CN115175203B - Intelligent track planning method for vehicle-mounted base station with hot spot area covered on demand - Google Patents
Intelligent track planning method for vehicle-mounted base station with hot spot area covered on demand Download PDFInfo
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- H—ELECTRICITY
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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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
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/04—Traffic adaptive resource partitioning
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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
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/48—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
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- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/06—TPC algorithms
- H04W52/14—Separate analysis of uplink or downlink
- H04W52/143—Downlink power control
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- H—ELECTRICITY
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- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses an intelligent track planning method of a vehicle-mounted base station with hot spot areas covered according to needs, which comprises the following steps: s1, classifying and identifying different dynamic hot spot traffic areas, dividing the service into N service clusters by adopting a K-means clustering method, and providing communication services for the N service clusters by N vehicle-mounted mobile base stations in a one-to-one correspondence manner; s2, analyzing the data transmission rate, coverage and capacity of the vehicle-mounted base station according to the space-time distribution of the service flow; and S3, establishing an optimization model by adopting a greedy search algorithm with the aim of maximizing the matching degree of the network capacity and the service demand, and planning and optimizing the track of the vehicle-mounted mobile base station. According to the invention, the vehicle-mounted base station provides service for the hot spot area, so that the aim of improving the network capacity is fulfilled, the network capacity provided by the vehicle-mounted base station and the macro base station is matched with the actual service demand flow, and the on-demand coverage of the vehicle-mounted base station is realized.
Description
Technical Field
The invention relates to an intelligent planning method for a vehicle-mounted base station, in particular to an intelligent track planning method for the vehicle-mounted base station, which is used for optimizing the on-demand coverage of a hot spot area.
Background
Existing fixed base station networks are designed based on worst case, and network traffic imbalance caused by service requirements can lead to resource waste of the networks. Therefore, future network architecture designs will be based on-demand coverage of resources, meeting business requirements at different locations and at different times.
On-demand coverage of resources and traffic in a network is a very attractive way to guarantee the serviceability of existing infrastructure against the above-mentioned demands. Furthermore, in the event of a natural disaster, on-demand coverage may help provide emergency communications in severely compromised infrastructure. Recently, the use of Unmanned Aerial Vehicle (UAV) communications has attracted considerable interest in many applications, UAVs may be used as flight base stations to improve the rate and coverage performance of wireless networks in different scenarios such as temporary hotspots and emergencies. An aerial base station based on an unmanned aerial vehicle may provide better coverage and resource utilization than a terrestrial base station while having the advantage of providing real-time communications.
However, the safety of unmanned aerial vehicles and the urban flying forbidden policy make the operation of the flying base station limited, and the ground vehicle-mounted base station plays an increasingly important role in realizing the on-demand coverage of the service.
Disclosure of Invention
The invention aims to: the invention aims to provide an intelligent track planning method of a vehicle-mounted base station, which can maximize the on-demand coverage of a hot spot area with the matching degree of network capacity and service requirements aiming at the service flow of the hot spot area which changes in a free dynamic manner at any time in a city.
The technical scheme is as follows: the invention relates to an intelligent track planning method for an on-board base station, which comprises the following steps:
s1, classifying and identifying different dynamic hot spot traffic areas, dividing the service into N service clusters by adopting a K-means clustering method, and providing communication services for the N service clusters by N vehicle-mounted mobile base stations in a one-to-one correspondence manner;
s2, analyzing the data transmission rate, coverage and capacity of the vehicle-mounted base station according to the space-time distribution of the service flow;
and S3, establishing an optimization model by adopting a greedy search algorithm with the aim of maximizing the matching degree of the network capacity and the service demand, and planning and optimizing the track of the vehicle-mounted mobile base station.
Further, in the step S1, in the t-th time slot, the i-th in-vehicle base station serves the service cluster
Indicating that the optimal communication position is defined as +.>
K is the number of cluster cores; let the total service time of the communication transmission be T,
representing the position coordinates of the service clusters in each time slot, the moving speed of the vehicle-mounted base station is V,
representing its position, defining a set of road networks +.>
The method comprises the following steps:
therefore, the position constraint of the in-vehicle base station during movement is expressed as:
let the area of each service cluster be
For each hot spot area, the number of business requirements obeys the parameters as follows
Is a poisson process of (2); let N (t) represent the time (0, t]The number of business demands initiated by the hot spot area is then for any t.gtoreq.0 and Δt.gtoreq.0:
where Δt is the length of the time interval; in interval (0, t)]Inner hot spot areaThe average number of service demands initiated by the domain is
Wherein t is any time;
the average service demand number initiated by the hot spot area is within the unit time
Further, in the step S2, all the services in the service cluster are set to be completed in one time slot, and the transmission rate is determined by the transmitting power of the vehicle-mounted base station;
the bandwidth of the communication between the vehicle-mounted base station and the service cluster is fixed as B, and the maximum transmission power of the vehicle-mounted base station is P max So the ith vehicle-mounted base station pair
Instantaneous rate of a cluster during communication in the t-th time slot +.>
The method comprises the following steps:
wherein ,
is that the ith vehicle-mounted base station is allocated to the nth time slot at the nth time slot i Transmission power, sigma, of individual clusters 2 Is the noise power spectral density,/">
Is the antenna gain +.>
The path loss of the LoS link and the NLoS link between the vehicle-mounted base station and the service cluster are respectively as follows:
wherein ,fc Is the carrier frequency, c is the speed of light,
is the distance between the vehicle-mounted base station and the service cluster +.>
η LoS and ηNLoS Average additional loss for LoS link and NLoS link, respectively;
the connection probability of the LoS link between the vehicle-mounted base station and the service cluster is as follows:
where a and b are constants dependent on the environment, h i Is the i-th vehicle-mounted base station antenna height; similarly, the connection probability of NLoS link is P NLoS ＝1-P LoS ；
Considering only average path loss, there is
wherein ,PLLoS PL for connection probability of LoS link NLoS For the connection probability of the NLoS link,
further, in the step S2, the coverage analysis of the vehicle-mounted base station is as follows:
setting the transmitting power of the vehicle-mounted base station as P i The power received by the service cluster is
Setting target service cluster N i The vehicle-mounted base station i and the downlink target service cluster N are accessed i The SINR of (2) is:
wherein ,
to accumulate interference:
defining a binary variable
Representing a service cluster N i If the coverage status of the vehicle-mounted base station i and the service cluster N i The downlink SINR therebetween is greater than the threshold SINR 0 Then consider service cluster N i Covered by the vehicle-mounted base station i, the following steps are:
to ensure that each service cluster is covered by one of the vehicle-mounted base stations, the coverage constraints that must be satisfied are:
n is the initial number of the vehicle-mounted base stations in the planning space.
Further, in the step S2, the capacity analysis of the in-vehicle base station is as follows:
let the capacity of each vehicle-mounted base station be C i The service cluster served by the ith vehicle-mounted base station is N i The ith vehicle-mounted base station can be provided for the service cluster in the t-th time slot
Capacity of->
Capacity of all the in-vehicle base stations at the t-th time slot
Expressed as:
the average service demand number initiated by the hot spot area is
Is the data rate required by the service cluster;
for any service cluster N i The ith vehicle-mounted base station provides service for all intra-cluster services and also meets the service target rate requirement, and the capacity constraint of the obtained hot spot area is as follows:
further, in the step S3, the matching degree between the network capacity and the service requirement is expressed as:
wherein ,
representing the total traffic demand in the t-th time slot: />
C N，F ((x N，F ，y N，F ) T) represents the total capacity that N vehicle-mounted base stations and F fixed base stations can provide in the t-th time slot;
delta is an adjustable parameter that represents the tolerance of the total demand of traffic in the network to the capacity of the network.
Further, in the step S3, an optimization model is established by adopting a greedy search algorithm as follows:
wherein, constraint C1 represents that the network capacity of the vehicle-mounted base station and the fixed base station passing through is higher than the service requirement, constraint C2 represents the constraint of the transmitting power of each vehicle-mounted base station, constraint C3 represents the constraint of the coverage of each vehicle-mounted base station, and constraint C4 represents the constraint of ensuring that the vehicle-mounted base station cannot exceed the maximum moving speed in the moving process.
Compared with the prior art, the invention has the following remarkable effects:
1. according to the invention, the vehicle-mounted base station provides service for the hot spot area, the purpose of improving the network capacity is realized, so that the network capacity provided by the vehicle-mounted base station and the macro base station can be matched with the actual service demand flow, the matching degree of the demand and the capacity is provided as an index of vehicle-mounted base station path planning, and the on-demand coverage of the vehicle-mounted base station is realized;
2. the invention optimizes the service clusters served by each vehicle-mounted base station, the transmitting power distributed to each service cluster by the vehicle-mounted base station and the track of the vehicle-mounted base station so as to maximize the matching degree;
3. compared with the traditional path planning method, the method can adjust the transmitting power of each vehicle-mounted base station and the number of the vehicle-mounted base stations through the service requirement at the current moment, can reduce the number of the vehicle-mounted base stations meeting the overall service requirement and the transmitting power distributed to each service cluster, and achieves the aim of green energy conservation; by matching with the actual service demand changing at any time and space, reasonable path planning of the vehicle-mounted base station at each moment is realized, and the service quality of the hot spot area is further improved.
Drawings
FIG. 1 is a schematic diagram of a service scenario deployed by the present invention;
fig. 2 is a schematic diagram of a three-dimensional virtual appearance of a system scene in a time slot t according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
According to the intelligent track planning method of the vehicle-mounted base station, firstly, different dynamic hot spot flow areas are classified and identified, and then, the track of the vehicle-mounted mobile base station is planned and optimized according to the space-time distribution of service flow; finally, the transmitting power and the number of the vehicle-mounted mobile base stations are optimized so that the network capacity provided by the base stations is matched with the actual service flow of the hot spot areas. The service scene deployed by the invention is shown in figure 1, and the specific implementation steps are as follows:
step 1, classifying and identifying different dynamic hot spot flow areas
Since the position of the service in each time slot is known, as shown in fig. 2, the position of the vehicle-mounted base station corresponding to each service cluster is the optimal communication position obtained after the optimization solution is needed. Therefore, the space position of each service in the current time slot can be used as an index for distinguishing different service clusters, and the services with similar spatio-temporal performance can be divided into the same cluster. Because the number of the service clusters after service clustering is the same as that of the vehicle-mounted base stations, the K-means clustering method is adopted to divide the service into N service clusters, and the N vehicle-mounted mobile base stations are in one-to-one correspondence to provide communication services for the N service clusters. In the t-th time slot, the service cluster served by the i-th vehicle-mounted base station is used
And (3) representing. Defining the optimal communication position as +.>
K is the number of cluster centers. Assuming that the total service time of the communication transmission is T, < >>
Representing the position coordinates of the service clusters in each time slot, wherein the moving speed of the vehicle-mounted base station is V, & lt/EN & gt>
Representing its position, defining a set of road networks +.>
The method comprises the following steps:
where W represents the number of road networks.
Therefore, the position constraint of the in-vehicle base station during movement is expressed as:
since the traffic distribution of the network scenario is non-uniform, the area of each traffic cluster is assumed
For each hotspot area, its business requirement number compliance parameter is +.>
Is a poisson process of (c). Let N (t) represent the time (0, t]The number of business demands initiated by the hot spot area is then for any t.gtoreq.0 and Δt.gtoreq.0:
where Δt is the length of the time interval.
The number of traffic demands is expressed in equation (3) to follow poisson distribution and is related only to the length of the time interval, where (0, t]The average service demand number initiated by the hot spot area is
Wherein t is any time, so that the average service demand number initiated by the hot spot area is +.>
The network capacity provided by the vehicle-mounted base station is matched with the actual service flow of the hot spot area, and the optimization problem is obtained through data transmission rate analysis, coverage analysis and capacity analysis of the vehicle-mounted base station. Because the network capacity and the service demand of each position at each moment are dynamically changed, the invention aims to maximize the matching degree of the network capacity and the service demand and optimize the service cluster served by each vehicle-mounted base station, the transmission power distributed by the vehicle-mounted base station to each service cluster and the moving track of the vehicle-mounted base station. The invention provides a vehicle-mounted base station path planning algorithm based on a greedy search algorithm, which divides an original problem into sub-problems and optimizes the sub-problems to obtain a suboptimal solution of the original problem.
The specific implementation steps are as follows:
step 21, analyzing the data transmission rate of the vehicle-mounted base station
After the service is clustered in each time slot, each vehicle-mounted base station correspondingly serves one service cluster, and in order to improve the communication efficiency of the vehicle-mounted base stations, the optimal communication position of each vehicle-mounted base station and the transmission power allocated to each service cluster need to be calculated. Assuming that the bandwidth of the communication between the vehicle-mounted base station and the service cluster is fixed as B, the maximum transmission power of the vehicle-mounted base station is P max So the ith vehicle-mounted base station pair
Instantaneous rate of a cluster during communication in the t-th time slot +.>
The method comprises the following steps:
wherein ,
is that the ith vehicle-mounted base station is allocated to the nth time slot at the nth time slot i Transmission power, sigma, of individual clusters 2 Is the noise power spectral density,/">
Is the antenna gain +.>
The invention sets that all the services in the service cluster are completed in one time slot, and the transmission rate is determined by the transmitting power of the vehicle-mounted base station.
A line-of-sight (LoS) link and a non-line-of-sight (NLoS) link exist between the vehicle-mounted base station and the service cluster, and the path loss of the LoS link and the path loss of the NLoS link are respectively:
wherein ,fc Is the carrier frequency, c is the speed of light;
is the distance between the vehicle-mounted base station and the service cluster, < >>
η LoS and ηNLoS The average parasitic losses of the LoS link and the NLoS link, respectively.
The probability of LoS connection between the vehicle-mounted base station and the service cluster is:
where a and b are constants dependent on the environment, where h i Respectively the height of the vehicle-mounted base station antenna. Similarly, the connection probability of NLoS is:
P NLoS ＝1-P LoS (8)
considering only average path loss, there is
wherein ,PLLoS PL for connection probability of LoS link NLoS For the connection probability of the NLoS link,
step 22, overlay analysis
When the vehicle-mounted base station plans a dense network for a hot spot area, defining that if the downlink SINR (Signal to Interference plus Noise Ratio signal to interference plus noise ratio) received by a service cluster is larger than a threshold SINR 0 Indicating that the service cluster is covered by the small base station, otherwise, defining that the service cluster is not covered. Setting the transmitting power of the vehicle-mounted base station as P i Thus the power received by the service cluster is
The invention is deployed in the scene that the macro base station and the vehicle-mounted base stations coexist but have different frequencies, so the interference suffered by the target service cluster is the accumulated interference generated by all vehicle-mounted base stations except the communication vehicle-mounted base station, and the noise is 0 as the mean value and sigma as the variance 2 Additive white gaussian noise of (c). Setting target service cluster N i The vehicle-mounted base station i and the downlink target service cluster N are accessed i The SINR of (2) is:
wherein ,
to accumulate interference, the following ∈>
So that
Updated to
wherein ,
is that the ith' vehicle-mounted base station is allocated to the nth time slot at the nth time slot i Transmission power, sigma, of individual clusters 2 Is the noise power spectral density,/">
Is the antenna gain of the i' th in-vehicle base station,/->
Is the ith' interfering vehicle base station to the nth i Average path loss of individual target clusters, expressed as
wherein ,
representing the ith interference vehicle base station to the target service cluster N i Distance of->
h i′ Is the antenna height of the i' th interfering vehicle base station.
Definition of the definitionBinary variable
Defining a service cluster N i Covering state of (2):
if the vehicle-mounted base station i and the service cluster N i The downlink SINR therebetween is greater than the threshold SINR 0 Then consider service cluster N i Is covered by the in-vehicle base station i, so equation (12) can be expressed as:
thus, the first and second substrates are bonded together,
the value of (2) is determined by the positions of all the vehicle-mounted base stations.
The initial number of the vehicle-mounted base stations in the planning space is N, and the coverage requirement of the planning space can be met only if each service cluster is covered by one vehicle-mounted base station, so that in order to ensure that each service cluster is covered by one vehicle-mounted base station, the following coverage constraint must be met:
step 23, capacity analysis
Let the capacity of each in-vehicle base station be C i N for business cluster served by ith vehicle-mounted base station i Indicating that the ith in-vehicle base station can be provided to the service cluster in the t-th time slot
Capacity of->
The method comprises the following steps:
capacity C of all the vehicle-mounted base stations at the t-th time slot N Expressed as:
the average service demand number initiated by the hot spot area is
Is the data rate required by the service cluster. For any service cluster N i The ith vehicle-mounted base station provides service for all intra-cluster services and also meets the target rate requirement of the services, and the capacity constraint of the hot spot area is as follows: />
In the invention, the load pressure of the fixed base station is reduced through the service of the vehicle-mounted base station to the hot spot area, thereby improving the capacity of the whole network. However, the network capacity and the service demand of each position at each moment are dynamically changed, the problem solved by the invention is to optimize the service cluster served by each vehicle-mounted base station, the transmission power distributed by the vehicle-mounted base station to each service cluster and the moving track of the vehicle-mounted base station by taking the matching degree of the maximized network capacity and the service demand as the target, and then the matching degree of the network capacity and the service demand is expressed as follows:
wherein ,
and />
The total service requirements of N service clusters and F non-service clusters served by the macro base station in the t time slot are respectively shown as follows:
C N ((x i ，y i),t) and CF ((x j ，y j ) T) represents the total capacity that N vehicle-mounted base stations and F fixed base stations can provide in the t-th time slot:
where Δ is an adjustable parameter that represents the tolerance of the total traffic demand in the network to the network capacity. In this study, the solution complexity of the optimization problem can be reduced by adjusting the value of Δ;
is the sum of the capacity each fixed base station can provide in the t-th time slot.
In conclusion, the method comprises the steps of,
thus, the optimization problem of maximizing the matching of network capacity to traffic demand can be expressed as:
the constraint C1 indicates that the network capacity through which the vehicle-mounted base station and the fixed base station can pass is higher than the service requirement, the constraint C2 restricts the transmitting power of each vehicle-mounted base station, the constraint C3 restricts the coverage of each vehicle-mounted base station, and the constraint C4 ensures that the vehicle-mounted base station cannot exceed the maximum moving speed in the moving process.
In (21), each of the service clusters serviced by the in-vehicle base station
Is limited to an integer, which makes it an MINLP (mixed integer nonlinear program) problem, and it is difficult to find the optimal solution. Based on the greedy search algorithm, the suboptimal solution of this problem can be found by decomposing it into three sub-problems, namely traffic clustering, power selection and path optimization. Firstly, the services in each time slot are clustered according to the known service distribution, and then the optimal communication position and power distribution of the vehicle-mounted base station serving each service cluster are optimized with the aim of maximizing the matching degree of the network capacity and the service requirement. And finally, matching the communication positions of the vehicle-mounted base stations in all time slots to form the optimal motion trail of the vehicle-mounted base stations.
In greedy algorithms, global optima are not considered, but rather the optimization space is searched for the best choice in the current state. The decomposed problem, while it results in an optimal solution in each individual time slot, the sub-optimal solution in the total service time is obtained by summing all solutions.
After solving the problem, optimization is performed in units of one slot. The objective function of the sub-problem, i.e. maximizing the matching of network capacity and traffic demand in each slot, is defined as
wherein ,
the capacities of all the vehicle-mounted base stations in each time slot are calculated first, and then the capacities of all the vehicle-mounted base stations in the total service time are obtained by adding the capacities. This is a non-convex optimization problem because of the number of clusters to be optimized
Optimal communication position of vehicle-mounted base station->
And transmission power->
There is a nonlinear coupling between them, the variable +.>
and />
Separate, then find the optimal solution for each variable by fixing the other two variables. Except for the number of clusters->
Besides, the result after service clustering also comprises the position of the vehicle-mounted base station corresponding to the center of each service cluster>
Therefore, the initial projection point of the road network center can be used as the initial vehicle-mounted base station communication position to optimize transmission power distribution.
By solving the optimization problem, a vehicle-mounted base station path planning scheme meeting service requirements can be finally obtained, and the corresponding number of vehicle-mounted base stations and the corresponding transmitting power can be obtained at different moments, so that green energy-saving on-demand coverage is realized. The service flow of the hot spot area changing in the idle state at any time in the city can be aimed at, the matching degree of the network capacity and the service demand is maximized, and the on-demand elastic coverage of the hot spot area is realized.
The greedy algorithm based on mobile base station track optimization is executed as a loop iteration, the iteration number required for obtaining the optimal solution is determined by an actual scene, and the concrete algorithm is as follows:
in each iteration, the cluster center positions under the current requirement are calculated according to a K-means clustering algorithm to determine the optimal communication positions of N mobile base stations, wherein the time complexity of the K-means clustering algorithm is O (kmmax_ite), K is the number of cluster centers, m is the number of service points of a hot spot area, and max_ite is the maximum iteration number of the algorithm. For N mobile base stations, the matching degree of each mobile base station is updated according to its transmit power and location, so the computational complexity of each iteration of each mobile base station is PN. And setting iteration T times, and calculating PNT times altogether, wherein the complexity of a greedy algorithm based on mobile base station track optimization is O (PNT+kmmax_ite).
Claims (3)
1. The intelligent track planning method for the vehicle-mounted base station with the hot spot area covered according to the requirement is characterized by comprising the following steps:
s1, classifying and identifying different dynamic hot spot traffic areas, dividing the service into N service clusters by adopting a K-means clustering method, and providing communication services for the N service clusters by N vehicle-mounted mobile base stations in a one-to-one correspondence manner;
s2, analyzing the data transmission rate, coverage and capacity of the vehicle-mounted base station according to the space-time distribution of the service flow;
s3, establishing an optimization model by adopting a greedy search algorithm with the aim of maximizing the matching degree of network capacity and service requirements, and planning the track of the vehicle-mounted mobile base station;
in the step S1, in the t-th time slot, the i-th vehicle-mounted base station serves the service cluster
The representation is made of a combination of a first and a second color,the optimal communication position is defined as->
K is the number of cluster cores; let the total service time of the communication transmission be T,
representing the position coordinates of the service clusters in each time slot, the moving speed of the vehicle-mounted base station is V,
representing its position, defining a set of road networks +.>
The method comprises the following steps:
therefore, the position constraint of the in-vehicle base station during movement is expressed as:
let the area of each service cluster be
For each hot spot area, the number of business requirements obeys the parameters as follows
Is a poisson process of (2); let N (t) represent the time (0, t]The number of business demands initiated by the hot spot area is then for any t.gtoreq.0 and Δt.gtoreq.0:
where Δt is the length of the time interval; in interval (0, t)]The average service demand number initiated by the hot spot area is
Wherein t is any time;
the average service demand number initiated by the hot spot area is within the unit time
In the step S2, all the services in the service cluster are set to be completed in one time slot, and the transmission rate is determined by the transmitting power of the vehicle-mounted base station;
the bandwidth of the communication between the vehicle-mounted base station and the service cluster is fixed as B, and the maximum transmission power of the vehicle-mounted base station is P max So the ith vehicle-mounted base station pair
Instantaneous rate of a cluster during communication in the t-th time slot +.>
The method comprises the following steps:
wherein ,
is that the ith vehicle-mounted base station is allocated to the nth time slot at the nth time slot i Transmission power, sigma, of individual clusters 2 Is the noise power spectral density,/">
Is the antenna gain +.>
The path loss of the LoS link and the NLoS link between the vehicle-mounted base station and the service cluster are respectively as follows:
wherein ,fc Is the carrier frequency, c is the speed of light,
is the distance between the vehicle-mounted base station and the service cluster
η LoS and ηNLoS Average additional loss for LoS link and NLoS link, respectively;
the connection probability of the LoS link between the vehicle-mounted base station and the service cluster is as follows:
where a and b are constants dependent on the environment, h i Dividing the antenna heights of the ith vehicle-mounted base station; similarly, the connection probability of NLoS link is P NLoS ＝1-P LoS ；
Considering only average path loss, there is
wherein ,PLLoS PL for connection probability of LoS link NLoS For the connection probability of the NLoS link,
in the step S3, the matching degree between the network capacity and the service requirement is expressed as:
wherein ,
representing the total traffic demand in the t-th time slot:
C N，F ((x N，F ，y N，F ) T) represents the total capacity that N vehicle-mounted base stations and F fixed base stations can provide in the t-th time slot;
delta is an adjustable parameter, which represents the tolerance of the total service demand in the network to the network capacity;
in the step S3, an optimization model established by adopting a greedy search algorithm is as follows:
wherein, constraint C1 represents that the network capacity of the vehicle-mounted base station and the fixed base station passing through is higher than the service requirement, constraint C2 represents the constraint of the transmitting power of each vehicle-mounted base station, constraint C3 represents the constraint of the coverage of each vehicle-mounted base station, and constraint C4 represents the constraint of ensuring that the vehicle-mounted base station does not exceed the maximum moving speed in the moving process.
2. The intelligent track planning method for the vehicle-mounted base station with the hot spot area covered on demand according to claim 1, wherein in the step S2, the coverage analysis of the vehicle-mounted base station is as follows:
setting the transmitting power of the vehicle-mounted base station as P i The power received by the service cluster is
Setting target service cluster N i The vehicle-mounted base station i and the downlink target service cluster N are accessed i The SINR of (2) is:
wherein ,
to accumulate interference:
defining a binary variable
Representing a service cluster N i If the coverage status of the vehicle-mounted base station i and the service cluster N i The downlink SINR therebetween is greater than the threshold SINR 0 Then consider service cluster N i Covered by the vehicle-mounted base station i, the following steps are:
to ensure that each service cluster is covered by one of the vehicle-mounted base stations, the coverage constraints that must be satisfied are:
n is the initial number of the vehicle-mounted base stations in the planning space.
3. The intelligent track planning method for the on-vehicle base station with the hot spot area covered on demand according to claim 1, wherein in the step S2, the capacity analysis of the on-vehicle base station is as follows:
let the capacity of each vehicle-mounted base station be C i The service cluster served by the ith vehicle-mounted base station is N i The ith vehicle-mounted base station can be provided for the service cluster in the t-th time slot
Capacity of->
Capacity of all the in-vehicle base stations at the t-th time slot
Expressed as:
the average service demand number initiated by the hot spot area is
Is the data rate required by the service cluster;
for any service cluster N i The ith vehicle-mounted base station provides service for all intra-cluster services and also meets the service target rate requirement, and the capacity constraint of the obtained hot spot area is as follows:
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| CN103442369A (en) * | 2013-07-31 | 2013-12-11 | 北京邮电大学 | Flexible networking method on wide-area coverage scene |
| CN109905848A (en) * | 2017-12-11 | 2019-06-18 | 上海交通大学 | A kind of converged network resource regulating method based on wireless traffic caching |
| CN111193536A (en) * | 2019-12-11 | 2020-05-22 | 西北工业大学 | Multi-unmanned aerial vehicle base station track optimization and power distribution method |
| CN113993067A (en) * | 2021-09-15 | 2022-01-28 | 北京邮电大学 | Interference coordination method of unmanned aerial vehicle auxiliary network under space constraint |
| WO2022043498A1 (en) * | 2020-08-31 | 2022-03-03 | Ipcom Gmbh & Co. Kg | Location dependent relay node configuration |
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| CN103442369A (en) * | 2013-07-31 | 2013-12-11 | 北京邮电大学 | Flexible networking method on wide-area coverage scene |
| CN109905848A (en) * | 2017-12-11 | 2019-06-18 | 上海交通大学 | A kind of converged network resource regulating method based on wireless traffic caching |
| CN111193536A (en) * | 2019-12-11 | 2020-05-22 | 西北工业大学 | Multi-unmanned aerial vehicle base station track optimization and power distribution method |
| WO2022043498A1 (en) * | 2020-08-31 | 2022-03-03 | Ipcom Gmbh & Co. Kg | Location dependent relay node configuration |
| CN113993067A (en) * | 2021-09-15 | 2022-01-28 | 北京邮电大学 | Interference coordination method of unmanned aerial vehicle auxiliary network under space constraint |
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