CN115379462B - Three-dimensional deployment method for 6G intelligent reflector auxiliary network - Google Patents
Three-dimensional deployment method for 6G intelligent reflector auxiliary network Download PDFInfo
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Abstract
Aiming at the problems of inaccurate position optimization and non-uniform parameter model in the existing intelligent reflector deployment research work, the invention establishes a general three-dimensional deployment model for covering and expanding an intelligent reflector auxiliary base station, unifies various existing two-dimensional/three-dimensional intelligent reflector models, defines relevant parameters of intelligent reflector deployment such as horizontal rotation angle, vertical rotation angle, distance from a base station, vertical height and the like, and lays a foundation for accurate analysis of network performance; deducing a closed expression of a base station coverage area under the intelligent reflecting surface three-dimensional deployment model, and considering a rice channel gain and a line-of-sight and non-line-of-sight statistical channel model; an intelligent reflecting surface three-dimensional deployment algorithm for maximizing the coverage area of the base station is designed; forming a coverage maximization problem into a convex optimization problem, and solving the problem by using a Lagrange multiplier method through introducing auxiliary variables; finally, under the condition of designing the optimal intelligent reflecting surface phase, the influence of various parameters on the coverage area of the base station is simulated and analyzed, and the deployment guidance proposal of the parameters of different intelligent reflecting surfaces is given.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a deployment method of an intelligent reflection surface (Intelligent reflecting surfaces, IRS) auxiliary network in sixth generation mobile communication (6th Generation,6G).
Background
The sixth generation of wireless communication systems aims at large-scale, transient-connection, data-driven intelligent networks, enabling ubiquitous wireless connections. But highly complex networks, high cost hardware and increasing energy consumption are critical issues facing future wireless communications. Thus, new transmission technologies are needed to support new applications and services. In many alternatives, IRSs have the ability to actively change the wireless communication environment, and have become the focus of wireless communication research to alleviate various challenges encountered in 6G wireless networks.
The IRS regulates and controls the wireless propagation environment between transmitters intelligently through the reconfigurable space electromagnetic wave regulator, and compared with a large-scale antenna transceiver and a relay node, the intelligent reflection surface has the main advantages that: the shape has plasticity, light weight and easy deployment on the surfaces of various scatterers in a wireless propagation environment; serving a remote user through a passive reflection reconfiguration Line Of Sight (LoS) link; the passive control of electromagnetic waves is realized by regulating and controlling the physical characteristics of electromagnetic materials, and high-power consumption devices of a radio frequency link are not needed.
In the traditional wireless network, the electromagnetic environment is not controlled by the network, the network performance is often limited by the environment, and the intelligent super surface is beneficial to the advantages, so that the environment can be converted into an intelligent reconfigurable electromagnetic space, and the paradigm change is brought for information transmission processing. For example, in a conventional urban cellular network, a communication link is blocked, a base station signal is not easily reached, and a user cannot obtain a good service due to the blocking of a wireless signal by an obstacle such as a large building. The intelligent reflecting surface can be deployed between the base station and the coverage blind area, and the transmission signals can reach users in the coverage hole through effective reflection/projection, so that effective connection is established between the base station and the users, and the coverage of the users in the hole area is ensured. In addition, the intelligent reflecting surface can be applied to the fields of cell edge interference suppression, line-of-sight multi-stream transmission, large-scale antenna transceivers, user scenes and the like, and can be combined with the large-scale antenna transceivers to further enhance network performance. The wide application scenario makes the intelligent super surface a promising research point for future wireless systems.
The intelligent super-surface deployment problem has the characteristic of diversity, and targets for different problem researches are not the same. In existing intelligent subsurface operations, it is often deployed on the side of the network near the base station or user to help improve communication performance with the serving base station. The user side intelligent super-surface deployment strategy is generally used for hot spots, cell edges and mobile user scenes so as to promote local coverage.
However, existing intelligent subsurface works, mostly assuming that the intelligent subsurface is deployed in a fixed location, without taking advantage of its deployment flexibility. It is well known that different smart subsurface deployment parameters, such as smart subsurface orientation, distance from the base station, smart subsurface height, etc., can result in different smart subsurface channels, thereby significantly affecting system capacity and spectral efficiency. In addition, the deployment of the intelligent super-surface should also consider the spatial user information density, i.e. the intelligent super-surface should be preferentially deployed in a hot spot area with a large number of users, or on the boundary of two cells to eliminate the co-channel interference between them, while expanding the cell coverage. From this point of view, existing intelligent subsurface deployments are inefficient for most of the work. Therefore, how to achieve optimal deployment of intelligent supersurfaces in wireless networks remains an important open problem.
Disclosure of Invention
Aiming at the problems of inaccurate position optimization and non-uniform parameter model in the existing intelligent reflecting surface deployment research work, the invention provides a general intelligent reflecting surface three-dimensional deployment method with maximized base station coverage, considers a rice channel gain and a LoS/None-LoS (NLoS) statistical channel model, deduces a closed expression of coverage about intelligent reflecting surface parameters, and designs an optimal deployment algorithm of the intelligent reflecting surface.
The invention relates to a dense urban intelligent reflecting surface deployment and three-dimensional parameter setting scheme, which comprises the following steps:
step 200, acquiring relevant parameters, and establishing a three-dimensional deployment model covered by the intelligent reflecting surface according to the distance between the base station and the user.
The central position of the intelligent super-surface is determined through the horizontal distance and the height between the super-surface and the base station, and the orientation of the intelligent super-surface is determined through the horizontal rotation angle and the vertical rotation angle, so that the structure shown in the figure 2 is obtained.
The user and the base station are in an xOy plane, and the centers of the base station and the intelligent reflecting surface are in an xOz plane;
representing the horizontal distance between the base station and the user;
representing the horizontal distance between the base station and the intelligent reflecting surface;
representing the horizontal distance between the intelligent reflecting surface and the user;
h BS representing the elevation of the base station;
h IRS representing the elevation of the base station;
φ 1 representing the horizontal rotation angle of the intelligent reflecting surface;
φ 2 representing the vertical rotation angle of the intelligent reflecting surface;
beta represents the horizontal azimuth angle of the user compared to the base station
The position of the intelligent reflecting surface is represented by phi 1 ,φ 2 ,h IRS And determining, namely optimizing the positions of the intelligent reflecting surfaces. After the model is established, the next step is entered.
Step 210, setting the phase of the reflection factor of the electromagnetic unit of the intelligent subsurface so as to maximize the signal-to-noise ratio of the signal received by the user.
The base station to user channel gain can be expressed as:
wherein Γ is m,n Reflection factor h representing the mth row and the nth column reflection units of the intelligent super surface m,n Indicating the channel gain, h, of the signal reflected by the m-th row and n-th column reflecting units of the intelligent super surface D Channel gain representation of the signal to user direct link. Further, the two links have LoS and NLoS paths respectively, and are modeled by rice model:
thus, the signal-to-noise ratio of the user received signal can be expressed as:
the specific channel gain model is brought in, and after a series of transformations, when the phase of the reflection unit satisfies:
maximum value is obtained:
wherein P is the base station transmitting power;
k 1 the rice channel parameters of the direct link from the base station to the user are obtained;
k 2 the method comprises the steps that the Laise channel parameters of a link are reflected by an intelligent reflecting surface for a base station to a user;
channel gain of LoS link expressed as direct;
channel gain of NLoS link denoted as direct;
represented as through intelligent inverseChannel gain of the LoS link reflected by the emission surface;
expressed as the channel gain of the NLoS link reflected by the smart reflective surface;
cos θ represents the incident angle of the base station to the smart reflective surface;
m and N represent the dimension of the intelligent reflecting surface, namely M rows of reflecting units M;
σ 2 is the noise power.
After obtaining the maximum value of the received signal-to-noise ratio of the user, the next step is carried out.
Step 220, calculating the farthest distance of each beta azimuth user of the base station according to the channel state.
Using the model created in step 200, a typical user is used as a reference, the angle of the user is β compared with the base station, and then the maximum linear distance that the base station can cover along the β angle is calculated.
First, four kinds of channel gains are calculated according to the existing conditionsSecondly, calculating a cosine value cos theta of an incident angle from the base station to the intelligent reflecting surface; finally, according to the set signal-to-noise ratio threshold gamma received by the user th The furthest distance of a typical user from the base station is calculated, which is the distance within which the base station can cover a straight line, before proceeding to the next step.
At step 230, an approximate solution for the coverage is calculated by discretizing the sum, followed by calculating an optimal solution for the deployment parameters using the Lagrangian multiplier method.
The coverage area of a base station can be expressed by integrating the angle β:
wherein the method comprises the steps ofFrom the deployment parameter phi of the intelligent reflecting surface, which has been found in step 220 1 ,φ 2 ,/>h IRS And (5) determining. It can thus be seen that the coverage area S is a relative phi 1 ,φ 2 ,/>h IRS Is a non-linear function of (2).
Furthermore, according to the model, whenh IRS Other variables are affected by the change, e.g. change phi 1 ,φ 2 And further affects the overall coverage area. However, it is difficult to describe the effect of a variable on the closed-form solution, while taking into account that the coverage area S is a function of 1 ,φ 2 ,/>h IRS The present design simplifies the area S and decomposes it into the form of the sum of the areas of K equal angle sectors:
where K is the parameter of the estimated area approximation solution, the opening angle per sector is
After the simplification, the problem of solving the optimal deployment parameters becomes the optimization problem constrained by inequality, and the optimal deployment parameters can be solved by using a Lagrangian multiplier method and a logarithmic barrier function
Advantageous effects
The three-dimensional deployment method of the intelligent reflector auxiliary network establishes a general three-dimensional deployment model of coverage extension of an intelligent reflector auxiliary base station, unifies various two-dimensional/three-dimensional intelligent reflector models, and defines phi 1 ,φ 2 ,h IRS And the intelligent reflection face deployment related parameters are used for accurately describing the space position and orientation of the intelligent reflection face, so that a foundation is laid for accurately analyzing the network performance. On the basis again, a closed expression of the coverage area of the base station under the intelligent reflecting surface three-dimensional deployment model is further deduced, and the rice channel gain and the LoS/NLoS statistical channel model are considered. Under the condition of designing the optimal intelligent reflecting surface phase, the influence of various parameters on the coverage area of the base station is simulated and analyzed, and the deployment guidance proposal of the parameters of different intelligent reflecting surfaces is given.
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In order to clearly and clearly explain the technical steps of the present invention, a brief description will be given below of all the drawings used in the description of the present invention. It should be noted that the drawings described below are only examples of implementations of the present invention, and other persons of ordinary skill in the art may still obtain other drawings in different situations according to the drawings.
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a diagram of a smart reflector of the present invention assisting a base station service user;
FIG. 3 is an explanatory view of the horizontal and vertical corners of the intelligent reflecting surface of the present invention;
FIG. 4 is a graph of the angle of incidence versus angle of rotation for the present invention;
FIG. 5 is a graph of coverage area as a function of horizontal rotational angle for the present invention;
FIGS. 6 and 7 are graphs of coverage area as a function of vertical rotational angle for the present invention;
FIG. 8 is a graph of coverage area as a function of horizontal distance from a smart reflective surface to a base station in accordance with the present invention;
FIGS. 9 and 10 are graphs of coverage area versus intelligent reflective surface height for the present invention;
Detailed Description
The steps and processes of the present invention will be fully and clearly described below with reference to the accompanying drawings in this application, and it is apparent that the examples described in this application are only one example application scenario of the present invention, and other results based on the present disclosure without substantial changes are all within the scope of the present invention.
Fig. 2 is an exemplary scenario of the present invention illustrating the step-wise implementation of the present invention. In the model, the base station is arranged in the center of the coordinates, an omni-directional antenna is adopted, and the intelligent reflecting surface is only required to be deployed on the optimal parameters calculated by the designAnd at the determined position, the maximization of the coverage area enhancement of the base station can be realized.
The invention uses the network scene as a single base station multi-user scene, does not consider the interference among users, and can provide service for multiple users in various multiple access modes such as frequency division multiple access, time division multiple access and the like. The mechanism implementation of the present invention is performed by the wireless overlay service provider at the time of deployment of the intelligent reflective surface.
The specific steps of the 3-dimensional deployment of the 6G IRS auxiliary network coverage extension are as follows:
and 300, acquiring relevant parameters, and establishing a three-dimensional deployment model covered by the intelligent reflecting surface according to the distance between the base station and the user.
The central position of the intelligent super-surface is determined through the horizontal distance and the height between the super-surface and the base station, and the orientation of the intelligent super-surface is determined through the horizontal rotation angle and the vertical rotation angle, so that the structure shown in the figure 2 is obtained.
The user and the base station are in an xOy plane, and the centers of the base station and the intelligent reflecting surface are in an xOz plane;
representing the horizontal distance between the base station and the user;
representing the horizontal distance between the base station and the intelligent reflecting surface;
representing the horizontal distance between the intelligent reflecting surface and the user;
h BS representing the elevation of the base station;
h IRS representing the elevation of the base station;
φ 1 representing the horizontal rotation angle of the intelligent reflecting surface;
φ 2 representing the vertical rotation angle of the intelligent reflecting surface;
beta represents the horizontal azimuth angle of the user compared to the base station.
In step 310, the phase of the reflection factor of the electromagnetic unit of the intelligent subsurface is set so that the signal-to-noise ratio of the signal received by the user is maximized.
On the basis of the formula (3)Then formula (3) may be further rewritten as:
the average of the SNR can then be decomposed into:
the model of the channel gain is brought in, and further deduction can be obtained:
then, the formula (9) can be rewritten as:
further, by the formula (14), in order toTaking the maximum value, it is necessary to satisfy:
step 320, calculating the farthest distance of each beta azimuth user of the base station according to the channel state.
Taking a typical user as a reference, the angle of the user compared with a base station is beta, and according to the geometric relationship among the user, the base station and the intelligent reflecting surface:
wherein, according to the cosine law:
in practice h BS And h UE Are all known, and therefore whenWhen determined, d can be calculated by taking in (16) BI ,d BU ,d IU Then substituting the channel gain model matched with the environment to calculate +>
In order to further clearly show the relation between the angle of incidence θ and other parameters, the present invention designs a more specific angle dependence on fig. 2, as shown in fig. 3.
The rectangles EBFP, EPLK and PFQL are in the x 'Pz', y 'Pz', x 'Py' plane, respectively. PA represents the normal vector of the intelligent reflecting surface, line PD represents the angle of incidence of the base station signal, point D is on line BF, and line AC is perpendicular to the x 'Py' plane.
Thus, it is possible to obtain:
while phi is 1 ,φ 2 ,h IRS When the ratio is determined, the proportional relation among the line segments is determined, so that cos theta can be calculated.
Thereafter, the reception threshold of the given user is γ th One user is covered with the requirement of meetingFrom this inequality +.>The maximum value of (2) is marked->
Step 330, calculate the approximate solution of the coverage by discretized summation, then calculate the optimal solution of the deployment parameters using Lagrangian multiplier.
First fromThe initial value meeting the condition is randomly selected in the value range omega, and the first derivative and the second derivative of S are obtained according to the formula (7)>
And then according to the following recurrence:
iterationUp to->Wherein the termination condition e is given in advance, the accuracy of the result can be controlled by controlling e.
The simulation results are shown in fig. 5-10.
FIG. 5 shows the coverage area S with the horizontal angle phi 1 From the graph, the maximum coverage area is always the same regardless of the values of other parametersAnd then obtained.
FIGS. 6 and 7 show the coverage area S with the horizontal angle phi 2 In most cases, the optimum vertical direction phi 2 =0, which means that the plane of the intelligent reflecting surface is perpendicular to the direction of the incident signal. In particular, it is noted in FIG. 6 that there is a tendency for coverage to increase as smart reflective surfaces are deployed higherEspecially when the element unit N of the intelligent reflecting surface is smaller and the optimal vertical angle is the sameWhen moving. From fig. 7 we can conclude that there is a tendency for coverage to increase as the smart reflector and base station are closer, especially as the number of smart reflector cell units is smaller, but this tendency gradually disappears as N increases. Thus, when the number of smart reflector units is limited or small, the smart reflector should be disposed at a position higher than the base station, and its plane should be parallel to the ground as much as possible. Otherwise, its plane should be perpendicular to the direction of the incoming signal.
FIG. 8 is a graph of coverage area versus horizontal distance from the intelligent reflecting surface to the base station for eliminating the effect of the vertical direction of the intelligent reflecting surface, we take the phi of each graph 2 Optimum value of (2)It can be found that when the number N of the intelligent reflection surface elements is small, the intelligent reflection surface and the base station are +.>When the horizontal distance is closer, the coverage area is more influenced by the height of the intelligent reflecting surface, and the coverage area is more influenced by the height of the intelligent reflecting surface along with N and +>Increasing, this effect gradually disappears. Furthermore, an increase in N will also extend the best deployment site away from the base station outwards. Thus, when the number of smart reflective pixels is small, the smart reflective surface should be disposed at the edge of the unit at a height below the base station, or at a position close to the base station and at a height above the base station. Otherwise, the number of intelligent reflector elements has little effect, and deployment of the intelligent reflector to the vicinity of the base station is a good strategy.
Fig. 9 and 10 are graphs of coverage area versus intelligent reflective surface height for the present invention. As can be seen from FIG. 9, when the intelligent reflecting surface is horizontally distant from the base stationAnd when the number N of the intelligent reflection surface elements is larger, the optimal height of the intelligent reflection surface exists, and the optimal solution is equal to the height of the base station. Looking at FIG. 10, it can be seen that in the range of 0-90 meters, the coverage is directly proportional to the height of the intelligent reflective surface. The closer the intelligent reflector is to the base station, the greater the impact of the intelligent reflector height on coverage, and this impact gradually diminishes as N increases. And follow->And N, the optimal solution for the intelligent reflector height disappears. Thus, in case of a large number of smart reflector elements, it is recommended to deploy the smart reflector near the base station and at the height of the base station, while in other scenarios it is recommended to deploy the smart reflector at the highest allowed position.
Claims (5)
1. A reflection angle setting and three-dimensional deployment method for a 6G intelligent super surface (Intelligent Metasurface, IM), comprising: firstly, establishing a universal three-dimensional deployment model of coverage extension of an IM auxiliary base station; the phase phi of the IM reflecting unit is then designed m,n Maximizing the received signal-to-noise ratio of the user; then under the condition of a certain signal-to-noise ratio threshold constraint, calculating the farthest linear distance of the user which can be served in the beta direction by taking the base station as the centerWherein->Is the IM horizontal rotation angle phi 1 IM vertical rotation angle phi 2 IM horizontal distance from base station->IM vertical height h IM Is a implicit function of (1); then gives three dimensions using integrationCalculation expression of base station coverage under deployment model +.>Finally, a Lagrange factor is introduced as an auxiliary variable by a fan-shaped discrete approximation method, and a Lagrange multiplier method is utilized to solve the coverage maximization problem.
2. The method of claim 1 wherein the three-dimensional deployment model unifies existing two-dimensional and three-dimensional IM models defining an IM horizontal angle of rotation Φ 1 IM vertical rotation angle phi 2 IM horizontal distance from base stationIM vertical height h IM And the relevant parameters of IM deployment are equal, the spatial position and orientation of the IM are accurately described, and a foundation is laid for accurate analysis of network performance.
3. The method of claim 1 wherein the IM element phase Φ m,n Is designed to give an IM phase condition that maximizes the received signal-to-noise ratio of the user by closed-form derivation based on the Rice channel gain and line-of-sight, non-line-of-sight statistical channel model
Wherein phi is m,n Representing the cell phase, phi, of the mth row and nth column on IM D As the phase shift value of the direct link,for the phase shift value of the reflective link through the mth row and nth column element on IM, based on phi m,n Closed-form solution giving a signal-to-noise ratio mean maximum
Wherein P is the base station transmitting power, k 1 Rice channel parameters, k, for direct base station to user link 2 For the rice channel parameters of the base station to user straight through IM reflection link,channel gain, denoted direct Line of Sight (LoS) link, +.>Channel gain, denoted direct (None Line of Sight, NLoS) link, +.>Channel gain, denoted LoS link by IM reflection,>expressed as the channel gain of the NLoS link reflected by the IM, θ represents the base station to IM incidence angle, M, N represents the IM transverse and longitudinal element numbers, σ 2 Is the noise power.
4. The method of claim 1, wherein the base station-centric user-serviceable furthest linear distance calculation method comprises first calculating direct LoS link channel gain based on channel conditionsDirect NLoS link channel gain +.>Channel gain of LoS link by IM reflection +.>Channels of NLoS links by IM reflectionGain ofThe four types of channel gains are used for calculating the incident angle of the base station signal, and finally carrying out closed solution of the maximum value of the average value of the signal to noise ratio, and calculating the longest distance of the user under the condition that the maximum signal to noise ratio of the user is larger than a threshold value.
5. The method of claim 1, wherein the IM three-dimensional deployment algorithm that maximizes base station coverage converts the closed-form solution of coverage area to an approximate solution that can be convex optimized by a discrete approximation method, and solves the problem by introducing a lagrangian factor auxiliary variable using a lagrangian multiplier method.
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