CN111988789B - Wireless network node deployment optimization method, system and device - Google Patents
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Abstract
The invention discloses a method, a system and a device for optimizing wireless network node deployment, wherein the method specifically comprises the following steps: the method comprises the steps of establishing a D2D node distribution model in a limited wireless network based on a binomial point process, deploying millimeter waves in the limited wireless network on the basis, setting a D2D antenna model, establishing a general model of blocking, channel distribution and path fading, deducing an SINR distribution model and the distance distribution from a target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model and the general model, completing the Laplace transform deduction of interference power based on the distance distribution and the characteristics of the binomial point process, and finally obtaining an accurate expression of coverage probability at the target receiver by utilizing the Laplace transform of the distance distribution and the interference power according to a coverage probability definition formula. The invention discovers that the method can accurately describe the coverage probability in the limited wireless network through the numerical simulation result.
Description
Technical Field
The invention belongs to the technical field of wireless communication networks, and particularly relates to a method, a system and a device for optimizing wireless network node deployment.
Background
With the rapid development of mobile internet devices and digital multimedia, the mobile data traffic is rapidly increasing, and the available spectrum resources in the microwave band do not seem to meet the requirements of future mobile communication, so people have a great interest in the research of new frequency bands, and the millimeter wave band is considered as an important component of the fifth generation (5G) cellular network due to the large amount of available spectrum resources. Compared with the existing microwave communication, the millimeter wave communication has the following characteristics: (1) Small wavelength, which facilitates deployment of large antenna arrays on transceivers to increase array gain; (2) Millimeter wave signals are sensitive to blocking effects and suffer greater penetration losses through obstacles than carriers below 6GHz, which are a necessary consideration in analyzing millimeter wave communications. Another possible solution to improve network capacity is to enable device-to-device (D2D) communication in a cellular network. D2D communication may enable file transfer between close range devices, thereby saving transmission power and network resources.
Stochastic geometry theory is used to study millimeter wave and D2D communication, and the homogeneous poisson point process is often used to model large-scale stochastic networks due to its simplicity and ease of processing, but it cannot be used to model limited networks for a given number of nodes. With the popularization of millimeter wave communication, it may become a mainstream demand to construct a D2D network in a limited area in a cellular network, and in such a case, the homogeneous poisson point process is not applicable.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method, a system and a device for optimizing the deployment of wireless network nodes.
In order to achieve the purpose, the invention adopts the technical scheme that: a wireless network node deployment optimization method based on a binomial point process comprises the following steps:
s1, establishing a distribution model of D2D nodes in an analysis area of a limited wireless network by using a uniform binomial point process;
s2, on the basis of the D2D node distribution model, deploying millimeter waves in an analysis area in the limited wireless network in the S1, setting an antenna model of D2D equipment, and establishing a general model of blocking, channel distribution and path fading;
s3, deducing an SINR distribution model and distance distribution from a D2D target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model in the S1 and a general model of blockage, channel distribution and path fading obtained in the S2;
s4, obtaining Laplace transform of interference power at the D2D target receiver based on the distance distribution obtained in the S3 and the characteristic of the binomial point process;
and S5, according to the coverage probability definition, combining the distance distribution obtained in the S3 and the Laplace transform distribution of the interference power obtained in the S4 to deduce the coverage probability at the target receiver.
In S1, an analysis area of a limited wireless network is determined, D2D devices in the analysis area are independently and uniformly distributed in the area according to a uniform binomial point process, one half of the devices in the analysis area are set as transmitters, the other half of the devices in the analysis area are set as receivers, each receiver corresponds to one transmitter as service equipment, and all the transmitters in the analysis area are in a transmission state at the same time; and acquiring the positions of the target receiver and the center of the analysis area, and then acquiring the distance between the target receiver and the center of the analysis area and the distance between the target receiver and the transmitter.
In S2, according to the propagation characteristics of millimeter waves, the D2D device is set to adopt an array antenna to obtain beam forming gain, a sector antenna is used for approximating an actual antenna, further the directional gain of a link is obtained, the link between a target transmitter and a receiver is set to obtain perfect directional gain, an interference link generates random directional gain, and four possible directional gain results and corresponding probabilities are obtained.
In S2, a line of sight (LOS) sphere model is used for modeling a blocking effect, a target receiver is defined to be located in the center of an LOS area, when the distance between the target receiver and a transmitter is smaller than or equal to the radius of the LOS area, the probability that a link between the target receiver and the transmitter is LOS is 1, when the distance between the target receiver and the transmitter is larger than or equal to the radius of the LOS area, the probability that the link between the target receiver and the transmitter is LOS is 0, the target receiver randomly selects one transmitter as a service transmitter in the LOS area, the rest transmitters are interference transmitters, and a path LOSs function l (S) = S of the LOS link is obtained -α And the channel gain of the LOS link, s denotes the distance between the receiver and the transmitter, and α is a path LOSs parameter.
In S3, based on S1The obtained D2D node distribution model and the general model in S2 can obtain an SINR distribution model of
The numerator part represents the received power at the target receiver, and the denominator part represents the interference and noise influence on the signal transmission process; interference power
From transmitters other than the serving transmitter in the LOS region of the target receiver;
the probability density function of the distance is:
the serving transmitter and the interfering transmitter of the target receiver are both randomly selected in the LOS region, and the probability density functions of the serving distance and the interfering distance both satisfy the derivation, so that the conditional probability density functions of the serving distance and the interfering distance are f R (r|v 0 )＝f S (r|v 0 ) And f W (w|v 0 )＝f S (w|v 0 ) And v is 0 Has a probability density function of
0≤v 0 ≤R。
In S4, calculating Laplace transform of interference power based on interference distance distribution and binomial point process characteristics,
when v is more than or equal to 0 0 ≤R-R B The laplace transform of the interference power is:
when R-R B ＜v 0 When R is less than or equal to R, the Laplace of the interference power is converted into:
wherein,
in S5, the coverage probability is defined as P c And = P { SINR > T }, which represents the probability that the SINR at the receiver is greater than the threshold T, and the coverage probability expression is:
wherein the probability P [ SINR > T]Is derived as follows:
combining interference power Laplace transform with coverage probability definitional formula, service distance distribution, v 0 The exact solution to the coverage probability is obtained as:
the device comprises a D2D node distribution model building module, a general model building module for blocking, channel distribution and path fading, an SINR distribution model obtaining module, a distance distribution calculating module, a Laplace transform module and a coverage probability calculating module;
the distribution model building module of the D2D nodes is used for building a distribution model of the D2D nodes in a limited wireless network by using a uniform binomial point process;
the general model building module for blocking, channel distribution and path fading is used for deploying millimeter waves in the limited wireless network S1 on the basis of a D2D node distribution model, setting an antenna model of D2D equipment and building a general model for blocking, channel distribution and path fading;
the SINR distribution model obtaining module deduces SINR distribution models based on the D2D node distribution model and the general model of the blockage, the channel distribution and the path fading
The distance distribution calculation module deduces the distance distribution from a D2D target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model and the general models of blockage, channel distribution and path fading;
the Laplace transform module deduces Laplace transform of interference power at the D2D target receiver according to the distance distribution and the binomial point process characteristics;
the coverage probability calculation module combines the distance distribution and the Laplace transform distribution of the interference power to derive a coverage probability at a target receiver based on a coverage probability definitional formula.
A computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the radio network node deployment optimization method of the invention when executing the computer program.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the radio network node deployment optimization method of the invention.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention is based on a random geometry method, utilizes a binomial point process to model D2D positions in a wired wireless network, considers service nodes as a part of the point process, populates the positions of service transmitters to any positions, accurately describes distance distribution, deploys millimeter waves into a network model, is more close to an actual communication scene, can accurately describe coverage probability in a limited wireless network, and has important significance for the research of future heterogeneous cellular networks.
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FIG. 1 is a block diagram of an implementation flow of the present invention.
FIG. 2 is a node distribution diagram for modeling a D2D millimeter wave network in a limited area based on a binomial point process.
Fig. 3 is a graph of the effect of the total number of devices of the present invention on the probability of coverage.
Fig. 4 is a diagram showing the effect of the antenna parameters of the present invention on the coverage probability.
Detailed Description
Firstly, a distribution model of D2D nodes is established in a limited wireless network by using a binomial point process of a random geometric theory, millimeter waves are deployed in the limited wireless network on the basis, an antenna model of D2D equipment is set, a general model of blocking, channel distribution and path fading is established, then a signal to interference plus noise ratio (SINR) distribution model and distance distribution from a target receiver to a transmitter in a visual range are deduced based on the D2D node distribution model and the general model, then Laplace transform derivation of interference power is completed based on the distance distribution and the characteristics of the binomial point process, and finally, according to a coverage probability definition formula, a coverage probability accurate expression at the target receiver can be obtained by utilizing the Laplace transform of the distance distribution and the interference power.
Referring to the flow chart 1, the specific implementation steps of the invention are as follows:
(1) A circular limited area b (0, R) with the radius of R is used as an analysis area of the limited wireless network, D2D devices are independently and uniformly distributed in the analysis area according to a uniform binomial point process, the position of each device in the analysis area is represented as y, and y belongs to R 2 The probability density function is
If the total number of the devices in the analysis area is N, half are transmitters and the other half are receivers, each receiver has one transmitter as service device, and all the transmitters in the area are set to be in a transmission state at the same time, and the transmission power of each transmitter is P d . The target receiver is positioned at the origin, and the center of the analysis area is positioned on the x-axis x 0 A distance v between the target receiver and the center of the analysis area 0 ＝||x 0 ||∈[0，R]The distance variable from the transmitter can be expressed as s = | | | x 0 + y | |, at v 0 ＝||x 0 Under the condition of | l, all distance variables are independent.
(2) Based on the D2D node distribution model, millimeter wave signals are deployed in a wired wireless network, an antenna model of D2D equipment is set, and a general model of blocking, channel distribution and path fading is established.
According to the propagation characteristics of millimeter waves, setting a D2D device to obtain beam forming gain by adopting an array antenna, approximating an actual antenna by using a sector antenna so as to obtain the directional gain of a link, and setting a link between a target receiver and a service transmitter thereof so as to obtain perfect directional gain D 0 ＝M t M r Wherein M is t 、M r Representing the main lobe gain of the transmitter and receiver sector antennas, respectively, while the interfering link produces a random directional gain D l Including four possible directional gain results a k Corresponding probability b k K is an element of {1,2,3,4}, as shown in the following table;
wherein m is t 、m r Indicating the side lobe gain, theta, of the transmitter and receiver sector antennas, respectively t 、θ r Representing the main lobe beamwidth of the transmitter and receiver sector antennas, respectively.
The blocking effect in the system model is modeled by a line of sight (LOS) sphere model, and the target receiver is defined to be positioned in the center of an LOS area which can be expressed as B (0, R) B ) The radius of the region is R B And s represents the distance between the target receiver and the transmitter, when 0 < s < R B When the probability that the link between the target receiver and the transmitter is LOS is equal to 1, when s ≧ R B The probability that the link is LOS equals 0. Pathloss function l(s) = s for LOS link -α The path loss parameter is α. And considering the propagation characteristics of millimeter waves, assuming that each link is affected by Nakagami fading, the channel gain of the link is expressed as | h 2 ，|h| 2 Is a normalized gamma random variable with parameter NL.
(3) Based on the D2D node distribution model and the general model provided in the step (2), the SINR distribution model can be obtained as
The numerator part represents the received power at the target receiver, | h 0 | 2 Channel gain for the serving link, r -α To service link path loss, r = | | | x 0 +y 0 | | is the distance between the target receiver and its serving transmitter, y 0 Is the position of the service transmitter in the analysis area, the denominator part represents the interference and noise influence on the signal transmission process, and the interference power
From transmitters other than the serving transmitter within the line of sight of the target receiver,
for a collection of transmitters within the analysis area,
and
channel gain and directional gain of the interfering link, respectively, and | | x 0 +y|| -α To interfere with link path loss, in addition, the system model considers an interference limited situation, let σ 2 And =0. And the distance variable s = | | x 0 The probability density function of + y | and the distance v between the target receiver and the center of the analysis area 0 When v is not more than 0 0 ≤R-R B The LOS area of the target receiver is entirely contained within the analysis area, and the distance between the target receiver and the transmitter within the line-of-sight range is 0 to R B With the distance variable s at v 0 ＝||x 0 The Cumulative Distribution Function (CDF) under | | conditions is
The variable s is derived to obtain a Probability Density Function (PDF) of the distance variable as
0＜s＜R B (ii) a When R-R B ＜v 0 R is less than or equal to R, only partial LOS area of the target receiver is positioned in the analysis area, and the partial LOS area is a circle
And (x-v) 0 ) 2 +y 2 ＝R 2 The abscissa of the intersection point of the two circles is
The area of the part is:
order to
Wherein,
at this time, the distance length between the transmitter and the target receiver can be divided into two parts: a) S is more than 0 and less than or equal to R-v 0 CDF of the distance variable is
The variable s is derived to obtain the PDF of the distance variable as
b)R-v 0 ＜s＜R B Obtaining the CDF of the distance variable by adopting a similar calculation method of the intersection area of the analysis area and the LOS area
Then the variable s is derived to obtain the conditional PDF of the distance variable as
Thus, the probability density function of the distance is
The service transmitter and the interference transmitter of the target receiver are randomly selected in the LOS area, and the probability density functions of the service distance and the interference distance both meet the derivation, so that the conditional PDFs of the service distance and the interference distance are f respectively R (r|v 0 )＝f S (r|v 0 ) And f W (w\v 0 )＝f S (w|v 0 ) And v is 0 Has a probability density function of
0≤v 0 ≤R。
(4) And (4) deducing the Laplace transform of the interference power in the system by using the interference distance distribution in the step (3) and the characteristics of the binomial point process, as follows:
by calculation of
Is obtained after the moment function of
The mean value of the number of the interference transmitters in the LOS area of the target receiver and the distribution of the interference distances are all equal to v 0 Is related to the value of, so according to v 0 Respectively with different value rangesA laplace transform of the interference power is derived.
When v is more than or equal to 0 0 ≤R-R B Time-of-flight laplace transform of interference power
Wherein,
and
indicating the probability of the transmitter being located within and outside the LOS region, respectively.
When R-R B ＜v 0 Laplace transform of interference power at R or less
Wherein,
indicating the probability of the transmitter being located within the LOS region and, correspondingly, 1-beta indicating the probability of the transmitter being located outside the LOS region.
(5) Coverage probability is defined as P c = P { SINR > T }, which represents the probability that the signal to interference noise ratio (SINR) at the receiver is greater than the threshold value T, and the system model coverage probability expression is:
wherein the probability P [ SINR > T]The derivation of (c) is as follows:
based on | h | 2 Is a condition for normalizing the random variable of gamma, consisting of
Can obtain
Due to N L Is an integer, obtained using the binomial theorem
Substituting the Laplace transform of the interference power into the above formula to combine the service distance distribution, v 0 The probability density function of (2) can obtain an accurate solution of the coverage probability
The effect of the invention can be further illustrated by simulation examples.
The simulation content and results are as follows: fig. 3 shows a comparison graph of the theoretical value and the simulated value of the coverage probability, and it can be seen that the theoretical curve is close to the simulated curve, which proves the accuracy of the theoretical analysis, and it can also be seen that the total number of devices in the analysis area has a large influence on the coverage probability. Fig. 4 shows a coverage probability curve under the condition of changing the antenna parameters, and it can be seen that when millimeter waves are deployed in a limited wireless network, the change of the antenna parameters has a certain influence on the coverage probability, and particularly, the change of the antenna main lobe gain has a large influence on the coverage probability.
The invention also provides a wireless network node deployment optimization system based on the binomial point process, which comprises a distribution model construction module of D2D nodes, a general model construction module for blocking, channel distribution and path fading, an SINR distribution model acquisition module, a distance distribution calculation module, a Laplace transform module and a coverage probability calculation module;
the distribution model building module of the D2D nodes is used for building a distribution model of the D2D nodes in a limited wireless network by using a uniform binomial point process;
the general model building module for blocking, channel distribution and path fading is used for deploying millimeter waves in the limited wireless network S1 on the basis of a D2D node distribution model, setting an antenna model of D2D equipment and building a general model for blocking, channel distribution and path fading;
the SINR distribution model obtaining module derives an SINR distribution model based on the D2D node distribution model and the general models of blockage, channel distribution and path fading
The distance distribution calculation module deduces the distance distribution from a D2D target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model and the general models of blockage, channel distribution and path fading;
the Laplace transform module deduces Laplace transform of interference power at the D2D target receiver according to the distance distribution and the binomial point process characteristics;
and the coverage probability calculation module is used for combining the distance distribution and the Laplace transform distribution of the interference power to derive the coverage probability at the target receiver based on a coverage probability definitional formula.
The present invention also provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the wireless network node deployment optimization method based on binomial point process according to the present invention.
The present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. The wireless network node deployment optimization method based on the binomial point process can be stored in a computer readable storage medium if the method is realized in the form of a software functional unit and sold or used as an independent product.
Based on such understanding, in the exemplary embodiment, a computer readable storage medium is further provided, all or part of the processes in the method of the above embodiments of the present invention can also be implemented by a computer program for instructing relevant hardware, where the computer program can be stored in the computer readable storage medium, and when the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. Computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice. The computer storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NANDFLASH), solid State Disk (SSD)), etc.
In an exemplary embodiment, a computer device is also provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the desuperheating water control method based on heating value calculation when executing the computer program. The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (6)
1. A wireless network node deployment optimization method is characterized by comprising the following steps:
s1, establishing a distribution model of D2D nodes in an analysis area of a limited wireless network by using a uniform binomial point process;
s2, on the basis of the D2D node distribution model, deploying millimeter waves in an analysis area in the limited wireless network in the S1, setting an antenna model of D2D equipment, and establishing a general model of blocking, channel distribution and path fading;
s3, deducing an SINR distribution model and distance distribution from a D2D target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model in the S1 and a general model of blockage, channel distribution and path fading obtained in the S2;
s4, obtaining Laplace transform of interference power at the D2D target receiver based on the distance distribution obtained in the S3 and the characteristic of the binomial point process;
(1) S5, according to the coverage probability definition, combining the distance distribution obtained in the S3 and the Laplace transform distribution of the interference power obtained in the S4 to deduce the coverage probability at the target receiver; in S1, an analysis area of a limited wireless network is determined, D2D devices in the analysis area are independently and uniformly distributed in the area according to a uniform binomial point process, one half of the devices in the analysis area are set as transmitters, the other half of the devices in the analysis area are set as receivers, each receiver corresponds to one transmitter as service equipment, and all the transmitters in the analysis area are in a transmission state at the same time; obtaining the positions of a target receiver and the center of the analysis area, and then obtaining the distance between the target receiver and the center of the analysis area and the target receiverDistance from the transmitter; in S3, the D2D node distribution model obtained based on S1 and the SINR distribution model obtained based on the general model in S2 are
The numerator part represents the received power at the target receiver, and the denominator part represents the interference and noise influence on the signal transmission process; interference power
From transmitters other than the serving transmitter in the LOS region of the target receiver; | h 0 | 2 Channel gain for the serving link, r -α To service link path loss, r = | | x 0 +y 0 I is the distance between the target receiver and its serving transmitter, y 0 Is the position of the service transmitter in the analysis area, the denominator part represents the interference and noise influence on the signal transmission process, and the interference power
From transmitters other than the serving transmitter within the line of sight of the target receiver,
for a collection of transmitters within the analysis area,
and
channel gain and directional gain of the interfering link, respectively, and | | x 0 +y|| -α To interfere with link path loss, and in addition, the system model considers an interference limited situation, let σ 2 =0, distance variable s = | | | x 0 The probability density function of + yI and the distance v between the target receiver and the center of the analysis area 0 When v is not more than 0 0 ≤R-R B The LOS area of the target receiver is entirely contained within the analysis area, and the distance between the target receiver and the transmitter within the line-of-sight range is 0 to R B With the distance variable s at v 0 ＝||x 0 The cumulative distribution function under the condition of | | is
The variable s is derived to obtain a probability density function of the distance variable as
When R-R B ＜v 0 R is less than or equal to R, only partial LOS area of the target receiver is positioned in the analysis area, and the partial LOS area is a circle
And (x-v) 0 ) 2 +y 2 ＝R 2 The intersection of the two circles having an intersection abscissa of
The area of the part is as follows:
order to
Wherein,
at this time, the distance length between the transmitter and the target receiver can be divided into two parts: a) S is more than 0 and less than or equal to R-v 0 CDF of the distance variable is
Derivation of variable sThen obtaining the PDF of the distance variable as
b)R-v 0 ＜s＜R B Obtaining the CDF of the distance variable by adopting a similar calculation method of the intersection area of the analysis area and the LOS area
Then the variable s is derived to obtain the conditional PDF of the distance variable as
The probability density function of the distance is:
the serving transmitter and the interfering transmitter of the target receiver are both randomly selected in the LOS region, and the probability density functions of the serving distance and the interfering distance both satisfy the derivation, so the conditional probability density functions of the serving distance and the interfering distance are f R (r|v 0 )＝f S (r|v 0 ) And f W (w|v 0 )＝f S (w|v 0 ) And v is 0 Has a probability density function of
In S4, calculating Laplace transform of interference power based on interference distance distribution and binomial point process characteristics,
when v is more than or equal to 0 0 ≤R-R B The laplace transform of the interference power is:
when R-R B ＜v 0 When R is less than or equal to R, the Laplace of the interference power is converted into:
wherein,
in S5, the coverage probability is defined as P c And = P { SINR > T }, which represents the probability that the SINR at the receiver is greater than the threshold T, and the coverage probability expression is: p c ＝E v0 [P[SINR＞T|v 0 ]]Wherein the probability P [ SINR > T]The derivation of (c) is as follows:
combining interference power Laplace transform with coverage probability definitional formula, service distance distribution, v 0 The exact solution to the coverage probability is obtained as:
2. the wireless network node deployment optimization method according to claim 1, wherein in S2, according to the propagation characteristics of millimeter waves, the D2D device is set to employ an array antenna to obtain a beam forming gain, a sector antenna is used to approximate an actual antenna to obtain a directional gain of a link, a link between a target transmitter and a receiver is set to obtain an optimal directional gain, and an interfering link generates a random directional gain to obtain four possible directional gain results and corresponding probabilities; according to the propagation characteristics of millimeter waves, setting that the D2D equipment adopts an array antenna to obtain beam forming gain, approximating an actual antenna by using a sector antenna to further obtain the directional gain of a link, and setting that the link between a target receiver and a service transmitter thereof can obtain the maximum directional gain D 0 ＝M t M r Wherein M is t 、M r Representing the main part of the sector antenna of the transmitter and receiver, respectivelyLobe gain, while interfering links produce random directional gain D l Including four possible directional gain results a k Corresponding probability b k K is equal to {1,2,3,4}, as shown in the following table;
wherein m is t 、m r Indicating the side lobe gain, theta, of the transmitter and receiver sector antennas, respectively t 、θ r Representing the main lobe beamwidth of the transmitter and receiver sector antennas, respectively.
3. The method of claim 1, wherein in S2, a line of sight (LOS) sphere model is used to model the blocking effect, and the target receiver is defined to be located in the center of the LOS region, when the distance between the target receiver and the transmitter is smaller than or equal to the radius of the LOS region, the probability that the link between the target receiver and the transmitter is LOS is 1, when the distance between the target receiver and the transmitter is larger than or equal to the radius of the LOS region, the probability that the link between the target receiver and the transmitter is LOS is 0, the target receiver randomly selects one transmitter as the service transmitter in the LOS region, and the remaining transmitters are interference transmitters, so as to obtain the path LOSs function l (S) = S of the LOS link -α And the channel gain of the LOS link, s represents the distance between the receiver and the transmitter, and α is a path LOSs parameter.
4. The wireless network node deployment optimization system based on the binomial point process is characterized by comprising a D2D node distribution model building module, a general model building module for blocking, channel distribution and path fading, an SINR distribution model obtaining module, a distance distribution calculating module, a Laplace transform module and a coverage probability calculating module;
the distribution model building module of the D2D nodes is used for building a distribution model of the D2D nodes in a limited wireless network by using a uniform binomial point process; firstly, determining an analysis area of a limited wireless network, wherein D2D devices in the analysis area are independently and uniformly distributed in the area according to a uniform binomial point process, half of the devices in the analysis area are set as transmitters, the other half of the devices are set as receivers, each receiver corresponds to one transmitter as service equipment, and all the transmitters in the analysis area are in a transmission state at the same time; acquiring the positions of a target receiver and the center of the analysis area, and then acquiring the distance between the target receiver and the center of the analysis area and the distance between the target receiver and the transmitter;
the general model building module for blocking, channel distribution and path fading is used for deploying millimeter waves in the limited wireless network S1 on the basis of a D2D node distribution model, setting an antenna model of D2D equipment and building a general model for blocking, channel distribution and path fading;
an SINR distribution model obtaining module deduces an SINR distribution model based on the D2D node distribution model and the general models of blocking, channel distribution and path fading; based on the obtained D2D node distribution model and the general model, an SINR distribution model is obtained
The numerator part represents the received power at the target receiver, and the denominator part represents the interference and noise influence on the signal transmission process; interference power
From transmitters other than the serving transmitter in the target receiver LOS region;
the probability density function of the distance is:
the serving transmitter and the interfering transmitter of the target receiver are both randomly selected in the LOS region, and the probability density functions for both the serving distance and the interfering distance satisfy the above derivation, so that the serving distance sumThe conditional probability density function of the interference distance is f R (r|v 0 )＝f S (r|v 0 ) And f W (w|v 0 )＝f S (w|v 0 ) And v is 0 Has a probability density function of
The distance distribution calculation module deduces the distance distribution from a D2D target receiver to a transmitter in a line-of-sight range based on the D2D node distribution model and the general models of blockage, channel distribution and path fading;
the Laplace transform module deduces Laplace transform of interference power at the D2D target receiver according to the distance distribution and the binomial point process characteristics; a laplacian transform of the interference power is calculated based on the interference distance distribution and the binomial point process characteristics,
when v is more than or equal to 0 0 ≤R-R B The laplace transform of the interference power is:
when R-R B ＜v 0 When R is less than or equal to R, the Laplace of the interference power is converted into:
wherein,
a coverage probability calculation module combines the distance distribution and the Laplacian transformation distribution of the interference power to deduce the coverage probability at a target receiver based on a coverage probability definitional formula; coverage probability is defined as P c P { SINR > T }, representing the probability that the signal to interference plus noise ratio SINR at the receiver is greater than a threshold T, and the coverage probability is expressed as:
wherein the probability P [ SINR > T]The derivation of (c) is as follows:
combining interference power Laplace transform with coverage probability definitional formula, service distance distribution, v 0 The exact solution to the coverage probability is obtained as:
5. a computer arrangement comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the radio network node deployment optimization method according to any one of claims 1 to 3 when executing the computer program.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimizing the deployment of a radio network node according to any one of claims 1 to 3.
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