CN113395709B - Service coverage analysis method facing user base station poisson distribution - Google Patents

Service coverage analysis method facing user base station poisson distribution Download PDF

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CN113395709B
CN113395709B CN202110676826.XA CN202110676826A CN113395709B CN 113395709 B CN113395709 B CN 113395709B CN 202110676826 A CN202110676826 A CN 202110676826A CN 113395709 B CN113395709 B CN 113395709B
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辛元雪
杨璇
史朋飞
张金波
苏新
张学武
范新南
周润康
黄伟盛
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    • 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
    • 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
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses wireless communication system coverageA service coverage analysis method facing user base station Poisson distribution in the analysis field aims at solving the technical problem that the analysis of a wireless communication system is complex. The method comprises the following steps: obtaining an uplink transmission signal y received by a service base station of the first cell l (ii) a According to the uplink transmission signal y l Combining the transmission channel g from the first cell to the central base station of the cell llk And calculating the intra-cell interference information when the base station of the first cell transmits uplink in the cell. The invention fully considers the randomness of the deployment positions of the user base stations in the actual scene and the hardcore distance between the adjacent base stations, analyzes the intra-cell interference and the inter-cell interference during the uplink transmission in the multi-cell and multi-user scene that the positions of the user base stations obey Poisson distribution by utilizing a random geometric method, can obtain the service coverage conditions of all the cell base stations in the whole system, and greatly reduces the complexity of the performance analysis of the wireless communication system.

Description

Service coverage analysis method facing user base station poisson distribution
Technical Field
The invention relates to a service coverage analysis method facing user base station poisson distribution, belonging to the technical field of wireless communication system coverage analysis.
Background
The communication network analysis technology based on random geometry is an effective method for coping with increasingly complex wireless communication network environments, such as a multi-cell multi-user communication scene and the like, and has the importance that not only is the processing process convenient, but also an accurate performance index is provided for a communication network. For a general network node, the total interference is a random process, which depends on the position of the interference source and the random channel gain obtained by the point process, and the random geometry analysis can just provide the statistical information of the interference suffered by the network node in the spatial domain, so the interference is one of the main parameters for analyzing the network performance by using the random geometry.
In a two-dimensional plane, when both the user location and the base station location obey the poisson point process, most of the conventional service coverage analysis methods are based on that base station points are randomly and uniformly distributed in a plane area, however, in reality, due to factors such as interference and cost, a minimum distance exists between any two base stations, that is, the base station is actually a hard core point process with a stricter obeying condition, however, because a probability mother functional of the hard core point process does not exist, the probability mother functional of the poisson point process is generally used for approximately analyzing the base station, but the wireless communication system analysis is complex, and therefore, a service coverage analysis method facing the poisson distribution of the user base station is provided.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a service coverage analysis method facing to the Poisson distribution of a user base station, and solves the technical problem that the performance analysis of a wireless communication system is more complex.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a service coverage analysis method facing to user equipment base station poisson distribution,
the method comprises the following steps:
obtaining an uplink transmission signal y received by a service base station of the first cell l ；
According to the uplink transmission signal y l Combining the transmission channel g from the first cell to the central base station of the cell llk Calculating the interference information in the first cell when the base station of the first cell transmits uplink in the cell;
according to the uplink transmission signal y l Combining the transmission channel g from the ith cell to the center base station of the ith cell ilk Calculating the inter-cell interference information when the ith cell base station transmits uplink to the ith cell;
according to the uplink transmission signal y l Calculating signal-to-interference ratio SIR lk And the signal-to-interference ratio threshold gamma of the successful access of the user to the base station is taken as a standard for judging whether the user is covered, and the service coverage rate eta of the system is obtained by combining the intra-cell interference information and the inter-cell interference information.
Further, the uplink transmission signal y l The calculation expression of (a) is:
Figure BDA0003120939070000021
wherein s is lk Expressing the power normalization of the transmitted signal of the kth user in the ith cell, wherein rho expresses the power of the transmitted signal, and w l Representing variance as σ 2 G noise of (c), g llk For the transmission channel from the kth user in the first cell to the central base station of this cell, g ilk Is the transmission channel from the kth user in the ith cell to the central base station of the ith cell.
Further, the signal-to-interference ratio SIR lk The calculation expression of (a) is:
Figure BDA0003120939070000022
wherein I intra Is an actual value of intra-cell interference, I inter Is an actual value of inter-cell interference.
Further, the calculation expression of the intra-cell interference information is as follows:
Figure BDA0003120939070000023
wherein
Figure BDA0003120939070000031
Gamma is the signal-to-interference ratio threshold, d llk The distance from the kth user in the ith cell to the central base station of the cell,α is the path loss factor, I intra Is an actual value of intra-cell interference.
Further, the calculation expression of the inter-cell interference information is as follows:
Figure BDA0003120939070000032
in which I inter Is an actual value of inter-cell interference.
Further, the transmission channel g llk The modeling method comprises a model of large-scale fading and small-scale fading, and comprises the following steps:
Figure BDA0003120939070000033
wherein h is llk For small scale fading and subject to Rayleigh fading, | h llk | 2 Subject to an exponential distribution with a parameter of 1, i.e. | h ll | k 2 ～exp(1)；κ llk Representing large scale fading, modeled as:
Figure BDA0003120939070000034
where c is the path loss coefficient of the signal at unit reference distance.
Further, the transmission channel g ilk The modeling method comprises the following modeling steps of a large-scale fading model and a small-scale fading model:
Figure BDA0003120939070000035
wherein h is ilk For small scale fading and subject to Rayleigh fading, | h ilk | 2 Obeying an exponential distribution with a parameter of 1, i.e. | h ilk | 2 ～exp(1)；κ ilk Representing large scale fading, modeled as:
Figure BDA0003120939070000036
wherein d is ilk The distance from the kth user in the ith cell to the center base station of the ith cell.
Further, the calculation expression of the system service coverage probability η is as follows:
Figure BDA0003120939070000037
Figure BDA0003120939070000038
wherein gamma is the signal-to-interference ratio threshold value of the successful access of the user to the base station, R is the cell service radius, and lambda u Distributing density, λ, for users b For the base station distribution density, K is the average of the number of users per cell.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a service coverage analysis method facing to user base station poisson distribution, which fully considers the randomness of the deployment position of the user base station in an actual scene and the hardcore distance between adjacent base stations, and analyzes the intra-cell interference and inter-cell interference when the user base station position obeys the poisson distribution under a multi-cell multi-user scene during uplink transmission by using a random geometric method, thereby obtaining the service coverage condition of all cell base stations in the whole system and greatly reducing the complexity of the performance analysis of a wireless communication system.
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Fig. 1 is a flowchart of a service coverage analysis method for poisson distribution of a user base station according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The specific implementation method discloses a service coverage analysis method facing to user base station poisson distribution, as shown in fig. 1, comprising the following steps:
(1) Acquiring an uplink transmission signal y received by a serving base station in the first cell l ；
(2) Combining the transmission channel g from the kth user in the first cell to the central base station of the cell llk Analyzing the interference information L in the cell when the base station of the first cell and the k user in the cell transmit uplink intra ；
(3) Considering the hardmac distance d between base stations, introducing a maintenance probability Pr (d) and combining a transmission channel g from a kth user in an ith cell to a central base station of the ith cell ilk Analyzing inter-cell interference information L inter The impact on service coverage performance η;
(4) Calculating signal-to-interference ratio (SIR) lk And the signal-to-interference ratio threshold value gamma of the successful access of the user to the base station is taken as the standard whether the user is covered or not, and the interference information L in the cell is combined intra And inter-cell interference information L inter And analyzing the system service coverage rate eta.
Considering such a communication network system, the single-antenna base station location obeys the poisson point process Φ b Distribution density of λ b The service radius is R, and a hardcore distance R exists between any two base stations 0 (ii) a User location obeying poisson point procedure phi in service range of base station u Distribution density of λ u . Then the base station of the ith cell in uplink transmission receives signal y l Comprises the following steps:
Figure BDA0003120939070000051
wherein s is lk The power of the kth user in the ith cell is expressed as normalized transmission signal, p represents the power, and w l Representing variance as σ 2 G noise of llk For the transmission channel from the kth user in the first cell to the central base station of this cell, g ilk For the transmission channel from the kth user in the ith cell to the central base station of the ith cellThe signal-to-interference-and-noise ratio of the uplink signal received by the base station of the cell is as follows:
Figure BDA0003120939070000052
where p denotes its power, σ 2 Represents the variance of the noise, g llk For the transmission channel from the kth user in the l cell to the central base station of the cell, g ilk Is the transmission channel from the kth user in the ith cell to the central base station of the ith cell, and K is the total number of users in the cell.
For convenience of analysis, considering the case of limited noise interference, the signal-to-interference ratio of the uplink signal received by the base station of the ith cell is:
Figure BDA0003120939070000053
wherein g is llk For the transmission channel from the kth user in the ith cell to the central base station of this cell, I intra Is an actual value of intra-cell interference, I inter Is an actual value of inter-cell interference.
Assuming that the signal-to-interference ratio threshold value of the user successfully accessing the base station is gamma, the system service coverage probability is as follows:
η＝E[P r (SIR lk ＞γ)]
as can be seen from the formula, the service coverage of the multi-cell multi-user communication system is determined by the intra-cell interference, the inter-cell interference and the distribution of the base station users. Considering that statistical information of interference behaviors experienced by nodes in a communication network can be obtained by using a random geometric analysis method, a probability density function of interference is selected to represent interference information, but the probability density function of interference in a multi-cell multi-user communication scene cannot be directly obtained, so that in a specific implementation process, based on the consideration that aggregative interference is a strict positive random variable, the laplace transform is determined to represent the interference information, and then the interference information in a cell is as follows:
Figure BDA0003120939070000061
wherein
Figure BDA0003120939070000062
Gamma is the signal-to-interference ratio threshold, d llk Is the distance from the kth user in the ith cell to the central base station of the cell, alpha is the path loss factor, I intra And e is an actual value of the interference in the cell and is a mathematical constant. The inter-cell interference information is expressed as:
Figure BDA0003120939070000063
wherein
Figure BDA0003120939070000064
Gamma is the signal-to-interference ratio threshold, d llk Is the distance from the kth user in the ith cell to the central base station of the cell, alpha is the path loss factor, I inter And e is an actual value of the inter-cell interference and is a mathematical constant.
In step (2), for the transmission channel g from the kth user in the ith cell to the central base station of the cell llk The modeling is regarded as a model including large-scale fading and small-scale fading and is as follows:
Figure BDA0003120939070000065
wherein h is llk For small scale fading, for simplicity of analysis, assume h llk Subject to Rayleigh fading, then | h llk | 2 Obeying an exponential distribution with a parameter of 1, i.e. | h llk | 2 Exp (1). In the above formula (1) (-) llk Representing large scale fading, modeled as:
Figure BDA0003120939070000066
d llk for the k user in the l cellThe distance of the central base station of the cell, α is a path loss factor, and c is a path loss coefficient of a signal at a unit reference distance, which is generally determined by the antenna height, carrier frequency and transmission environment of the communication device, and is set to 1 in the analysis process for reducing the calculation amount.
In step (3), the transmission channel g from the kth user in the ith cell to the center base station of the ith cell is transmitted ilk Also denoted as large-scale fading and small-scale fading:
Figure BDA0003120939070000071
wherein h is ilk For small scale fading, similarly for simplicity of analysis h is assumed ilk Subject to Rayleigh fading, then | h ilk | 2 Obeying an exponential distribution with a parameter of 1, i.e. | h ilk | 2 ～exp(1)。κ ilk Representing large scale fading:
Figure BDA0003120939070000072
d ilk the distance from the kth user in the ith cell to the center base station of the ith cell.
Since the probability mother functional of the hard-core point process cannot be directly obtained, the hard-core point process is considered to be thinned based on the poisson point process. The hard core point process between the base stations is established according to the poisson point process, and the most remarkable difference is that the hard core point process has a hard core distance d, so that the hard core point process can be regarded as a sparse poisson point process. The introduction of a dimensionality probability Pr (d) to the original Poisson point process sparsification:
Figure BDA0003120939070000073
wherein q is a correction factor used to approximate the probability of the multi-cell hardmac thinning process; lambda [ alpha ] b Is the original poisson point process density; d is the hardmac target distance,it is set to 0 here for simplicity of calculation; e is a mathematical constant.
In step (4), it is assumed that the users are all distributed in the cell, i.e. the distribution positions of the users obey the poisson cluster process. Taking a signal-to-interference ratio threshold gamma of a user successfully accessing a base station as a standard for whether the user is covered, and combining a probability mother functional of a Poisson cluster process, the system service coverage probability eta is as follows:
Figure BDA0003120939070000074
wherein:
Figure BDA0003120939070000075
wherein the SIR lk Sending the signal-to-interference ratio of uplink signals to the serving base station of the cell for the kth user in the ith cell, wherein gamma is the threshold value of the signal-to-interference ratio of the successful access of the user to the base station, alpha is a path loss factor, R is the serving radius of the cell, and lambda is the threshold value of the access of the user to the base station u Distributing density, λ, for users b For the base station distribution density, i.e. the density of the original Poisson point process in equation (5) above, it is convenient to take in equation (7) for analysis
Figure BDA0003120939070000081
The average of the number of users per cell is expressed as:
Figure BDA0003120939070000082
where R is the cell service radius, λ u The density is distributed for the users.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (7)

1. A service coverage analysis method facing to user base station Poisson distribution comprises the following steps:
obtaining an uplink transmission signal y received by a service base station of the first cell l ；
According to the uplink transmission signal y l Combining the transmission channel g from the first cell to the central base station of the cell llk Calculating the interference information in the first cell when the base station of the first cell transmits uplink in the cell;
according to the uplink transmission signal y l Combining the transmission channel g from the ith cell to the central base station of the ith cell ilk Calculating the inter-cell interference information when the ith cell base station transmits uplink to the ith cell;
according to the uplink transmission signal y l Calculating signal-to-interference ratio (SIR) lk The signal-to-interference ratio threshold gamma of the successful access of the user to the base station is used as a standard for judging whether the user is covered, and the service coverage rate eta of the system is obtained by combining the intra-cell interference information and the inter-cell interference information;
the uplink transmission signal y l The calculation expression of (a) is:
Figure FDA0003799253460000011
wherein s is lk Expressing the power normalization of the transmitted signal of the kth user in the ith cell, wherein rho expresses the power of the transmitted signal, and w l Representing the variance σ 2 G noise of llk For the transmission channel from the kth user in the ith cell to the central base station of the cell,g ilk is the transmission channel from the kth user in the ith cell to the central base station of the ith cell.
2. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: the signal-to-interference ratio SIR lk The calculation expression of (a) is:
Figure FDA0003799253460000012
wherein I intra Actual value of intra-cell interference, I inter Is an actual value of inter-cell interference.
3. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: the calculation expression of the intra-cell interference information is as follows:
Figure FDA0003799253460000021
wherein
Figure FDA0003799253460000022
Gamma is the signal-to-interference ratio threshold, d llk Is the distance from the kth user in the l cell to the central base station of the cell, alpha is the path loss factor, I intra Is an actual value of intra-cell interference.
4. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: the calculation expression of the inter-cell interference information is as follows:
Figure FDA0003799253460000023
wherein I inter Is an actual value of inter-cell interference.
5. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: said transmission channel g llk The modeling method comprises the following modeling steps of a large-scale fading model and a small-scale fading model:
Figure FDA0003799253460000024
wherein h is llk For small scale fading and subject to Rayleigh fading, | h llk | 2 Obeying an exponential distribution with a parameter of 1, i.e. | h llk | 2 ～exp(1)；κ llk Representing large scale fading, modeled as:
Figure FDA0003799253460000025
where c is the path loss coefficient of the signal at a unit reference distance.
6. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: said transmission channel g ilk The modeling method comprises the following modeling steps of a large-scale fading model and a small-scale fading model:
Figure FDA0003799253460000026
wherein h is ilk For small scale fading and subject to Rayleigh fading, | h ilk | 2 Obeying an exponential distribution with a parameter of 1, i.e. | h ilk | 2 ～exp(1)；κ ilk Representing large scale fading, modeled as:
Figure FDA0003799253460000031
whereind ilk The distance from the kth user in the ith cell to the center base station of the ith cell.
7. The method for analyzing service coverage oriented to poisson distribution of user equipment as claimed in claim 1, wherein: the calculation expression of the system service coverage probability eta is as follows:
Figure FDA0003799253460000032
Figure FDA0003799253460000033
wherein gamma is the signal-to-interference ratio threshold value of the successful access of the user to the base station, R is the cell service radius, and lambda u Distributing density, λ, for users b In order to distribute the density of the base stations,
Figure FDA0003799253460000034
is the average of the number of users per cell.
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