CN114980205A - QoE (quality of experience) maximization method and device for multi-antenna unmanned aerial vehicle video transmission system - Google Patents
QoE (quality of experience) maximization method and device for multi-antenna unmanned aerial vehicle video transmission system Download PDFInfo
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Abstract
The application relates to a QoE maximization method and a QoE maximization device for a multi-antenna unmanned aerial vehicle video transmission system, wherein a multi-antenna unmanned aerial vehicle video stream transmission framework is constructed, the interference among a plurality of ground users is eliminated by applying beam forming based on ZF, and video services are simultaneously provided for the plurality of ground users; the method comprises the steps of maximizing the minimum value of all ground user QoEs through joint optimization of unmanned aerial vehicle tracks, multi-antenna transmission scheduling and playing rate distribution, and determining a mathematical optimization model P1 and constraint conditions according to a minimum value model maximizing all user QoEs on the basis of optimization of unmanned aerial vehicle tracks, multi-antenna transmission scheduling and video playing rate distribution. The method and the device convert a mathematical optimization model which is difficult to obtain a global optimal solution into an equivalent form with a Penalty term, adopt a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) to process the converted optimization model to obtain a suboptimal solution, only need several iterations to obtain the suboptimal solution, and have good convergence and low complexity.
Description
Technical Field
The application relates to the field of wireless communication, in particular to a QoE (quality of experience) maximization method and device for a multi-antenna unmanned aerial vehicle video transmission system.
Background
With the rapid development of smart phones and 4G/5G cellular networks, applications of mobile videos such as Virtual Reality (VR), video monitoring, Ultra-High-Definition (UHD) videos, and the like are rapidly emerging. Although current mobile networks provide a large amount Of bandwidth, high Quality and smooth video streaming cannot be guaranteed, thereby degrading the Quality Of Experience (QoE) Of the user. Therefore, it is very important to improve QoE of users in video streaming under limited wireless network resources.
In recent years, Unmanned Aerial Vehicles (UAVs) have been utilized as Aerial base stations, establishing higher quality wireless connections with ground users, and extending communication coverage. But traditional unmanned aerial vehicle only has single antenna, can only communicate with a ground user at the same moment, and this can lead to unmanned aerial vehicle's flying distance to lengthen, increases unmanned aerial vehicle's energy consumption. In order to solve the problem, the following research loads a plurality of antennas on the unmanned aerial vehicle, and makes full use of spatial multiplexing gain and multi-antenna beam forming gain, so that the unmanned aerial vehicle can communicate with a plurality of ground users simultaneously.
However, none of the existing research on multi-antenna drones considers video streaming applications with user QoE requirements. The user QoE may be affected by various factors such as video quality and play jitter. Due to the high flexibility of the unmanned aerial vehicle, large-amplitude bandwidth fluctuation often occurs during video stream transmission supported by the unmanned aerial vehicle, so that it is very important to ensure smooth playing (reducing playing jitter) of the video. Therefore, the video quality and the playing jitter in the multi-user video streaming system supported by the multi-antenna unmanned aerial vehicle are balanced.
Disclosure of Invention
The application provides a method and a device for maximizing experience quality in a multi-antenna unmanned aerial vehicle video transmission system, so that the video quality is improved, the video playing fluctuation is reduced, and the fairness among different ground users is ensured.
The technical scheme of the application is as follows:
according to a first aspect of embodiments of the present application, there is provided a method and an apparatus for maximizing QoE of a multi-antenna drone video transmission system, the method including:
constructing a multi-antenna unmanned aerial vehicle video stream transmission structure, and eliminating the interference among a plurality of ground users by applying beam forming based on ZF (zero frequency warping) so as to simultaneously provide video service for the plurality of ground users;
and constructing a channel model according to the small-scale fading and the large-scale channel power gain, and obtaining the reachable rate under the worst condition by adopting a lower bound expression.
Determining a quality of experience (QoE) model according to the video playing rate and the video jitter;
determining a mathematical optimization model P1 and constraint conditions according to a minimum model for maximizing QoE of all users based on the optimized unmanned aerial vehicle track, multi-antenna transmission scheduling and video playing rate distribution;
and converting the mathematical optimization model into an equivalent form with a Penalty term, and processing the converted optimization model by adopting a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) to obtain a suboptimal solution.
Optionally, the minimum model of QoE of all users specifically includes:
where N is the slot sequence number, N is the total number of slots in the total time range T, ρ is a weighting factor that balances video quality and video jitter, θ and β are constant parameters that depend on the specific application, r k [n]Representing user u k The video playback rate at time slot n,
is with user u k Screen size related required playback rate.
Optionally, the mathematical optimization model P1 is:
and the constraint conditions specifically include:
q[1]＝q[N], (14)
wherein q [ n ]]Representing the horizontal position of the drone at time slot n,
r k [n]representing user u k Video playback rate at time slot n, a k [n]Represents the transmission schedule between the unmanned aerial vehicle and the ground user, delta is the time slot time length,
user u for time slot τ k The reachable rate of (K) is the total number of users connected to the same drone, V max Representing the maximum horizontal velocity of the drone, and the constraint (11) representing that the drone provides service to the user periodically.
Optionally, the mathematical optimization model is converted into an equivalent form with a penalty term, and the converted optimization model is processed by using a P-BCD-based dual-cycle algorithm to obtain a suboptimal solution, specifically including
Adding a penalty item into the mathematical problem model, and converting the model into a second penalty problem model P2 containing a penalty parameter lambda which is greater than zero;
in an outer loop, continuously updating a penalty parameter lambda to amplify the penalty brought by not meeting the equality constraint until convergence;
in the first internal cycle, a Block Coordinate Descent (BCD) method, a ConCave-Convex process (CCCP) method and a Successive Convex Approximation (SCA) method are adopted to approximately solve a given penalty parameter lambda l Second penalty problem model P2.
Optionally, the second penalty problem model P2:
and the constraint conditions are formulas (5) to (8), and formulas (10) to (12)
Wherein q [ n ]]Representing the horizontal position of the drone at time slot n,
r k [n]representing user u k Video playback rate at time slot n, α k [n]Indicating nobodyAnd (3) scheduling transmission between the unmanned aerial vehicle and the ground user, wherein K is the total number of users connected with the same unmanned aerial vehicle, N is the total number of discrete time slots with the length delta in the total time range T, and lambda is a punishment parameter larger than zero.
Optionally, the second penalty problem model P2 is decomposed into two submodels, which are a playback rate assignment and drone trajectory optimization submodel under the given transmission schedule, and a transmission schedule optimization submodel under the given playback rate assignment and drone trajectory, respectively.
Optionally, the first mathematical optimization model is solved by using a first algorithm of P-BCD, and the specific process is as follows:
initialization
λ 0 >0 and c>1, setting the iteration number l of the outer loop to be 0;
in an outer loop, continuously updating a penalty parameter lambda to amplify the penalty brought by not meeting the equality constraint until the final convergence;
in the inner circulation, at a given local point
Next, the problem (P2) is solved using BCD, CCCP, and SCA techniques to obtain
Wherein, setting a first inner circulation parameter: lambda [ alpha ] l+1 ＝cλ l (ii) a Second inner loop parameters: l +1 until the amplification proportion of the target value of the second penalty problem model P2 is lower than a preset threshold value epsilon>0。
Optionally, the inner loop in the first algorithm is a second algorithm, and specifically includes:
at initialization
After the iteration number r of the inner loop is set to be 0, the inner loop is carried out until the target value of the second penalty problem model P2 is converged; the internal circulation includes:
initialization
Setting the iteration number r of the inner loop to be 0;
at a given local point
Next, a fourth penalty problem model P4 is solved to obtain
At a given local point
Next, a seventh mathematical optimization model P7 is solved to obtain
Setting r to r +1, and performing inner loop until the target value of the second penalty problem model P2 converges;
wherein the fourth penalty problem model P4 is generated by the second penalty problem model P2 at a given transmission schedule { a } k [n]Lower simplification to obtain a third penalty problem model P3, which is approximated by the third penalty problem model P3, and the target value of the fourth penalty problem model P4 is the lower bound of the target value of the third penalty problem model P3
And the seventh mathematical optimization model P7 is represented by the second penalty problem model P2 at a given drone trajectory { q [ n ]]And video playback rate r k [n]Reduce to a fifth penalty problem model P5, introduce a slack variable y k [n]And obtaining a sixth mathematical optimization model P6, and then obtaining by conversion of the sixth mathematical optimization model P6, wherein the target value of the seventh mathematical optimization model P7 is the lower bound of the sixth mathematical optimization model P6.
According to a second aspect of the embodiments of the present application, there is provided an apparatus for maximizing quality of experience in a multi-antenna drone video transmission system, including:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of maximizing quality of experience in a multi-antenna drone video transmission system of any one of the methods mentioned in the first aspect above.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the method comprises the steps of constructing a multi-antenna unmanned aerial vehicle video stream transmission framework, and eliminating interference among a plurality of ground users by using beam forming based on ZF (zero frequency warping) so as to simultaneously provide video services for the plurality of ground users; the method comprises the steps of maximizing the minimum value of all the ground user QoE through joint optimization of unmanned aerial vehicle tracks, multi-antenna transmission scheduling and playing rate distribution, and determining a mathematical optimization model P1 and constraint conditions according to the minimum value model maximizing all the user QoE on the basis of optimization of unmanned aerial vehicle tracks, multi-antenna transmission scheduling and video playing rate distribution. The method and the device convert a mathematical optimization model which is difficult to obtain a global optimal solution into an equivalent form with a Penalty term, and process the converted optimization model by a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) to obtain a suboptimal solution.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application and are not to be construed as limiting the application.
Fig. 1 is an architectural diagram illustrating the construction of a downlink system supporting drone communications in accordance with an exemplary embodiment;
fig. 2 is a flow diagram illustrating a method of maximizing quality of experience in a multi-antenna drone video transmission system in accordance with an exemplary embodiment;
FIG. 3 is an exemplary illustration of a P-BCD framework incorporating a penalty method according to an exemplary embodiment;
fig. 4 is a schematic diagram illustrating the convergence of the mathematical optimization model when T is 100s, M is 15, and ρ is 0.01, according to an exemplary embodiment.
Detailed Description
In order to make the technical solutions of the present application better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The application scenario described in the embodiment of the present application is for more clearly illustrating the technical solution of the embodiment of the present application, and does not form a limitation on the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that with the occurrence of a new application scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems. In the description of the present application, the meaning of "a plurality" means more than one unless otherwise stated.
In this context, the N × M dimensional complex and real matrix sets are used respectively
And
denotes that the kth diagonal element of the square matrix S is represented by [ S] k,k Representing transpose and conjugate of matrix MTranspose respectively by M T And M H And (4) showing.
As shown in fig. 2, the method for maximizing QoE of a multi-antenna unmanned aerial vehicle video transmission system provided by the present application specifically includes:
step 1: a multi-antenna unmanned aerial vehicle video streaming framework is constructed, and beam forming based on ZF is applied to eliminate interference among a plurality of ground users, so that video service is provided for the ground users at the same time.
As shown in fig. 1, in the unmanned aerial vehicle video streaming architecture of the present application, a video streaming system is created, which is composed of K single-antenna ground users and an unmanned aerial vehicle with M antennas. Terrestrial user set of users
Denotes u k The coordinates in a three-dimensional Cartesian coordinate system are
Wherein g is k ＝[x k ,y k ] T Represents u k K is more than or equal to 1 and less than or equal to K at the horizontal position on the ground.
The video data requested by the user is stored on a drone configured with a Uniform Rectangular Array (URA) containing M antennas, where adjacent antennas are evenly dispersed in the vertical and horizontal directions so that the drone can transmit video data to multiple ground users simultaneously.
In some exemplary embodiments, the total time range T is discretized into N time slots of length δ, where δ is small enough that the position of the drone can be considered to be invariant within each time slot. To ensure flight safety, z [ n ] is required]≥H min ,
Wherein H min Associated with the highest obstacle of the service area. Thus, slot n time drone and groundUser u k The distance between can be expressed as:
wherein,
and z [ n ]]Respectively, the horizontal and vertical positions of the drone at time slot n, thus [ q [ n ]] T ,
Representing the three-dimensional coordinates of the drone at time slot n.
In some exemplary embodiments, the binary variable a k [n]E {0,1} represents the transmission schedule between the drone and the terrestrial user. In particular, if the drone is in slot n to u k Transmitting video, then a k [n]1, otherwise a k [n]＝0。
In some exemplary embodiments, the sets
Representing a set of users communicating simultaneously with the drone at time slot n, then
Wherein,
that is, the unmanned aerial vehicle can simultaneously move towards K through a plurality of antennas n Each user transmits an independent signal.
Further, in the above-mentioned case,
and
representing video streams separatelyInput information symbols and users
Corresponding beamforming vector at time slot n, wherein
Thus, with w k [n]s k [n]Represents the direction of travel u k The transmission signal of (1).
Step 2: and constructing a channel model according to the small-scale fading and the large-scale channel power gain, and obtaining the reachable rate under the worst condition by adopting a lower bound expression.
In this application, let h be k [n]For slot n unmanned aerial vehicle and u k Inter-base-band equivalent complex channel coefficient, then
Wherein,
representing small scale fading, beta k [n]Representing the large scale channel power gain. In particular, the method comprises the following steps of,
unmanned aerial vehicle and u with large-scale channel power gain being formed by time slots n k Distance d of k [n]Determination of, i.e.. beta.) k [n]＝β 0 d k [n] α Wherein, β 0 And α represents a channel power gain and a path loss exponent at 1 meter, respectively.
Next, a Rice fading model is used to characterize small-scale fading, i.e.
Wherein G represents a Rice index,
representing line-of-sight channel components, θ k,m [n]Representing the mth antenna of the unmanned plane and the user u at the time slot n k The phase of the line-of-sight path between,
representing the random scatter component.
In some exemplary embodiments, user u is at slot n k The received signal may be expressed as:
wherein
Representing additive white Gaussian noise, σ 2 Receiving user u for time slot n k The noise power of (d).
And, a ground user u k The received Signal-To-Noise Ratio (SNR) at slot n can be expressed as
Wherein,
p denotes drone transmission power.
Thus, slot n time u k The achievable rate of can be expressed as R k [n]＝Blog 2 (1+Γ k [n]) Where B is the channel bandwidth in Hertz (Hertz, Hz).
Note that R is the random channel coefficient k [n]Is a random variable and therefore takes into account mainly the average rate, i.e.
Due to the fact that
In the expectation ofThe existence of operations, in general, is difficult to obtain
A closed form expression of (c). However, the derivation may be based on the independence of the line of sight component and the random scatter component in the Rice channel model
Is a strict lower bound, i.e.
Wherein
Representing the reference received SNR at 1 meter. In the following resource allocation and trajectory design, this lower bound expression is employed to obtain the worst case achievable rate, namely:
note that in equation (2), to achieve maximum achievable rate, the flying height of the drone should always be H min I.e. z [ n ]]＝H min ,
And step 3: and determining the user experience quality of a single user according to the video playing speed and the video jitter.
The application adopts Dynamic Adaptive Streaming Over HTTP (DASH) for video transmission, and the video rate can dynamically adapt to different channel conditions. It is assumed that the user has a sufficiently large cache to store the received video data. In order to avoid video playing pause, a video playing cause and effect constraint condition is introduced:
wherein r is k [n]Representing user u k The video playback rate at time slot n, delta the time length of the time slot,
user u for time slot τ k The left side in inequality (3) represents the amount of video data that has been received up to time slot n-1, and the right side represents the amount of video data that has been played up to time slot n.
In inequality (3), it is assumed that a video processing delay such as data decoding, playback preparation, etc. requires one time slot, and let r be k [1]＝0,
a k [N]＝0,
The user does not play the video in the first time slot, and the unmanned aerial vehicle does not transmit the video in the last time slot.
The method and the device take the QoE of the user as a performance index, and the QoE of the user mainly depends on the quality of the received video and the video jitter. Generally, video quality increases with increasing playback rate, and saturates when the playback rate is sufficiently large. Thus, a logarithmic function with diminishing returns characteristic is employed
Come to depict user u k Video quality at time slot n, and u is characterized by the variance of the current playback rate and the average playback rate k Play jitter in time slot n, i.e.
To sum up, user u k The total QoE of is expressed as:
where p is a weighting factor that balances video quality and video jitter, theta and beta are constant parameters depending on the particular application,
is with user u k Screen size related required playback rate.
And 4, step 4: and determining a mathematical optimization model P1 according to a minimum model for maximizing QoE of all users based on the optimized unmanned aerial vehicle track, multi-antenna transmission scheduling and video playing rate distribution.
In this application, according to formula (4), the minimum value of the total QoE of all users is determined as:
n is the slot sequence number, N is the total number of slots in the total time range T, p is the weighting factor that balances video quality and video jitter, θ and β are constant parameters depending on the specific application, r k [n]Representing user u k The video playback rate at time slot n,
is with user u k Screen size related required playback rate.
The optimization problem is expressed as the following mathematical optimization model P1:
the constraint conditions of the mathematical optimization model are as follows:
q[1]＝q[N], (25)
wherein, V max Representing the maximum horizontal velocity of the drone, and the constraint (11) representing that the drone provides service to the user periodically.
And 5: and converting the mathematical optimization model into an equivalent form with a Penalty term, and processing the converted optimization model by adopting a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) to obtain a suboptimal solution.
In the present application, the mathematical optimization model P1 includes a binary variable a k [n]And a non-convex constraint condition (6), under the condition that the global optimal solution of the mathematical optimization model is difficult to obtain, firstly converting the model into an equivalent form with a Penalty term, and then processing the converted optimization problem by adopting a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) so as to obtain a suboptimal solution. That is, the penalty parameter is updated at the outer loop to increase the penalty value generated by violating the equality constraint, and the penalty parameter is updated at the inner loop by the alternate optimization, the Block Coordinate Descent (BCD) method, the ConCave-Convex process (CCCP) method, and the Successive Convex approximation (Successive Convex approximation)ion, SCA) method to solve the penalty optimization problem of given penalty parameters.
Specifically, as shown in fig. 3, an exemplary P-BCD framework including a penalty method is shown.
And 51, adding a penalty term to the mathematical problem model, and converting into a model for solving a second penalty problem, wherein the second penalty problem model comprises a penalty parameter lambda which is greater than zero.
In the outer loop, the penalty parameter λ is continuously updated to amplify the penalty due to the unsatisfied equality constraint until the final convergence, step 52.
Step 53, in the first internal cycle, adopting BCD, CCCP and SCA technologies to approximately solve the given penalty parameter lambda l Second penalty problem model P2.
Specifically, to solve the binary constraint (9), a penalty term is added to the mathematical problem model to penalize the effects of non-binary solutions. Specifically, constraint (9) is first converted to the intersection of the following regions:
this only requires proving that the constraint (9) is true if and only if both the constraint (12) and the condition (13) are true. Through the method, penalty terms are added in the mathematical problem model to penalty the condition that the equality constraint (13) is not satisfied, and the method is converted into a second penalty problem model P2:
wherein the constraints are (5) - (8), (10) - (12). In addition, λ is a penalty parameter greater than zero, and when λ is large enough (P2) the solution is equivalent to that of the original problem (P1). In the outer loop, the penalty parameter lambda is continuously updated to amplify the penalty caused by not meeting the equality constraint until the final convergence.
In the application, a first mathematical optimization model for solving problems by using a P-BCD through a first algorithm is specifically as follows:
step 61: initialization
λ 0 >0 and c>And 1, setting the iteration number l of the outer loop to be 0.
Step 62: in the outer loop, the penalty parameter λ is continuously updated to amplify the penalty incurred not satisfying the equality constraint until final convergence.
And step 63: in the inner circulation, at a given local point
Next, the problem (P2) is solved using BCD, CCCP, and SCA techniques to obtain
Wherein, setting a first inner circulation parameter: lambda [ alpha ] l+1 ＝cλ l (ii) a Second inner loop parameters: the amplification proportion of the target value from l to l +1 to the second penalty problem model P2 is lower than a preset threshold value epsilon>0。
Specifically, the second penalty problem model P2 is decomposed into two submodels, a playback rate assignment and drone trajectory optimization submodel for a given transmission schedule, and a transmission schedule optimization submodel for a given playback rate assignment and drone trajectory. The second penalty problem model P2 is solved by alternately iteratively optimizing the two sub-problems and maximizing their local strict lower bound.
Sub-model for distributing playing speed and optimizing unmanned aerial vehicle track
At a given transmission schedule a k [n]Next, the second penalty problem model P2 can be simplified to a third penalty problem model:
P3:
wherein the constraint conditions are equations (5) - (7), (10), (11).
In the present application, it is preferred that,
is about | q [ n]-g k ‖ 2 A convex function of (a). By pairs
Of (II) q [ n ]]-g k ‖ 2 Term at given local point q r [τ]Applying a first order Taylor expansion approximation, the following lower bound expression can be obtained:
wherein
Can be obtained by means of SCA technique
Replacement is to lower bound
Approximating the third penalty problem model P3 as a fourth penalty problem model P4:
wherein the constraint conditions are formulas (5) - (7), (10), (11)
That is, the fourth penalty problem model P4 is simplified by the second penalty problem model P2 to obtain a third penalty problem model P3, which is also approximated by the third penalty problem model P3.
Due to the fact that
Is about q [ n ]]The concave function of (a), in this case (P4), is a standard convex optimization problem that can be solved efficiently by means of a CVX solver. Furthermore, by replacing the lower rate bound expression, any feasible solution of the fourth penalty problem model P4 must be a feasible solution of the third penalty problem model P3, so the target value of the fourth penalty problem model P4 is the lower bound of the target value of the third penalty problem model P3.
(II) Transmission scheduling optimization submodel
In the summary of the application, at a given unmanned aerial vehicle trajectory { q [ n ]]And video playback rate r k [n]The second penalty problem model P2 can be simplified to a fifth penalty problem model P5:
wherein the constraint conditions are equations (6) - (8), (12).
The fifth penalty problem model P5 is still a non-convex optimization problem due to the presence of the non-convex constraint (6) and the non-concave objective function. To this end, a slack variable y is first introduced k [n]To get the following problem sixth mathematical optimization model P6:
wherein the constraint conditions are formulas (7), (8), (12),
in the present application, the equation (17) in the optimal solution of the sixth mathematical optimization model P6 must be satisfied, otherwise y can be always increased under the condition that the target value is not changed and other constraint conditions are still satisfied k [τ]Therefore, the fifth penalty problem model P5 is equivalent to the sixth mathematical optimization model P6.
The non-concave term on the left side of (16) is rewritten as a Difference-of-Convex (DC) function, i.e.
It is then approximately represented as a concave function by means of linearization based on the concept of CCCP.
In particular, at a given local point of the r-th iteration
Above, using a first order Taylor expansion
Approximately represents a convex function (a) k [τ]+y k [τ]) 2 To obtain
Therefore, the constraint (16) can be approximately expressed as
Non-concave objective functions can be treated in the same way:
in summary, the following approximation problem of the constraint (6), namely the seventh mathematical optimization model P7, can be obtained:
the constraint equations (7), (8), (12), (17), (19) are given.
In the present application, the seventh mathematical optimization model P7 is a standard convex optimization problem that can be solved efficiently using CVX tools. By replacing the above lower bound expression, any feasible solution of the seventh mathematical optimization model P7 is always a feasible solution of the sixth mathematical optimization model P6, and therefore, the target value of the seventh mathematical optimization model P7 is the lower bound of the sixth mathematical optimization model P6.
That is, the seventh mathematical optimization model P7 is modeled by the second penalty problem model P2 at a given drone trajectory { q [ n ]]And video playback rate r k [n]Reduce to a fifth penalty problem model P5, introduce a relaxation variable y k [n]And obtaining a sixth mathematical optimization model P6, and converting the sixth mathematical optimization model P6, wherein the target value of the seventh mathematical optimization model P7 is the lower bound of the sixth mathematical optimization model P6.
In this application, the inner loop in the first algorithm is a second algorithm, and specifically includes:
at initialization
After the iteration number r of the inner loop is set to be 0, the inner loop is carried out until the target value of the second penalty problem model P2 is converged; the internal circulation includes:
step 71: initialization
And setting the iteration number r of the inner loop to be 0.
Step 72: at a given local point
Next, a fourth penalty problem model P4 is solved to obtain
Step 73: at a given local point
Next, a seventh mathematical optimization model P7 is solved to obtain
R is set to r + 1.
In some exemplary embodiments, the present application shows a specific video streaming scenario, and after the model is optimized, the user experience quality value is obtained through simulation, so as to illustrate the improvement of the user experience quality after the present application uses the above embodiments.
The system comprises a multi-antenna unmanned aerial vehicle and 6 ground users which are randomly distributed in a square area with the side length of 800 meters. Each row on the URA that the unmanned aerial vehicle was equipped with has 3 evenly distributed antennas. Therefore, the number of antennas installed in the drone may be M — 3,6,9, and so on. We set the channel correlation coefficient to beta 0 ＝-60dB，α＝2，σ 2 -110dBm, P ═ 0.1W, B ═ 1MHz, QoE correlation coefficients set to θ ═ 0.8, β ═ 400,
algorithm-dependent parameter setting to λ 0 ＝1，c＝2，∈＝10 -4 And other parameters are set to H min ＝100m，δ＝1s，V max ＝50m/s。
As shown in fig. 4, the convergence of the proposed algorithm is given when T is 100s, M is 15, and ρ is 0.01. Wherein the small graph in fig. 4 illustrates the inner loop convergence process of the second algorithm and the large graph illustrates the outer loop convergence process of the first algorithm. It can be seen that the maximum-minimum QoE increases with the number of iterations, and converges after about ten iterations, which verifies the convergence and low complexity of the proposed algorithm.
ρ is a weighting factor that balances video quality and video quality fluctuation in the user QoE. Because the trajectory of the drone decreases with the increase of ρ, although the drone can utilize its own mobility to approach each user in turn with better channel quality to improve throughput and video quality, the drone inevitably keeps away from other users when approaching a certain user, thereby causing the video playing jitter of other users. Thus, an increase in ρ indicates that it becomes more important to avoid video jitter, where the drone trajectory is more severely constrained to avoid moving away from any one ground user to maintain stable video playback. Unless otherwise stated, ρ is set to 0.01 in the following tests.
Furthermore, as the number of antennas M increases, the drone will shrink in trajectory and approach the center of all ground users. This is because when a drone is equipped with more antennas, more ground users can be served simultaneously, so the drone tends to hover at the center of all users to provide stable video playback services. In addition, as the time T increases, the drone can make full use of its own mobility, freely providing higher quality and more stable video streaming service to each user.
In the application, for the spatial multiplexing gain brought by the mobility of the unmanned aerial vehicle and multiple antennas, the minimum value of all ground users QoE is maximized by jointly optimizing transmission scheduling, video playing rate and unmanned aerial vehicle track. In order to solve the difficult joint non-convex optimization problem, a double-layer iterative algorithm utilizing P-BCD, CCCP and SCA technologies is provided. Simulation results also prove that the QoE of the proposed algorithm is remarkably improved compared with that of a reference scheme, and the balance between the video quality of a user and the video playing jitter is disclosed.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
In some possible implementations, an electronic device according to the present application may include at least one processor, and at least one memory. Wherein the memory stores program code which, when executed by the processor, causes the processor to perform the operational data management method according to various exemplary embodiments of the present application described above in this specification. For example, the processor may perform steps as in an operational data management method.
Further, according to the system for maximizing quality of experience in the multi-antenna drone video transmission system according to the embodiment of the present application, the steps in the method for maximizing quality of experience in the multi-antenna drone video transmission system mentioned in the above embodiments may be performed.
In exemplary embodiments, the various aspects of a multi-antenna drone video transmission system QoE maximization method and apparatus provided herein may also be embodied in the form of a program product that includes program code for causing a computer device to perform the steps in the method for maximizing quality of experience in a multi-antenna drone video transmission system according to various exemplary embodiments of the present application described above in this specification, when the program product is run on the computer device.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable image scaling apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable image scaling apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable image scaling device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer implemented process such that the instructions which execute on the computer or other programmable device provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (9)
1. A method and a device for maximizing QoE (quality of experience) of a multi-antenna unmanned aerial vehicle video transmission system are disclosed, wherein the method comprises the following steps:
constructing a multi-antenna unmanned aerial vehicle video stream transmission structure, and eliminating the interference among a plurality of ground users by applying beam forming based on ZF (zero frequency warping) so as to simultaneously provide video service for the plurality of ground users;
constructing a channel model according to small-scale fading and large-scale channel power gain, and adopting a lower bound expression to obtain an achievable rate under the worst condition;
determining a user QoE model according to the video playing rate and the video jitter;
determining a mathematical optimization model P1 and constraint conditions according to a minimum model for maximizing QoE of all users based on the optimized unmanned aerial vehicle track, multi-antenna transmission scheduling and video playing rate distribution;
and converting the mathematical optimization model into an equivalent form with a Penalty term, and processing the converted optimization model by adopting a double-loop algorithm based on a Penalty Block Coordinate Descent method (P-BCD) to obtain a suboptimal solution.
2. The method according to claim 1, wherein the minimum model for all user QoE specifically comprises:
where N is the slot sequence number, N is the total number of slots in the total time range T, ρ is a weighting factor that balances video quality and video jitter, θ and β are constant parameters that depend on the specific application, r k [n]Representing user u k The video playback rate at time slot n,
is with user u k Screen size related required playback rate.
3. The method of claim 2, wherein the mathematical optimization model P1 is:
and the constraint conditions specifically include:
q[1]＝q[N], (7)
wherein q [ n ]]Representing the horizontal position of the drone at time slot n,
a k [n]represents the transmission schedule between the unmanned aerial vehicle and the ground user, delta is the time slot time length,
user u for time slot τ k K is the total number of users connected to the same drone, V max Representing the maximum horizontal velocity of the drone, and the constraint (11) representing that the drone provides service to the user periodically.
4. The method according to claim 3, wherein the mathematical optimization model is converted into an equivalent form with a penalty term, and the converted optimization model is processed by a P-BCD-based double-loop algorithm to obtain a sub-optimal solution, specifically comprising:
adding a penalty item to the mathematical problem model, and converting the model into a second penalty problem model P2 for solving a penalty parameter lambda which is greater than zero;
in an outer loop, continuously updating a penalty parameter lambda to amplify the penalty brought by not meeting the equality constraint until convergence;
in the first internal cycle, a Block Coordinate Descent (BCD) method, a ConCave-Convex process (CCCP) method and a Successive Convex Approximation (SCA) method are adopted to approximately solve a given penalty parameter lambda l Second penalty problem model P2.
5. The method according to any of the claims 4, wherein the second penalty problem model P2 is specifically:
and the constraint conditions are formulas (5) to (8), and formulas (10) to (12)
Wherein q [ n ]]Representing the horizontal position of the drone at time slot n,
r k [n]representing user u k Video playback rate at time slot n, a k [n]And the transmission scheduling between the unmanned aerial vehicle and the ground user is represented, K is the total number of users connected with the same unmanned aerial vehicle, N is the total number of time slots in the total time range T, and lambda is a punishment parameter larger than zero.
6. The method of claim 5, wherein the second penalty problem model P2 is decomposed into two submodels, a playback rate assignment and a drone trajectory optimization submodel for a given transmission schedule, and a transmission schedule optimization submodel for a given playback rate assignment and drone trajectory.
7. The method according to claim 3, wherein the first mathematical optimization model is solved by a first algorithm using P-BCD as follows:
initialization
λ 0 >0 and c>1, setting the iteration number l of the outer loop to be 0;
in an outer loop, continuously updating a penalty parameter lambda to amplify the penalty brought by not meeting the equality constraint until the final convergence;
in the inner circulation, at a given local point
Next, the problem (P2) is solved using BCD, CCCP, and SCA techniques to obtain
Wherein, setting a first inner circulation parameter: lambda [ alpha ] l+1 ＝cλ l (ii) a Second inner loop parameters: l +1 until the amplification proportion of the target value of the second penalty problem model P2 is lower than a preset threshold value epsilon>0。
8. The method according to claim 7, comprising in particular: the inner loop in the first algorithm is a second algorithm, and specifically comprises:
at initialization
After the iteration number r of the inner loop is set to be 0, the inner loop is carried out until the target value of the second penalty problem model P2 is converged; the internal circulation includes:
at a given local point
Next, a fourth penalty problem model P4 is solved to obtain
At a given local point
Next, a seventh mathematical optimization model P7 is solved to obtain
Setting r to r + 1;
wherein the fourth penalty problem model P4 is given by the second penalty problem model P2Transmission scheduling { a k [n]The lower simplification obtains a third penalty problem model P3, which is also approximated by the third penalty problem model P3, and the target value of the fourth penalty problem model P4 is the lower bound of the target value of the third penalty problem model P3.
And the seventh mathematical optimization model P7 is represented by the second penalty problem model P2 at a given drone trajectory { q [ n ]]And video playback rate r k [n]Reduce to a fifth penalty problem model P5, introduce a relaxation variable y k [n]Obtaining a sixth mathematical optimization model P6, and then obtaining the sixth mathematical optimization model P6 through conversion; the target value of the seventh mathematical optimization model P7 is the lower bound of the sixth mathematical optimization model P6.
9. The utility model provides a maximize quality of experience's device among many antennas unmanned aerial vehicle video transmission system which characterized in that includes:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement a method of maximizing quality of experience in a multi-antenna drone video transmission system according to any one of claims 1 to 8.
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