CN115696264A - Energy-saving data unloading device and method of cloud wireless access network based on unmanned aerial vehicle - Google Patents

Energy-saving data unloading device and method of cloud wireless access network based on unmanned aerial vehicle Download PDF

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CN115696264A
CN115696264A CN202110867309.0A CN202110867309A CN115696264A CN 115696264 A CN115696264 A CN 115696264A CN 202110867309 A CN202110867309 A CN 202110867309A CN 115696264 A CN115696264 A CN 115696264A
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unmanned aerial
aerial vehicle
end user
access network
rrh
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张驰亚
李兴泉
何春龙
赵汝军
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Shenzhen Graduate School Harbin Institute of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • 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]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • 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 discloses an energy-saving data unloading device and method of a cloud wireless access network based on an unmanned aerial vehicle, wherein the energy-saving data unloading device comprises the following steps: establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network; constructing a data unloading optimization problem according to an energy-saving data unloading system model based on an unmanned aerial vehicle-assisted cloud wireless access network; and solving the optimal parameters of the data unloading optimization problem. The beneficial effects are that: the unmanned aerial vehicle communication technology is combined with the cloud wireless access network technology, so that the service quality of a communication system can be effectively improved, the requirements of terminal Users (UE) can be met, and due to the characteristic of high maneuverability of the unmanned aerial vehicle, the system can also cope with emergency situations, ensure communication under special conditions and is also suitable for areas with complex terrain; the method optimizes the access allocation and the calculation capability allocation of the terminal User (UE), the transmission power of the terminal User (UE) and the unmanned aerial vehicle path of the system, reduces the power loss of the terminal User (UE) in the communication process of the system, and improves the performance of the whole system.

Description

Energy-saving data unloading device and method of cloud wireless access network based on unmanned aerial vehicle
Technical Field
The invention relates to the technical field of communication, in particular to an energy-saving data unloading device and method of a cloud wireless access network based on an unmanned aerial vehicle.
Background
With the continuous development of scientific technology driving the gradual maturity of unmanned aerial vehicle technology, it has been merged into many aspects of people's life. Nowadays, unmanned Aerial Vehicles (UAVs) are widely used in the industries of camera surveillance, aerial image acquisition, cargo transportation and the like. It is reported that global commercial drone use is estimated to cost 20 billion dollars in 2016, and will rapidly increase to 1270 billion dollars by 2020. Because equipped advanced receiving and dispatching information device and high capacity battery, consequently unmanned aerial vehicle has very high mobility and flexibility in the overall arrangement, can be applied to the information science field effectively. Particularly, the unmanned aerial vehicle flies in the high altitude, has line-of-sight transmission connection, and can effectively reduce interference and energy consumption during communication.
Information exchange plays an increasingly important role in people's daily life, and with the explosive growth of data, great challenges are brought to communication services. In order to improve the service quality of the Communication system, researchers have proposed many advanced schemes, such as Distributed Antenna Systems (DAS), cloud Radio Access Networks (C-RAN), device to Device Communication (D2D), and Ultra-Dense Networks (UDN). Although these advanced communication technologies can effectively improve the service quality of the communication system, there still exist many disadvantages, such as poor service quality of edge terminal Users (UEs), overloading of base stations, high communication delay, inflexible communication network, and inability to cope with emergencies such as earthquakes. Therefore, how to improve the service quality of the communication system, the communication rate of the edge end User (UE) and the flexibility of the communication system have become a major task in the communication industry.
To solve the related problems of the communication system, the academic community has proposed a Mobile Edge Computing (MEC) concept. It pushes cloud service functionality to the edge of the radio access network, aiming to provide cloud-based computing offload services near the mobile terminal. In MEC networks, the process of computing task offloading includes task transfers and task execution, which involves the allocation of communication resources, computing resources and caching resources. Mainly can carry out the overall arrangement through unmanned aerial vehicle's flexibility and mobility and realize. In the C-RAN architecture, the fixed connection relationship between the remote radio frequency unit and the baseband processing unit is broken. Each remote radio unit does not belong to any baseband processing unit entity. The processing of the transmitted and received signals at each remote RF unit is performed at a virtual base station, and the processing capability of the virtual base station is formed by allocating part of processors in a base band pool by real-time virtual technology.
Disclosure of Invention
The invention aims to solve the problems and provide an energy-saving data unloading device and method for a cloud wireless access network based on unmanned aerial vehicles, which combine the advantages of the unmanned aerial vehicles, take a plurality of unmanned aerial vehicles as relays, receive ground terminal User (UE) information and unload the information into a baseband unit (BBU) of the cloud wireless access network.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention provides an energy-saving data unloading method of a cloud wireless access network based on an unmanned aerial vehicle, which comprises the following steps:
s1: establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network;
s2, constructing a data unloading optimization problem according to the energy-saving data unloading system model based on the unmanned aerial vehicle assisted cloud wireless access network;
and S3, solving the optimal parameters of the data unloading optimization problem.
Further, the step S1 specifically includes:
establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network, wherein the system model comprises a BBU (base band unit), a terminal User (UE), an RRH (remote radio head) and an unmanned aerial vehicle;
the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals of a hot spot area or a blind spot area;
the unmanned aerial vehicle is located in the air, the RRHs are located on the ground, N terminal Users (UE), M RRHs and J unmanned aerial vehicles are set, each terminal User (UE) has a calculation task to be executed, and the task can unload data to the BBU through the unmanned aerial vehicle or the RRHs.
Further, the step S2 specifically includes:
constructing a time formula for each end User (UE) to execute an unloading task:
Figure BDA0003187837200000031
wherein, F i Representing the amount of computation required by the CPU of the ith end User (UE), f i,b Representing the computing power, D, of the BBU assigned to the i-th end User (UE) i Indicates the amount of data transmitted by the ith end User (UE), r i,j Represents a transmission rate of an ith end User (UE);
constructing a power formula of an end User (UE) for executing an unloading task:
Figure BDA0003187837200000032
wherein the content of the first and second substances,
Figure BDA0003187837200000033
for the ith end User (UE) own power,
Figure BDA0003187837200000034
κ i and vi are both constant values of the number of,
Figure BDA0003187837200000041
power to perform offloading tasks for the ith end User (UE) via the drone,
Figure BDA0003187837200000042
is the ith oneAn end User (UE) offloading power for performing a task with an RRH;
calculating the transmission rate of the end User (UE) to the drone, the transmission rate to the RRH, and the transmission rate from the drone to the BBU as:
Figure BDA0003187837200000043
Figure BDA0003187837200000044
Figure BDA0003187837200000045
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003187837200000046
Figure BDA0003187837200000047
σ 2 for noise interference, beta U Is the channel power gain, B is a constant
Figure BDA0003187837200000048
The distance of the drone to the end User (UE),
Figure BDA0003187837200000049
is the distance from the RRH to the end User (UE), alpha is the antenna gain,
Figure BDA00031878372000000410
for the power of the jth drone,
Figure BDA00031878372000000411
is the distance from the drone to the BBU.
Further, the step S2 is specifically to express the data offloading optimization problem as:
Figure BDA00031878372000000412
Figure BDA00031878372000000413
Figure BDA00031878372000000414
Figure BDA0003187837200000051
Figure BDA0003187837200000052
Figure BDA0003187837200000053
Figure BDA0003187837200000054
Figure BDA0003187837200000055
Figure BDA0003187837200000056
Figure BDA0003187837200000057
Figure BDA0003187837200000058
wherein the content of the first and second substances,
Figure BDA0003187837200000059
is the maximum transmission rate between the mth RRH and the BBU.
Further, step S3 specifically converts the data offloading optimization problem into four sub-problems, which are respectively:
optimizing an access variable of an end User (UE), optimizing a calculation capacity variable of the end User (UE), optimizing transmission power of the end User (UE) and optimizing a track of the unmanned aerial vehicle;
by iterating through each sub-problem, a solution to the problem is derived.
Further, optimizing an end User (UE) access variable specifically includes:
given other optimization variables than end User (UE) access variables, the problem can translate into:
Figure BDA00031878372000000510
Figure BDA00031878372000000511
Figure BDA0003187837200000061
Figure BDA0003187837200000062
Figure BDA0003187837200000063
Figure BDA0003187837200000064
since the problem is a convex function for the end User (UE) access variable, it is directly solved for optimization.
Further, the optimizing the end User (UE) computing capability variables specifically includes:
when the terminal User (UE) completes the unloading task, the calculation capacity variable of the terminal User (UE) is optimized, and the problem is converted into that:
Figure BDA0003187837200000065
Figure BDA0003187837200000066
Figure BDA0003187837200000067
Figure BDA0003187837200000068
Figure BDA0003187837200000069
the problem is a convex function, and can be directly optimized and solved;
when an end User (UE) completes a data unloading task by means of an unmanned aerial vehicle and an RRH, computing capacity variables of the end User (UE) are optimized, and two variables s = [ s ] are added 1 ,...,s N ]And t = [ t ] 1 ,...,t N ]This problem is translated into:
Figure BDA00031878372000000610
Figure BDA0003187837200000071
Figure BDA0003187837200000072
s≥0,t≥0
the problem is a convex function and can be directly optimized and solved.
Further, optimizing the transmission power of the end User (UE) specifically includes:
given other variables than the transmission power of the end User (UE), and transforming the problem into:
Figure BDA0003187837200000073
Figure BDA0003187837200000074
Figure BDA0003187837200000075
Figure BDA0003187837200000076
Figure BDA0003187837200000077
Figure BDA0003187837200000078
Figure BDA0003187837200000079
the problem is converted into a convex function, and can be directly optimized and solved.
Further, the track of the optimized unmanned aerial vehicle is specifically as follows:
using successive convex approximation iteration method and first order Taylor expansion formula and by adding b = [ b ] i,j ]，c＝[c 1 ,...c J ]This problem is translated into:
Figure BDA0003187837200000081
s.t.
Figure BDA0003187837200000082
Figure BDA0003187837200000083
b≥0,c≥0
and the problem is solved optimally.
The invention also provides an energy-saving data unloading device based on the unmanned aerial vehicle assisted cloud wireless access network, which is applied to the unmanned aerial vehicle assisted cloud wireless access network system and comprises a BBU, a terminal User (UE), an RRH and an unmanned aerial vehicle; the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area; wherein, unmanned aerial vehicle is located aloft, RRH is located ground, we have set for N end User (UE), M RRHs, J unmanned aerial vehicle, and every end User (UE) all has the calculation task that needs to carry out, and this task can be through unmanned aerial vehicle or RRH with data uninstallation to BBU, the device includes:
the model establishing module is used for establishing a system model based on the unmanned aerial vehicle assisted cloud wireless access network;
the equation construction module is used for constructing a data unloading optimization problem;
and the solving module is used for solving the optimal parameters of the data unloading optimization problem.
Has the advantages that:
1. the unmanned aerial vehicle communication technology is combined with the cloud wireless access network technology, a novel communication system model is provided, the service quality of the communication system can be effectively improved, the requirements of terminal Users (UE) can be met, the system performance is improved, meanwhile, due to the characteristic of high maneuverability of the unmanned aerial vehicle, the system can also cope with sudden situations, communication under special conditions is guaranteed, special situations such as earthquakes and forest fires can be coped with, and the system is also suitable for areas with complex terrain;
2. the method optimizes the access allocation and the calculation capability allocation of the terminal User (UE), the transmission power of the terminal User (UE) and the unmanned aerial vehicle path of the system, reduces the power loss of the terminal User (UE) in the communication process of the system, and improves the performance of the whole system.
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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 used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a model diagram of an energy-saving data offloading system based on an unmanned aerial vehicle assisted cloud wireless access network of the present invention;
fig. 2 is a flowchart of an energy-saving data offloading method based on an unmanned aerial vehicle assisted cloud wireless access network of the present invention;
FIG. 3 is a graph of the convergence rate of algorithm 2 of the present invention;
FIG. 4 is a graph of total power consumed by an end User (UE) versus maximum delay for the present invention;
FIG. 5 is a graph of total power consumed by an end User (UE) versus the amount of data offloaded in accordance with the present invention;
FIG. 6 is a graph of total power consumed by an end User (UE) of the present invention versus the number of end User (UE) CPU processing cycles.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Referring to fig. 1-2, fig. 1 is a model diagram of an energy-saving data offloading system based on an unmanned aerial vehicle-assisted cloud wireless access network of the present invention, and fig. 2 is a flowchart of an energy-saving data offloading method based on an unmanned aerial vehicle-assisted cloud wireless access network of the present invention. The invention provides an energy-saving data unloading method of a cloud wireless access network based on an unmanned aerial vehicle, which comprises the following steps:
s1: establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network;
s2, constructing a data unloading optimization problem according to the energy-saving data unloading system model based on the unmanned aerial vehicle assisted cloud wireless access network;
and S3, solving the optimal parameters of the data unloading optimization problem.
In some embodiments, the step S1 specifically includes:
establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network, wherein the system model comprises a BBU (base band unit), a terminal User (UE), an RRH (remote radio head) and an unmanned aerial vehicle;
the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area;
the unmanned aerial vehicle is located in the air, the RRHs are located on the ground, N terminal Users (UE), M RRHs and J unmanned aerial vehicles are set, each terminal User (UE) has a calculation task to be executed, and the task can unload data to the BBU through the unmanned aerial vehicle or the RRHs.
In some embodiments, the step S2 is specifically,
Figure BDA0003187837200000111
when the number is 1, it means that the ith end User (UE) performs the task by itself, and when the number is 0, it means that the task is executed by itselfIndicating no execution;
Figure BDA0003187837200000112
when the number is 1, the ith terminal User (UE) unloads the task through the jth unmanned aerial vehicle, and when the number is 0, the task is not executed;
Figure BDA0003187837200000113
when the number is 1, the ith terminal User (UE) unloads the task through the mth RRH, and when the number is 0, the task is not executed;
because an end User (UE) can only select one way to perform the offloading task, there are:
Figure BDA0003187837200000114
when the ith end User (UE) needs to perform an offloading task, the time consumed is:
Figure BDA0003187837200000115
wherein F i Representing the amount of computation required by the CPU of the ith end User (UE), f i,b Which represents the computational power allocated by the baseband processing unit BBU to the i-th end User (UE).
If the ith terminal User (UE) needs to perform task offloading by the drone or RRH, the consumed time is:
Figure BDA0003187837200000116
wherein D i Indicates the amount of data transmitted by the ith end User (UE), r i,j Representing the transmission rate of the ith end User (UE).
Because each end User (UE) has a finite time to perform the offloading task, the time formula for each end User (UE) to perform the offloading task is constructed as follows:
Figure BDA0003187837200000121
when the end User (UE) completes the offloading task itself:
Figure BDA0003187837200000122
in conjunction with the above access variables, there are therefore:
Figure BDA0003187837200000123
in addition, the computing power allocated to the end User (UE) by itself and BBU is limited
Figure BDA0003187837200000124
Wherein f is i For the ith end User (UE) own computing power,
Figure BDA0003187837200000125
the maximum computing power of the end User (UE) itself.
Figure BDA0003187837200000126
Figure BDA0003187837200000127
Maximum computing power allocated to end User (UE) for BBU
The power loss of the end User (UE) is as follows:
when the end User (UE) completes the offloading task itself, the power loss of the end User (UE) is:
Figure BDA0003187837200000128
wherein the content of the first and second substances,
Figure BDA0003187837200000129
κ i and v i Are all constants
Figure BDA00031878372000001210
The power of the ith end User (UE) itself.
When the end User (UE) completes the offloading task through the drone or the RRH, the power loss of the end User (UE) is:
Figure BDA0003187837200000131
wherein the content of the first and second substances,
Figure BDA0003187837200000132
power to perform offloading tasks for end Users (UEs) via drones,
Figure BDA0003187837200000133
offloading power for end User (UE) tasks performed by RRHs.
However, there is also a maximum value for power so the power formula that builds an end User (UE) to perform the offloading task is:
Figure BDA0003187837200000134
wherein the content of the first and second substances,
Figure BDA0003187837200000135
maximum power transmitted for the end User (UE).
Calculating the transmission rate from the end User (UE) to the drone and to the RRH and from the drone to the BBU as:
Figure BDA0003187837200000136
Figure BDA0003187837200000137
Figure BDA0003187837200000138
wherein the content of the first and second substances,
Figure BDA0003187837200000139
Figure BDA00031878372000001310
σ 2 for noise interference, beta U Is the channel power gain, B is a constant
Figure BDA00031878372000001311
The distance of the drone to the end User (UE),
Figure BDA00031878372000001312
is the RRH to end User (UE) distance, alpha is the antenna gain,
Figure BDA00031878372000001313
for the power of the jth drone,
Figure BDA00031878372000001314
the distance from the unmanned aerial vehicle to the BBU;
the transmission of the fronthaul data between the drone and the baseband processing unit needs to meet the constraints:
Figure BDA0003187837200000141
the transmission of the fronthaul data between the RRH and the baseband processing unit needs to meet the constraint:
Figure BDA0003187837200000142
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003187837200000143
is the maximum transmission rate between the mth RRH and the BBU.
In some embodiments, the step S2 is specifically to offload the data optimization problem
The expression is as follows:
Figure BDA0003187837200000144
Figure BDA0003187837200000145
Figure BDA0003187837200000146
Figure BDA0003187837200000147
Figure BDA0003187837200000148
Figure BDA0003187837200000149
Figure BDA00031878372000001410
Figure BDA00031878372000001411
Figure BDA00031878372000001412
Figure BDA00031878372000001413
Figure BDA00031878372000001414
in some embodiments, since there are many variables in the problem, the problem is divided into four sub-problems to be optimized separately, including optimizing end User (UE) access variables, optimizing end User (UE) computational power variables, optimizing end User (UE) transmission power, and optimizing drone trajectory, optimizing one variable in each sub-problem, so the other variables are fixed,
by iterating through each sub-problem, a solution to the problem is derived.
In some embodiments, optimizing end User (UE) access variables is specifically that when the end User (UE) offloads data into the baseband processing unit by means of the drone, the transmission rate should be such that
Figure BDA0003187837200000151
Similarly, when using RRH
Figure BDA0003187837200000152
And changing the binary variable into a continuous variable, the problem can be transformed into:
Figure BDA0003187837200000153
Figure BDA0003187837200000154
Figure BDA0003187837200000155
Figure BDA0003187837200000156
Figure BDA0003187837200000157
Figure BDA0003187837200000158
Figure BDA0003187837200000159
Figure BDA00031878372000001510
since the problem is a convex function for the end User (UE) access variable, it can be directly optimized using matlab or other means.
In some embodiments, the optimizing the end User (UE) computing capability variable is specifically that the optimization of the variable needs to be divided into two cases, because the end User (UE) can complete the offloading task by itself, and can also complete the data offloading task by means of the drone and the RRH.
When the end User (UE) completes the offloading task itself, the problem can be translated into:
Figure BDA0003187837200000161
Figure BDA0003187837200000162
Figure BDA0003187837200000163
Figure BDA0003187837200000164
Figure BDA0003187837200000165
the problem is also a convex function and can therefore be optimized using matlab or other means.
When an end User (UE) completes a data unloading task by means of an unmanned aerial vehicle and an RRH, since an objective function is unrelated to an optimization variable at the moment, the objective function needs to be modified, and the objective function is linked with the optimization variable. We add two variables s = [ s ] 1 ,...,s N ]And t = [ t ] 1 ,...,t N ]The whole optimization problem is converted into:
Figure BDA0003187837200000166
Figure BDA0003187837200000167
Figure BDA0003187837200000171
s≥0,t≥0
the problem is also a convex function problem and can therefore also be directly optimized.
In some embodiments, optimizing the transmission power of an end User (UE) is specifically:
after fixing other variables to be optimized, the problem is converted into:
Figure BDA0003187837200000172
Figure BDA0003187837200000173
Figure BDA0003187837200000174
Figure BDA0003187837200000175
Figure BDA0003187837200000176
Figure BDA0003187837200000177
Figure BDA0003187837200000178
wherein, in order to solve the optimization problem, the method comprises the following steps
Figure BDA0003187837200000179
Even then, the left side of this constraint does not fit the constraint of the convex function, so we can do the following:
Figure BDA00031878372000001710
the second term of the polynomial is non-convex and the function is still a non-convex function, we can use the second term in
Figure BDA00031878372000001711
Performing a first-order taylor expansion to obtain:
Figure BDA0003187837200000181
using the same method, limiting conditions
Figure BDA0003187837200000182
Can be converted into:
Figure BDA0003187837200000183
let us order
Figure BDA0003187837200000184
Using the same method, limiting conditions
Figure BDA0003187837200000185
Can be converted into:
Figure BDA0003187837200000186
let us order
Figure BDA0003187837200000187
Similarly, limiting conditions
Figure BDA0003187837200000188
Can be converted into:
Figure BDA0003187837200000189
let us order
Figure BDA00031878372000001810
Eventually our problem turns into a convex one:
Figure BDA0003187837200000191
Figure BDA0003187837200000192
Figure BDA0003187837200000193
Figure BDA0003187837200000194
Figure BDA0003187837200000195
Figure BDA0003187837200000196
Figure BDA0003187837200000197
this problem can be directly optimized since it has already been transformed into a convex problem.
In some embodiments, the trajectory of the unmanned aerial vehicle is specifically:
we first use S i,j ＝||U j -x i || 2 Limitation of conditions
Figure BDA0003187837200000198
Can be written as:
Figure BDA0003187837200000199
let us order
Figure BDA00031878372000001910
Since the constraint is a non-convex constraint, some conversion is needed, we useThe method of successive convex approximation puts the first term on the left side of the inequality at
Figure BDA00031878372000001911
The first order taylor expansion is performed, and the constraint conditions are converted into:
Figure BDA00031878372000001912
let us order
Figure BDA0003187837200000201
In addition, the limiting conditions
Figure BDA0003187837200000202
Also in the non-convex case, we use the same method to convert it to:
Figure BDA0003187837200000203
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003187837200000204
Figure BDA0003187837200000205
similarly, the objective function of the optimization problem is not linked to the optimization variables, so we add b = [ b ] i,j ]，c＝[c 1 ,...c J ]Thus, the optimization problem translates into:
Figure BDA0003187837200000206
s.t.
Figure BDA0003187837200000207
Figure BDA0003187837200000208
b≥0,c≥0
in the specific problem of optimizing the trajectory of the unmanned aerial vehicle, an iteration method of continuous convex approximation is used, so that the algorithm is as follows:
algorithm 1 optimization of unmanned aerial vehicle trajectory U
Step 1: initializing variable U (0) 、γ＞1、κ (0) 、κ max 、
Figure BDA0003187837200000211
Iteration number t =0 and convergence parameter;
step 2: by solving the above problem P 4 Get updated U (t+1) ，κ (t+1) ＝max{γκ (t) ,κ max The iteration times t +1 are judged once each time;
and step 3: until a condition is satisfied
Figure BDA0003187837200000212
And | | | U (t) -U (t-1) || 1 And xi, the algorithm is executed.
In summary, we divide the problem into four sub-problems, and we can also integrate the four sub-problems into one algorithm as follows:
and 2, algorithm: total power minimization iterative algorithm of cloud wireless access network system based on unmanned aerial vehicle assistance
Step 1: initializing an access variable A (0) Calculating capacity distribution variable F (0) Power distribution variable P (0) And unmanned plane position variable U (0) The number of iterations T =0 and the maximum number of iterations T max And a convergence variable ξ.
And 2, step: by passing
Figure BDA0003187837200000213
Calculating the value of an objective function
Figure BDA0003187837200000214
And step 3: and (3) circularly executing:
let t = t +1
(1) By fixing F (t-1) 、P (t-1) 、U (t-1) To solve the problem P 1-E1 To obtain a new solution A (t) ；
(2) By fixing A (t) 、P (t-1) 、U (t-1) To solve the problem P 2-E1 And P 2-E2 To obtain a new solution F (t) ；
(3) By fixing A (t) 、F (t) 、U (t-1) To solve the problem P 3-E1 To obtain a new solution P (t) ；
(4) By fixing A (t) 、F (t) 、P (t) To solve the problem P 4 To obtain a new solution U (t) ；
And 3, step 3: if the condition is satisfied
Figure BDA0003187837200000221
Or T > T max When the cycle is over, the algorithm is also stopped to obtain the optimal solution.
As shown in fig. 3, fig. 3 is a graph of the convergence rate of the algorithm 2 of the present invention, and it can be seen from the graph that the convergence rate of the algorithm 2 is very fast and is hardly affected in case that the number of end Users (UEs) increases.
As shown in fig. 4, fig. 4 is a graph of the total power consumed by the end User (UE) and the maximum delay of the present invention, and it can be seen from the graph that the total power consumed by the end User (UE) decreases with the increase of the maximum delay, and it can be seen that this trend is more obvious in the case of a larger number of end Users (UE).
As shown in fig. 5, fig. 5 is a graph of the total power consumed by the end User (UE) and the amount of data to be offloaded according to the present invention, and it can be seen from the graph that when the amount of data to be offloaded by the end User (UE) increases, the total power consumed by the end User (UE) also increases, because the increase in the amount of data causes the end User (UE) to need more power to meet the requirement of the delay.
As shown in fig. 6, fig. 6 is a graph of the total power consumed by the end User (UE) according to the present invention and the number of CPU processing cycles of the end User (UE), and it can be seen from the graph that when the number of CPU processing cycles of the end User (UE) increases, the total power consumed by the end User (UE) also increases, because the end User (UE) needs to increase the power to process more CPU cycles to ensure that the requirement of the time delay is satisfied.
The invention also provides an energy-saving data unloading device based on the unmanned aerial vehicle assisted cloud wireless access network, which is applied to the unmanned aerial vehicle assisted cloud wireless access network system and comprises a BBU, a terminal User (UE), an RRH and an unmanned aerial vehicle; the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area; wherein, unmanned aerial vehicle is located aloft, RRH is located ground, and we have set for N end User (UE), M RRHs, J unmanned aerial vehicle, and every end User (UE) all has the calculation task that needs the execution, and this task can be through unmanned aerial vehicle or RRH with data uninstallation to BBU, the device includes:
the model establishing module is used for establishing a wireless access network system model based on unmanned aerial vehicle auxiliary cloud;
the equation construction module is used for constructing a data unloading optimization problem;
and the solving module is used for solving the optimal parameters of the data unloading optimization problem.
Has the advantages that:
1. the unmanned aerial vehicle communication technology is combined with the cloud wireless access network technology, a novel communication system model is provided, the service quality of the communication system can be effectively improved, the requirements of terminal Users (UE) can be met, the system performance is improved, meanwhile, due to the characteristic of high maneuverability of the unmanned aerial vehicle, the system can also cope with sudden situations, communication under special conditions is guaranteed, special situations such as earthquakes and forest fires can be coped with, and the system is also suitable for areas with complex terrain;
2. the method optimizes the access allocation and the calculation capability allocation of the terminal User (UE), the transmission power of the terminal User (UE) and the unmanned aerial vehicle path of the system, reduces the power loss of the terminal User (UE) in the communication process of the system, and improves the performance of the whole system.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An energy-saving data unloading method of a cloud wireless access network based on an unmanned aerial vehicle is characterized by comprising the following steps:
s1: establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network;
s2, constructing a data unloading optimization problem according to the energy-saving data unloading system model based on the unmanned aerial vehicle assisted cloud wireless access network;
and S3, solving the optimal parameters of the data unloading optimization problem.
2. The energy-saving data offloading method for a cloud radio access network based on a drone of claim 1, wherein the step S1 specifically includes:
establishing a system model based on an unmanned aerial vehicle assisted cloud wireless access network, wherein the system model comprises a BBU, a terminal User (UE), an RRH and an unmanned aerial vehicle;
the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area;
the unmanned aerial vehicle is located in the air, the RRHs are located on the ground, N terminal Users (UE), M RRHs and J unmanned aerial vehicles are set, each terminal User (UE) has a calculation task to be executed, and the task can unload data to the BBU through the unmanned aerial vehicle or the RRHs.
3. The energy-saving data offloading method for a cloud radio access network based on an unmanned aerial vehicle of claim 1, wherein the step S2 is specifically:
constructing a time formula for each end User (UE) to perform an offloading task:
Figure FDA0003187837190000011
wherein, F i Representing the amount of computation required by the CPU of the ith end User (UE), f i,b Representing the computing power allocated by the BBU to the ith end User (UE), D i Indicates the amount of data transmitted by the ith end User (UE), r i,j Represents a transmission rate of an ith end User (UE);
constructing a power formula of an end User (UE) for executing an unloading task:
Figure FDA0003187837190000021
wherein the content of the first and second substances,
Figure FDA0003187837190000022
for the ith end User (UE) own power,
Figure FDA0003187837190000023
κ i and vi are both constants that are constant in number,
Figure FDA0003187837190000024
power to perform offloading tasks for the ith end User (UE) via the drone,
Figure FDA0003187837190000025
offloading power for an ith end User (UE) to perform a task with the RRH;
calculating the transmission rate from the end User (UE) to the drone and to the RRH and from the drone to the BBU as:
Figure FDA0003187837190000026
Figure FDA0003187837190000027
Figure FDA0003187837190000028
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003187837190000029
Figure FDA00031878371900000210
σ 2 for noise interference, beta U Is the channel power gain, B is a constant
Figure FDA00031878371900000211
The distance of the drone to the end User (UE),
Figure FDA00031878371900000212
is the distance from the RRH to the end User (UE), alpha is the antenna gain,
Figure FDA00031878371900000213
for the power of the jth drone,
Figure FDA00031878371900000214
distance from drone to BBU.
4. The energy-saving data offloading method for unmanned aerial vehicle-based cloud wireless access network of claim 3, wherein the step S2 is specifically to formulate the data offloading optimization problem as：
Figure FDA0003187837190000031
Figure FDA00031878371900000313
Figure FDA0003187837190000033
Figure FDA0003187837190000034
Figure FDA0003187837190000035
Figure FDA0003187837190000036
Figure FDA0003187837190000037
Figure FDA0003187837190000038
Figure FDA0003187837190000039
Figure FDA00031878371900000310
Figure FDA00031878371900000311
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00031878371900000312
is the maximum transmission rate between the mth RRH and the BBU.
5. The energy-saving data offloading method for a cloud radio access network based on an unmanned aerial vehicle of claim 4, wherein step S3 is specifically to convert the data offloading optimization problem into four sub-problems, respectively:
optimizing an access variable of an end User (UE), optimizing a calculation capacity variable of the end User (UE), optimizing transmission power of the end User (UE) and optimizing a track of the unmanned aerial vehicle;
by iterating through each sub-problem, a solution to the problem is derived.
6. The energy-saving data offloading method of a cloud radio access network based on an unmanned aerial vehicle of claim 5, wherein optimizing end User (UE) access variables is specifically:
given other optimization variables than end User (UE) access variables, the problem can translate into:
Figure FDA0003187837190000041
Figure FDA0003187837190000042
Figure FDA0003187837190000043
Figure FDA0003187837190000044
Figure FDA0003187837190000045
Figure FDA0003187837190000046
since the problem is a convex function for the end User (UE) access variable, it is directly solved for optimization.
7. The energy-saving data offloading method for unmanned aerial vehicle-based cloud radio access network of claim 5, wherein the optimization of end User (UE) computing capability variables is specifically:
when the terminal User (UE) completes the unloading task, the calculation capacity variable of the terminal User (UE) is optimized, and the problem is converted into that:
Figure FDA0003187837190000047
Figure FDA0003187837190000048
Figure FDA0003187837190000051
Figure FDA0003187837190000052
Figure FDA0003187837190000053
the problem is a convex function and can be directly optimized and solved;
when an end User (UE) completes a data unloading task by means of an unmanned aerial vehicle and an RRH, computing capacity variables of the end User (UE) are optimized, and two variables s = [ s ] are added 1 ,...,s N ]And t = [ t ] 1 ,...,t N ]This problem is translated into:
Figure FDA0003187837190000054
Figure FDA0003187837190000055
Figure FDA0003187837190000056
s≥0,t≥0
the problem is a convex function and can be directly optimized and solved.
8. The energy-saving data offloading method for unmanned aerial vehicle-based cloud radio access network of claim 5, wherein optimizing transmission power of an end User (UE) is specifically:
given other variables than the transmission power of the end User (UE), and transforming the problem into:
Figure FDA0003187837190000057
Figure FDA0003187837190000058
Figure FDA0003187837190000061
Figure FDA0003187837190000062
Figure FDA0003187837190000063
Figure FDA0003187837190000064
Figure FDA0003187837190000065
the problem is converted into a convex function, and can be directly optimized and solved.
9. The energy-saving data offloading method for a cloud radio access network based on an unmanned aerial vehicle of claim 5, wherein the optimizing the trajectory of the unmanned aerial vehicle is specifically:
using successive convex approximation iterative method and first order Taylor expansion formula and by adding b = [ b ] i,j ]，c＝[c 1 ,...c J ]This problem is translated into:
Figure FDA0003187837190000066
s.t.
Figure FDA0003187837190000067
Figure FDA0003187837190000068
b≥0,c≥0
and the problem is solved optimally.
10. An energy-saving data unloading device based on an unmanned aerial vehicle assisted cloud wireless access network is applied to an unmanned aerial vehicle assisted cloud wireless access network system and is characterized by comprising a BBU, an end User (UE), an RRH and an unmanned aerial vehicle; the BBU is used for being connected with the RRH through optical fiber communication, and the RRH is used for covering wireless signals in a hot spot area or a blind spot area; wherein, unmanned aerial vehicle is located aloft, RRH is located ground, we have set for N end User (UE), M RRHs, J unmanned aerial vehicle, and every end User (UE) all has the calculation task that needs to carry out, and this task can be through unmanned aerial vehicle or RRH with data uninstallation to BBU, the device includes:
the model establishing module is used for establishing a system model based on the unmanned aerial vehicle assisted cloud wireless access network;
the equation construction module is used for constructing a data unloading optimization problem;
and the solving module is used for solving the optimal parameters of the data unloading optimization problem.
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