CN113099423B - Deployment method of narrowband cellular Internet of things based on Poisson cluster process - Google Patents
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Abstract
The invention discloses a deployment method of a narrowband cellular Internet of things based on a Poisson cluster process, which comprises the following steps: s1, constructing the position of the cellular mobile user as a PPP model in the area to be tested; s2, modeling the position of the NB-IoT base station as a PPP model in the area to be measured, and modeling the position of the NB-IoT user as a cluster with the NB-IoT base station as the center and the radius as the center; s3, constructing an uplink interference model and carrying out interference analysis on the coexistence model; s4, obtaining an interruption probability expression and a user transmission capacity expression based on the interference analysis, simulating the interference process, and analyzing the performance of the coexistence model. The method can optimize the interruption performance of the network and increase the user transmission capacity by adjusting the cluster radius and the NB-IoT base station distribution density.
Description
Technical Field
The invention relates to the field of narrowband Internet of things, in particular to a deployment method of narrowband cellular Internet of things based on a Poisson cluster process.
Background
With the gradual maturity of the application technology of the internet of things, the number of terminal devices of the internet of things exceeds 800 hundred million by 2025 years, the mobile data traffic is exponentially increased, and a single cellular mobile network cannot meet the access of mass devices, so that the problems of low wireless resource utilization rate and low energy efficiency are caused. Release 13 in the third generation partnership project (3GPP) standard proposes a cellular-based narrowband internet of things. An operator adds an NB-IoT micro base station and tightly combines the NB-IoT micro base station with a Long Term Evolution (LTE) macro base station network to realize wide area coverage, guarantee network performance and service quality of cell edge equipment and improve the utilization rate of frequency spectrum. Large-scale Machine Type Communication (mtc) provides delay-tolerant Low-rate Communication for a large number of devices, and in the context of internet of things of mtc, a Low Power Wide-Area Network (LPWAN) technology based on an existing cellular architecture gradually becomes a suitable solution. The NB-IoT is one of LPWAN technologies based on cellular Internet of things, supports large-scale low-throughput equipment, and has the advantages of wide area coverage, ultralow power consumption, ultralow cost and the like. Therefore, the combination of the NB-IoT system and the cellular mobile network system can provide a feasible solution for future mass device access and efficient resource management and control. There are three deployment modes for NB-IoT, which are: independent deployment, guardband deployment, and in-band deployment.
In the prior art, only the performance of NB-IoT in a two-layer architecture is analyzed, and the performance of NB-IoT and a cellular network coexisting network is not analyzed; different deployment strategies for NB-IoT in heterogeneous networks are introduced, and the scenario of LTE independent deployment in NB-IoT is analyzed, which shows that the average throughput of independent deployment is improved by up to 66% compared with synchronous or asynchronous deployment, the energy consumption is reduced by 72%, but the performance result is lower because the multi-tone transmission of an uplink is poorer than the single-tone transmission. In the prior art, the NB-IoT is deployed in a heterogeneous network by means of a hardware platform, the performance of the NB-IoT is analyzed, but a specific coexistence model of the NB-IoT and a cellular network co-frequency networking is not established, particularly the problem of fusion around the heterogeneous network is solved, and as the heterogeneous network is increasingly complex, the configuration between various users and base stations needs to be considered for actual performance evaluation and system design, how to establish a flexible dual-network framework structure is required, so that co-frequency interference is effectively reduced, and the throughput is improved.
Disclosure of Invention
The invention aims to provide a deployment method of a narrowband cellular Internet of things based on a Poisson cluster process, which aims to solve the problems in the prior art, and how to establish a flexible dual-network frame structure, thereby effectively reducing co-channel interference and improving throughput.
In order to achieve the purpose, the invention provides the following scheme:
the invention provides a deployment method of a narrowband cellular Internet of things based on a Poisson cluster process, which comprises the following steps:
s1, constructing a plurality of narrowband Internet of things NB-IoT base stations and cellular mobile users in an area to be tested, wherein the narrowband Internet of things NB-IoT base stations cover the plurality of narrowband Internet of things NB-IoT users;
s2, constructing a PCP-based NB-IoT and cellular mobile network coexistence model, specifically including: modeling the position of the NB-IoT base station as a PPP model in the area to be tested, modeling the position of the NB-IoT user as a cluster taking the NB-IoT base station as the center, and constructing the position of the cellular mobile user as the PPP model;
s3, constructing an uplink interference model based on the NB-IoT and the cellular mobile network, and performing interference analysis on the PCP-based NB-IoT and cellular mobile network coexistence model based on the uplink interference model;
s4, based on the interference analysis, obtaining an interruption probability expression and a user transmission capacity expression through interruption probability and user transmission capacity respectively, simulating an interference process, and analyzing the performance of the PCP-based NB-IoT and cellular mobile network coexistence model.
Preferably, in S2, the distance between the NB-IoT base station and the NB-IoT users is set to be a random variable, and the cluster includes a number of NB-IoT users.
Preferably, the uplink interference model in S3 includes but is not limited to: target base station, other NB-IoT base stations, NB-IoT users, cellular mobile users, useful information, inter-cluster interference, cellular mobile user interference.
Preferably, in S3, the interference of the interference model further includes: the NB-IoT users experience path loss when uploading information, Rayleigh fading when the NB-IoT base station receives data transmitted by the NB-IoT users, and interference of the NB-IoT base station with NB-IoT users of adjacent cells.
Preferably, the interruption probability expression in S4 is obtained by calculation based on NB-IoT base station density, NB-IoT base station coverage radius, and maximum signal to interference plus noise ratio criterion.
Preferably, the interruption probability expression is:
wherein P is out Representing the probability of interruption, a representing the cluster radius,
which represents the laplacian operator, is,
and
respectively represent interference I UE And interference I CU Of Laplace transformation, I UE Representing interference from other NB-IoT cell user equipments, I CU Representing interference from cellular mobile users.
Preferably, the user transmission capacity expression in S4 is obtained based on the outage probability, the signal to interference plus noise ratio, and the shannon theorem formula.
Preferably, the user transmission capacity expression is:
where C represents the user transmission capacity, p i The upload power on behalf of the NB-IoT users,
represents a transmission channel of the NB-IoT user, | | x | | | represents the Euclidean distance between the NB-IoT user and the target base station,
represents the power, σ, that the target base station receives to NB-IoT users within the coverage area 2 Representing the system noise, Γ () representing the gamma function,
α represents a path loss factor and s represents a laplacian operator.
Preferably, the performance analysis in S4 is based on the outage probability and the user transmission capacity.
Preferably, in the simulation process, cellular mobile users, NB-IoT base stations and NB-IoT users are randomly scattered. The invention discloses the following technical effects:
the invention establishes a PCP distribution-based NB-IoT and cellular network co-frequency coexistence model, analyzes co-frequency interference of an uplink of a coexistence network by means of a random geometric theory, deduces an expression of interruption probability and user transmission capacity, and performs Monte Carlo simulation verification. The simulation result in this embodiment shows that, compared with a model based on PPP distribution, the model based on PCP distribution established in the present application is more suitable for realistic deployment, selects an appropriate NB-IoT base station coverage radius and NB-IoT base station distribution density, and improves the interruption performance of a coexisting network, thereby increasing user transmission capacity, having a guiding significance for actual deployment of NB-IoT, and providing a solution for implementing mass large-connection internet.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a diagram showing a comparison between a PPP model and a PCP model, wherein (a) is a diagram showing the PPP model and (b) is a diagram showing the PCP model;
FIG. 2 is a schematic diagram of NB-IoT co-frequency deployment uplink interference model with a cellular mobile network;
FIG. 3 is a diagram illustrating the relationship between the interruption probability and the cluster radius in this embodiment;
FIG. 4 is a diagram illustrating the relationship between outage probability and NB-IoT base station density in the present embodiment;
fig. 5 is a three-dimensional graph of user transmission capacity, SINR threshold and cluster radius in the present embodiment;
fig. 6 is a three-dimensional graph of user transmission capacity versus SINR threshold and density in the present embodiment;
fig. 7 is a diagram illustrating a comparison of PPP and PCP user transmission capacities in this embodiment.
Detailed Description
Reference will now be made in detail to various exemplary embodiments of the invention, the detailed description should not be construed as limiting the invention but as a more detailed description of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Further, for numerical ranges in this disclosure, it is understood that each intervening value, between the upper and lower limit of that range, is also specifically disclosed. Every smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in a stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the documents are cited. In case of conflict with any incorporated document, the present specification will control.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the present disclosure without departing from the scope or spirit of the disclosure. Other embodiments will be apparent to those skilled in the art from consideration of the specification. The specification and examples are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are open-ended terms that mean including, but not limited to.
The "parts" in the present invention are all parts by mass unless otherwise specified.
Example 1
The narrow Internet of Things (NB-IoT, Narrowband Internet of Things) has the advantages of massive connection, wide coverage and the like, but at present, low-rate services are not supported by a good cellular technology. Therefore, the NB-IoT and cellular mobile network co-frequency coexistence network is researched, the position of an NB-IoT user node is modeled by adopting a Poisson Cluster Process (PCP) by means of a random geometric theory tool, the analytic expressions of the uplink interruption probability and the user transmission capacity of the heterogeneous network are deduced, the influence of the Cluster radius and the NB-IoT base station deployment density on the network interruption performance and the user transmission capacity is researched, and the heterogeneous network interruption performance modeled based on the Poisson Point Process (PPP, Poisson Point Process) is compared. The theory and simulation results show that the interruption performance and the user transmission capacity of the coexisting network can be effectively improved by adjusting the cluster radius and the NB-IoT base station deployment density.
The variable notations used in this application are shown in table 1:
TABLE 1
Comparison of PPP model and PCP model in the present application:
as shown in fig. 1 (a), modeling NB-IoT systems and cellular mobile users using a PPP method, both NB-IoT base stations and NB-IoT users follow PPP modeling, independent of each other, which increases the flexibility of analysis, but in fact, the user devices are more likely to cluster around the NB-IoT base stations and the different distributions may differ according to channel conditions and fading conditions.
Then, the PCP-based modeling method is adopted in the present application, and as shown in fig. 1 (b), the location of the clustering center NB-IoT base station is modeled as PPP with a density of λ B Each father node B i ∈Φ B Forming the center of a cluster, and taking NB-IoT users as sub-nodes to be independently and uniformly distributed to form clusters in a circle with the radius of a and the density of lambda UE The location of cellular mobile users is modeled as a density of λ CU Phi of CU PPP model.
To simplify the analysis step, considering that the target base station BS is located at the origin of coordinates, the distance d between the NB-IoT base station and the users in its coverage area is a random variable, and since the NB-IoT users are uniformly distributed in a circle, the density function of the distance from any one NB-IoT user to the NB-IoT base station is:
converting equation (1) to polar coordinates, the Probability Density Function (PDF) of the cluster radius is expressed as:
and (3) constructing a channel and interference model:
as shown in fig. 2, the uplink interference model is deployed co-frequency with the cellular mobile network for NB-IoT, assuming that there are N NB-IoT users in each NB-IoT base station coverage area, numbering NB-IoT users in each NB-IoT base station coverage area from 1 to N. The environment of the radio channel determines the performance of the wireless cellular communication system, and an ideal Gaussian noise channel is adopted, and the noise power is sigma 2 . NB-IoT users upload information, propagation in the uplink is affected by path fading, path loss is represented by a constant α, and α > 2. In addition, the NB-IoT base station receives the data transmitted by the NB-IoT user and is affected by Rayleigh fading, h N In an exponential distribution with a parameter of 1, i.e. h N Exp (1). At time t, NB-IoT base station (shown as BS in FIG. 2) covers NB-IoT user 1 (UE in coverage of BS in FIG. 2) of cell 1 Shown) upload information, and cells neighboring the BS cell (e.g., BS in fig. 2) 1 、BS 2 Shown) UE within a coverage area 1 Uploading information simultaneously, when the BS is interfered by NB-IoT users from neighboring cells, in addition to this, the application considers cellular mobile users and UEs 1 The BS also receives interference from cellular mobile users, since it transmits on the same channel.
Suppose user upload power is p i Due to the influence of path loss and fading, the power of the target BS receiving the user 1 in the coverage area is:
wherein the content of the first and second substances,
is a UE 1 Is the UE, | x | | | is the transmission channel of (2) 1 Euclidean distance to the target BS.
Interference I from other NB-IoT cell user equipment UE Expressed as:
Interference from cellular mobile users CU Expressed as:
h z is the transmission channel of the cellular mobile user, | | z | | | represents the euclidean distance between the cellular mobile user and the target BS.
Thus, the SINR received at the target BS is:
substituting equations (3) to (5) into equation (6) yields:
wherein σ 2 Is the system noise.
The interruption probability:
and adopting a maximum signal to interference plus noise ratio (SINR) criterion, and when the SINR received by the NB-IoT base station is smaller than a signal to interference plus noise ratio threshold beta at the NB-IoT base station, considering that the NB-IoT user cannot normally communicate with the NB-IoT base station. Therefore, the outage probability of the co-frequency coexistence network of the NB-IoT and the cellular mobile network is defined as follows:
P out ＝P(SINR＜β) (8)
the average outage probability of the same-frequency coexistence network of the NB-IoT and the cellular mobile network is as follows:
wherein
It is seen from equation (9) that the outage probability is related to the NB-IoT base station density and NB-IoT base station coverage radius.
As can be obtained from equation (7), the SINR received by the NB-IoT base station is related to the distance from the user to the NB-IoT base station, and is obtained according to the definition of the average outage probability:
substituting the formula (2) and the formula (7) into the formula (10) yields:
according to the desired definition of the conditions, will be expressed in formula (11)
Wherein
Subject to an exponential distribution with a parameter of 1, i.e.
Equation (12) is expanded to:
wherein step (a) utilizes desired properties,
and
respectively represent interference I UE And interference I CU Is performed by the laplace transform. Substituting equation (13) into equation (11), the average outage probability is expressed as:
order to
To obtain I UE Is subjected to laplace transform
Comprises the following steps:
wherein the content of the first and second substances,
derived from the definition of the laplace transform:
wherein step (b) utilizes the generating function of the parent process in each cluster, and step (c) is performed by h N Obtaining a rayleigh distribution with a mean value of 1, converting cartesian coordinates into polar coordinates in step (d), and simplifying the formula (15) as follows:
according to Table of integratals, Series and Products:
combining equation (16) with equation (18) yields μ ═ 2- α, v ═ α, q ═ 1, and n ═ 0, and substituting equation (17) yields equation (15). In the same way, I is obtained CU Laplace change of (a):
the formula (18) and the formula (19) are substituted into the formula (14) to obtain the formula (9).
User transmission capacity:
the shannon theorem formula can know that the system user capacity is as follows:
the application defines the user transmission capacity as the number of successful user transmission under the effect of the interruption probability at any time, namely:
C＝B(1-P out )log 2 (1+SINR) (21)
substituting equation (9) and equation (7) into equation (21) yields the user transmission capacity:
where B is the transmission bandwidth, it can be seen from equation (22) that the user transmission capacity is related to the NB-IoT base station coverage radius a and the NB-IoT base station distribution density.
Starting simulation, simulation result and analysis:
the performance of the NB-IoT and cellular mobile network coexistence system is simulated in a given area to verify the correctness of the theoretical analysis. In the established model, the influence of system noise is ignored, the point spreading is carried out on the cellular mobile users, the NB-IoT base stations and the NB-IoT users in the area with the radius of 10km, and the simulation result is 10 6 The sub-scattering simulation is averaged, and the specific parameter settings are shown in table 2.
TABLE 2
Fig. 3 shows the variation trend of the interruption probability under different cluster radius conditions. It is seen from the figure that the simulation result is completely consistent with the theoretical analysis result, and secondly, it can be known through the simulation of 4 different cluster radius situations that the interruption probability is small when the cluster radius is small, and when the cluster radius is 2000m, the interruption is more likely to occur, so that if the interruption performance is taken as the primary requirement, the coverage radius of the NB-IoT base station needs to be controlled within a certain range. Based on simulation analysis, when the cluster radius is smaller, the communication distance is reduced, and the inter-cluster interference is reduced, so that the condition of communication interruption is reduced. In addition, the interruption performance at a cluster radius of 600m is improved by 40% compared with a network model based on PPP distribution.
λ B ＝λ CU ＝10 -6 km -3
Fig. 4 shows the variation trend of the outage probability under different NB-IoT base station density states. As can be known from simulation of 3 different NB-IoT base station density situations, the interruption probability is lower when the NB-IoT base station density is lower, and the NB-IoT base station density is 10 -5 km -3 When the network is in a break state, the NB-IoT base station density needs to be controlled to a smaller level if the break performance is the primary requirement. Based on simulation analysis, when the density of the NB-IoT base stations is small, the number of scattering points is reduced, so that the inter-cluster interference is reduced, and the system communication interruption performance is reduced. In addition, it can be seen from FIG. 4 that the network outage probability of the PPP-based model is concentrated within the range of 0.7-0.9, and the density of the proposed model in NB-IoT base stations is 10 -7 km 3 The interruption probability is within the range of 0.2-0.9, and the interruption performance is improved by 33.5%.
(a＝600m，λ CU ＝10 -6 km -3 )
Fig. 5 shows the variation trend of user transmission capacity with SINR threshold and cluster radius. As seen from fig. 5, when the SINR threshold is 10dB and the cluster radius is small, the user transmission capacity reaches the maximum value. If the user transmission capacity is used as a primary index for measuring the network, a proper threshold value and a smaller cluster radius are selected. Based on simulation analysis, when the SINR threshold is fixed, as the cluster radius is increased, the number of scattering points is increased, the interruption probability is increased, the coverage capability of the network is weakened, and the quantity which finally influences the successful transmission of a user is reduced; when the cluster radius is constant, the user transmission capacity gradually increases with the increase of the SINR threshold, and starts to decrease when reaching a certain value, and the interruption probability becomes larger as the threshold becomes larger, resulting in a smaller power for successful transmission, while the logarithmic function is an increasing function, resulting in a maximum value when the user transmission capacity is 10dB at the threshold.
Fig. 6 shows the relationship between the user transmission capacity and the SINR threshold and density. As seen in fig. 6, the SINR threshold is 10dB, and when the NB-IoT base station density is low, the user transmission capacity reaches the maximum value. Based on simulation analysis, when the SINR threshold is fixed, as the density of NB-IoT base stations increases, the number of scattering points increases, and the interference becomes larger, so that the average outage probability becomes larger, the coverage area is reduced, the number of successful user transmissions is reduced, and finally the user transmission capacity is reduced; when the NB-IoT base station density is constant, as the SINR threshold increases, the user transmission capacity increases to the highest point and then decreases, because the outage probability increases as the threshold increases, resulting in a lower probability of successful transmission, however, the logarithmic function is an increasing function, and there is an SINR threshold that maximizes the user transmission capacity.
Fig. 7 shows a comparison of user transmission capacities in different PPP and PCP deployment modes. Simulation results show that the maximum value of the user capacity in the PPP deployment mode is 74.43kbps, the maximum value of the user transmission capacity in the PCP deployment mode is 258.3kbps, and based on simulation analysis, compared with the PPP mode, the user transmission capacity in the PCP mode is improved by about 70%.
The method establishes a PCP distribution-based NB-IoT and cellular network co-frequency coexistence model, analyzes co-frequency interference existing in an uplink of a coexistence network by means of a random geometric theory, deduces an expression of interruption probability and user transmission capacity, and performs Monte Carlo simulation verification. Simulation results show that compared with a model based on PPP distribution, the established model based on PCP distribution is more suitable for actual deployment, the interrupt performance of the coexistence network can be improved by 40% by selecting a proper NB-IoT base station coverage radius and NB-IoT base station distribution density, so that the transmission capacity of a user is increased, the model has guiding significance for the actual deployment of NB-IoT, and a solution is provided for realizing massive internet with large connection.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (4)
1. A deployment method of a narrowband cellular Internet of things based on a Poisson cluster process is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a plurality of narrowband Internet of things NB-IoT base stations and cellular mobile users in an area to be tested, wherein the narrowband Internet of things NB-IoT base stations cover the plurality of narrowband Internet of things NB-IoT users;
s2, constructing a PCP-based NB-IoT and cellular mobile network coexistence model, specifically including: modeling the position of the NB-IoT base station as a PPP model in the area to be tested, modeling the position of the NB-IoT user as a cluster taking the NB-IoT base station as a center, and constructing the position of the cellular mobile user as the PPP model, wherein the PPP model comprises the following steps: the position of the clustering center NB-IoT base station is modeled as PPP and the density is lambda B Each father node B i ∈Φ B Forming the center of a cluster, and taking NB-IoT users as sub-nodes to be independently and uniformly distributed in a circle with the radius of a to form clusters, wherein the density is lambda UE The location of cellular mobile users is modeled as a density of λ CU Phi of CU PPP model;
s3, constructing an uplink interference model based on the NB-IoT and the cellular mobile network, and performing interference analysis on the PCP-based NB-IoT and cellular mobile network coexistence model based on the uplink interference model;
the uplink interference model includes, but is not limited to: target base station, other NB-IoT base stations, NB-IoT users, cellular mobile users, useful information, inter-cluster interference, cellular mobile user interference;
s4, based on the interference analysis, obtaining an interruption probability expression and a user transmission capacity expression through interruption probability and user transmission capacity respectively, simulating an interference process, and analyzing the performance of the PCP-based NB-IoT and cellular mobile network coexistence model;
the interrupt probability expression is obtained by calculation based on NB-IoT base station density, NB-IoT base station coverage radius and a maximum signal to interference plus noise ratio criterion;
the interruption probability expression is:
wherein P is out Representing the probability of interruption, a representing the cluster radius,
which represents the laplacian operator, is,
and
respectively represent interference I UE And interference I CU Of Laplace transformation, I UE Representing interference from other NB-IoT cell user equipments, I CU Representing interference from cellular mobile users, σ 2 Representing system noise;
the user transmission capacity expression is obtained based on the interruption probability, the signal-to-interference-and-noise ratio and a Shannon theorem formula;
the user transmission capacity expression is as follows:
where C represents the user transmission capacity, p i The upload power on behalf of the NB-IoT users,
transmission on behalf of NB-IoT usersA channel, wherein | x | represents the Euclidean distance between the NB-IoT user and the target base station,
represents the power, σ, that the target base station receives to NB-IoT users within the coverage area 2 Representing the system noise, Γ () representing the gamma function,
alpha represents the path loss factor, s represents the Laplace operator, B represents the operating bandwidth, and B representing an NB-IoT base station density; lambda [ alpha ] CU Representing cellular mobile subscriber density.
2. The deployment method of narrowband cellular internet of things based on poisson cluster process as claimed in claim 1, wherein: in S2, the distance between the NB-IoT base station and the NB-IoT users is set to be a random variable, and the cluster includes a plurality of NB-IoT users.
3. The deployment method of narrowband cellular internet of things based on poisson cluster process as claimed in claim 1, wherein: the performance analysis in S4 is based on the outage probability and the user transmission capacity.
4. The deployment method of narrowband cellular internet of things based on poisson cluster process as claimed in claim 1, wherein: in the simulation process, random point scattering is carried out on cellular mobile users, NB-IoT base stations and NB-IoT users.
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