CN115379462A - Three-dimensional deployment method for 6G intelligent reflector auxiliary network - Google Patents
Three-dimensional deployment method for 6G intelligent reflector auxiliary network Download PDFInfo
- Publication number
- CN115379462A CN115379462A CN202210766373.4A CN202210766373A CN115379462A CN 115379462 A CN115379462 A CN 115379462A CN 202210766373 A CN202210766373 A CN 202210766373A CN 115379462 A CN115379462 A CN 115379462A
- Authority
- CN
- China
- Prior art keywords
- base station
- irs
- intelligent
- reflecting surface
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 16
- 235000007164 Oryza sativa Nutrition 0.000 claims abstract description 8
- 235000009566 rice Nutrition 0.000 claims abstract description 8
- 238000005457 optimization Methods 0.000 claims abstract description 7
- 241000209094 Oryza Species 0.000 claims description 7
- 238000013461 design Methods 0.000 claims description 4
- 238000009795 derivation Methods 0.000 claims description 2
- 230000010363 phase shift Effects 0.000 claims 2
- 238000011160 research Methods 0.000 abstract description 5
- 240000007594 Oryza sativa Species 0.000 abstract 1
- 238000004891 communication Methods 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 108091005941 EBFP Proteins 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/145—Passive relay systems
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/24—Cell structures
- H04W16/26—Cell enhancers or enhancement, e.g. for tunnels, building shadow
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
Aiming at the problems of inaccurate position optimization and non-uniform parameter models in the existing intelligent reflecting surface deployment research work, the invention establishes a universal three-dimensional deployment model of intelligent reflecting surface auxiliary base station coverage extension, unifies various existing two-dimensional/three-dimensional intelligent reflecting surface models, defines intelligent reflecting surface deployment related parameters such as horizontal turning angle, vertical turning angle, distance from a base station, vertical height and the like, and lays a foundation for network performance accurate analysis; deducing a closed expression of the coverage area of the base station under an intelligent reflector three-dimensional deployment model, and considering a Rice channel gain, a line-of-sight and a 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 convex optimization problem by the coverage maximization problem, and solving the problem by introducing auxiliary variables and utilizing a Lagrange multiplier method; and finally, under the condition of designing the optimal intelligent reflecting surface phase, simulating and analyzing the influence of various parameters on the coverage area of the base station, and giving a deployment guidance suggestion of different intelligent reflecting surface parameters.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a method for deploying an Intelligent Reflection Surface (IRS) auxiliary network in the sixth generation mobile communication (6 generation, 6G).
Background
The sixth generation wireless communication systems target large-scale, instantaneous connectivity, data-driven intelligent networks, enabling ubiquitous wireless connectivity. Highly complex networks, high cost hardware and increasing energy consumption are key issues facing future wireless communications. Therefore, new transmission technologies are needed to support new applications and services. Among many alternatives, IRS has the ability to actively change the wireless communication environment, which has been the focus of wireless communication research to alleviate various challenges encountered in 6G wireless networks.
IRS passes through reconfigurable space electromagnetic wave modulator, regulates and control intelligently to the wireless propagation environment between the transmitter, compares in large-scale antenna transceiver and relay node, and the main advantage of intelligent plane of reflection has: the shape is plastic, the weight is light, and the device is easy to be arranged on the surfaces of various scatterers in a wireless propagation environment; reconstructing a Line Of Sight (LoS) link through passive reflection to serve remote users; the passive control of the electromagnetic wave is realized by utilizing the regulation and control of the physical properties of the electromagnetic material, and a high-power-consumption device of a radio frequency link is not needed.
In a traditional wireless network, an electromagnetic environment is not controlled by the network, the network performance is limited by the environment, and the intelligent super surface can convert the environment into an intelligent reconfigurable electromagnetic space by benefiting from the advantages, so that paradigm change is brought to information transmission processing. For example, in a conventional urban cellular network, due to the obstruction of wireless signals by large buildings and other obstacles, communication links are blocked, base station signals are not easy to reach, and users cannot obtain good service. The intelligent reflecting surface can be deployed between the base station and the coverage blind area, and the transmission signal reaches the user in the coverage hole through effective reflection/projection, so that effective connection is established between the base station and the user, and the coverage of the user in the hole area is ensured. In addition, the intelligent reflecting surface can also 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 also be combined with the large-scale antenna transceivers to further enhance the network performance. The wide application scenario makes intelligent super-surface a promising research point for future wireless systems.
The intelligent super-surface deployment problem has the characteristic of diversity, and the research targets for different problems are not very same. In existing intelligent super-surface work, it is typically deployed on the side of the network near the base station or user to help improve the performance of communications 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 improve local coverage.
However, existing intelligent super-surface works, mostly assuming that the intelligent super-surface is deployed in a fixed location, without taking advantage of its deployment flexibility. It is well known that different intelligent super-surface deployment parameters, such as intelligent super-surface orientation, distance from a base station, intelligent super-surface height, etc., can result in different intelligent super-surface channels, thereby significantly affecting system capacity and spectral efficiency. In addition, the deployment of the intelligent super surface also considers the information density of space users, namely the intelligent super surface is preferentially deployed in a hot spot area with a large number of users or on the boundary of two cells to eliminate the same-frequency interference of channels between the two cells and simultaneously expand the cell coverage. From this perspective, intelligent super-surface deployment is inefficient in most of the existing work. Therefore, how to achieve optimal deployment of intelligent hypersurfaces in wireless networks remains an important open question.
Disclosure of Invention
Aiming at the problems of inaccurate position optimization and non-uniform parameter models in the existing intelligent reflecting surface deployment research work, the general intelligent reflecting surface three-dimensional deployment method for maximizing the base station coverage area is provided, the Rice channel gain and a LoS/None-LoS (NLoS) statistical channel model are considered, a closed expression of the coverage area on the intelligent reflecting surface parameters is deduced, and the optimal deployment algorithm of the intelligent reflecting surface is designed.
The scheme for intelligent reflecting surface deployment and three-dimensional parameter setting in the dense urban area comprises the following steps:
and 200, acquiring related 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 center position of the intelligent super surface is determined according to the horizontal distance and the height between the super surface and the base station, and the orientation of the intelligent super surface is determined according to the horizontal and vertical rotation angles, so that the structure shown in the attached figure 2 is obtained.
Wherein the user and the base station are on an xOy plane, and the base station and the center of the intelligent reflecting surface are on an xOz plane;
h BS indicating the altitude of the base station;
h IRS indicating the altitude of the base station;
φ 1 representing the horizontal rotation angle of the intelligent reflecting surface;
φ 2 representing a vertical corner 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 phi 1 ,φ 2 ,
h IRS And determining and optimizing the position of the intelligent reflecting surface, namely optimizing the four parameters. After the model is established, the next step is carried out.
From the model determined in step 200, the base station to user channel gain can be expressed as:
wherein gamma is m,n The reflection factor h of the m row and n column reflection unit of the intelligent super surface is represented m,n Representing the channel gain, h, of the signal reflected by the m-th row and n-th column of the reflecting unit of the intelligent super surface D Channel gain representation of the signal-to-user direct link. Further, the two links respectively have LoS and NLoS paths, and are modeled through a Rice model:
thus, the signal-to-noise ratio of the user's received signal can be expressed as:
and substituting a specific channel gain model, and performing a series of transformations when the phase of the reflection unit satisfies:
wherein, P is the transmitting power of the base station;
k 1 a rice channel parameter for a direct link from a base station to a user;
k 2 the parameters of the Leise channel from the base station to the user are reflected by the intelligent reflecting surface;
cos theta represents the incident angle of the base station to the intelligent reflecting surface;
m and N represent the size of the intelligent reflecting surface, namely M rows of reflecting units of M are provided;
σ 2 is the noise power.
And after the maximum value of the receiving signal-to-noise ratio of the user is obtained, the next step is carried out.
Using the model established in step 200, a typical user is used as a reference, the angle of the user relative to the base station is β, and then the maximum linear distance that the base station can cover along the β angle is calculated.
Firstly, four kinds of channel gains are calculated according to the existing conditions
Secondly, 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 value gamma received by the user th And calculating the farthest distance of the typical user from the base station, wherein the distance is the straight-line distance covered by the base station, and then entering the next step.
The coverage area of a base station can be calculated by integrating the angle β, which is expressed as:
wherein
The deployment parameter phi of the intelligent reflector obtained in step 220 1 ,φ 2 ,
h IRS And (5) determining. It follows that the coverage area S is a function of phi 1 ,φ 2 ,
h IRS Is a non-linear function of (a).
Furthermore, according to the model, when
h IRS Other variables are affected when changed, e.g. by changing phi 1 ,φ 2 And further affects the overall coverage area. However, it is difficult to describe the effect of a variable on a closed-form solution, considering that the coverage area S is a function of φ 1 ,φ 2 ,
h IRS The design simplifies the area S and decomposes the area S into a plurality of fan-shaped area summation forms with equal angles, wherein the area summation forms are as follows:
where K is a parameter of the approximate solution for the estimated area and the opening angle per sector is
After the simplification, the problem of solving the optimal deployment parameter becomes an optimization problem constrained by inequality, and the optimal deployment parameter can be solved by using a Lagrange multiplier method and a logarithm barrier function
Advantageous effects
The three-dimensional deployment method of the intelligent reflector auxiliary network establishes a universal three-dimensional deployment model of the coverage extension of the intelligent reflector auxiliary base station, unifies various existing two-dimensional/three-dimensional intelligent reflector models, and defines phi 1 ,φ 2 ,
h IRS And relevant parameters are deployed on the intelligent reflecting surface, so that the spatial position and orientation of the intelligent reflecting surface are accurately described, and a foundation is laid for accurate analysis of network performance. On the basis, a closed expression of the coverage range of the base station under the intelligent reflector three-dimensional deployment model is further deduced, and a Rice channel gain and a LoS/NLoS statistical channel model are considered. Under the condition of designing the optimal intelligent reflector phase, the influence of various parameters on the coverage area of the base station is simulated and analyzed, and the deployment guidance suggestions of different intelligent reflector parameters are given.
Drawings
In order to clearly and clearly explain the technical steps of the present invention, all the drawings used in the description of the present invention will be briefly described below. It should be noted that the drawings described below are only exemplary words of the present invention, and those skilled in the art can still obtain other drawings in other different scenarios according to the drawings.
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a diagram of a model of an intelligent reflector for assisting a base station to service a user according to the present invention;
FIG. 3 is an explanatory diagram of horizontal and vertical corners of the intelligent reflective surface of the present invention;
FIG. 4 is a geometric plot of angle of incidence versus angle of rotation for the present invention;
FIG. 5 is a plot of coverage area as a function of horizontal rotation angle for the present invention;
FIGS. 6 and 7 are plots of coverage area of the present invention as a function of vertical rotation;
FIG. 8 is a graph of coverage area of the present invention as a function of horizontal distance of the intelligent reflector from the base station;
FIGS. 9 and 10 are graphs of coverage area of the present invention as a function of intelligent reflector height;
Detailed Description
The steps and processes of the present invention are described in detail below with reference to the drawings in the present application, and it is obvious that the example described in the present application is only an example application scenario of the present invention, and other results based on the present disclosure without substantial changes are all within the protection scope of the present invention.
FIG. 2 is an exemplary scenario of the present invention illustrating a step-wise variation of the implementation of the present invention. In the model, the base station is arranged at the center of coordinates, an omnidirectional antenna is adopted, and the intelligent reflecting surface is only required to be arranged on the optimal parameters calculated by the design
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 and multi-user scene, does not consider the interference among users, and can provide service for the multi-users by adopting various multi-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 coverage service provider at the time of deployment of the intelligent reflector.
The specific steps of 3-dimensional deployment of the 6G IRS-assisted network coverage extension are as follows:
and 300, acquiring related 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 center position of the intelligent super surface is determined according to the horizontal distance and the height between the super surface and the base station, and the orientation of the intelligent super surface is determined according to the horizontal and vertical rotation angles, so that the structure shown in the attached figure 2 is obtained.
Wherein the user and the base station are on an xOy plane, and the base station and the center of the intelligent reflecting surface are on an xOz plane;
h BS indicating the altitude of the base station;
h IRS indicating the altitude of the base station;
φ 1 representing the horizontal rotation angle of the intelligent reflecting surface;
φ 2 representing a vertical corner of the intelligent reflecting surface;
beta represents the horizontal azimuth of the user compared to the base station.
Step 310, setting the phase of the reflection factor of the electromagnetic unit of the intelligent super-surface to maximize the signal-to-noise ratio of the signal received by the user.
On the basis of formula (3)
Equation (3) can be further rewritten as:
the mean value of the SNR can then be decomposed into:
taking the model of the channel gain into account, further derivation can yield:
equation (9) can then be rewritten as:
further, it is obtained from the formula (14) in order to
To achieve the maximum, it must be satisfied that:
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 the base station is beta, and according to the geometrical relationship among the user, the base station and the intelligent reflecting surface:
wherein, according to the cosine theorem:
in practice h BS And h UE Are all known and therefore when
When determined, d can be calculated by substituting equation (16) BI ,d BU ,d IU Then substituting the model into the channel gain model matched with the environment to calculate
In order to further clearly show the relationship between the incident angle θ and other parameters, the present invention designs a more specific angle correlation diagram based 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:
when is phi 1 ,φ 2 ,
h IRS When all the line segments are determined, the proportional relationship between the line segments is determined, so that cos theta can be calculated.
The receive threshold for a given user is then γ th One user is covered and needs to be satisfied
According to this inequality can be obtained
Is recorded as
Step 330, calculating an approximate solution of the coverage range by a discretized summation method, and then calculating an optimal solution of the deployment parameters by using a Lagrange multiplier method.
First from
Randomly selecting initial values meeting the conditions in the value range omega, and obtaining the first derivative and the second derivative of S according to the formula (7)
Followed by a recursion according to:
iteration
Up to
Wherein the terminal condition epsilon is given in advance, and the precision of the result can be controlled by controlling the epsilon.
The simulation results are shown in fig. 5-10.
FIG. 5 shows the angle phi of the covered area S with the horizontal 1 The graph shows that the maximum coverage area is always present no matter what value other parameters are taken
Is obtained when the compound is used.
FIGS. 6 and 7 show the horizontal rotation phi of the covered area S 2 In most cases, the optimum vertical direction phi 2 =0, this means that the plane of the intelligent reflective surface is perpendicular to the direction of the incident signal. In particular, it is noted in fig. 6 that as the smart reflector is deployed higher, there is a tendency for the coverage area to increase, particularly as the smart reflector element units N are smaller and their optimal vertical-to-angular orientation is achieved
When moving. From fig. 7, we can conclude that the coverage area tends to increase as the intelligent reflector is closer to the base station, especially when the unit cell number of the intelligent reflector is smaller, but the trend gradually disappears with the increase of N. Therefore, when the number of the intelligent reflecting surface units is limited or less, the intelligent reflecting surface should be disposed at a position higher than the base station, and the plane thereof should be parallel to the ground as much as possible. Otherwise, its plane should be perpendicular to the direction of the incident signal.
FIG. 8 is a graph showing the variation of the coverage area of the present invention with the horizontal distance from the intelligent reflector to the base station, where phi is shown in each graph to eliminate the effect of the intelligent reflector in the vertical direction 2 Optimum value of (2)
It can be found that when the number of the intelligent reflection surface elements N is small, the intelligent reflection surface and the base station
When the horizontal distance is close, the coverage area is greatly 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 spread out away from the optimal deployment location of the base station. Therefore, when the number of smart reflective surface elements is small, the smart reflective surface should be disposed at the edge of the cell, which is highThe altitude is lower than the base station or is arranged at a position close to the base station and higher than the base station. Otherwise, the number of the elements of the intelligent reflecting surface has little influence, and the deployment of the intelligent reflecting surface near the base station is a good strategy.
Fig. 9 and 10 are graphs of the coverage area of the present invention as a function of the height of the intelligent reflective surface. As can be seen from FIG. 9, when the intelligent reflection surface is horizontally distant from the base station
When the number N of the elements of the intelligent reflecting surface is large, the optimal height of the intelligent reflecting surface exists, and the optimal solution is equal to the height of the base station. Looking closely at fig. 10, it can be seen that coverage is proportional to the height of the intelligent reflective surface, in the range of 0-90 meters. The closer the intelligent reflector is to the base station, the greater the influence of the height of the intelligent reflector on the coverage rate, and the influence is gradually weakened along with the increase of N. While following with
The optimal solution of the intelligent reflecting surface height disappears due to the increase of N and the decrease of N. Therefore, in the case of a large number of intelligent reflective surface elements, it is recommended to dispose the intelligent reflective surface near and at the height of the base station, while in other scenarios, it is recommended to dispose the intelligent reflective surface at the highest allowable position.
Claims (5)
1. A three-dimensional deployment method for a 6G-oriented Intelligent Reflecting Surface (IRS) auxiliary network is characterized by comprising the following steps: firstly, establishing a universal three-dimensional deployment model for coverage extension of an IRS auxiliary base station; then the phase phi of the IRS reflection unit is designed m,n Maximizing the received signal-to-noise ratio of the user; then, calculating the farthest straight line distance between the user and the base station under the condition of a certain signal-to-noise ratio threshold value for the user in each orientation of the base station; finally, a closed expression of the coverage range of the base station under the three-dimensional deployment model is given, an IRS three-dimensional deployment algorithm for maximizing the coverage range of the base station is designed, the coverage range maximization problem is formed into a convex optimization problem, and the convex optimization problem is processed by introducing auxiliary variables and utilizing a Lagrange multiplier methodAnd (5) solving the problem.
2. The three-dimensional deployment model of claim 1, wherein existing two-dimensional and three-dimensional IRS models are unified, and a horizontal rotation angle phi is defined 1 Angle of vertical rotation phi 2 Horizontal distance from base station
Vertical height h BS And when the IRS deploys related parameters, the spatial position and orientation of the IRS are accurately described, and a foundation is laid for accurate analysis of network performance.
3. IRS unit phase phi according to claim 1 m,n The design of (2) is characterized in that an IRS phase condition for maximizing the receiving signal-to-noise ratio of a user is given through closed derivation in consideration of a Rice channel gain and line-of-sight, non-line-of-sight statistical channel model
Wherein phi is m,n Indicates the phase of the cell at row m and column n on IRS D Is the phase shift value of the direct link,
phase shift values for the reflective links through the m row and n column elements on the IRS based on phi m,n Closed-form solution giving the maximum value of the mean value of the signal-to-noise ratio
Where P is the base station transmit power, k 1 Rice channel parameter, k, for direct base station-to-user link 2 For the rice channel parameters of the base station to user reflecting the link straight through the IRS,
expressed as the channel gain of a direct Line of Sight (LoS) link,
expressed as the channel gain of a direct Line of Sight (NLoS) link,
expressed as the channel gain of the LoS link reflected by the IRS,
expressed as channel gain of NLoS link reflected by IRS, theta represents incident angle of base station to IRS, M, N represents IRS horizontal and vertical unit number, sigma 2 Is the noise power.
4. The method of claim 1, wherein the farthest linear distance from each base station to the user is calculated based on channel conditions
And fourthly, calculating the incidence angle of the base station signal, finally carrying in the closed-form solution sum of the maximum value of the average value of the signal to noise ratio, and calculating the maximum distance of the user under the condition that the maximum signal to noise ratio of the user is greater than the threshold value.
5. The IRS three-dimensional deployment algorithm for maximizing the coverage of the base station as claimed in claim 1, wherein the closed solution of the coverage area is converted into an approximate solution capable of convex optimization by a discrete approximation method, and the problem is solved by introducing an auxiliary variable and using a Lagrange multiplier method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210766373.4A CN115379462B (en) | 2022-06-30 | 2022-06-30 | Three-dimensional deployment method for 6G intelligent reflector auxiliary network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210766373.4A CN115379462B (en) | 2022-06-30 | 2022-06-30 | Three-dimensional deployment method for 6G intelligent reflector auxiliary network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115379462A true CN115379462A (en) | 2022-11-22 |
| CN115379462B CN115379462B (en) | 2024-02-13 |
Family
ID=84062537
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210766373.4A Active CN115379462B (en) | 2022-06-30 | 2022-06-30 | Three-dimensional deployment method for 6G intelligent reflector auxiliary network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115379462B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200120630A1 (en) * | 2018-10-10 | 2020-04-16 | Beijing University Of Posts And Telecommunications | Method, apparatus and storage medium for determining location of intelligent base station |
| CN111698010A (en) * | 2020-06-11 | 2020-09-22 | 电子科技大学 | Resource allocation method based on intelligent reflection curved surface auxiliary communication |
| CN113315547A (en) * | 2021-05-28 | 2021-08-27 | 北京邮电大学 | Robust joint transmission beam optimization method for intelligent reflecting surface assisted multiple cells |
| US20210288698A1 (en) * | 2020-03-10 | 2021-09-16 | University Of Electronic Science And Technology Of China | Method for Intelligent Reflecting Surface Aided Terahertz Secure Communication System |
| CN113573293A (en) * | 2021-07-14 | 2021-10-29 | 南通大学 | Intelligent emergency communication system based on RIS |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
| US20220077919A1 (en) * | 2020-09-10 | 2022-03-10 | Qualcomm Iincorporated | Techniques to use reference signals for intelligent reflecting surface systems |
| WO2022121497A1 (en) * | 2020-12-11 | 2022-06-16 | 华南理工大学 | Millimeter wave intelligent reflecting surface communication-based large-scale antenna channel estimation method |
-
2022
- 2022-06-30 CN CN202210766373.4A patent/CN115379462B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200120630A1 (en) * | 2018-10-10 | 2020-04-16 | Beijing University Of Posts And Telecommunications | Method, apparatus and storage medium for determining location of intelligent base station |
| US20210288698A1 (en) * | 2020-03-10 | 2021-09-16 | University Of Electronic Science And Technology Of China | Method for Intelligent Reflecting Surface Aided Terahertz Secure Communication System |
| CN111698010A (en) * | 2020-06-11 | 2020-09-22 | 电子科技大学 | Resource allocation method based on intelligent reflection curved surface auxiliary communication |
| US20220077919A1 (en) * | 2020-09-10 | 2022-03-10 | Qualcomm Iincorporated | Techniques to use reference signals for intelligent reflecting surface systems |
| WO2022121497A1 (en) * | 2020-12-11 | 2022-06-16 | 华南理工大学 | Millimeter wave intelligent reflecting surface communication-based large-scale antenna channel estimation method |
| CN113315547A (en) * | 2021-05-28 | 2021-08-27 | 北京邮电大学 | Robust joint transmission beam optimization method for intelligent reflecting surface assisted multiple cells |
| CN113573293A (en) * | 2021-07-14 | 2021-10-29 | 南通大学 | Intelligent emergency communication system based on RIS |
| CN113645635A (en) * | 2021-08-12 | 2021-11-12 | 大连理工大学 | Design method of intelligent reflector-assisted high-energy-efficiency unmanned aerial vehicle communication system |
Non-Patent Citations (3)
| Title |
|---|
| ABDULLA MAHMOUD ET AL.: "Intelligent Reflecting Surfaces Assisted UAV Communications for IoT Networks: Performance Analysis", 《 IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING》 * |
| JIANGHUI LIU ET AL.: "Deployment Optimization of UAV-Aided Networks Through a Dynamic Tunable Model", 《 IEEE COMMUNICATIONS LETTERS 》 * |
| 崔亦军 等: "无线网络中智能超表面部署研究", 《无线电通信技术》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115379462B (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102204783B1 (en) | Deep learning-based beamforming communication system and method | |
| Li et al. | Channel model for millimeter-wave communications based on geometry statistics | |
| Chan et al. | 3D printing your wireless coverage | |
| Astapenya et al. | Last mile technique for wireless delivery system using an accelerating lens | |
| Parada et al. | Cell discovery based on historical user's location in mmWave 5G | |
| Zhang et al. | Optimizing coverage with intelligent surfaces for indoor mmWave networks | |
| Banday et al. | SINR analysis and interference management of macrocell cellular networks in dense urban environments | |
| Kokkoniemi et al. | Stochastic geometry based interference analysis of multiuser mmWave networks with RIS | |
| Nguyen-Kha et al. | Joint UAV placement and IRS phase shift optimization in downlink networks | |
| Şahin et al. | Control of electromagnetic radiation on coexisting smart radio environment | |
| Tatino et al. | Beam based stochastic model of the coverage probability in 5G millimeter wave systems | |
| Vrontos et al. | Performance evaluation of spectrum sharing in mmWave cellular networks using ray-tracing | |
| US20070046538A1 (en) | Wireless network apparatus and adaptive digital beamforming method thereof | |
| Lu et al. | Performance model and deployment strategy for mm-Wave multi-cellular systems | |
| CN115379462B (en) | Three-dimensional deployment method for 6G intelligent reflector auxiliary network | |
| Tian et al. | Optimizing the deployment of reconfigurable intelligent surfaces in mmWave vehicular systems | |
| Adebo et al. | A hybrid localization scheme for detection of primary user emulator in cognitive radio networks | |
| Tsoulos et al. | Adaptive antennas for microcellular and mixed cell environments with DS-CDMA | |
| CN115038121A (en) | Semi-static phase design method, system and medium for RIS auxiliary communication system | |
| Kumari et al. | Optimization of street canyon outdoor channel deployment geometry for mmWave 5G communication | |
| Van der Perre et al. | RadioWeaves for efficient connectivity: analysis andimpact of constraints in actual deployments | |
| Onireti et al. | Coverage and rate analysis in the uplink of millimeter wave cellular networks with fractional power control | |
| Getahun et al. | Performance analysis of mmWave radio propagations in an indoor environment for 5G networks | |
| Chopra et al. | Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment | |
| Abdulrazak | Coexistence of IMT-Advanced Systems for Spectrum Sharing with FSS Receivers in C-Band and Extended C-Band |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |