CN113613198A - Unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method - Google Patents
Unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method Download PDFInfo
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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. The invention uses the unmanned aerial vehicle 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 an overlay spectrum access mode, and collected radio frequency signals are converted into energy for communication. The system and rate are maximized by jointly optimizing drone transmit power, D2D transmit power, transmission time, user scheduling factors, and drone trajectory by considering cellular and D2D user minimum rate requirements, drone transmit power and energy harvesting constraints. And converting the mixed integer nonlinear programming problem into a convex optimization problem by using a continuous convex approximation method and a variable replacement method, and obtaining a closed-form solution by using a Lagrange dual method. The invention can prolong the service life of D2D equipment, improve the frequency spectrum utilization rate, and expand the system capacity, and has 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
The unmanned aerial vehicle provides data service for cellular users by utilizing the characteristics of high mobility, low cost and capability of providing line-of-sight link service, and becomes the focus of attention of people in the next generation of wireless communication system. When the emergency communication system is faced with emergency emergencies and natural disasters, emergency communication services can be provided for areas without signal coverage, and possible loss caused in the emergencies is reduced. The method can play a role in expanding data capacity in a hotspot place and provide a more flexible networking form.
With the development of the internet of things technology, the introduction of the direct terminal (D2D) technology and the energy collection technology makes it possible to solve the problems of spectrum resource shortage and system total energy consumption overhigh due to the access of massive devices, in order to further improve spectrum efficiency and realize the reasonable utilization of radio frequency signals. Specifically, the D2D device can effectively improve spectrum efficiency and reduce core network load by sharing spectrum resources of cellular users, and due to the broadcast 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 reused and the service life of the device can be prolonged.
The data service is provided for cellular users and D2D users with the energy collection function by using the unmanned aerial vehicle, and the problem of dynamic resource allocation of the heterogeneous network with the energy collection function is solved. Under the limitation of the energy collection condition of the D2D users, the trade-off problem in spectrum resource allocation between cellular users and D2D users, between cellular users and between D2D users is involved, and the network situation is further complicated by the channel change caused by the movement of the drone.
Disclosure of Invention
In view of this, the present invention provides 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 transmission 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 system and rate maximization as an optimization target. And converting the mixed integer nonlinear programming problem into a convex optimization problem by using a continuous convex approximation and variable replacement method, and obtaining a closed-form solution by using a Lagrange dual method.
The invention provides the following technical scheme to realize the technical goals:
a drone-assisted wireless energy-carrying D2D network resource allocation method, the method comprising:
s1: the unmanned aerial vehicle provides energy for the cellular users in the user cluster to perform downlink data transmission, and the D2D users in the user cluster communicate by using the collected energy, so that a transmission model is established;
s2: respectively constructing channel models of 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 a maximized rate by combining the minimum rate requirement of cellular users and D2D users, energy collection constraint and unmanned aerial vehicle transmission power constraint;
s4: traversing the connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, bringing the connection conditions into a resource allocation model with maximized system and rate, and calculating to obtain the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time, the D2D transmitting power calculation system and the rate;
s5: and if the system and the speed reach convergence, taking the maximum value of the system and the speed obtained by exhaustive search as a final solution, and performing 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 of the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time, the D2D transmitting power and the velocity in step S4 includes:
s41: bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the transmission time and the unmanned aerial vehicle transmitting power by using a variable replacement method;
s42: bringing the connection conditions of the unmanned aerial vehicles and the user clusters in all time slots into the resource allocation model with the system and the maximized rate, and obtaining D2D transmitting power by using an exponential transformation and continuous convex approximation method;
s43: bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the unmanned aerial vehicle track by using a variable replacement method;
s44: and calculating the system and the speed according to the obtained unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power.
The invention has the beneficial effects that:
the invention can provide emergency communication service for a heterogeneous network formed by a cellular user and a D2D user, has the functions of expanding data capacity and prolonging the service life of equipment, and provides a more flexible networking form by using the unmanned aerial vehicle as a mobile base station. The alternating iteration algorithm provided according to the established model has good convergence performance, can maximize the system and the rate on the premise of meeting the minimum rate requirements of cellular users and D2D users, and realizes reasonable distribution of spectrum resources between the cellular users and the D2D users.
Drawings
Fig. 1 is a flowchart illustrating a method for allocating resources of a wireless energy-carrying D2D network assisted by an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 2 is a diagram of a communication scenario of a wireless energy-carrying D2D network assisted by an unmanned aerial vehicle according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for allocating resources of a wireless energy-carrying D2D network assisted by an unmanned aerial vehicle according to a preferred embodiment of the present invention;
FIG. 4 is a graph of rate convergence simulation in an embodiment of the present invention;
FIG. 5 is a simulation diagram of the system and rate versus the D2D minimum rate threshold for different cellular users in accordance with an embodiment of the present invention;
FIG. 6 is a simulation diagram of the relationship between the system and the velocity and the flying height of the UAV under the minimum velocity thresholds of different D2D users and different D2D users in the embodiment of the present invention;
FIG. 7 is a comparative simulation diagram of the present invention with existing average power algorithm, average time algorithm and no energy collection scenario algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a method for allocating resources of a wireless energy-carrying D2D network 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 the cellular users in the user cluster to perform downlink data transmission, and the D2D users in the user cluster communicate by using the collected energy, so that a transmission model is established;
to illustrate the transmission model in the embodiment of the present invention, first, refer to a diagram of a communication scenario of a drone-assisted wireless energy-carrying D2D network as shown in fig. 2, where the communication scenario includes a drone serving as a mobile base station, and K clusters of users, that is, a total of cellular users and K clusters of D2D; each user cluster comprises a cellular user and MkAnd D2D users and D2D users share orthogonal time resources with cellular users in an overlay spectrum multiplexing mode. Definition of
A serial number indicating a cluster of users,
indicating the sequence number of the mth pair D2D in the kth user cluster. Assuming that the drone is flying at fixed height H, the drone is flying at TmaxFor the flight cycle, the flight cycle of the unmanned aerial vehicle is divided into N flight cycles with the length of T ═ TmaxN equal and sufficiently small time slots such that the position of the drone remains approximately constant within each time slot and adjacent time slot drones have a change in position. The time slot number is defined here
The first stage tau of the unmanned plane in the nth time slotk,nProviding downlink data service for k cellular user, and M of k user clusterkEnergy harvesting by D2D user, M of kth user ClusterkD2D user in the second stage T-tauk,nUsing a first phase τk,nThe collected energy is communicated.
It will be appreciated that in the first phase, the drone provides downlink data services to cellular users while the D2D users are performing energy harvesting, and in the second phase, the drone will enter a sleep mode in which the D2D user begins operating and is able to communicate with other D2D users.
In a preferred embodiment of the present invention, the D2D user pair may refer to a D2D transmitter and a D2D receiver, and the D2D receiver may receive data messages transmitted from its paired D2D transmitter, and may also receive data messages transmitted from other interfering D2D transmitters.
To facilitate an understanding of the present invention, the present invention provides an illustration of system parameters including cellular user coordinates
D2D transmitter coordinates
Maximum flying speed V of unmanned aerial vehiclemaxLength of each time slot T, number of time slots N and flight period TmaxUnit distance channel gain beta0Channel gain between the nth time slot and the drone for cellular users in the kth user cluster
Channel gain between the nth time slot and the drone for the mth pair of D2D transmitters in the kth user cluster
Channel gain at nth slot between mth pair D2D transmitter and receiver in kth D2D cluster, and channel gain at nth slot between mth pair D2D transmitter and receiver in kth D2D cluster
Interference channel gain of jth pair D2D transmitter to mth pair D2D receiver in nth time slot for kth D2D cluster
Maximum transmitting power P of unmanned aerial vehiclemaxUnmanned aerial vehicle is used for signal transmission maximum energy consumption E and cellular user noise
Minimum rate threshold for cellular users
D2D user noise
D2D user minimum rate threshold
102: respectively constructing channel models of the unmanned aerial vehicle to the cellular user and the D2D user and channel models between the D2D users;
assuming that the communication link from the drone to the cellular user and the energy harvesting link for the D2D user are line-of-sight links, the cellular user in the kth user cluster and the m-th pair of D2D transmitters are in the nth time slot and noneThe channel gains between the man-machines can be expressed as
And
wherein ,β0Channel gain, q, expressed in units of distancenIndicating the horizontal coordinate of the drone at the nth slot,
representing cellular user coordinates;
representing D2D user coordinates and H representing drone flight altitude.
Unlike the line-of-sight link from drone to ground device, the path loss per unit distance between D2D users is large, and the channel between different D2D users is subject to randomly varying additive fading. Thus, the channel gain between the m-th pair of D2D transmitters and the D2D receiver in the k-th cluster of D2D at the n-th slot is expressed as
wherein ,
representing the distance between the D2D transmitter and receiver,
represents the path loss exponent of a non-line-of-sight link,
representing rayleigh fading between D2D users. Due to the proximity interference between the D2D pairs, use
Indicating the interference channel gain of the kth D2D cluster from the D2D transmitter to the m pair D2D receiver in the nth slot。
103: establishing a resource allocation model with a system and a maximized rate by combining the minimum rate requirement of cellular users and D2D users, energy collection constraint and unmanned aerial vehicle transmission power constraint;
firstly, according to the shannon formula, the data rate of the cellular user in the time slot of the user cluster k is:
wherein ,
indicating additive white gaussian noise at the kth cellular user location in the nth slot,
αk,nscheduling a factor, τ, for a userk,nThe duration of time to serve the cellular user for the drone. In the first phase, during the downlink data transmission of the drone to the cellular user, the transmitter in the kth D2D cluster collects the radio frequency signal, 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 m-th pair of D2D in the k-th cluster D2D in the n-th slot can be expressed as:
wherein ,
indicating co-channel interference between D2D,
which represents the additive white gaussian noise of the m-th pair D2D in the kth user cluster in the nth time slot. Establishing a resource allocation model with a system and a rate maximized, and expressing as follows:
s.t.C1:||qn-qn-1||2≤(VmaxT)2
C2:
C3:
C4:
C5:0≤τk,n≤T
C6:
C7:
C8:
wherein ,qnHorizontal coordinates representing the unmanned aerial vehicle at the nth time slot; alpha is alphak,nIndicating that the unmanned plane serves a user cluster k at the nth time slot; tau isk,nIndicating the time length of the cellular user k served by the unmanned plane at the nth time slot;
to indicate nobodyThe transmitting power of the machine to a cellular user k in the nth time slot;
represents the transmission power of the m-th pair of D2D in the k-th D2D cluster in the nth time slot; n represents the number of time slots in one flight cycle of the drone; k represents the number of user clusters.
C1Representing unmanned aerial vehicle mobility constraints, VmaxIs the maximum flight speed;
C2for the cellular user minimum rate requirement constraint,
a minimum rate threshold for a cellular user;
C3is a D2D user minimum rate requirement constraint, wherein
Indicating co-channel interference between D2D,
a minimum rate threshold for the D2D user;
C4definition of alphak,nWith 1, UAV serves a user cluster k at the nth slot, otherwise αk,n＝0；
C5A duration constraint representing that the drone serves a cellular user;
C6representing a drone maximum transmit power constraint;
C7representing an energy consumption constraint for signal transmission by the drone during the flight period;
C8representing a D2D transmit power constraint, wherein
For the energy harvesting formula, ρ represents the energy conversion efficiency.
104: traversing the connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, bringing the connection conditions into a resource allocation model with maximized system and rate, and calculating to obtain the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time, the D2D transmitting power calculation system and the rate;
in the 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 by an exhaustive search methodk,nThe selection condition of (1); converting the transmission time and unmanned aerial vehicle transmitting power optimization subproblems into convex optimization problems in a variable replacement mode, and solving an analytic solution by using a Lagrangian dual method; then, converting the D2D transmitting power optimization subproblem into a convex optimization problem by using a continuous convex approximation and index replacement method, and solving an analytic solution by using a Lagrangian dual method; then, converting the unmanned aerial vehicle trajectory 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 comparedk,nThe 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, transmission time and unmanned aerial vehicle track are the optimized solutions.
105: and if the system and the speed reach convergence, taking the maximum value of the system and the speed obtained by exhaustive search as a final solution, and performing 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 condition of selecting different user scheduling factors, the user scheduling factor selection condition corresponding to the maximum system and rate is the final solution, and other corresponding variables are the optimized solutions.
In order to improve the feasibility and the reliability of the invention, an exhaustive search method is utilized to traverse the user scheduling factor alphak,nAnd the user is scheduled by the factor alpha by adopting the traditional variable relaxation methodk,nRelaxation to alphak,n∈[0,1]The analytic solutions of the cellular users and the D2D users cannot be obtained in the subsequent process of solving the analytic solutions of other optimized variables, and the maximum analytic solutions cannot be guaranteedAnd (4) high quality. The matching algorithm has limitation in solving the user scheduling factor in the situation 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 and reduce the complexity of the algorithm, log is utilized2Perspective function x log of (1+ y)2(1+ y/x) is the concave function, and is applicable to the situation of the transmission time and unmanned aerial vehicle emission power optimization subproblemk,nAnd
and decoupling, namely decoupling the D2D transmitting power from co-channel interference by using an exponential transformation and continuous convex approximation method.
Fig. 3 is a flowchart of a method for allocating resources of a wireless portable D2D network assisted by a drone in a preferred embodiment of the present invention, as shown in fig. 3, the method includes:
201. the unmanned aerial vehicle provides energy for the cellular users in the user cluster to perform downlink data transmission, and the D2D users in the user cluster communicate by using the collected energy, so that a transmission model is established;
the transmission model can also refer to a communication scene diagram shown in fig. 2, and the system comprises a drone serving as a mobile base station, wherein the number of user clusters is K, and each user cluster comprises a cellular user and MkAnd D2D users and D2D users share orthogonal time resources with cellular users in an overlay spectrum multiplexing mode. Unmanned plane TmaxFor the flight cycle, flying at a fixed altitude H, cellular subscribers are provided with downstream data services during a first phase, and D2D subscribers have energy harvesting capabilities during a second phase, using the energy harvested during the first phase for communication. 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 almost kept unchanged in each time slot.
202. Respectively constructing channel models of the unmanned aerial vehicle to the cellular user and the D2D user and channel models between the D2D users;
each channel model is represented as:
wherein ,
representing the channel gain between the drone and the cellular user;
represents the channel gain between drone to D2D user pair;
represents the channel gain between the D2D user pairs; beta is a0A channel gain representing a unit distance; q. q.snHorizontal coordinates representing the unmanned aerial vehicle at the nth time slot;
representing cellular user coordinates;
representing D2D user coordinates; h represents the flight height of the unmanned aerial vehicle;
represents the distance between the D2D transmitter and receiver;
representing a path loss exponent for a non-line-of-sight link;
representing rayleigh fading between D2D users.
203. Establishing a resource allocation model with a system and a maximized rate by combining the minimum rate requirement of cellular users and D2D users, energy collection constraint and unmanned aerial vehicle transmission power constraint;
the resource allocation model for system and rate maximization is represented as:
s.t.C1:||qn-qn-1||2≤(VmaxT)2
C2:
C3:
C4:
C5:0≤τk,n≤T
C6:
C7:
C8:
wherein ,qnHorizontal coordinates representing the unmanned aerial vehicle at the nth time slot; alpha is alphak,nIndicating that the unmanned plane serves a user cluster k at the nth time slot; tau isk,nIndicates that the drone is atDuration of n time slots serving cell user k;
indicating the transmitting power of the unmanned plane to a cellular user k in the nth time slot;
represents the transmission power of the m-th pair of D2D in the k-th D2D cluster in the nth time slot; n represents the number of time slots in one flight cycle of the drone; k represents the number of user clusters.
C1Representing unmanned aerial vehicle mobility constraints, VmaxIs the maximum flight speed;
C2for the cellular user minimum rate requirement constraint,
a minimum rate threshold for a cellular user;
C3is a D2D user minimum rate requirement constraint, wherein
Indicating co-channel interference between D2D,
a minimum rate threshold for the D2D user;
C4definition of alphak,nWith 1, UAV serves a user cluster k at the nth slot, otherwise αk,n＝0；
C5A duration constraint representing that the drone serves a cellular user;
C6representing a drone maximum transmit power constraint;
C7representing an energy consumption constraint for signal transmission by the drone during the flight period;
C8representing a D2D transmit power constraint, wherein
For the energy harvesting formula, ρ represents the energy conversion efficiency.
204. Traversing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, and bringing the connection condition into a resource allocation model with maximized system and rate;
in the embodiment of the invention, the user scheduling condition is solved by using an exhaustive search method.
205. Bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the transmission time and the unmanned aerial vehicle transmitting power by using a variable replacement method;
due to tauk,nAnd
the coupling relationship between them, which causes the above problem to be non-convex. Using variable substitution methods, defining
The following convex problem can be obtained:
s.t.C3,C5
wherein ,
definition of
By using Lagrangian functions
wherein ,
βk≥0，χk,m≥0，φk,n≥0，θk,nnot less than 0, η not less than 0 and κk,n,mLagrange multipliers are greater than or equal to 0. According to the KKT conditions, can be obtained
Analytic solution of (2):
wherein ,[x]+Max (0, x). Based on the sub-gradient method, the optimization variable tau can be obtainedk,nStep of updating
l is the number of iterations, ΔτIs the corresponding iteration step. When in use
When the temperature of the water is higher than the set temperature,
the value of (a) is significant. Definition of Δβ，Δχ，Δφ，Δθ，Δη，ΔκThe updating steps of the iteration step of the Lagrange multiplier are respectively as follows:
206. bringing the connection conditions of the unmanned aerial vehicles and the user clusters in all time slots into the resource allocation model with the system and the maximized rate, and obtaining D2D transmitting power by using an exponential transformation and continuous convex approximation method;
using the continuous convex approximation method can result in:
wherein ,
is shown onThe value is taken in one iteration,
due to the fact that
In
The coupling relationship with adjacent interference causes the problem to remain a non-convex one by introducing a relaxation variable
Can further obtain:
wherein ,
definition of
Can obtain
With respect to D2D transmit power
The problem of (a) can be described as:
s.t.
definition of
By using Lagrangian functions
wherein ,
λk,m≥0，
and ξk,n,mLagrange multipliers are greater than or equal to 0. According to the KKT conditions, can be obtained
Analytic solution of (2):
similarly, the analytic solution of the auxiliary variable is:
defining Delta based on the sub-gradient methodλ，
ΔξFor the Lagrange multiplier iteration step size, a Lagrange multiplier updating step can be obtained:
207. bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the unmanned aerial vehicle track by using a variable replacement method;
due to the fact that
About unmanned aerial vehicle orbit qnIs a convex function obtained by a first-order Taylor approximation based on a continuous convex approximation method
Lower bound of (d):
wherein ,
the problem with drone trajectories can be approximated as:
s.t.C1:||qn-qn-1||2≤(VmaxT)2
the optimization problem is a convex optimization problem that can be solved using standard convex optimization methods.
208. And calculating the system and the speed according to the obtained unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power.
209. And if the system and the speed reach convergence, taking the maximum value of the system and the speed obtained by exhaustive search as a final solution, and performing 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 a user scheduling factor alpha by an exhaustive search methodk,nAnd 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 final solutions, the corresponding other variables are optimized solutions, and the corresponding network resource allocation can be completed by using the optimized solutions.
The application effect of the present invention will be described in detail with reference to the simulation.
1) Simulation conditions
Suppose there are two cellular users in the system, and the horizontal coordinates are
And
each cellular user shares time resources with two pairs of D2D users, and the position coordinates of the D2D transmitter are respectively
The flight period T of the unmanned aerial vehicle is 2s, and the maximum flight speed Vmax100m/s, number of time slots N2, flying height H100 m, unit gain β per meter channel0-30dB, LoS link channel fading index
NLoS link channel fading index
The energy conversion efficiency rho is 0.8, the noise of a honeycomb user is-45 dBm/Hz, the noise of a D2D user is-130 dBm/Hz, the lowest rate requirement of the honeycomb user is 0.2bits/s/Hz, the lowest rate requirement of the D2D user is 0.1bits/s/Hz, the maximum transmitting power of the unmanned aerial vehicle is 1W, and the total energy consumption E of the transmitting power in the flight period is N (P)max/2)J。
2) Simulation result
In this embodiment, fig. 4 shows a rate convergence diagram of the algorithm proposed by the present invention. Figure 5 shows a graph of system and rate versus D2D minimum rate threshold for different cellular users minimum rate thresholds. Fig. 6 shows a plot of system and velocity versus drone altitude at different numbers of D2D users and different D2D user minimum velocity thresholds. Fig. 7 shows a comparison graph of the algorithm proposed by the present invention with the average power algorithm, the average time algorithm and the no energy collection scenario algorithm. Fig. 4 shows that the algorithm of the present invention has good convergence, and more time resources are allocated to D2D users while satisfying the minimum rate threshold of cellular users. Fig. 5 shows that the system and rate decrease as the D2D user minimum rate threshold increases, due to the channel difference between the two pairs of D2D devices. To maximize D2D and rate, allocating more power to D2D users with better channel conditions may contribute more to the increase in D2D and rate. Fig. 6 shows that the system and speed decrease with increasing flying height of the drone, due to the channel condition between the drone and the ground device becoming worse, and increase with increasing number of D2D, at the same flying height of the drone and minimum speed threshold value of D2D, to compensate to some extent for the system and speed loss caused by increasing flying height of the drone. Figure 6 shows that as the UAV maximum transmit power increases, both the system and the rate are increasing. It can be seen that the performance of the invention is significantly better than that of the average power algorithm and the average time algorithm, because the invention increases an optimization degree of freedom compared with the two algorithms, and has significant contribution to the system and speed improvement. Compared with a network without energy collection, the method provided by the invention has the advantages that the radio frequency signal of the unmanned aerial vehicle is converted into the energy signal at the D2D equipment end, so that the operation time of the D2D can be effectively prolonged, the system and the speed are increased, and the spectrum utilization efficiency is improved.
In the description of the present invention, it is to be understood that the terms "coaxial", "bottom", "one end", "top", "middle", "other end", "upper", "one side", "top", "inner", "outer", "front", "center", "both ends", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "disposed," "connected," "fixed," "rotated," and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method, the method comprising:
the unmanned aerial vehicle provides energy for the cellular users in the user cluster to perform downlink data transmission, and the D2D users in the user cluster communicate by using the collected energy, so that a transmission model is established;
respectively constructing channel models of the unmanned aerial vehicle to the cellular user and the D2D user and channel models between the D2D users;
establishing a resource allocation model with a system and a maximized rate by combining the minimum rate requirement of cellular users and D2D users, energy collection constraint and unmanned aerial vehicle transmission power constraint;
traversing the connection conditions of the unmanned aerial vehicle and the user cluster in all time slots by using an exhaustive search method, bringing the connection conditions into a resource allocation model with maximized system and rate, and calculating to obtain the unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time, the D2D transmitting power calculation system and the rate;
and if the system and the speed reach convergence, taking the maximum value of the system and the speed obtained by exhaustive search as a final solution, and performing 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.
2. The unmanned-vehicle-assisted wireless energy-carrying D2D network resource allocation method according to claim 1, wherein the transmission model comprises a unmanned vehicle serving as a mobile base station and K user clusters, each user cluster comprises a cellular user and MkD2D user pairs, D2D users adopt an overlay spectrum multiplexing mode, and share orthogonal time resources with cellular users; unmanned plane TmaxFor the flight cycle, split into N time slots that length is T with unmanned aerial vehicle's flight cycle, fly at fixed altitude H, unmanned aerial vehicle provides the energy for the cell user in the first stage of a certain time slot and for downlink data serviceThe D2D user collecting energy from the cellular user; the D2D user communicates in a second phase using the energy collected in the first phase.
3. The unmanned-vehicle-assisted wireless energy-carrying D2D network resource allocation method according to claim 1, wherein each channel model is represented as:
wherein ,
representing the channel gain between the drone and the cellular user;
represents the channel gain between drone to D2D user pair;
represents the channel gain between the D2D user pairs; beta is a0A channel gain representing a unit distance; q. q.snHorizontal coordinates representing the unmanned aerial vehicle at the nth time slot;
representing cellular user coordinates;
for D2DThe coordinates of the user; h represents the flight height of the unmanned aerial vehicle;
represents the distance between the D2D transmitter and receiver;
representing a path loss exponent for a non-line-of-sight link;
representing rayleigh fading between D2D users.
4. The unmanned-plane-assisted wireless energy-carrying D2D network resource allocation method according to claim 1, wherein the system and rate-maximized resource allocation model are expressed as:
s.t.C1:||qn-qn-1||2≤(VmaxT)2
C5:0≤τk,n≤T
wherein ,qnHorizontal coordinates representing the unmanned aerial vehicle at the nth time slot; alpha is alphak,nIndicating that the unmanned plane serves a user cluster k at the nth time slot; tau isk,nIndicating the time length of the cellular user k served by the unmanned plane at the nth time slot;
indicating the transmitting power of the unmanned plane to a cellular user k in the nth time slot;
represents the transmission power of the m-th pair of D2D in the k-th D2D cluster in the nth time slot; n represents the number of time slots in one flight cycle of the drone; k represents the number of user clusters;
indicating the transmission rate of a cellular user in the kth user cluster in the nth slot;
the transmission rate of the D2D user pair in the kth user cluster at the nth time slot; c1Representing unmanned aerial vehicle mobility constraints, VmaxIs the maximum flight speed; c2For the cellular user minimum rate requirement constraint,
representing the channel gain between the nth time slot and the unmanned aerial vehicle of the cellular user in the kth user cluster;
representing the noise of cellular users in the kth user cluster in the nth slot;
a minimum rate threshold for a cellular user; c3For the D2D user minimum rate requirement constraint,
indicating the transmission rate of the m-th pair of D2D users in the kth user cluster at the nth time slot; wherein
T denotes the length of the time slot,
indicating the channel gain at the nth time slot between the mth pair of D2D users in the kth user cluster,
indicating co-channel interference between D2D,
a minimum rate threshold for the D2D user; c4Definition of alphak,nWith 1, UAV serves a user cluster k at the nth slot, otherwise αk,n＝0；C5Duration constraints representing drone serving cellular users；C6Representing a maximum transmit power constraint, P, of the dronemaxRepresenting a maximum transmit power of the drone; c7Representing the energy consumption constraint for signal transmission by the drone during the flight period, E representing the energy that the drone can provide; c8Representing a D2D transmit power constraint, wherein
For the energy collection formula, ρ represents the energy conversion efficiency,
indicating the channel gain between the nth slot and the drone for the mth pair D2D of users in the kth user cluster.
5. The unmanned aerial vehicle-assisted wireless energy-carrying D2D network resource allocation method according to claim 1 or 4, wherein the process of calculating the unmanned aerial vehicle transmission power, the unmanned aerial vehicle position, the transmission time, the D2D transmission power calculation system and the speed comprises:
s41: bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the transmission time and the unmanned aerial vehicle transmitting power by using a variable replacement method;
s42: bringing the connection conditions of the unmanned aerial vehicles and the user clusters in all time slots into the resource allocation model with the system and the maximized rate, and obtaining D2D transmitting power by using an exponential transformation and continuous convex approximation method;
s43: bringing the connection condition of the unmanned aerial vehicle and the user cluster in all time slots into the resource allocation model with the system and the maximized rate, and obtaining the unmanned aerial vehicle track by using a variable replacement method;
s44: and calculating the system and the speed according to the obtained unmanned aerial vehicle transmitting power, the unmanned aerial vehicle position, the transmission time and the D2D transmitting power.
6. The unmanned-assisted wireless energy-carrying D2D network resource allocation method according to claim 5, wherein the transmission time and the unmanned aerial vehicle transmission power obtained by the variable substitution method comprise:
using variable substitution methods, defining
Will be related to the transmission time τk,nAnd unmanned aerial vehicle transmitting power
The optimization problem sub-problem is converted into a convex optimization problem, and an analytic solution is obtained by utilizing a Lagrangian dual method
wherein ,βkRepresents constraint C2Determined lagrange multiplier, thetak,nRepresents constraint C6Determined lagrange multiplier, η representing constraint C7Determined lagrange multiplier, κk,n,mRepresents constraint C8Determining Lagrange multiplier, N is the number of time slots,
7. the unmanned-assisted wireless energy-carrying D2D network resource allocation method according to claim 5, wherein the D2D transmission power obtained by exponential transformation and successive convex approximation method comprises:
by using a continuous convex approximation method to obtain
wherein ,
to represent
The last iteration of the value of (a) is taken,
and introducing a relaxation variable
To obtain
wherein ,
then through an exponential transformation method
Transmitting D2D power
The optimization sub-problem is converted into a convex optimization problem, and an analytic solution is obtained by utilizing a Lagrange dual method:
wherein ,λk,mRepresents constraint C3The determined lagrangian multiplier is used,
represents constraint C8Definite Lagrange multiplier xik,n,mRepresents the introduction of a relaxation variable and
obtaining constraints after conversion
N is the number of slots.
8. The unmanned-aerial-vehicle-assisted wireless energy-carrying D2D network resource allocation method according to claim 5, wherein the unmanned aerial vehicle trajectory obtained by using the variable substitution method comprises:
using variable substitution methods
wherein ,
the unmanned aerial vehicle trajectory optimization problem is converted into a convex optimization problem,
values are taken for the unmanned aerial vehicle track of the last iteration,
β0representing the channel gain per unit distance.
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