CN108566645A - Network user's cut-in method based on Neyman-Scott cluster processes - Google Patents
Network user's cut-in method based on Neyman-Scott cluster processes Download PDFInfo
- Publication number
- CN108566645A CN108566645A CN201810258383.0A CN201810258383A CN108566645A CN 108566645 A CN108566645 A CN 108566645A CN 201810258383 A CN201810258383 A CN 201810258383A CN 108566645 A CN108566645 A CN 108566645A
- Authority
- CN
- China
- Prior art keywords
- user
- cluster
- network
- neyman
- scott
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 104
- 238000005562 fading Methods 0.000 claims description 12
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 239000000654 additive Substances 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000009795 derivation Methods 0.000 claims 1
- 230000001413 cellular effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 238000005315 distribution function Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/345—Interference values
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3911—Fading models or fading generators
-
- 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
- H04W16/22—Traffic simulation tools or models
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Quality & Reliability (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The present invention provides a kind of network user's cut-in method based on Neyman Scott cluster processes, includes the following steps：1) system model is established based on Neyman Scott cluster processes；2) the distance between different base station distributed model in user and cluster is derived；3) SINR distributed models and interference profile model are derived；4) average achievable rate when user is serviced by some base station in SINR distributed models and range distribution model inference network is utilized；5) the interference profile model of super-intensive network is analyzed；6) final user is obtained by multiple base stations while the average achievable rate of service using the above results；7) by comparing, it is known that the present invention is obviously improved the average achievable rate of user.Be compared to for poisson process, the present invention can more closing to reality super-intensive network environment, obtain higher network and be averaged achievable rate, the actual deployment of super-intensive network is of great significance to future.
Description
Technical Field
The invention belongs to the technical field of ultra-dense network user access, and relates to a network user access method based on a Neyman-Scott cluster process.
Background
In recent years, significant progress has been made in modeling small cellular networks (small cells) using random geometry. Due to the characteristics of randomness and unpredictability of the deployment position of the micro base station, the micro base station can be modeled into one or more random point processes on an Euclidean plane, compared with a traditional hexagonal network model, the position distribution of the base station is modeled by using a Poisson point process, the interruption probability and the coverage probability of a user are respectively obtained by analyzing the cumulative distribution function and the complementary cumulative distribution function of SINR, and the network is more suitable for the actual scene of the network. However, the poisson point process assumes that all base stations and users are independent of each other, and in fact, have some relationship to each other.
In a future ultra-dense networking environment, the same user may be served by a plurality of base stations, and due to the simplification of the technology for deploying the base stations, the user can deploy the base stations by himself to obtain a better user experience, so that the base stations and the user distribution have a certain dependency relationship, and even the base stations are distributed around the user. The characteristic causes wide attention of academics, and a plurality of scholars propose various novel network models based on a Neyman-Scott cluster process at present, and can obtain an average reachable rate theoretical expression of the network by using random geometry as a mathematical tool.
The invention provides a method for accessing a network to users facing a future ultra-dense network, which has the characteristic that the density of base stations of the future network is greatly improved due to the trend that more and more users can deploy the base stations by themselves to form the ultra-dense network and the users are used as a center for clustering. The invention adopts a Neyman-Scott cluster process to construct a system model, designs a brand new service acquisition mode that a user can simultaneously access all base stations which take the user as a father node cluster, and deduces a theoretical expression of average reachable rate by using a random geometric theory. Compared with the traditional modeling mode, the method can better fit the actual network communication scene, analyzes the theoretical expression of the average reachable rate of the user, achieves higher average reachable rate of the network, and has important significance for the development and research of the ultra-dense network in the future.
Disclosure of Invention
In order to achieve the purpose, the invention provides a network user access method based on a Neyman-Scott cluster process, which solves the problem that the existing system model for analyzing the ultra-dense network has insufficient consideration on the relationship between a user and a base station, and can be closer to the actual communication environment by utilizing the Neyman-Scott cluster process to obtain higher average reachable rate of the network.
The technical scheme adopted by the invention is that a network user access method based on a Neyman-Scott cluster process is characterized in that: the method comprises the following steps:
establishing a system model based on a Neyman-Scott cluster process, and establishing a connection mode in which a user can be simultaneously served by a plurality of base stations;
deducing a distance distribution model between a user and different base stations in a cluster;
deducing an SINR distribution model and an interference distribution model;
deducing the average reachable rate of a user in the network when the user is served by a certain base station by utilizing an SINR distribution model and a distance distribution model;
analyzing an interference distribution model of the ultra-dense network by utilizing the Barhm distribution and the Reduced Barhm distribution of the Neyman-Scott cluster process;
obtaining the final average reachable rate of the user served by a plurality of base stations simultaneously by using the result;
by comparing the average reachable rate of the user under the same condition of the Neyman-Scott cluster process and the Poisson point process, the user can be provided with services by a plurality of base stations in the network at the same time, and the average reachable rate of the user is obviously improved.
Further, the user distribution is modeled as an intensity function with a constant λPThe base station location is modeled as a sub-process distributed with the user location as a parent process and the intensity function isWhereinThe base station is uniformly distributed in a circle which takes the user as the center of a circle and gamma as the radius according to a certain probability, and has a probability density function with the following form:
further, by utilizing the characteristics of the poisson point process, the obtained conclusion is popularized to users located at any position in the ultra-dense network.
Further, based on a Neyman-Scott cluster process, a system model and a corresponding SINR model are established, and a distance distribution model between a user and different base stations in a cluster is deduced, wherein the specific analysis process of the distance distribution model is as follows:
deriving probability density function of available distance
Wherein,is a gamma function and r is the distance between the user and the different base stations in the cluster.
Further, the expression form of the SINR distribution model is as follows:
wherein, the expression form is an SINR distribution model of a base station which is close to the first base station of a typical user at the origin as a father node in a cluster k,indicating the cumulative interference experienced by the user from other base stations not belonging to the same cluster, N0Is an additive white gaussian noise, and is,indicating the useful received power received by the user from the base station that is l-th closer to him; all base stations in the network have the same deployment timeand transmitting power, wherein the fading model is Rayleigh distribution, the average value is 1, and the index α in the path fading model is more than 2.
Further, in the case of a very dense network, the average reachable rate provided by the base station in the serving cluster k that is close to the user's first base station for the user is as follows:
further, the interference distribution model utilizes a balun distribution and Reduced balun distribution analysis, and utilizes the definition of the laplace functional of the cumulative interference received at the user:
wherein (a) is obtained by substituting H-exp (1) without affecting the distribution characteristic of the poisson point process after removing a certain point in the poisson point processTherefore, it willSubstitution can simplify the above result to:
wherein
The lower limit of integration in brackets starts from γ because the base stations in range γ are all serving base stations and do not cause interference to users in the network.
Further, substituting the Laplace transform result intoAnd summing and deducing all base stations in the cluster to obtain the average reachable rate of the network users, thereby effectively improving the average reachable rate of the users in the network, and the result is as follows:
further, comparing the average reachable rates of users under the same conditions as the poisson process, and deriving the average reachable rate of users in the poisson-point-available process as follows:
wherein,
in the same situation, when a user is served by the nearest base station in the course of the Neyman-Scott cluster, the average achievable rate model is as follows:
wherein,
according to the formula
1-exp(-ξx)≤ξx
When the same density lambda is taken, there are
Irrespective of noise, i.e. N0at 0, α 4, the transmitted power is 20dBm, and the fading model employs rayleigh fading, which is:
the invention has the beneficial effects that:
the invention provides a network user access method based on a Neyman-Scott cluster process, which provides a brand-new cellular network working mode that a user is simultaneously provided with services by a plurality of base stations, and can effectively avoid the problem that the user cannot access at a high speed due to the limitation of a return link; the position relation between the base station and the user is modeled by utilizing a Neyman-Scott cluster process, the average reachable rate of the user in the network is analyzed through a distance distribution model and an interference distribution model, the method can be closer to the actual ultra-dense network environment, and the method has important significance for the actual deployment of the ultra-dense network in the future.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a working model diagram of the network user access method based on the Neyman-Scott cluster process in the invention;
fig. 2 is a flowchart of an implementation of the network user access method based on the Neyman-Scott cluster process in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present invention provides a scenario that a user is used as a center and a plurality of base stations simultaneously provide services for the user, and network performance parameters are analyzed by using a Neyman-Scott cluster process in the scenario. As base stations will become more dense in future networks, the number of base stations per square kilometer may be much greater than 103And the base stations can be deployed by users, so that the base station deployment has the characteristic of clustering around the users, and the users can be served by the base stations simultaneously, so that the scene can better fit the actual network communication scene, and the theoretical expression of the average reachable rate of the users is analyzed.
As shown in fig. 2, the network user access method based on the Neyman-Scott cluster process provided by the present invention includes the following steps:
establishing a system model based on a Neyman-Scott cluster process, and establishing a connection mode in which a user can be served by a plurality of base stations at the same time;
deducing a distance distribution model between a user and different base stations in a cluster;
deducing an SINR distribution model and an interference distribution model;
deducing the average reachable rate of a user in the network when the user is served by a certain base station by utilizing an SINR distribution model and a distance distribution model;
analyzing an interference distribution model of the ultra-dense network by utilizing the Barhm distribution and the Reduced Barhm distribution of the Neyman-Scott cluster process;
and under the condition of an ultra-dense network, obtaining a final network average reachable rate model of a user simultaneously served by a plurality of base stations by using the result.
The concrete method of the steps is as follows:
step 201: so the distribution of users is modeled as a constant λ in the intensity functionPAnd the location of the base station is modeled as a sub-process distributed with the location of the user as a parent process, and the strength function isWhereinAs the average of the number of points within each cluster.
The base stations are uniformly distributed in a circle with a user as a center and gamma as a radius by a certain probability, and have a probability density function with the following form:
assuming that the mobile user is located at the origin, due to the stationary characteristic of the poisson point process, the whole model can be known to have the characteristic of mobility invariance, so that the obtained conclusion can be popularized to the user located at any position in the ultra-dense network.
Step 202: based on a comparative well-known Neyman-Scott cluster process, a system model and a corresponding SINR model are established, a distance distribution model between a user and different base stations in a cluster is deduced, and a specific analysis process of the distance distribution of the base station which is close to the nth user is as follows:
derived distance probability density function
Step 203: in the cluster k with the typical user at the origin as the parent node, the base station closer to the user has the SINR expressed as follows
WhereinIndicating the cumulative interference experienced by the user from other base stations not belonging to the same cluster, N0Is an additive white gaussian noise, and is,indicating the useful received power received by the user from the base station that is closest to him.
all base stations in the network have the same transmitting power when deployed, the fading model is Rayleigh distribution, the average value is 1, H-exp (1) exists, and the index α in the path fading model is larger than 2.
And a user can be simultaneously served by a plurality of base stations in the network, and the average reachable rate of the user can be obviously improved.
Step 204: under the condition of a super-dense network, an average reachable rate model provided for a user by a base station which is close to the user I in a service cluster k is deduced as follows:
step 205: and analyzing an interference distribution model of the ultra-dense network by using the Barmer distribution and the Reduced Barmer distribution of the Neyman-Scott cluster process. And using the definition of the Laplace transform to give a Laplace functional of the accumulated interference received at the user:
wherein (a) is obtained by substituting H-exp (1) without affecting the distribution characteristic of the poisson point process after removing a certain point in the poisson point processTherefore, it willSubstitution can simplify the above result to:
wherein
The lower limit of integration in brackets starts from γ because the base stations in range γ are all serving base stations and do not cause interference to users in the network.
Step 206: substituting the above Laplace transform result intoAnd toThe average achievable rate of the network user can be derived by summing all the base stations in the cluster as follows:
the average achievable rate of the user of the proposed algorithm and the poisson point process under the same situation is compared. The user average achievable rate results for the derived poisson point procedure are as follows:
wherein,
considering the equivalent situation in the course of a Neyman-Scott cluster, when a user is served only by the base station closest to the user, the average achievable rate model is as follows:
wherein,
according to the formula
1-exp(-ξx)≤ξx
It can be obtained that when the same density lambda is obtained, there are
in order to compare the present invention with the base station deployment based on the poisson point process, the present invention considers the verification of the embodiment under a special condition, that is, noise is not considered, that is, N0 is 0, α is 4, the transmission power is 20dBm, the fading model adopts rayleigh fading, and the following can be obtained under the same distance:
it can be easily found that the access method based on the Neyman-Scott cluster process can provide better average user reachable rate than the access method based on the Poisson point process when a single base station provides service. This performance is also improved better when a multiple access method is introduced.
The invention provides a brand-new cellular network working mode that one user is simultaneously provided with service by a plurality of base stations based on a network user access method of a Neyman-Scott cluster process, which can effectively avoid the problem that the user rate is not increased due to the limitation of a return link; the position relation between the base station and the user is modeled by utilizing a Neyman-Scott cluster process, the average reachable rate of the user in the network is analyzed through a distance distribution model and an interference distribution model, the method can be closer to the actual ultra-dense network environment, and the method has important significance for the actual deployment of the ultra-dense network in the future.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (9)
1. A network user access method based on a Neyman-Scott cluster process is characterized in that: the method comprises the following steps:
establishing a system model based on a Neyman-Scott cluster process, and establishing a connection mode in which a user can be simultaneously served by a plurality of base stations;
deducing a distance distribution model between a user and different base stations in a cluster;
deducing an SINR distribution model and an interference distribution model;
deducing the average reachable rate of a user in the network when the user is served by a certain base station by utilizing an SINR distribution model and a distance distribution model;
analyzing an interference distribution model of the ultra-dense network by utilizing the Barhm distribution and the Reduced Barhm distribution of the Neyman-Scott cluster process;
obtaining the final average reachable rate of the user served by a plurality of base stations simultaneously by using the result;
by comparing the average reachable rate of the user under the same condition of the Neyman-Scott cluster process and the Poisson point process, the user can be provided with services by a plurality of base stations in the network at the same time, and the average reachable rate of the user is obviously improved.
2. The network user access method based on the Neyman-Scott cluster process of claim 1, wherein: the user distribution is modeled as a poisson point process with an intensity function of a constant lambdap, the base station location is modeled as a subprocess distributed with the user location as a parent process, and the intensity function isWhereinThe average value of the number of points in each cluster;
the base stations are uniformly distributed in a circle which takes the user as the center of the circle and gamma as the radius, and have the probability density function with the following form:
3. the network user access method based on the Neyman-Scott cluster process of claim 2, wherein: and (4) utilizing the characteristics of the Poisson point process to promote the obtained conclusion to users located at any position in the ultra-dense network.
4. The network user access method based on the Neyman-Scott cluster process of claim 3, wherein: based on a Neyman-Scott cluster process, a system model and a corresponding SINR model are established, and a distance distribution model between a user and different base stations in a cluster is deduced, wherein the specific analysis process is as follows:
derived distance probability density function
Wherein,is a gamma function and r is the distance between the user and the different base stations in the cluster.
5. The network user access method based on the Neyman-Scott cluster process of claim 4, wherein: the expression form of the SINR distribution model is as follows:
wherein, the expression form is an SINR distribution model of a base station which is close to the first base station of a typical user at the origin as a father node in a cluster k,indicating the cumulative interference experienced by the user from other base stations not belonging to the same cluster, N0Is additive white Gaussian noise, PHrl -αall base stations in the network have the same transmitting power when deployed, the fading model is Rayleigh distribution, the mean value is 1, and the index α in the path fading model is larger than 2.
6. The network user access method based on the Neyman-Scott cluster process of claim 5, wherein under the condition of the ultra-dense network, the model of the average reachable rate provided by the base station close to the user I in the service cluster k for the user is as follows:
7. the network user access method based on the Neyman-Scott cluster process of claim 6, wherein: the interference distribution model utilizes the Baram distribution and Reduced Baram distribution analysis, and utilizes the Laplace functional of the Laplace transform to give the accumulated interference received by the user:
wherein (a) is obtained by substituting H-exp (1) without affecting the distribution characteristic of the poisson point process after removing a certain point in the poisson point processTherefore, it willSubstitution can simplify the above result to:
wherein
The lower limit of integration in brackets starts from γ because the base stations in range γ are all serving base stations and do not cause interference to users in the network.
8. The network user access method based on the Neyman-Scott cluster process of claim 7, wherein: substituting the Laplace transform result intoAnd summing all base stations in the cluster to derive the average reachable rate of the network user:
9. the network user access method based on the Neyman-Scott cluster process of claim 8, wherein the average reachable rate of the user in the poisson process under the same condition is compared, and the user average reachable rate in the poisson point derivation process is derived as follows:
wherein,
in the same situation, when a user is served by the nearest base station in the course of the Neyman-Scott cluster, the average achievable rate model is as follows:
wherein,
according to the formula
1-exp(-ξx)≤ξx
When the same density lambda is taken, there are
Irrespective of noise, i.e. N0at 0, α 4, the transmitted power is 20dBm, and the fading model employs rayleigh fading, which is:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810258383.0A CN108566645A (en) | 2018-03-27 | 2018-03-27 | Network user's cut-in method based on Neyman-Scott cluster processes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810258383.0A CN108566645A (en) | 2018-03-27 | 2018-03-27 | Network user's cut-in method based on Neyman-Scott cluster processes |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108566645A true CN108566645A (en) | 2018-09-21 |
Family
ID=63533472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810258383.0A Pending CN108566645A (en) | 2018-03-27 | 2018-03-27 | Network user's cut-in method based on Neyman-Scott cluster processes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108566645A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110912747A (en) * | 2019-11-28 | 2020-03-24 | 江苏电力信息技术有限公司 | Random geometry-based power wireless private network performance analysis method |
| CN111193646A (en) * | 2020-01-07 | 2020-05-22 | 上海置维信息科技有限公司 | C-RAN-based smart city high-speed data transmission method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040068667A1 (en) * | 2002-10-03 | 2004-04-08 | International Business Machines Corporation | Method and apparatus for securing and managing cluster computing in a network data processing system |
| EP2490378A1 (en) * | 2011-02-18 | 2012-08-22 | NTT DoCoMo, Inc. | Apparatus and method for determining a control unit using feasibility requests and feasibility responses |
| CN105933940A (en) * | 2016-05-24 | 2016-09-07 | 安徽科技学院 | Seamless handover method based on collaborative base station clustering in ultra-dense network |
| CN106454919A (en) * | 2016-10-25 | 2017-02-22 | 北京科技大学 | Heterogeneous cellular network base station deployment method based on Poisson cluster process |
-
2018
- 2018-03-27 CN CN201810258383.0A patent/CN108566645A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040068667A1 (en) * | 2002-10-03 | 2004-04-08 | International Business Machines Corporation | Method and apparatus for securing and managing cluster computing in a network data processing system |
| EP2490378A1 (en) * | 2011-02-18 | 2012-08-22 | NTT DoCoMo, Inc. | Apparatus and method for determining a control unit using feasibility requests and feasibility responses |
| CN105933940A (en) * | 2016-05-24 | 2016-09-07 | 安徽科技学院 | Seamless handover method based on collaborative base station clustering in ultra-dense network |
| CN106454919A (en) * | 2016-10-25 | 2017-02-22 | 北京科技大学 | Heterogeneous cellular network base station deployment method based on Poisson cluster process |
Non-Patent Citations (3)
| Title |
|---|
| YOUNG JIN CHUN 等: "Analysis of Heterogeneous Cellular Networks Interference with Biased Cell Association Using Poisson Cluster Processes", 《2014 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE》 * |
| 王飞: "异构网中干扰管理技术研究", 《中国博士学位论文全文数据库第I信息科技辑》 * |
| 马忠贵 等: "基于泊松簇过程的三层异构蜂窝网络部署模型", 《工程科学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110912747A (en) * | 2019-11-28 | 2020-03-24 | 江苏电力信息技术有限公司 | Random geometry-based power wireless private network performance analysis method |
| CN110912747B (en) * | 2019-11-28 | 2021-08-13 | 江苏电力信息技术有限公司 | Random geometry-based power wireless private network performance analysis method |
| CN111193646A (en) * | 2020-01-07 | 2020-05-22 | 上海置维信息科技有限公司 | C-RAN-based smart city high-speed data transmission method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109474980B (en) | Wireless network resource allocation method based on deep reinforcement learning | |
| CN106792451B (en) | D2D communication resource optimization method based on multi-population genetic algorithm | |
| CN106507393B (en) | Access selection method based on comprehensive overhead function | |
| CN108965009B (en) | Load known user association method based on potential game | |
| CN110225578A (en) | A method of the D2D resource allocation based on graph coloring | |
| Shahid et al. | Self-organized energy-efficient cross-layer optimization for device to device communication in heterogeneous cellular networks | |
| CN108924934B (en) | Heterogeneous network interference management method based on multi-dimensional resource allocation | |
| Huang et al. | Resource allocation for D2D communications with a novel distributed Q-learning algorithm in heterogeneous networks | |
| CN108566645A (en) | Network user's cut-in method based on Neyman-Scott cluster processes | |
| CN113099423B (en) | Deployment method of narrowband cellular Internet of things based on Poisson cluster process | |
| CN108282888B (en) | D2D resource allocation method based on improved fuzzy clustering | |
| Cheung et al. | Stochastic analysis of two-tier networks: Effect of spectrum allocation | |
| CN114423070A (en) | D2D-based heterogeneous wireless network power distribution method and system | |
| CN110139282A (en) | A kind of energy acquisition D2D communication resource allocation method neural network based | |
| CN107612745A (en) | A kind of method of determination D2D network models and the method for assessing D2D network model performances | |
| CN105992219A (en) | Method and device for obtaining management strategy of heterogeneous network | |
| CN111866895A (en) | Unauthorized frequency band mode selection method of 5G NR base station | |
| CN108810855B (en) | Clustering D2D resource allocation method based on energy constraint and interference limited area | |
| Jiang et al. | User rate and energy efficiency of HetNets based on Poisson cluster process | |
| Liu et al. | Power allocation and channel selection in small cell networks based on traffic-offloading | |
| CN110380808B (en) | Micro-cell semi-clustering interference coordination method taking user equipment as center | |
| CN109041016B (en) | Method for optimizing terminal access number of 5G communication system in dense scene | |
| Benchaabene et al. | A genetic algorithm for solving the radio network planning problem in 5g cellular networks | |
| Fukushima et al. | Smoothing Method of User-equipment Accommodation for Blockchain-based Wireless Network Sharing | |
| CN113993098B (en) | Power control factor setting method for 6G unmanned aerial vehicle user |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180921 |