CN113613198B - Unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method - Google Patents
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Abstract
The invention relates to an unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method, and belongs to the technical field of resource allocation in wireless networks. According to the invention, the unmanned aerial vehicle is used as a mobile base station to provide data service for a plurality of cellular users, a plurality of pairs of D2D users share spectrum resources in a coverage type spectrum access mode, and the collected radio frequency signals are converted into energy for communication. The system and rate are maximized by jointly optimizing the drone transmit power, D2D transmit power, transmission time, user scheduling factors, and drone trajectory by considering constraints such as cellular and D2D user minimum rate requirements, drone transmit power and energy collection. The mixed integer nonlinear programming problem is converted into a convex optimization problem by using a continuous convex approximation method and a variable substitution method, and a closed solution is obtained by using a Lagrange dual method. The invention can prolong the service life of the D2D equipment, improve the frequency spectrum utilization rate, play a role in expanding the system capacity and have wide application range.
Description
Technical Field
The invention belongs to the technical field of wireless network resource allocation, and relates to an unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method.
Background
Unmanned aerial vehicles are focused on providing data services for cellular users by utilizing the characteristics of high mobility, low cost and capability of providing line-of-sight link services. When facing emergency incidents and natural disasters, the emergency communication service can be provided for areas without signal coverage, and loss possibly caused in the emergency is reduced. The method can play a role in expanding data capacity in hot spot places and provide a more flexible networking form.
With the development of the internet of things technology, the problems of spectrum resource shortage and overhigh total energy consumption of a system are caused by mass equipment access, and in order to further improve spectrum efficiency and realize reasonable utilization of radio frequency signals, the introduction of a terminal direct connection (D2D) technology and an energy collection technology makes the solution of the problems possible. Specifically, the D2D device can effectively improve spectrum efficiency and reduce core network load by sharing spectrum resources of cellular users, and due to broadcasting characteristics of radio, radio frequency signals generated by mass devices in the environment can be used as sources for energy collection, so that the radio frequency signals can be secondarily utilized, and meanwhile, the service life of the device can be prolonged.
The unmanned aerial vehicle is used for providing data services for cellular users and D2D users with an energy collection function, and is a problem of dynamic resource allocation of a heterogeneous network with the energy collection function. Under the limitation of the energy collection condition of the D2D user, the problem of balance between cellular users and the D2D user, between the cellular users and between the D2D users in spectrum resource allocation is related, and the network condition is more complicated due to channel change caused by unmanned aerial vehicle movement.
Disclosure of Invention
In view of the above, the present invention aims to provide an unmanned aerial vehicle assisted wireless energy-carrying D2D network resource allocation method, which considers a cellular user minimum rate constraint, a D2D user minimum rate constraint, an unmanned aerial vehicle transmit power constraint, an energy collection constraint, a user scheduling constraint, and an unmanned aerial vehicle mobility constraint, and establishes an unmanned aerial vehicle assisted energy collection D2D network model and a system model with a system and a rate maximization as optimization targets. And converting the mixed integer nonlinear programming problem into a convex optimization problem by using a continuous convex approximation and variable substitution method, and obtaining a closed solution by using a Lagrange dual method.
The invention provides the following technical scheme to achieve the technical aim:
an unmanned aerial vehicle assisted wireless energy-carrying D2D network resource allocation method, the method comprising:
s1: the unmanned aerial vehicle provides energy for cellular users in the user cluster to carry out downlink data transmission, and D2D users in the user cluster communicate with the collected energy, so that a transmission model is established;
s2: respectively constructing channel models from the unmanned aerial vehicle to the cellular user and the D2D user, and channel models between the D2D users;
s3: establishing a resource allocation model with a system and maximized rate by combining minimum rate requirements of a cellular user and a D2D user and energy collection constraint and unmanned aerial vehicle emission power constraint;
s4: traversing connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, and introducing the connection conditions into a resource allocation model with a maximized system and speed, and calculating to obtain unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power calculating system and speed;
s5: if the system and the rate reach convergence, taking the maximum value of the system and the rate obtained by exhaustive search as a final solution, and carrying out network resource allocation according to the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power corresponding to the final solution.
Preferably, the calculating the unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmitting time, D2D transmitting power calculating system and rate in step S4 includes:
s41: the connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the transmission time and the unmanned aerial vehicle transmitting power obtained by using a variable replacement method are utilized;
s42: the connection condition of the unmanned plane and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and D2D transmitting power is obtained by using an exponential transformation and continuous convex approximation method;
s43: the connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the unmanned aerial vehicle track is obtained by using a variable replacement method;
s44: and calculating a system and a rate according to the obtained unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power.
The invention has the beneficial effects that:
the invention can provide emergency communication service for heterogeneous networks consisting of cellular users and D2D users, has the functions of expanding data capacity and prolonging service life of equipment, and provides a more flexible networking form for the unmanned aerial vehicle as a mobile base station. The alternating iterative algorithm proposed according to the modeling type has good convergence performance, and can maximize the system and the rate on the premise of meeting the minimum rate requirements of the cellular user and the D2D user, so that the reasonable distribution of spectrum resources between the cellular user and the D2D user is realized.
Drawings
Fig. 1 is a flowchart of a method for allocating wireless energy-carrying D2D network resources assisted by an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a wireless energy-carrying D2D network communication scenario assisted by an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for allocating wireless energy-carrying D2D network resources assisted by an unmanned aerial vehicle according to a preferred embodiment of the present invention;
FIG. 4 is a rate convergence simulation graph in an embodiment of the invention;
FIG. 5 is a simulation diagram of the relationship between the system and the rate and the D2D minimum rate threshold under different minimum rate thresholds of cellular users according to an embodiment of the present invention;
FIG. 6 is a simulation diagram of the relationship between the system and the speed and the flight altitude of the unmanned aerial vehicle under different D2D user numbers and different D2D user minimum speed thresholds in the embodiment of the invention;
FIG. 7 is a comparison simulation of the present invention with the prior art average power algorithm, average time algorithm and no energy harvesting scenario algorithm.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a wireless energy-carrying D2D network resource allocation method assisted by an unmanned aerial vehicle in an embodiment of the present invention, as shown in fig. 1, specifically including the following steps:
101: the unmanned aerial vehicle provides energy for cellular users in the user cluster to carry out downlink data transmission, and D2D users in the user cluster communicate with the collected energy, so that a transmission model is established;
in order to illustrate the transmission model in the embodiment of the present invention, reference is first made to a wireless energy-carrying D2D network communication scenario diagram assisted by an unmanned aerial vehicle as shown in fig. 2, where the communication scenario includes K unmanned aerial vehicles serving as mobile base stations, i.e. a total of K cellular users and K D2D clusters; each user cluster contains a cellular user and M k And D2D user pairs, wherein the D2D users share orthogonal time resources with the cellular users by adopting a coverage type spectrum multiplexing mode. Definition of the definition
Serial number representing user cluster,/-, and>
representing the sequence number of the mth D2D pair in the kth user cluster. Suppose that unmanned aerial vehicle flies at a fixed height H, unmanned aerial vehicle takes the form of T max For the flight period, the flight period of the unmanned aerial vehicle is divided into N pieces with the length of T=T max N equal and small enough time slots such that the position of the drone remains nearly unchanged within each time slot and adjacent time slots have position changes for the drone. Here is defined a slot sequence number +>
The first stage tau of the nth time slot of the unmanned aerial vehicle k,n Providing downlink data service for kth cellular user, and M of kth user cluster k Energy harvesting by D2D users, M of kth user cluster k The D2D users are in the second stage T-tau k,n Using the first phase τ k,n The collected energy is communicated.
It will be appreciated that in the first phase the drone provides downstream data services to cellular users while the D2D users are collecting energy, and in the second phase the drone will enter a sleep mode in which the D2D users start to operate and can communicate with other D2D users.
In a preferred embodiment of the invention, a D2D user pair may refer to a D2D transmitter and a D2D receiver, which D2D receiver may receive data messages sent from its paired D2D transmitter, and possibly also from other interfering D2D transmitters.
To facilitate an understanding of the present invention, the present invention provides the following system parameters, including cellular user coordinates
D2D transmitter coordinates->
Unmanned planeHigh flying speed V max Length of each time slot T, number of time slots N, flight period T max Channel gain per unit distance beta 0 Channel gain between the nth time slot and the drone for cellular users in the kth user cluster +.>
Channel gain of the mth pair of D2D transmitters in the kth user cluster between the nth time slot and the drone +.>
Channel gain between the mth pair of D2D transmitters and receivers in the kth D2D cluster, channel gain between the nth time slot>
Interference channel gain of the jth pair of D2D transmitters to the mth pair of D2D receivers in the nth time slot of the kth D2D cluster +.>
Maximum emission power P of unmanned aerial vehicle max Unmanned aerial vehicle for maximum energy consumption E of signal emission and noise of cellular users +.>
Minimum rate threshold for cellular users>
D2D user noise->
D2D user minimum rate threshold +.>
102: respectively constructing channel models from the unmanned aerial vehicle to the cellular user and the D2D user, and channel models between the D2D users;
assume thatThe communication link from the unmanned aerial vehicle to the cellular user and the energy collection link of the D2D user are line-of-sight links, and the channel gains of the cellular user and the m pair of D2D transmitters in the kth user cluster between the nth time slot and the unmanned aerial vehicle can be respectively expressed as
and />
wherein ,β0 Channel gain, q, representing unit distance n Horizontal coordinates representing the unmanned plane in the nth time slot,/->
Representing cellular user coordinates;
and D2D user coordinates are represented, and H represents the flying height of the unmanned aerial vehicle.
Unlike the line-of-sight link of the drone to the ground equipment, the path loss per unit distance between D2D users is large and the channel between different D2D users can be subject to additional fading effects that vary randomly. Thus, the channel gain between the mth pair of D2D transmitters and D2D receivers in the kth D2D cluster at the nth time slot is expressed as
wherein ,
represents the distance between D2D transmitter and receiver,/->
Representing the path loss index of the non-line-of-sight link,
representing rayleigh fading between D2D users. Due to proximity interference between D2D pairs, use +.>
Representing the interference channel gains of the kth D2D cluster from the nth pair of D2D transmitters to the mth pair of D2D receivers in the nth time slot.
103: establishing a resource allocation model with a system and maximized rate by combining minimum rate requirements of a cellular user and a D2D user and energy collection constraint and unmanned aerial vehicle emission power constraint;
first, according to shannon's formula, the data rate of the cellular users of user cluster k in the time slot is:
wherein ,
indicating additive white gaussian noise at the kth cellular subscriber position in the nth time slot,/->
α k,n For the user scheduling factor τ k,n The length of time for which the cell user is served for the drone. In the first stage, during the downlink data transmission process for the cellular user, the unmanned aerial vehicle collects radio frequency signals by the transmitters in the kth D2D cluster, and the collected energy can be expressed as:
where ρ represents the energy harvesting efficiency of the D2D transmitter. According to shannon's formula, the data rate of the mth pair of D2D in the kth D2D cluster at the nth slot can be expressed as:
wherein ,
indicating co-channel interference between D2D, < >>
Representing the additive white gaussian noise of the mth pair D2D in the kth user cluster in the nth time slot. Establishing a resource allocation model of a system and rate maximization, which is expressed as:
s.t.C 1 :||q n -q n-1 || 2 ≤(V max T) 2
C 2 :
C 3 :
C 4 :
C 5 :0≤τ k,n ≤T
C 6 :
C 7 :
C 8 :
wherein ,qn Representing the horizontal coordinate of the unmanned aerial vehicle in the nth time slot; alpha k,n Indicating that the unmanned plane serves a user cluster k in the nth time slot; τ k,n The time length of the unmanned plane serving the cellular user k in the nth time slot is represented;
representing the transmitting power of the unmanned plane to the cellular user k in the nth time slot; />
Representing the transmission power of the m-th pair of D2D in the nth time slot in the kth D2D cluster; n represents the number of time slots in one flight cycle of the drone; k denotes the number of user clusters.
C 1 Representing unmanned plane mobility constraints, V max Is the maximum flying speed;
C 2 for the cellular user minimum rate requirement constraint,
a minimum rate threshold for a cellular user;
C 3 minimum rate requirement constraint for D2D users, wherein
Indicating co-channel interference between D2D, < >>
A minimum rate threshold for the D2D user;
C 4 definition alpha k,n =1 means that the UAV serves one user cluster k in the nth slot, otherwise α k,n ＝0；
C 5 Representing duration constraints of the unmanned aerial vehicle service cellular users;
C 6 representing maximum transmit power constraints of the drone;
C 7 representing energy consumption constraint of the unmanned aerial vehicle for signal transmission in a flight period;
C 8 representing D2D transmit power constraints, where
For the energy collection formula, ρ represents the energy conversion efficiency.
104: traversing connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, and introducing the connection conditions into a resource allocation model with a maximized system and speed, and calculating to obtain unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power calculating system and speed;
in an embodiment of the invention, the original optimization problem is decomposed into a plurality of optimization sub-problems. First, the user scheduling factor alpha is traversed using an exhaustive search method k,n Is selected according to the selection conditions; converting the transmission time and the unmanned aerial vehicle transmitting power optimization sub-problem into a convex optimization problem in a variable replacement mode, and obtaining an analytic solution by utilizing a Lagrange dual method; then, converting the D2D transmitting power optimization sub-problem into a convex optimization problem by using a continuous convex approximation and index substitution method, and obtaining an analytic solution by using a Lagrange dual method; then, converting the unmanned aerial vehicle track optimization problem into a convex optimization problem by using a continuous convex approximation method based on first-order Taylor expansion; finally, the user scheduling factor alpha is compared k,n The maximum value of the system and the speed obtained under each selection condition is the final solution, and the corresponding unmanned aerial vehicle transmitting power, D2D transmitting power, transmitting time and unmanned aerial vehicle track are the optimal solutions.
105: if the system and the rate reach convergence, taking the maximum value of the system and the rate obtained by exhaustive search as a final solution, and carrying out network resource allocation according to the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power corresponding to the final solution.
Under the selection condition of different user scheduling factors, the selection condition of the corresponding user scheduling factors when the system and the rate are maximum is the final solution, and the corresponding other variables are the optimal solution.
To improve the feasibility and reliability of the invention, the user scheduling factor alpha is traversed by an exhaustive search method k,n By conventional variable relaxation methodsScheduling the user by a factor alpha k,n Relaxed to alpha k,n ∈[0,1]The continuous variable of (2) can not be solved in the process of solving the analytic solutions of other optimized variables, and the analytic solutions of the cellular user and the D2D user can not be ensured. The matching algorithm has limitation in solving the user scheduling factor under the condition that the unmanned aerial vehicle has mobility, is directly related to the initialized unmanned aerial vehicle track, and is not suitable for solving the model. In order to ensure the feasibility and convergence of the algorithm provided by the invention, the complexity of the algorithm is reduced, and log is utilized 2 Perspective function x log of (1+y) 2 (1+y/x) is a concave function, for tau, where this theory applies in the transmit time and unmanned transmit power optimization sub-problem k,n And
and decoupling is carried out, and decoupling between D2D transmitting power and co-channel interference is realized by using an exponential transformation and continuous convex approximation method.
Fig. 3 is a flowchart of a method for allocating wireless energy-carrying D2D network resources assisted by a drone according to a preferred embodiment of the present invention, as shown in fig. 3, the method includes:
201. the unmanned aerial vehicle provides energy for cellular users in the user cluster to carry out downlink data transmission, and D2D users in the user cluster communicate with the collected energy, so that a transmission model is established;
the transmission model can refer to the communication scene diagram shown in fig. 2, the system comprises an unmanned plane serving as a mobile base station, the number of user clusters is K, and each user cluster comprises a cellular user and M k And D2D user pairs, wherein the D2D users share orthogonal time resources with the cellular users by adopting a coverage type spectrum multiplexing mode. Unmanned plane T max For the flight cycle, flying at a fixed height H, the cellular subscribers are provided with downlink data services in a first phase, and the D2D subscribers have the function of energy collection, and communicate in a second phase using the energy collected in the first phase. The flight cycle of the unmanned aerial vehicle is divided into N time slots with the length of T, and the position of the unmanned aerial vehicle is kept almost unchanged in each time slot.
202. Respectively constructing channel models from the unmanned aerial vehicle to the cellular user and the D2D user, and channel models between the D2D users;
each channel model is expressed as:
wherein ,
representing a channel gain between the drone and the cellular user; />
Representing channel gain between the drone and the D2D user pair; />
Representing channel gain between the D2D user pair; beta 0 Channel gain representing unit distance; q n Representing the horizontal coordinate of the unmanned aerial vehicle in the nth time slot; />
Representing cellular user coordinates; />
Representing D2D user coordinates; h represents the unmanned aerial vehicle flight height; />
Representing the distance between the D2D transmitter and the receiver; />
A path loss index representing a non-line-of-sight link; />
Representing rayleigh fading between D2D users.
203. Establishing a resource allocation model with a system and maximized rate by combining minimum rate requirements of a cellular user and a D2D user and energy collection constraint and unmanned aerial vehicle emission power constraint;
the system and rate maximized resource allocation model is expressed as:
s.t.C 1 :||q n -q n-1 || 2 ≤(V max T) 2
C 2 :
C 3 :
C 4 :
C 5 :0≤τ k,n ≤T
C 6 :
C 7 :
C 8 :
wherein ,qn Representing the horizontal coordinate of the unmanned aerial vehicle in the nth time slot; alpha k,n Indicating that the unmanned plane serves a user cluster k in the nth time slot; τ k,n The time length of the unmanned plane serving the cellular user k in the nth time slot is represented;
representing the transmitting power of the unmanned plane to the cellular user k in the nth time slot; />
Representing the transmission power of the m-th pair of D2D in the nth time slot in the kth D2D cluster; n represents the number of time slots in one flight cycle of the drone; k denotes the number of user clusters.
C 1 Representing unmanned plane mobility constraints, V max Is the maximum flying speed;
C 2 for the cellular user minimum rate requirement constraint,
a minimum rate threshold for a cellular user;
C 3 minimum rate requirement constraint for D2D users, wherein
Indicating co-channel interference between D2D, < >>
A minimum rate threshold for the D2D user;
C 4 definition alpha k,n =1 means that the UAV serves one user cluster k in the nth slot, otherwise α k,n ＝0；
C 5 Representing duration constraints of the unmanned aerial vehicle service cellular users;
C 6 indicating maximum hair of unmanned planeA transmit power constraint;
C 7 representing energy consumption constraint of the unmanned aerial vehicle for signal transmission in a flight period;
C 8 representing D2D transmit power constraints, where
For the energy collection formula, ρ represents the energy conversion efficiency.
204. Traversing the connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, and bringing the connection conditions into a resource allocation model with a maximized system and rate;
in the embodiment of the invention, the user scheduling condition is solved by using an exhaustive search method.
205. The connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the transmission time and the unmanned aerial vehicle transmitting power obtained by using a variable replacement method are utilized;
due to tau k,n And
the coupling relationship between the two results in the above-mentioned problem being non-convex. Definition using variable replacement methods
The following convex problems can be obtained:
s.t.C 3 ,C 5
wherein ,
definitions->
By using Lagrangian functions
wherein ,
β k ≥0，χ k,m ≥0，φ k,n ≥0，θ k,n not less than 0, eta not less than 0 and kappa k,n,m And 0 is Lagrangian multiplier. According to the KKT condition, +.>
Is solved by (a) analysis:
wherein ,[x]+ =max (0, x). Based on the sub-gradient method, the optimized variable tau can be obtained k,n Is updated by the update step of (a)
l is the iteration number, delta τ For the corresponding iteration step. When->
When (I)>
The value of (2) is significant. Definition of delta β ，Δ χ ，Δ φ ，Δ θ ，Δ η ，Δ κ For Lagrangian multiplier iteration step, the updating steps are as follows:
206. the connection condition of the unmanned plane and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and D2D transmitting power is obtained by using an exponential transformation and continuous convex approximation method;
using the continuous convex approximation method, it is possible to obtain:
wherein ,
indicating that the value of the last iteration is taken,
due to->
Middle->
The coupling relation with the adjacent disturbance results in the problem that still is a non-convex problem by introducing the relaxation variable +.>
It is possible to further obtain:
wherein ,
definitions->
Can get +.>
About D2D transmit power->
The problem of (2) can be described as:
s.t.
definition of the definition
By using Lagrangian functions
wherein ,
λ k,m ≥0，/>
and ξk,n,m And 0 is Lagrangian multiplier. According to the KKT condition, +.>
Is solved by (a) analysis:
similarly, the resolution of the auxiliary variables is:
based on the sub-gradient method, define Δ λ ，
Δ ξ For the lagrangian multiplier iteration step, the lagrangian multiplier updating step can be obtained:
207. the connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the unmanned aerial vehicle track is obtained by using a variable replacement method;
due to
With respect to unmanned plane trajectory q n Is a convex function, which can be obtained by a first-order Taylor approximation based on a continuous convex approximation method>
Is defined as the lower bound of:
wherein ,
the problem with the unmanned aerial vehicle trajectory can be approximated as:
s.t.C 1 :||q n -q n-1 || 2 ≤(V max T) 2
the optimization problem described above is a convex optimization problem that can be solved using standard convex optimization methods.
208. And calculating a system and a rate according to the obtained unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power.
209. If the system and the rate reach convergence, taking the maximum value of the system and the rate obtained by exhaustive search as a final solution, and carrying out network resource allocation according to the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power corresponding to the final solution.
Obtaining user scheduling factor alpha through exhaustive search method k,n And selecting the system and the rate under the result, wherein the maximum value of the system and the rate and the corresponding scheduling selection condition of the user are the final solution, and the corresponding other variables are the optimal solution, so that the corresponding network resource allocation can be completed by using the optimal solution.
The application effect of the present invention will be described in detail with reference to simulation.
1) Simulation conditions
Assuming that two cellular users exist in the system, the horizontal coordinates are respectively
and />
Each cellular user shares time resources with two pairs of D2D users, and the position coordinates of the D2D transmitters are respectively as follows
Unmanned plane flight cycle t=2s, maximum flight speed V max =100 m/s, number of slots n=2, flight height h=100deg.m, unit channel gain per meter β 0 = -30db, los link channel fading index +.>
NLoS link channel fading index +.>
Energy conversion efficiency ρ=0.8, cellular user noise of-45 dBm/Hz, D2D user noise of-130 dBm/Hz, cellular user minimum rate requirement of 0.2bits/s/Hz, D2D user minimum rate requirement of 0.1bits/s/Hz, unmanned aerial vehicle maximum transmit power of 1W, total transmit power energy consumption e=n (P max /2)J。
2) Simulation results
In this embodiment, fig. 4 shows a rate convergence chart of the algorithm proposed by the present invention. Fig. 5 shows a graph of system and rate versus D2D minimum rate threshold for different cellular users minimum rate thresholds. Fig. 6 shows a graph of system and rate versus drone flight altitude for different numbers of D2D users and different D2D user minimum rate thresholds. Fig. 7 shows a comparison of the proposed algorithm with the average power algorithm, the average time algorithm and the no energy harvesting scenario algorithm. Fig. 4 shows that the algorithm provided by the present invention has good convergence, and allocates more time resources to the D2D user while satisfying the minimum rate threshold of the cellular user. Fig. 5 shows that the system and rate decrease with increasing D2D user minimum rate threshold due to channel differences between the two pairs of D2D devices. To maximize D2D and rate, allocating more power to D2D users with better channel conditions may make a greater contribution to the increase in D2D and rate. Fig. 6 shows that the system and rate decrease with increasing unmanned aerial vehicle flight altitude due to poor channel conditions between the unmanned aerial vehicle and the ground equipment, and the system and rate increase with increasing D2D number when the same unmanned aerial vehicle flight altitude and D2D minimum rate threshold are valued, compensating for the system and rate loss caused by increasing unmanned aerial vehicle flight altitude to some extent. Figure 6 shows that both the system and rate are increasing as the maximum transmit power of the UAV increases. It can be seen that the performance of the present invention is significantly better than the average power algorithm and the average time algorithm, since the present invention adds an optimized degree of freedom compared to the two algorithms described above, which significantly contributes to the system and rate improvement. Compared with a network without energy collection, the method and the device for converting the unmanned aerial vehicle radio frequency signals into the energy signals at the D2D equipment end can effectively improve the D2D operation time length, increase the system and the speed, and improve the spectrum utilization efficiency.
In the description of the present invention, it should be understood that the terms "coaxial," "bottom," "one end," "top," "middle," "another end," "upper," "one side," "top," "inner," "outer," "front," "center," "two ends," etc. indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the invention.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "configured," "connected," "secured," "rotated," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intermediaries, or in communication with each other or in interaction with each other, unless explicitly defined otherwise, the meaning of the terms described above in this application will be understood by those of ordinary skill in the art in view of the specific circumstances.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. An unmanned aerial vehicle assisted wireless energy-carrying D2D network resource allocation method, comprising:
the unmanned aerial vehicle provides energy for cellular users in the user cluster to carry out downlink data transmission, and D2D users in the user cluster communicate with the collected energy, so that a transmission model is established;
the transmission model comprises an unmanned plane serving as a mobile base station and K user clusters, wherein each user cluster comprises a cellular user and M k The method comprises the steps that D2D user pairs are adopted, and the D2D users share orthogonal time resources with cellular users in a coverage type spectrum multiplexing mode; unmanned plane T max For the flight period, dividing the flight period of the unmanned aerial vehicle into N time slots with the length of T, and flying at a fixed height H, wherein the unmanned aerial vehicle provides energy for a cellular user for downlink data service in a first stage of a certain time slot, and a D2D user collects energy from the cellular user; the D2D user communicates in a second stage by using the energy collected in the first stage;
respectively constructing channel models from the unmanned aerial vehicle to the cellular user and the D2D user, and channel models between the D2D users;
each channel model is expressed as:
wherein ,
representing a channel gain between the drone and the cellular user; />
Representing channel gain between the drone and the D2D user pair; />
Representing channel gain between the D2D user pair; beta 0 Channel gain representing unit distance; q n Representing the horizontal coordinate of the unmanned aerial vehicle in the nth time slot; />
Representing cellular user coordinates; />
Representing D2D user coordinates; h represents the unmanned aerial vehicle flight height; />
Representing the distance between the D2D transmitter and the receiver; />
A path loss index representing a non-line-of-sight link; />
Representing rayleigh fading between D2D users;
establishing a resource allocation model with a system and maximized rate by combining minimum rate requirements of a cellular user and a D2D user and energy collection constraint and unmanned aerial vehicle emission power constraint;
the system and rate maximized resource allocation model is expressed as:
s.t.C 1 :||q n -q n-1 || 2 ≤(V max T) 2
C 2 :
C 3 :
C 4 :
C 5 :0≤τ k,n ≤T
C 6 :
C 7 :
C 8 :
wherein ,qn Representing the horizontal coordinate of the unmanned aerial vehicle in the nth time slot; alpha k,n Indicating that the unmanned plane serves a user cluster k in the nth time slot; τ k,n The time length of the unmanned plane serving the cellular user k in the nth time slot is represented;
representing the transmitting power of the unmanned plane to the cellular user k in the nth time slot; />
Representing the transmission power of the m-th pair of D2D in the nth time slot in the kth D2D cluster; n represents the number of time slots in one flight cycle of the drone; k represents the number of user clusters; />
Representing the transmission rate of cellular users in the kth user cluster in the nth time slot; />
The transmission rate of the D2D user pair in the kth user cluster in the nth time slot; c (C) 1 Representing unmanned plane mobility constraints, V max Is the maximum flying speed; c (C) 2 For the cellular user minimum rate requirement constraint,
representing channel gains of cellular users in the kth user cluster between the nth time slot and the unmanned aerial vehicle; />
Noise representing cellular users in the kth user cluster in the nth time slot; />
A minimum rate threshold for a cellular user; c (C) 3 Constraint for minimum rate requirement for D2D users, < >>
Indicated at the nthThe transmission rate of the m-th pair of D2D user pairs in the kth user cluster of the time slot; wherein->
T represents the slot length, < >>
Represents the channel gain between the mth pair of D2D user pairs in the kth user cluster at the nth time slot,/for>
X k,n,m Representing co-channel interference between D2D,
a minimum rate threshold for the D2D user; c (C) 4 Definition alpha k,n =1 means that the UAV serves one user cluster k in the nth slot, otherwise α k,n ＝0；C 5 Representing duration constraints of the unmanned aerial vehicle service cellular users; c (C) 6 Representing maximum transmit power constraint of unmanned aerial vehicle, P max Representing the maximum transmitting power of the unmanned aerial vehicle; c (C) 7 Representing energy consumption constraint of the unmanned aerial vehicle for signal transmission in a flight period, wherein E represents energy which can be provided by the unmanned aerial vehicle; c (C) 8 Representing D2D transmit power constraints, where
For the energy harvesting formula, ρ represents the energy conversion efficiency, +.>
Representing channel gains between the nth time slot and the unmanned for the mth pair of D2D user pairs in the kth user cluster;
traversing connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, and introducing the connection conditions into a resource allocation model with a maximized system and speed, and calculating to obtain unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power calculating system and speed;
s41: the connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the transmission time and the unmanned aerial vehicle transmitting power obtained by using a variable replacement method are utilized;
definition using variable replacement methods
Will be related to the transmission time τ k,n And unmanned plane transmit power +.>
The optimization problem optimization sub-problem of (2) is converted into a convex optimization problem, and an analytical solution is obtained by using a Lagrange dual method
wherein ,βk Representing constraint C 2 Determined Lagrangian multiplier, θ k,n Representing constraint C 6 Determined Lagrangian multiplier, η represents constraint C 7 Determined Lagrangian multiplier, κ k,n,m Representing constraint C 8 The determined lagrangian multiplier, N being the number of slots,
s42: the connection condition of the unmanned plane and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and D2D transmitting power is obtained by using an exponential transformation and continuous convex approximation method;
using a continuous convex approximation method to obtain
wherein ,
representation->
Is the value of the last iteration of (a),
and introducing the relaxation variable +.>
Obtain->
wherein ,
then->
D2D transmit power->
The optimization sub-problem of (2) is converted into a convex optimization problem, and an analytical solution is obtained by using a Lagrange dual method:
wherein ,λk,m Representing constraint C 3 The determined lagrangian multiplier,
representing constraint C 8 Determined Lagrangian multiplier, ζ k,n,m Represents the introduction of a relaxation variable and ∈ ->
Obtaining constraints after conversion
The Lagrangian multiplier of (2), N is the number of time slots;
s43: the connection condition of the unmanned aerial vehicle and the user cluster in all time slots is brought into a resource allocation model with the maximized system and rate, and the unmanned aerial vehicle track is obtained by using a variable replacement method;
the unmanned aerial vehicle track obtained by using the variable substitution method comprises the following steps:
method for replacing by using variable
wherein ,
the unmanned aerial vehicle track optimization problem is converted into a convex optimization problem,
the unmanned aerial vehicle track of the last iteration is valued,
β 0 channel gain representing unit distance;
s44: calculating a system and a rate according to the obtained unmanned aerial vehicle transmitting power, unmanned aerial vehicle position, transmission time and D2D transmitting power;
if the system and the rate reach convergence, taking the maximum value of the system and the rate obtained by exhaustive search as a final solution, and carrying out network resource allocation according to the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power corresponding to the final solution.
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