CN113285777A - 5G communication system user association, unmanned aerial vehicle deployment and resource allocation method - Google Patents

5G communication system user association, unmanned aerial vehicle deployment and resource allocation method Download PDF

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CN113285777A
CN113285777A CN202110572618.5A CN202110572618A CN113285777A CN 113285777 A CN113285777 A CN 113285777A CN 202110572618 A CN202110572618 A CN 202110572618A CN 113285777 A CN113285777 A CN 113285777A
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modeling
user
unmanned aerial
aerial vehicle
association
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CN113285777B (en
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柴蓉
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陈米铃
陈前斌
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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 relates to a 5G communication system user association, unmanned aerial vehicle deployment and resource allocation method, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: modeling a user-channel associated variable; s2: modeling an unmanned aerial vehicle deployment area; s3: modeling link transmission rate; s4: modeling link transmission time delay; s5: modeling EU and rate; s6: modeling an RU time delay sum; s7: modeling MU energy consumption sum; s8: modeling user-channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions; s9: and determining user association, unmanned aerial vehicle deployment and resource allocation strategies based on system multi-objective optimization. The invention realizes EU sum rate, RU time delay sum and MU energy consumption and optimization by jointly optimizing user association, unmanned plane deployment and power distribution strategies.

Description

5G communication system user association, unmanned aerial vehicle deployment and resource allocation method
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a 5G communication system user association, unmanned aerial vehicle deployment and resource allocation method.
Background
In recent years, the pace of application of unmanned aerial vehicles to civilian and commercial fields has been significantly accelerated due to advances in manufacturing technology and reduction in cost of unmanned aerial vehicles. The use of Unmanned Aerial Vehicles (UAVs) in wireless communication systems has received increasing attention, and the performance of the communication systems and the user experience can be effectively improved by flexible and efficient deployment of UAVs compared to conventional ground communication systems. Meanwhile, in order to meet the differentiated user service quality requirements, the 5G will support three application scenarios: eMBB, mtc, and URLLC. However, limited time-frequency resources in the 5G communication system have contradicted with rapidly-developing user service requirements, and how to design efficient user association, unmanned aerial vehicle deployment and resource allocation strategies, and to achieve performance increase of the 5G communication system has become an important research subject.
Existing research has considered various application scenarios in a 5G scenario, but few research considers resource sharing between the three application scenarios together. In addition, existing work is less concerned with user multi-hop drone deployment and drone backhaul link design.
Disclosure of Invention
In view of this, the present invention aims to provide a method for user association, unmanned aerial vehicle deployment and resource allocation in a 5G communication system, in which EU users and rates, RU user delays, and MU user energy consumptions are modeled for optimization objectives including multiple unmanned aerial vehicle base stations, multiple unmanned aerial vehicle relays, multiple unmanned aerial vehicle gateways, and three types of 5G users, so as to implement a user association, unmanned aerial vehicle deployment, and resource allocation strategy.
In order to achieve the purpose, the invention provides the following technical scheme:
A5G communication system user association, unmanned aerial vehicle deployment and resource allocation method comprises the following steps:
s1: modeling a user-channel associated variable;
s2: modeling a UAV base station deployment area;
s3: modeling link transmission rate;
s4: modeling link transmission time delay;
s5: modeling Enhanced Mobile Broadband user (EU) and rate;
s6: modeling an Ultra-high-Reliable Low-Latency Communication user (RU) time delay sum, wherein the RU represents the Ultra-high-Reliable Low-Latency user;
s7: modeling the sum of energy consumption of Massive Machine Type Communication users (MU), wherein the MU represents the Massive Machine Type Communication users;
s8: modeling user-channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions;
s9: determining user association, unmanned aerial vehicle deployment and resource allocation strategies based on system multi-objective optimization;
suppose the system has multiple 5G users, including M1EU, M2Each RU and M3Each MU, each user needs to send data to the core network; let EUiRepresenting the ith EU user, i is more than or equal to 1 and less than or equal to M1；RUjJ is more than or equal to 1 and less than or equal to M and represents the jth RU user2；MUlRepresenting the first MU user, l is more than or equal to 1 and less than or equal to M3(ii) a Let M be M1+M2+M3Indicating the number of users, UEs, in the systemmRepresents the mth user; order SmRepresenting a UEmM is more than or equal to 1 and less than or equal to M;
in order to realize the transmission of user data to the core network through the unmanned aerial vehicle, the UAV base station, the UAV relay and the UAV gateway are deployed, and the total number N of the deployed UAVs is assumed to make
Figure BDA0003083291500000021
Denotes the n-th1A UAV base station, n is more than or equal to 11≤N1；
Figure BDA0003083291500000022
Denotes the n-th2Number of UAV relays, n is more than or equal to 12≤N2；
Figure BDA0003083291500000023
Denotes the n-th3A UAV gateway, n is more than or equal to 13≤N3(ii) a Let N be N1+N2+N3To representTotal number of UAVs, U, deployed in the systemnRepresenting the nth UAV, wherein N is more than or equal to 1 and less than or equal to N;
assuming that EU and RU can access the UAV base station, MU can forward data to the core network by associating with EU; the system adopts an orthogonal frequency division multiple access mode to transmit data, and the bandwidth of each sub-channel is B.
Further, in step S1, modeling the user-channel associated variable specifically includes: order to
Figure BDA0003083291500000024
A variable is selected for the user's access mode,
Figure BDA0003083291500000025
representing a UEmAnd
Figure BDA0003083291500000026
the association is performed and, conversely,
Figure BDA0003083291500000027
1≤m≤M1+M2(ii) a Order to
Figure BDA0003083291500000028
In order to associate the variables for the MU,
Figure BDA0003083291500000029
represents MUlAnd EUiThe association is performed and, conversely,
Figure BDA00030832915000000210
1≤i≤M1(ii) a Order to
Figure BDA00030832915000000211
The variables are associated with the unmanned aerial vehicle,
Figure BDA00030832915000000212
to represent
Figure BDA00030832915000000213
And
Figure BDA00030832915000000214
associating transmission UEmData of (1) and vice versa
Figure BDA00030832915000000215
To represent
Figure BDA00030832915000000216
And
Figure BDA00030832915000000217
associating transmission UEmThe data of (a) and, conversely,
Figure BDA00030832915000000218
further, in step S2, modeling the unmanned aerial vehicle base station deployment area specifically includes: modeling unmanned aerial vehicle deployment area as three-dimensional grid
Figure BDA00030832915000000219
Respectively the maximum point number in the row, column and vertical directions in the grid, alphax,y,z,nOptimize the variables for UAV deployment location, if αx,y,z,n1 represents UnIs the x-th row in the horizontal direction, the y-th column in the horizontal direction, and the z-th row in the vertical direction in the three-dimensional grid, wherein
Figure BDA00030832915000000220
Further, in step S3, modeling the link transmission rate specifically includes:
(1) order to
Figure BDA00030832915000000221
For the UEmAnd
Figure BDA00030832915000000222
associating the transmission rate corresponding to the data transmission, and modeling as follows:
Figure BDA0003083291500000031
wherein ,PmFor the UEmIs transmitted byPower, σ2In order to be able to measure the power of the noise,
Figure BDA0003083291500000032
for the UEmAnd
Figure BDA0003083291500000033
the gain of the link channel between is modeled as
Figure BDA0003083291500000034
ho represents the channel gain per unit distance,
Figure BDA0003083291500000035
representing a UEmAnd
Figure BDA0003083291500000036
the distance between the two, λ represents the channel propagation fading index;
(2) order to
Figure BDA0003083291500000037
To represent
Figure BDA0003083291500000038
Transmit data to
Figure BDA0003083291500000039
The corresponding link transmission rate is modeled as:
Figure BDA00030832915000000310
wherein ,
Figure BDA00030832915000000311
is composed of
Figure BDA00030832915000000312
The transmission power of the antenna is set to be,
Figure BDA00030832915000000313
is composed of
Figure BDA00030832915000000314
And
Figure BDA00030832915000000315
link channel gain between;
(3) order to
Figure BDA00030832915000000316
To represent
Figure BDA00030832915000000317
And
Figure BDA00030832915000000318
the link transmission rate between the two is modeled as:
Figure BDA00030832915000000319
wherein ,
Figure BDA00030832915000000320
is composed of
Figure BDA00030832915000000321
The transmission power of the antenna is set to be,
Figure BDA00030832915000000322
is composed of
Figure BDA00030832915000000323
And
Figure BDA00030832915000000324
inter-link channel gain;
(4) order to
Figure BDA00030832915000000325
Expressed as MUlAccess EUiThe transmission rate when data transmission is performed is modeled as:
Figure BDA00030832915000000326
wherein ,
Figure BDA00030832915000000327
is MUlThe transmission power of the antenna is set to be,
Figure BDA00030832915000000328
is MUlAnd EUiThe gain of the inter-link.
Further, in step S4, modeling the link transmission delay specifically includes:
(1) order to
Figure BDA00030832915000000329
Representing a UEmTransmit data to
Figure BDA00030832915000000330
Corresponding to the time delay, can be modeled as
Figure BDA00030832915000000331
SmRepresenting a UEmThe amount of data to be transmitted;
(2) order to
Figure BDA00030832915000000332
To represent
Figure BDA00030832915000000333
Transmitting UEmData to
Figure BDA00030832915000000334
Corresponding to the time delay, can be modeled as
Figure BDA00030832915000000335
(3) Order to
Figure BDA00030832915000000336
To represent
Figure BDA00030832915000000337
Transmitting UEmData to
Figure BDA00030832915000000338
The corresponding time delay can be modeled as
Figure BDA00030832915000000339
Further, in step S5, modeling EU and rate specifically includes: let ReuRepresents the sum rate of the EU, modeled as:
Figure BDA00030832915000000340
wherein ,
Figure BDA00030832915000000341
is EUiIs modeled as
Figure BDA00030832915000000342
Further, in step S6, modeling an RU time delay sum specifically includes: let DruRepresenting the sum of time delays of RUs, modeled as
Figure BDA00030832915000000344
wherein ,
Figure BDA00030832915000000343
denotes RUjThe time delay of the user for transmitting data to the core network is modeled as follows:
Figure BDA0003083291500000041
further, in step S7, modeling the MU power consumption sum specifically includes: let EmuFor MU energy consumption sum, modeling is as follows:
Figure BDA0003083291500000042
wherein ,
Figure BDA0003083291500000043
represents MUlEnergy consumption for transmitting data to EU, modeled as:
Figure BDA0003083291500000044
wherein ,
Figure BDA0003083291500000045
is MUlAccess EUiThe corresponding energy consumption during data transmission is modeled as
Figure BDA0003083291500000046
Further, the step S8 specifically includes:
(1) modeling user-channel association constraints, including:
①MUlthe channel association constraints of (a) are:
Figure BDA0003083291500000047
②UEmthe channel association constraints of (a) are:
Figure BDA0003083291500000048
③ the channel association constraint of the UAV is:
Figure BDA0003083291500000049
(2) modeling drone deployment constraints, including:
①
Figure BDA00030832915000000410
②
Figure BDA00030832915000000411
(3) modeling resource allocation constraints, including:
①UEmthe transmission rate limiting condition is
Figure BDA00030832915000000412
wherein ,Rm，
Figure BDA00030832915000000413
Are respectively UEmM is more than or equal to 1 and less than or equal to M.
②UEmThe transmission power limiting condition is
Figure BDA00030832915000000414
wherein ,
Figure BDA00030832915000000415
for the UEmA maximum transmit power threshold of;
③Unthe transmission power limiting condition is
Figure BDA00030832915000000416
wherein ,
Figure BDA00030832915000000417
is UnThe maximum transmit power threshold.
Further, the step S9 specifically includes: under the condition of meeting the user channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions, a target modeling multi-objective optimization problem is solved by maximizing EU and rate, minimizing total MU energy consumption and RU end-to-end time delay sum, and unmanned aerial vehicle deployment, user association and resource allocation strategies are determined based on a multi-objective optimization method as follows:
Figure BDA0003083291500000051
wherein,
Figure BDA0003083291500000052
respectively representing optimized
Figure BDA0003083291500000053
The invention has the beneficial effects that: the method can effectively ensure the service quality requirements of different types of service users, and combines unmanned aerial vehicle deployment and user association based on EU and rate maximization, RU time delay and minimization and MU energy consumption and minimization criteria, thereby improving the comprehensive performance of the system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic view of a 5G communication system;
fig. 2 is a schematic flow chart of a 5G communication system user association, unmanned aerial vehicle deployment and resource allocation method of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 2, fig. 1 is a schematic view of a 5G communication system scenario, as shown in fig. 1, a network includes multiple drones, including multiple drone base stations, multiple drone relays and drone gateways, multiple EUs, RUs, and MUs, and EU and rate maximization, RU latency, and MU energy consumption and minimization can be achieved by jointly designing an optimal user association, drone deployment, and resource allocation strategy.
Fig. 2 is a schematic flow chart of a method for user association, unmanned aerial vehicle deployment and resource allocation in a 5G communication system according to the present invention, and as shown in fig. 2, the method specifically includes the following steps:
step 1: modeling user-channel associated variables
Modeling user-channel associated variables, order
Figure BDA0003083291500000061
A variable is selected for the user's access mode,
Figure BDA0003083291500000062
representing a UEmAnd
Figure BDA0003083291500000063
the association is performed and, conversely,
Figure BDA0003083291500000064
1≤m≤M1+M2(ii) a Order to
Figure BDA0003083291500000065
In order to associate the variables for the MU,
Figure BDA0003083291500000066
represents MUlAnd EUiThe association is performed and, conversely,
Figure BDA0003083291500000067
1≤i≤M1(ii) a Order to
Figure BDA0003083291500000068
The variables are associated with the unmanned aerial vehicle,
Figure BDA0003083291500000069
to represent
Figure BDA00030832915000000610
And
Figure BDA00030832915000000611
associating transmission UEmData of (1) and vice versa
Figure BDA00030832915000000612
To represent
Figure BDA00030832915000000613
And
Figure BDA00030832915000000614
associating transmission UEmThe data of (a) and, conversely,
Figure BDA00030832915000000615
step 2: unmanned aerial vehicle deployment area of modelling
Modeling unmanned aerial vehicle deployment area, modeling unmanned aerial vehicle deployment area into three-dimensional grid, and ordering
Figure BDA00030832915000000616
Respectively the maximum point number in the row, column and vertical directions in the grid, alphax,y,z,nOptimize the variables for UAV deployment location, if αx,y,z,n1 represents UnIs the x-th row in the horizontal direction, the y-th column in the horizontal direction, and the z-th row in the vertical direction in the three-dimensional grid, wherein
Figure BDA00030832915000000617
Figure BDA00030832915000000618
And step 3: modeling link transmission rates
Modeling link transmission rate, order
Figure BDA00030832915000000619
For the UEmAnd
Figure BDA00030832915000000620
the transmission rate corresponding to the data transmission is correlated, modeled as,
Figure BDA00030832915000000621
wherein ,PmFor the UEmOf the transmission power, σ2In order to be able to measure the power of the noise,
Figure BDA00030832915000000622
for the UEmAnd
Figure BDA00030832915000000623
the channel gain of the link between can be modeled as
Figure BDA00030832915000000624
ho represents the channel gain per unit distance,
Figure BDA00030832915000000625
representing a UEmAnd
Figure BDA00030832915000000626
the distance between the two, λ represents the channel propagation fading index;
order to
Figure BDA00030832915000000627
To represent
Figure BDA00030832915000000628
Transmit data to
Figure BDA00030832915000000629
Corresponding link transmission rate is modeled as
Figure BDA00030832915000000630
wherein ,
Figure BDA00030832915000000631
is composed of
Figure BDA00030832915000000632
The transmission power of the antenna is set to be,
Figure BDA00030832915000000633
is composed of
Figure BDA00030832915000000634
And
Figure BDA00030832915000000635
link channel gain between;
order to
Figure BDA00030832915000000636
To represent
Figure BDA00030832915000000637
And
Figure BDA00030832915000000638
the link transmission rate between is modeled as
Figure BDA00030832915000000639
wherein ,
Figure BDA00030832915000000640
is composed of
Figure BDA00030832915000000641
The transmission power of the antenna is set to be,
Figure BDA00030832915000000642
is composed of
Figure BDA00030832915000000643
And
Figure BDA00030832915000000644
inter-link channel gain;
order to
Figure BDA00030832915000000645
Expressed as MUlAccess EUiThe transmission rate of data transmission is modeled as
Figure BDA00030832915000000646
wherein ,Pl muIs MUlThe transmission power of the antenna is set to be,
Figure BDA00030832915000000647
is MUlAnd EUiThe inter-link gain;
and 4, step 4: modeling link transmission delay
Modeling link propagation delay, order
Figure BDA0003083291500000071
Representing a UEmTransmit data to
Figure BDA0003083291500000072
Corresponding to the time delay, can be modeled as
Figure BDA0003083291500000073
SmRepresenting a UEmThe amount of data to be transmitted;
Figure BDA0003083291500000074
to represent
Figure BDA0003083291500000075
Transmitting UEmData to
Figure BDA0003083291500000076
Corresponding to the time delay, can be modeled as
Figure BDA0003083291500000077
To represent
Figure BDA0003083291500000078
Transmitting UEmData to
Figure BDA0003083291500000079
The corresponding time delay can be modeled as
Figure BDA00030832915000000710
And 5: modeling EU and Rate
Modeling enhances mobile broadband users and rates, let ReuRepresents the sum rate of the EU, modeled as:
Figure BDA00030832915000000711
wherein ,
Figure BDA00030832915000000712
is EUiIs modeled as
Figure BDA00030832915000000713
Step 6: modeling RU time delay sum
Modeling ultra-high reliability low-delay user time delay sum, order DruRepresenting the sum of time delays of RUs, modeled as
Figure BDA00030832915000000714
wherein ,
Figure BDA00030832915000000715
denotes RUjThe time delay of the user for transmitting data to the core network is modeled as follows:
Figure BDA00030832915000000716
and 7: modeling MU energy consumption and
modeling mass machine type communication user energy consumption sum order EmuFor MU energy consumption sum, modeling is as follows:
Figure BDA00030832915000000717
wherein ,
Figure BDA00030832915000000718
represents MUlEnergy consumption for transmitting data to EU, modeled as:
Figure BDA00030832915000000719
wherein ,
Figure BDA00030832915000000720
is MUlAccess EUiThe corresponding energy consumption during data transmission is modeled as
Figure BDA00030832915000000721
And 8: modeling user-channel associations, drone deployment, and resource allocation constraints
Modeling user-channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions, wherein the user-channel association limiting conditions specifically comprise:
1)MUlthe channel association constraints of (a) are:
Figure BDA00030832915000000722
2)UEmthe channel association constraints of (a) are:
Figure BDA00030832915000000723
3) the channel association constraints of the UAV are:
Figure BDA0003083291500000081
the modeling unmanned aerial vehicle deployment limiting conditions specifically include:
1)
Figure BDA0003083291500000082
2)
Figure BDA0003083291500000083
the modeling resource allocation limiting condition specifically includes:
1)UEmthe transmission rate limiting condition is
Figure BDA0003083291500000084
wherein ,Rm，
Figure BDA0003083291500000085
Are respectively UEmM is more than or equal to 1 and less than or equal to M.
2)UEmThe transmission power limiting condition is
Figure BDA0003083291500000086
wherein ,
Figure BDA0003083291500000087
for the UEmA maximum transmit power threshold of;
3)Unthe transmission power limiting condition is
Figure BDA0003083291500000088
wherein ,
Figure BDA0003083291500000089
is UnThe maximum transmit power threshold.
And step 9: determining unmanned aerial vehicle deployment and user association strategy based on system multi-objective optimization
Determining unmanned aerial vehicle deployment and user association strategies based on system multi-objective optimization, obtaining an optimal solution of a multi-objective optimization problem by aiming at maximizing EU and rate, minimizing total MU energy consumption and RU end-to-end delay sum under the condition of meeting user channel association, unmanned aerial vehicle deployment and resource allocation limitation, and determining the user association, unmanned aerial vehicle deployment and resource allocation strategies based on the system multi-objective optimization as follows:
Figure BDA00030832915000000810
wherein,
Figure BDA00030832915000000811
respectively representing optimized
Figure BDA00030832915000000812
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (10)

1. A5G communication system user association, unmanned aerial vehicle deployment and resource allocation method is characterized by comprising the following steps:
s1: modeling a user-channel associated variable;
s2: modeling a UAV base station deployment area, wherein UAV represents an unmanned aerial vehicle;
s3: modeling link transmission rate;
s4: modeling link transmission time delay;
s5: modeling EU and rate, wherein EU represents an enhanced mobile broadband user;
s6: modeling an RU time delay sum, wherein RU represents an ultra-high-reliability low-time-delay user;
s7: modeling MU energy consumption sum, wherein MU represents mass machine type communication users;
s8: modeling user-channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions;
s9: determining user association, unmanned aerial vehicle deployment and resource allocation strategies based on system multi-objective optimization;
suppose the system has multiple 5G users, including M1EU, M2Each RU and M3Each MU, each user needs to send data to the core network; let EUiRepresenting the ith EU user, i is more than or equal to 1 and less than or equal to M1；RUjJ is more than or equal to 1 and less than or equal to M and represents the jth RU user2；MUlRepresenting the first MU user, l is more than or equal to 1 and less than or equal to M3(ii) a Let M be M1+M2+M3Indicating the number of users, UEs, in the systemmRepresents the mth user; order SmRepresenting a UEmM is more than or equal to 1 and less than or equal to M;
deploying UAV base stations, UAV relays and UAV gateways for enabling user data to be transmitted to a core network via unmanned aerial vehicles, assuming a total number N of deployed UAVs, let
Figure FDA0003083291490000011
To representN th1A UAV base station, n is more than or equal to 11≤N1；
Figure FDA0003083291490000012
Denotes the n-th2Number of UAV relays, n is more than or equal to 12≤N2；
Figure FDA0003083291490000013
Denotes the n-th3A UAV gateway, n is more than or equal to 13≤N3(ii) a Let N be N1+N2+N3Indicates the total number of UAVs deployed in the system, UnRepresenting the nth UAV, wherein N is more than or equal to 1 and less than or equal to N;
assuming that EU and RU can access the UAV base station, MU can forward data to the core network by associating with EU; the system adopts an orthogonal frequency division multiple access mode to transmit data, and the bandwidth of each sub-channel is B.
2. The method according to claim 1, wherein in step S1, modeling a user-channel association variable specifically comprises: order to
Figure FDA0003083291490000014
A variable is selected for the user's access mode,
Figure FDA0003083291490000015
representing a UEmAnd
Figure FDA0003083291490000016
the association is performed and, conversely,
Figure FDA0003083291490000017
order to
Figure FDA0003083291490000018
In order to associate the variables for the MU,
Figure FDA0003083291490000019
represents MUlAnd EUiThe association is performed and, conversely,
Figure FDA00030832914900000110
order to
Figure FDA00030832914900000111
The variables are associated with the unmanned aerial vehicle,
Figure FDA00030832914900000112
to represent
Figure FDA00030832914900000113
And
Figure FDA00030832914900000114
associating transmission UEmData of (1) and vice versa
Figure FDA00030832914900000115
To represent
Figure FDA00030832914900000116
And
Figure FDA00030832914900000117
associating transmission UEmThe data of (a) and, conversely,
Figure FDA00030832914900000118
3. the user association, unmanned aerial vehicle deployment and resource allocation method according to claim 2, wherein in step S2, modeling an unmanned aerial vehicle base station deployment area specifically comprises: modeling unmanned aerial vehicle deployment area as three-dimensional grid
Figure FDA0003083291490000021
Figure FDA0003083291490000022
Respectively the maximum point number in the row, column and vertical directions in the grid, alphax,y,z,nOptimize the variables for UAV deployment location, if αx,y,z,n1 represents UnIs the x-th row in the horizontal direction, the y-th column in the horizontal direction, and the z-th row in the vertical direction in the three-dimensional grid, wherein
Figure FDA0003083291490000023
4. The method according to claim 3, wherein in step S3, modeling link transmission rate specifically comprises:
(1) order to
Figure FDA0003083291490000024
For the UEmAnd
Figure FDA0003083291490000025
associating the transmission rate corresponding to the data transmission, and modeling as follows:
Figure FDA0003083291490000026
wherein ,PmFor the UEmOf the transmission power, σ2In order to be able to measure the power of the noise,
Figure FDA0003083291490000027
for the UEmAnd
Figure FDA0003083291490000028
the gain of the link channel between is modeled as
Figure FDA0003083291490000029
hoThe channel gain is expressed in terms of a unit distance,
Figure FDA00030832914900000210
representing a UEmAnd
Figure FDA00030832914900000211
the distance between the two, λ represents the channel propagation fading index;
(2) order to
Figure FDA00030832914900000212
To represent
Figure FDA00030832914900000213
Transmit data to
Figure FDA00030832914900000214
The corresponding link transmission rate is modeled as:
Figure FDA00030832914900000215
wherein ,
Figure FDA00030832914900000216
is composed of
Figure FDA00030832914900000217
The transmission power of the antenna is set to be,
Figure FDA00030832914900000218
is composed of
Figure FDA00030832914900000219
And
Figure FDA00030832914900000220
link channel gain between;
(3) order to
Figure FDA00030832914900000221
To represent
Figure FDA00030832914900000222
And
Figure FDA00030832914900000223
the link transmission rate between the two is modeled as:
Figure FDA00030832914900000224
wherein ,
Figure FDA00030832914900000225
is composed of
Figure FDA00030832914900000226
The transmission power of the antenna is set to be,
Figure FDA00030832914900000227
is composed of
Figure FDA00030832914900000228
And
Figure FDA00030832914900000229
inter-link channel gain;
(4) order to
Figure FDA00030832914900000230
Expressed as MUlAccess EUiThe transmission rate when data transmission is performed is modeled as:
Figure FDA00030832914900000231
wherein ,Pl muIs MUlThe transmission power of the antenna is set to be,
Figure FDA00030832914900000232
is MUlAnd EUiThe gain of the inter-link.
5. The user association, unmanned aerial vehicle deployment and resource allocation method according to claim 4, wherein in step S4, modeling link transmission delay specifically comprises:
(1) order to
Figure FDA00030832914900000233
Representing a UEmTransmit data to
Figure FDA00030832914900000234
Corresponding to the time delay, is modeled as
Figure FDA00030832914900000235
SmRepresenting a UEmThe amount of data to be transmitted;
(2) order to
Figure FDA00030832914900000236
To represent
Figure FDA00030832914900000237
Transmitting UEmData to
Figure FDA00030832914900000238
Corresponding to the time delay, is modeled as
Figure FDA00030832914900000239
(3) Order to
Figure FDA0003083291490000031
To represent
Figure FDA0003083291490000032
Transmitting UEmData to
Figure FDA0003083291490000033
Corresponding time delay is modeled as
Figure FDA0003083291490000034
6. According to claim 5The user association, unmanned aerial vehicle deployment and resource allocation method is characterized in that in step S5, modeling EU and rate specifically includes: let ReuRepresents the sum rate of the EU, modeled as:
Figure FDA0003083291490000035
wherein ,
Figure FDA0003083291490000036
is EUiIs modeled as
Figure FDA0003083291490000037
7. The method of claim 6, wherein in step S6, modeling the sum of RU time delays specifically comprises: let DruRepresenting the sum of time delays of RUs, modeled as
Figure FDA0003083291490000038
wherein ,
Figure FDA0003083291490000039
denotes RUjThe time delay of the user for transmitting data to the core network is modeled as follows:
Figure FDA00030832914900000310
8. the method according to claim 7, wherein in step S7, modeling MU energy consumption sum specifically comprises: let EmuFor MU energy consumption sum, modeling is as follows:
Figure FDA00030832914900000311
wherein ,
Figure FDA00030832914900000312
represents MUlEnergy consumption for transmitting data to EU, modeled as:
Figure FDA00030832914900000313
wherein ,
Figure FDA00030832914900000314
is MUlAccess EUiThe corresponding energy consumption during data transmission is modeled as
Figure FDA00030832914900000315
9. The method according to claim 8, wherein the step S8 specifically includes:
(1) modeling user-channel association constraints, including:
①MUlthe channel association constraints of (a) are:
Figure FDA00030832914900000316
②UEmthe channel association constraints of (a) are:
Figure FDA00030832914900000317
③ the channel association constraint of the UAV is:
Figure FDA00030832914900000318
(2) modeling drone deployment constraints, including:
①
Figure FDA0003083291490000041
②
Figure FDA0003083291490000042
(3) modeling resource allocation constraints, including:
①UEmthe transmission rate limiting condition is
Figure FDA0003083291490000043
wherein ,Rm，
Figure FDA0003083291490000044
Are respectively UEmM is more than or equal to 1 and less than or equal to M;
②UEmthe transmission power limiting condition is
Figure FDA0003083291490000045
wherein ,
Figure FDA0003083291490000046
for the UEmA maximum transmit power threshold of;
③Unthe transmission power limiting condition is
Figure FDA0003083291490000047
wherein ,
Figure FDA0003083291490000048
is UnThe maximum transmit power threshold.
10. The method according to claim 9, wherein the step S9 specifically includes: under the condition of meeting the user channel association, unmanned aerial vehicle deployment and resource allocation limiting conditions, a target modeling multi-objective optimization problem is solved by maximizing EU and rate, minimizing total MU energy consumption and RU end-to-end time delay sum, and unmanned aerial vehicle deployment, user association and resource allocation strategies are determined based on a multi-objective optimization method as follows:
Figure FDA0003083291490000049
wherein ,
Figure FDA00030832914900000410
respectively represent the optimized alphax,y,z,n，
Figure FDA00030832914900000411
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