to each AP to be deployed includes:

ordering the APs to be deployed according to the position relation among the APs to be deployed to obtain an AP ordering result;

according to the AP sequencing result, respectively combining optimal parameters corresponding to each candidate AP position of a first AP to be deployed into a first path node, and sequentially selecting a next path node based on the metric value and the reservation maximum value principle corresponding to each AP to be deployed to obtain a plurality of surviving paths;

Screening target surviving paths in a plurality of surviving paths to obtain the optimal AP position combination and the corresponding optimal parameter combination; and the whole network throughput corresponding to the target surviving path is maximum.

In a second aspect, an embodiment of the present invention further provides a network deployment optimization device, including:

the acquisition module is used for acquiring network deployment basic information corresponding to the target scene; the network deployment basic information comprises a three-dimensional model and a plurality of alternative AP positions;

the determining module is used for determining a target deployment result reaching a preset throughput threshold corresponding to the target scene by adopting a Viterbi algorithm based on the network deployment basic information and a preset constraint condition; the preset constraint conditions comprise that K optimal AP positions selected from the multiple candidate AP positions are required to meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is highest.

In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can run on the processor, and the processor implements the network deployment optimization method according to the first aspect when executing the computer program.

In a fourth aspect, an embodiment of the present invention further provides a storage medium, where a computer program is stored, where the computer program is executed by a processor to perform the network deployment optimization method according to the first aspect.

According to the network deployment optimization method, the network deployment optimization device, the electronic equipment and the storage medium, when grid deployment is carried out, network deployment basic information corresponding to a target scene is acquired; the network deployment basic information comprises a three-dimensional model and a plurality of alternative AP positions; determining a target deployment result reaching a preset throughput threshold corresponding to a target scene by adopting a Viterbi algorithm based on the network deployment basic information and a preset constraint condition; the preset constraint conditions comprise that K optimal AP positions selected from a plurality of alternative AP positions meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is maximum. Therefore, aiming at the characteristics of the 2B scene, the Viterbi algorithm is adopted to reduce the optimizing complexity, and on the premise of ensuring network coverage, the throughput of the whole network is maximized and the use quantity of the APs is minimized, so that the reliability and fluency of the network coverage are ensured, and meanwhile, the economical efficiency of network deployment is ensured.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a network deployment optimization method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of three-dimensional models corresponding to an indoor scene provided by an embodiment of the present invention;

fig. 3 is a schematic diagram of two three-dimensional models corresponding to an outdoor scene according to an embodiment of the present invention;

FIG. 4 is a diagram of a gridding result according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of a network deployment optimizing device according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

At present, wi-Fi and other wireless technologies are increasingly widely applied to 2B scenes of large wireless local area networks. Aiming at the network deployment problem in the 2B scene, the network deployment optimization method, the device, the electronic equipment and the storage medium provided by the embodiment of the invention adopt the network planning optimization technology in the 2B scene, namely the Viterbi algorithm is adopted to reduce the optimizing complexity, can be used for network deployment and planning optimizing in the 2B scene, realize large-scale AP (Access Point) deployment in the 2B scene and enhance the reliability of network coverage; the network rate can be maximized and the use quantity of the APs can be minimized on the premise of ensuring network coverage, so that the reliability and fluency of the network coverage are ensured, and the economical efficiency of network deployment is also ensured.

For the sake of understanding the present embodiment, a detailed description is first provided of a network deployment optimization method disclosed in the present embodiment.

The embodiment of the invention provides a network deployment optimization method which can be executed by electronic equipment with data processing capability. Referring to a flow chart of a network deployment optimization method shown in fig. 1, the method mainly includes the following steps S102 to S104:

Step S102, obtaining network deployment basic information corresponding to a target scene; wherein the network deployment basis information includes a three-dimensional model and a plurality of alternative AP locations.

The target scene mayBut are not limited to, 2B scenes, for example, target scenes including large offices, large factories/super factories, ports, steel works, shops, coal mines, etc. The three-dimensional model is obtained by three-dimensional modeling of a target scene; three-dimensional modeling can be performed on an indoor scene by using 3D modeling software, and the obtained three-dimensional model comprises walls, furniture, tables and chairs, lamps, green plants and other objects capable of reflecting electromagnetic waves, as shown in FIG. 2; the outdoor scene can also be modeled in three dimensions, and the obtained three-dimensional model comprises buildings such as houses and the like, as shown in fig. 3. Multiple alternative AP locations are known, i.e. two-dimensional coordinates are known<x,y>The purpose of the embodiment of the invention is to select K optimal position parameters from M candidate AP positions through an optimization algorithm, wherein K is as small as possible, the throughput of the whole network is maximum, and the optimal position parameters comprise the optimal AP position and parameter combinations thereof (three-dimensional coordinates of the AP)<x,y,z>And the downtilt phi and azimuth angle of the antenna

I.e. the parameter to be optimized is +.>

)。

It should be noted that the network device deployed above is not limited to Wi-Fi AP, and in other embodiments, the network device may be a cell base station or a network planning of a cellular 4G/5G base station in a city scenario.

Step S104, determining a target deployment result reaching a preset throughput threshold corresponding to a target scene by adopting a Viterbi algorithm based on the network deployment basic information and a preset constraint condition; the preset constraint conditions comprise that K optimal AP positions selected from a plurality of alternative AP positions meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is maximum.

The distance constraint condition refers to that the distance between any two APs to be deployed is greater than a preset distance threshold value. In some possible embodiments, the step S104 may be implemented by the following procedure: firstly, obtaining the current value of the initialized AP quantity; then, based on the network deployment basic information and the Viterbi algorithm, determining a current deployment result corresponding to the current value; the current deployment result comprises an optimal AP position combination and an optimal parameter combination of each target AP position in the optimal AP position combination, wherein the coverage rate of the whole network corresponding to the optimal AP position combination under the corresponding optimal parameter combination is highest and the throughput of the whole network is maximum; when the throughput of the whole network corresponding to the current deployment result does not reach a preset throughput threshold, updating the current value according to a preset step length; when the throughput of the whole network corresponding to the current deployment result reaches a preset throughput threshold, determining the current deployment result as a target deployment result corresponding to a target scene.

A smaller value may be initialized as the current value of the number of APs, for example, the initialized current value is 3. The preset step length can be set according to actual requirements, for example, if the preset step length is 2, the sum of the current value and 2 is used as the updated current value. Thus, by continuously updating the current value, the target deployment result corresponding to the minimum AP number which ensures the highest coverage rate and the highest throughput of the whole network and reaches the preset throughput threshold can be found.

According to the network deployment optimization method provided by the embodiment of the invention, when grid deployment is carried out, network deployment basic information corresponding to a target scene is acquired first; determining a target deployment result reaching a preset throughput threshold corresponding to a target scene by adopting a Viterbi algorithm based on the network deployment basic information and a preset constraint condition; the preset constraint conditions comprise that K optimal AP positions selected from a plurality of alternative AP positions meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is maximum. Therefore, aiming at the characteristics of the 2B scene, the Viterbi algorithm is adopted to reduce the optimizing complexity, and on the premise of ensuring network coverage, the throughput of the whole network is maximized and the use quantity of the APs is minimized, so that the reliability and fluency of the network coverage are ensured, and meanwhile, the economical efficiency of network deployment is ensured.

The embodiment of the invention can reconstruct a wireless channel by utilizing a three-dimensional reconstruction and ray tracing technology, calculate the channel capacity based on channel parameters, find an optimal solution in an oversized parameter space formed by a plurality of dimensions, and reduce the optimization complexity of the algorithm by adopting a Viterbi algorithm (namely a Viterbi algorithm) under an MLSE (Maximum likelihood sequence estimation) criterion. Based on this, the step of determining the current deployment result corresponding to the current value based on the network deployment basic information and the viterbi algorithm in the above step S104 may be implemented by the following sub-steps 1 to 4:

and a substep 1, determining a plurality of candidate AP positions corresponding to each to-be-deployed AP in the current value to-be-deployed APs based on the plurality of candidate AP positions and the distance constraint condition.

The method comprises the steps that firstly, a plurality of AP position combinations corresponding to the APs to be deployed with the current value can be determined based on a plurality of alternative AP positions and preset distance constraint conditions; and then determining a plurality of candidate AP positions corresponding to each AP to be deployed based on the plurality of AP position combinations.

Step 2, determining an optimal parameter combination corresponding to each candidate AP position of each AP to be deployed based on the three-dimensional model and the ray tracing technology; and the total channel capacity corresponding to the candidate AP position under the corresponding optimal parameter combination is the largest.

For each candidate AP position of each AP to be deployed, calculating to obtain the corresponding total channel capacity of the AP to be deployed under each parameter combination of the candidate AP position based on a three-dimensional model and a ray tracing technology; and then determining the parameter combination with the maximum total channel capacity as the optimal parameter combination corresponding to the candidate AP position of the AP to be deployed.

Optionally, the network deployment basic information further includes a meshing result, where the meshing result includes a plurality of meshes obtained by performing a networking process on a plan view of the target scene, and taking an indoor scene as an example, the indoor plan view may be meshed, as shown in fig. 4, for example, the mesh size is l×l, and there are N meshes in total. Based on this, the total channel capacity corresponding to the AP to be deployed under each parameter combination of the candidate AP positions can be calculated by the following procedure: for each parameter combination, firstly, calculating to obtain corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed by utilizing a ray tracing technology based on a three-dimensional model; the ray parameter information comprises the number of rays, the amplitude of each ray and time delay information; then, according to the corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed, calculating to obtain the channel capacity of the AP to be deployed to each grid under the parameter combination of the candidate AP position; and finally, calculating to obtain the corresponding total channel capacity (the total channel capacity is equal to the sum of the corresponding channel capacities) of the to-be-deployed AP under the parameter combination of the candidate AP position according to the channel capacity of the to-be-deployed AP to each grid under the parameter combination of the candidate AP position.

Step 3, determining a metric value corresponding to each AP to be deployed; the metric value is composed of total channel capacity corresponding to each candidate AP position under the corresponding optimal parameter combination.

And step 4, determining an optimal AP position combination and a corresponding optimal parameter combination corresponding to each AP to be deployed through a Viterbi algorithm based on the metric value corresponding to each AP to be deployed.

In some possible embodiments, the APs to be deployed may be sequenced according to the positional relationship between the APs to be deployed, so as to obtain an AP sequencing result; then according to the AP sequencing result, respectively combining optimal parameters corresponding to each candidate AP position of a first AP to be deployed into a first path node, and sequentially selecting a next path node based on the metric value and the reservation maximum value principle corresponding to each AP to be deployed to obtain a plurality of surviving paths; screening target surviving paths in the plurality of surviving paths to obtain an optimal AP position combination and a corresponding optimal parameter combination; wherein, the whole network throughput corresponding to the target surviving path is maximum.

Considering that when the number of APs to be deployed is large, the surviving paths are too long, the association between different APs to be deployed which are too far away is weak, the path merging depth can be set, after the depth of the surviving paths (i.e. the number of path nodes) reaches the path merging depth, surviving path merging (i.e. screening of target surviving paths) is carried out, and the rest APs to be deployed continue to generate surviving paths and carry out surviving path merging according to the path merging depth. Based on the above, after the AP ordering result is obtained, each AP to be deployed may be grouped according to the AP ordering result and a preset path merging depth, so as to obtain a plurality of groups of AP sets; each group of AP sets at most comprises a path merging depth number of APs to be deployed; for each group of AP sets, respectively combining optimal parameters corresponding to each candidate AP position of a first AP to be deployed in the group of AP sets into a first path node, and sequentially selecting a next path node based on a metric value and a reservation maximum value principle corresponding to each AP to be deployed in the group of AP sets to obtain a plurality of surviving paths corresponding to the group of AP sets; and finally, screening the target surviving paths for a plurality of surviving paths corresponding to each group of AP sets respectively to obtain the target surviving paths corresponding to each group of AP sets, namely obtaining the optimal AP position combination and the corresponding optimal parameter combination.

The embodiment of the invention is a pure software simulation scheme. And after receiving the requirement of the target scene, carrying out three-dimensional modeling on the environment. Based on the three-dimensional model of three-dimensional modeling and the alternative AP position (the alternative AP position can only comprise the first two coordinates of the AP in the < x, y and z > of the model, wherein the height z is taken as an optimization parameter), ray changes such as direct radiation/reflection/refraction/diffraction/transmission of electromagnetic waves emitted by the AP in the three-dimensional space and the influence of parameters such as materials, reflection coefficients, refraction coefficients and the like of the surfaces of contacted objects received in the electromagnetic wave transmission process are calculated by a ray tracing technology (ray-tracing) and ray parameters (the ray parameters comprise the amplitude, time delay, arrival angle and the like of rays) in the grid after the meshing in each space are calculated. The time domain channel information is obtained based on the received rays of each grid, and the degrees of freedom of the channel in space (note that here, MIMO (multiple input multiple output, multiple input multiple output) channels are assumed, i.e., the AP is a multi-antenna and two-dimensional antenna array) are analyzed to calculate the channel capacity or throughput. Under the constraint of ensuring network coverage, the throughput of the whole network (the sum of the throughput of all grids) is maximized and the number of APs is minimized, so that the economy is maximized under the condition of ensuring the reliability of the whole network. Taking Wi-Fi AP station distribution in an indoor scene as an example, the network deployment optimization method comprises the following specific steps:

1. And 3D modeling software is utilized to perform three-dimensional modeling on indoor scenes, wherein the three-dimensional modeling comprises walls, furniture, tables and chairs, lamps, green plants and other objects capable of reflecting electromagnetic waves.

2. The indoor plane diagram is gridded, and the grid size is l×l, assuming that there are N grids in total.

3. Assuming that the positions < x, y > of the M candidate APs are set, the information such as the number of rays received on each grid, the amplitude of each ray, the time delay, the arrival angle and the like is calculated by using a ray-tracing technology.

4. Optimizing strategies:

the position of K optimal APs (K should be as small as possible) is selected from the positions of M candidate APs (two-dimensional coordinates < x, y >) by an optimization algorithm (optimal position parameters comprise three-dimensional coordinates of the APs)

<x,y,z>And the downtilt phi and azimuth angle of the antenna

I.e. the parameters to be optimized are

). I.e. the optimization objective is 1: the number of K is the smallest; 2: the overall network coverage of the full area is the highest (for example, 100% coverage) and the throughput is the largest, as shown in the following equation:

s.t.K≤M

wherein arg represents a variable such that variable C _total Maximum, K minimum, C _total Referring to m traversing from 1 to K, i traversing from 1 to N results in a throughput C _i,m And (3) summing; c (C) _i,m Representing throughput of alternative AP m to grid i, C _i,m Is a function of the parameter combinations of the alternative AP m; s.t. refers to subject to, representing a constraint;

Representing any number (i.e., full scale word); the distance between alternative AP m and alternative AP n is greater than distance threshold value dist.

a) K locations are randomly selected from M candidate AP locations (assuming the AP is located high, e.g., indoor ceiling locations). After meshing the space, assume that M positions occupy M meshes within the meshing space, and label the ID of each position: m:<x _m ，y _m >. In addition, selecting K positions from M positions requires satisfying distance constraint conditions (i.e. the distance between any two selected positions is greater than a distance threshold value dist), i.e

The set of K positions screened out under the above distance constraint is denoted as d= { D ₁ ，d ₂ ，…，d _G G combinations, each of which d _{g,g＝1,2,…,G} ＝{<x ₁ ,y ₁ >,<x ₂ ,y ₂ >,<x _K ,y _K }. Note that: the coordinate combinations are arranged from large to small (or from small to large) in the order of the gridding numbers.

b) Assuming that the ith grid receives the amplitude of the ray from the AP with ID k, time-ordered according to the delay information is (zero padding to J values): h is a _i，k (j) J=1, 2, … J, i.e. the time domain impulse response CIR of grid i to AP k (Channel Impulse Response ).

c) Inverse fast fourier transform by IFFT (Inverse Fast Fourier Transform) ) The CIR is transformed to obtain a frequency domain channel response CFR (Channel FrequencyResponse ) of grid i: h _i，k (j) J=1, 2, …, J (column vector).

d) Computing channel capacity C from AP k to grid i _i，k (throughput).

Wherein det represents determinant, I represents identity matrix, E _s The transmission power of the AP is represented, α represents a preset constant, and H represents the conjugate transpose of H.

e) Assume that the range of downtilt angle phi that the antenna of each alternative AP can adjust is (phi) _dn ，φ _up ) And azimuth angle

Variable Range->

(for some APs it may be equivalent to the AP body rotating within this angular range), the height z of the AP is at (z _low ，z _high ) The in-range adjustment, K, is selected from the M candidate AP locations under the distance constraints as described above.

f) Assume that the target value of the overall network throughput is Ω (i.e., a preset throughput threshold, or search threshold).

g) Initializing a smaller K _ini From K _ini Start searching for optimum value K _opt 。

i. Optimization target: at D= { D ₁ ，d ₂ ，…，d _G Finding a position combination that maximizes overall network throughput

Each combination is treated as a continuous sequence and then the optimal solution is found based on the Viterbi algorithm under the MLSE criterion.

Starting from the first AP: candidate location of first AP<x ₁ ，y ₁ >At most G possibilities, traverse each

Wherein G' is less than or equal to G. Each candidate position of the first AP is respectively provided with a step length phi ₀ ，

Z ₀ Calculating and recording each +.>

The following channel capacities:

For the first AP in the sequence, the channel capacities of all meshes seen by the AP for each candidate position (assuming total is I) are summed +.>

Traversal of phi under a certain G 'e {1,2, …, G' } ₁ ：{φ _up ～φ _dn }，

z ₁ ：{z _low ～z _high } under->

Maximum value reserved->

And record +.>

Traversing G 'e {1,2, …, G' }, calculating the maximum value at each G

And record the corresponding

v. record all values

Corresponding->

Wherein->

Is a metric, i.e., a metric of network coverage and throughput of the first AP.

Calculating the corresponding metric value of the second AP by the same procedure, namely<x ₂ ，y ₂ >Repeating the above operation for the following candidate positions to obtain:

sequentially calculating all K _ini Metric values corresponding to the APs:

viii definition Γ _k (s) is

The k-th AP (or mapping to k moment, and looking up the whole as a time sequence state) in the sequence is the maximum metric, Γ, corresponding to the state s (i.e. a certain parameter combination) _k+1 (s ') is the maximum metric corresponding to the next AP k+1 (or mapped to time k+1) state s'), then according to the optimal principle:

Wherein,,

representing the maximizing operation for all those s that can be transferred to the s' state and constrained within set D. And if the path merging depth of the Viterbi algorithm is V, merging the current multiple surviving paths when the node number of the surviving paths reaches V, namely selecting the target surviving path with the maximum total metric value.

Specific Viterbi algorithm:

add-compare-select (ACS: add-compare-select).

Addition: for all possible s that transition to the new state s', Γ will be _k (s) and Λ _k (s.fwdarw.s') are added.

Comparison: in all valid s, the last step Γ is compared _k (s)+Λ _k The size of (s.fwdarw.s').

Selecting: preserving maximum value (selecting surviving path), discarding other paths to obtain Γ _k+1 A value of (s').

The complete surviving path is obtained in this way, namely, an optimal AP position combination and an optimal parameter combination in the set D are obtained:

calculating the whole network throughput under the optimal path (namely the target surviving path) under the optimal AP position combination and the optimal parameter combination

And comparing with a threshold value omega, if the threshold value is reached, the optimal AP position combination and the optimal parameter combination are the optimal AP deployment scheme. Otherwise, the next step is entered.

h) If the initial value K _ini Failing to reach the search threshold, increasing K by delta _ini Is of the value of (2)Repeating the operation of the step g) until the throughput of the whole network reaches a threshold value. Suppose that after n searches, k=k _ini When +n.delta reaches the threshold value, the optimal AP position combination and the optimal parameter combination are as follows:

in summary, the embodiment of the invention provides a network deployment optimizing scheme in a 2B scene, and the network rate is maximized and the usage amount of an AP is minimized on the premise of ensuring network coverage. The reliability and fluency of the network are guaranteed, and the economy of network deployment is also guaranteed.

Corresponding to the above network deployment optimization method, the embodiment of the present invention further provides a network deployment optimization device, referring to a schematic structural diagram of the network deployment optimization device shown in fig. 5, where the device includes:

the acquiring module 501 is configured to acquire network deployment basic information corresponding to a target scene; the network deployment basic information comprises a three-dimensional model and a plurality of alternative AP positions;

the determining module 502 is configured to determine, based on the network deployment basic information and a preset constraint condition, a target deployment result reaching a preset throughput threshold corresponding to a target scene by adopting a viterbi algorithm; the preset constraint conditions comprise that K optimal AP positions selected from a plurality of alternative AP positions meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is maximum.

The network deployment optimizing device provided by the embodiment of the invention firstly acquires the network deployment basic information corresponding to the target scene when grid deployment is carried out; determining a target deployment result reaching a preset throughput threshold corresponding to a target scene by adopting a Viterbi algorithm based on the network deployment basic information and a preset constraint condition; the preset constraint conditions comprise that K optimal AP positions selected from a plurality of alternative AP positions meet preset distance constraint conditions, K is minimum, the coverage rate of the whole network is highest, and the throughput of the whole network is maximum. Therefore, aiming at the characteristics of the 2B scene, the Viterbi algorithm is adopted to reduce the optimizing complexity, and on the premise of ensuring network coverage, the throughput of the whole network is maximized and the use quantity of the APs is minimized, so that the reliability and fluency of the network coverage are ensured, and meanwhile, the economical efficiency of network deployment is ensured.

Further, the determining module 502 includes:

an obtaining unit, configured to obtain a current value of the initialized AP number;

a first determining unit, configured to determine a current deployment result corresponding to the current value based on the network deployment basic information and the viterbi algorithm; the current deployment result comprises an optimal AP position combination and an optimal parameter combination of each target AP position in the optimal AP position combination, wherein the coverage rate of the whole network corresponding to the optimal AP position combination under the corresponding optimal parameter combination is highest and the throughput of the whole network is maximum;

The updating unit is used for updating the current value according to a preset step length when the throughput of the whole network corresponding to the current deployment result does not reach a preset throughput threshold;

and the second determining unit is used for determining the current deployment result as a target deployment result corresponding to the target scene when the whole network throughput corresponding to the current deployment result reaches a preset throughput threshold.

Further, the first determining unit is specifically configured to: determining a plurality of candidate AP positions corresponding to each AP to be deployed in the current value AP to be deployed based on the plurality of candidate AP positions and the distance constraint condition; based on the three-dimensional model and the ray tracing technology, determining an optimal parameter combination corresponding to each candidate AP position of each AP to be deployed; the total channel capacity corresponding to the candidate AP position under the corresponding optimal parameter combination is the largest; determining a metric value corresponding to each AP to be deployed; the measurement value consists of total channel capacity corresponding to each candidate AP position under the corresponding optimal parameter combination; and determining an optimal AP position combination and a corresponding optimal parameter combination corresponding to each AP to be deployed through a Viterbi algorithm based on the metric value corresponding to each AP to be deployed.

Further, the first determining unit is further configured to: based on the multiple alternative AP positions and the distance constraint conditions, determining multiple AP position combinations corresponding to the APs to be deployed with the current values; and determining a plurality of candidate AP positions corresponding to each AP to be deployed based on the plurality of AP position combinations.

Further, the first determining unit is further configured to: for each candidate AP position of each AP to be deployed, calculating to obtain the corresponding total channel capacity of the AP to be deployed under each parameter combination of the candidate AP position based on a three-dimensional model and a ray tracing technology; and determining the parameter combination with the maximum total channel capacity as the optimal parameter combination corresponding to the candidate AP position of the AP to be deployed.

Further, the network deployment basic information further comprises a gridding result, wherein the gridding result comprises a plurality of grids obtained by carrying out networking processing on the plan view of the target scene; the first determining unit is further configured to: for each parameter combination, calculating to obtain corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed by utilizing a ray tracing technology based on a three-dimensional model; the ray parameter information comprises the number of rays, the amplitude of each ray and time delay information; according to the corresponding ray parameter information of each grid under the parameter combination of the candidate AP position of the AP to be deployed, calculating to obtain the channel capacity of the AP to be deployed to each grid under the parameter combination of the candidate AP position; and calculating to obtain the corresponding total channel capacity of the AP to be deployed under the parameter combination of the candidate AP position according to the channel capacity of the AP to be deployed to each grid under the parameter combination of the candidate AP position.

Further, the first determining unit is further configured to: ordering the APs to be deployed according to the position relation among the APs to be deployed to obtain an AP ordering result; according to the AP sequencing result, respectively combining optimal parameters corresponding to each candidate AP position of a first AP to be deployed into a first path node, and sequentially selecting a next path node based on the metric value and the reservation maximum value principle corresponding to each AP to be deployed to obtain a plurality of surviving paths; screening target surviving paths in the plurality of surviving paths to obtain an optimal AP position combination and a corresponding optimal parameter combination; wherein, the whole network throughput corresponding to the target surviving path is maximum.

The implementation principle and the generated technical effects of the network deployment optimization device provided by the embodiment are the same as those of the network deployment optimization method embodiment, and for the sake of brief description, the corresponding content in the network deployment optimization method embodiment can be referred to where the network deployment optimization device embodiment is not mentioned.

As shown in fig. 6, an electronic device 600 provided in an embodiment of the present invention includes: the network deployment optimization method comprises a processor 601, a memory 602 and a bus, wherein the memory 602 stores a computer program capable of running on the processor 601, and when the electronic device 600 runs, the processor 601 and the memory 602 communicate through the bus, and the processor 601 executes the computer program to realize the network deployment optimization method.

Specifically, the memory 602 and the processor 601 can be general-purpose memories and processors, which are not particularly limited herein.

The embodiment of the invention also provides a storage medium, and a computer program is stored on the storage medium, and the computer program is executed by a processor to execute the network deployment optimization method in the previous method embodiment. The storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a RAM, a magnetic disk, or an optical disk, etc., which can store program codes.

Any particular values in all examples shown and described herein are to be construed as merely illustrative and not a limitation, and thus other examples of exemplary embodiments may have different values.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims

1. A method for optimizing network deployment, comprising:

2. The network deployment optimization method according to claim 1, wherein the determining, by using a viterbi algorithm, a target deployment result corresponding to the target scenario and reaching a preset throughput threshold based on the network deployment basic information and a preset constraint condition includes:

acquiring the current value of the initialized AP quantity;

3. The network deployment optimization method according to claim 2, wherein the determining a current deployment result corresponding to the current value based on the network deployment base information and a viterbi algorithm includes:

4. The network deployment optimization method of claim 3, wherein the determining, based on the plurality of candidate AP locations and the distance constraint, a plurality of candidate AP locations corresponding to each of the APs to be deployed in the current number of APs to be deployed comprises:

5. The network deployment optimization method of claim 3, wherein the determining, based on the three-dimensional model and ray tracing technique, an optimal parameter combination corresponding to each candidate AP location of each AP to be deployed comprises:

6. The network deployment optimization method according to claim 5, wherein the network deployment basic information further comprises a gridding result, and the gridding result comprises a plurality of grids obtained by carrying out networking processing on the plan view of the target scene; the calculating, based on the three-dimensional model and the ray tracing technology, a total channel capacity corresponding to the AP to be deployed under each parameter combination of the candidate AP position includes:

7. The network deployment optimization method according to claim 3, wherein the determining, by a viterbi algorithm, an optimal AP position combination and a corresponding optimal parameter combination corresponding to each AP to be deployed based on the metric value corresponding to each AP to be deployed includes:

8. A network deployment optimization device, comprising:

9. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, wherein the processor implements the network deployment optimization method of any of claims 1-7 when the computer program is executed by the processor.

10. A storage medium having a computer program stored thereon, which when executed by a processor performs the network deployment optimization method of any of claims 1-7.