CN112996121A - U2U distributed dynamic resource allocation method for intra-cluster communication - Google Patents

Abstract

The invention provides a U2U distributed dynamic resource allocation method facing intra-cluster communication, which comprises the steps of constructing an IaS-U2U communication scene model and a communication flow; constructing an objective function of IaS-U2U communication to obtain a multi-objective optimization problem of IaS-U2U communication; decomposing the multi-objective optimization problem, and carrying out a UAV matching process by using the H-UAV and the R-UAV by adopting a classical GS algorithm to complete UAV pairing; according to the UAV pairing result, completing the position optimization of the R-UAV by adopting an SPO algorithm, and completing the solution of a multi-objective optimization problem; and according to the solving result, completing the distribution of the U2U distributed dynamic resources. The invention provides a U2U distributed dynamic resource allocation method facing intra-cluster communication, and provides a distributed U2U communication flow, which is suitable for IaS-U2U transmission in an off-line state and overcomes the defects that actual conditions such as UAV propulsion power neglect, UAV interference neglect and QoS constraint neglect in the existing U2U document.

Description

U2U distributed dynamic resource allocation method for intra-cluster communication

Technical Field

The invention relates to the technical field of communication, in particular to a U2U distributed dynamic resource allocation method for intra-cluster communication.

Background

With the rapid development of communication technology, people can enjoy high-speed and reliable mobile communication services in most land areas. However, in remote areas where the eNB cannot effectively cover, and in a scenario where the number of users in a cell is overloaded, the eNB is not sufficient to meet the communication requirements of all users. The UAV plays a remarkable role in expanding the wireless coverage of the eNB and improving the communication quality of cell users.

In recent years, UAVs are also gradually being put into new application scenarios and cover different needs of military and commercial applications. The role played by UAVs is also gradually changing from relaying assisted ground communications to users of air or ground network cells. UAVs have a variety of application scenarios including building planning, rescue implementation, cargo transportation, real-time monitoring, etc. The types of services supported by UAVs are also gradually going from singular to diverse, making the communications demand of the industry for UAVs escalating. However, the UAV is a cell user and is not necessarily able to obtain high communication quality. For example, when the UAV is responsible for ground monitoring and detection work, it may be in an area with poor signal coverage, and it is difficult for the UAV to upload acquired video information to the eNB in time. In addition, when search and rescue actions are performed in remote areas, the UAVs may be in an off-line state, and communication between the UAVs is not possible, so that it is difficult for the UAV network to perform search and rescue activities normally. The U2U technology can reduce the load of eNB and increase the capacity of the system, and thus becomes one of the key technologies for improving the UAV communication quality.

Due to the limited functionality of UAVs, it is difficult to support the above-mentioned complex and burdensome business requirements with only a single UAV. Compared with the traditional single UAV, the UAV cluster can efficiently and quickly complete various complex tasks. For example, in military scenarios, UAVs often perform patrol and monitoring tasks in a cluster. Therefore, in order to cope with the development trend of UAV system clustering, clustered UAVs become one of the important application scenarios considered by the U2U communication technology.

Another key issue in supporting U2U communications is the limited onboard energy of the UAV. To achieve reliable communication of U2U and to extend the flight time of the UAV cluster, efficient allocation of UAV cluster energy resources is needed. Unlike ground based devices, UAVs generate additional propulsion energy costs to maintain flight and support their movement. Therefore, optimization of the propulsion energy consumption also becomes a problem to be considered in the optimization design of the U2U communication system.

Many studies both at home and abroad take into account the communication and energy consumption problems of the UAVs. Wherein, Qiu et al [1] C.Qiu, Z.Wei, Z.Feng, and P.Zhang, "Backhau-aware projection optimization of fixed-wing UAV-mounted base station for coherent available wireless Service," IEEE Access, vol.8, pp.60940-60950,2020, doi:10.1109/ACCESS 2020.2983516, refers to the propulsion energy consumption model of fixed wing UAVs, and under the constraint of Quality of Service (QoS) considering UAV assisted wireless communication, constructs the problem of minimum propulsion energy consumption and jointly optimizes the trajectory and transmitting power of UAVs. However, this study only focuses on the case where the UAV hovers around a ground eNB, and the propulsion model is difficult to apply directly to a rotorcraft UAV. Although rotorcraft UAVs have a completely different propulsion principle than fixed-wing UAVs, they are more flexible. Aiming at the application scene of the rotor UAV, Zeng et al [2] Y.Zeng and R.Zhang, "Energy-efficiency UAV communication with project optimization," IEEE Transactions on Wireless Communications, vol.16, No.6, pp.3747-3760, Jun.2017, doi:10.1109/TWC.2017.2688328. Zhan et al [3] C.Zhan and H.Lai, "Energy minimization in internet-of-threads system based on rotary-wing UAV," IEEE Wireless Communications Letters, vol.8, No.5, pp.1341-1344, Oct.2019, doi:10.1109/LWC.2019.2916549. Ahmed et al [4] S.Ahmed, M.Z.Chowdhury, and Y.M.Jang, "Energy-efficiency UAV-to-user scheduling to maximum throughput in wireless networks," IEEE Access, vol.8, pp.21215-21225,2020, doi:10.1109/ACCESS.2020.2969357. UAV-assisted wireless communication scenarios with Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) are considered, the maximum throughput problem is constructed under the constraint of considering UAV propulsion Energy consumption, and UAV trajectory, transmission power and UAV speed are jointly optimized. It is noted that documents [2] to [4] both consider only the problem of position deployment or trajectory optimization of a single UAV, thus assuming that the UAV does not suffer interference from the rest of the equipment. However, in practical scenarios, the interference strength of the UAV varies as its location varies. In addition, in a cluster scene, if co-frequency networking is considered, the UAV cannot ignore interference from other UAVs in the same cluster. Therefore, in optimizing the UAV trajectory, it is more realistic to consider the impact of the interference. Based on this, Liu et al [5] A.Liu and V.K.N.lau, "Optimization of multi-UAV-aided Wireless network over a raw-routing channel model," IEEE Transactions on Wireless Communications, vol.18, No.9, pp.4518-4530, Sep.2019, doi:10.1109/TWC.2019.2926088. consider UAV assisted Wireless communication scenarios in the case of line of sight and non-line of sight, and consider interference from ground users, and design a SCA-based position Optimization algorithm. The algorithm may select a suitable location to deploy the UAV based on the interference of the ground user. However, this solution only optimizes the hover position of the UAV, does not optimize the trajectory of the UAV over a certain period of time, and ignores the energy consumption issue of the UAV during flight. It is noted that the above schemes mostly focus on UAV assisted wireless communication scenarios only, and are difficult to be directly applied to cluster U2U communication scenarios.

Currently, only a small amount of research is directed to the U2U communication scenario. Bellido-Manganell et al, among others, analyzed the potential application scenario of U2U technology and discussed the applicability of different media access schemes, forward error correction coding and modulation schemes to U2U data link design. Ranjha et al studied the problem of multi-hop UAV relay links delivering short ultra-reliable and low-latency (URLLC) instruction packets between Internet of Things (IoT) devices on the ground, and used a non-linear optimization method to minimize the probability of erroneous decoding. However, such schemes do not involve the resource allocation problem of the Media Access Control (MAC) layer. Du et al [6] b.du, r.xue, l.zhao, and v.c.m.Leung, "cosmetic graphic gateway for air-to-air and air-to-ground cognitive mapping," IEEE Transactions on Audio and Electronic Systems, doi:10.1109/TAES.2019.2958162. the spectrum sensing and sharing problem between U2U and cell users was studied assuming that U2U shares the same frequency resources with cell users for secondary users as a cell. However, in a cluster UAV scene, multiple UAVs compete for the same channel in a spectrum sensing and sharing manner, so that good fairness is not easily ensured, and a higher communication service requirement of a cluster UAV system is difficult to meet. Zhang et al [7] S.Zhang, H.Zhang, B.Di, and L.Song, "Cellular UAV-to-X Communications: design and optimization for multi-UAV networks," IEEE Transactions on Wireless Communications, vol.18, No.2, pp.1346-1359, Feb.2019, doi:10.1109/TWC.2019.2892131. However, this study only optimizes pairing between UAVs and does not involve optimization of trajectory and launch power of the UAVs. In practical scenarios, optimizing for the trajectory of the UAV may further improve the performance of the system. Wang et al [8] H.Wang, J.Wang, G.Ding, J.Chen, and J.Yang, "Completion time minimization for turning angle-constrained UAV-to-UAV communications," IEEE Transactions on vehicle Technology, vol.69, No.4, pp.4569-4574, April 2020, doi:10.1109/TVT.2020.2976938. the flight trajectory and launch power of the U2U pair are jointly optimized with the goal of minimizing the task Completion time. However, this solution only considers trajectory optimization for one U2U pair, neglecting interference from the remaining UAVs, and does not analyze the propulsion energy consumption problem during flight. It is noted that documents [6] to [8] strongly depend on a centralized processing mechanism of network nodes such as eNB, but when the cluster is in a offline state, it is difficult to find a centralized node to support the above scheme. Therefore, in the communication scenario of the cluster U2U, a distributed algorithm needs to be designed to deal with the problem of U2U communication in the offline state.

Disclosure of Invention

The invention provides a U2U distributed dynamic resource allocation method for intra-cluster communication, aiming at overcoming the technical defect that when a cluster is in an off-line state in the conventional U2U communication scene, a centralized node is difficult to find to support communication.

In order to solve the technical problems, the technical scheme of the invention is as follows:

a U2U distributed dynamic resource allocation method facing intra-cluster communication comprises the following steps:

s1: constructing an IaS-U2U communication scenario model and communication flow, wherein three kinds of UAVs are considered, including a central UAV at a cluster center location, i.e., a C-UAV, an assistor UAV that is actively providing U2U communication service, i.e., an H-UAV, and a requester UAV that requires U2U service, i.e., an R-UAV;

s2: constructing an objective function of IaS-U2U communication to obtain a multi-objective optimization problem of IaS-U2U communication;

s3: decomposing the multi-objective optimization problem, and carrying out a UAV matching process by using the H-UAV and the R-UAV by adopting a classical GS algorithm to complete UAV pairing;

s4: according to the UAV pairing result, completing the position optimization of the R-UAV by adopting an SPO algorithm, and completing the solution of a multi-objective optimization problem;

s5: and according to the solving result, completing the distribution of the U2U distributed dynamic resources.

In the scheme, the invention constructs a multi-objective optimization problem of transmission rate and propulsion power, and jointly optimizes parameters such as UAV matching and position. In order to solve the multi-objective optimization problem, the design provides a distributed communication process of the UAVs in the cluster, and the optimization problem is decomposed into a UAV pairing and position optimization process:

firstly, forming an effective U2U pair based on the similarity of content features between UAVs;

secondly, in order to solve the position optimization problem, the design provides a distributed algorithm based on Sequential Convex Approximation (SCA). The algorithm is divided into two stages, wherein the first stage constructs the problem of the maximum transmission rate, and a feasible solution meeting the constraint of the original problem is obtained by adopting an SCA algorithm according to the initial position of the UAV as a starting point; and in the second stage, the feasible solution obtained in the first stage is used as a starting point, and the local optimal solution of the original problem is obtained by adopting an SCA algorithm.

In the above scheme, the present invention provides an SCA-based Position Optimization (SPO) scheme in a cluster; the contributions of this scheme mainly include: aiming at an IaS-U2U communication scene which is considered less, different from a mechanism that the existing U2U document mainly depends on a centralized node, a distributed U2U communication flow is provided, and the distributed U2U communication flow is suitable for IaS-U2U transmission in an off-line (Out-of-Coverage, OOC) state; the defects that actual conditions such as UAV propelling power, interference among UAVs, QoS constraint and the like are ignored in the existing U2U literature are overcome, the influence of UAV mobility inside a cluster on performance is concerned, the transmission rate and the propelling power of the cluster UAV are considered in a combined mode, a multi-objective optimization problem with QoS constraint is constructed, and UAV matching and position are optimized in a combined mode; the maximum transmission rate problem is constructed and solved, and a starting point is provided for iterative solution of the multi-objective optimization problem by judging whether the multi-objective optimization problem has a feasible solution, so that a feasible starting point can be provided for an SCA algorithm; simulation results show that compared with other existing schemes, the scheme can obtain remarkable performance gain.

Compared with the prior art, the technical scheme of the invention has the beneficial effects that:

the invention provides a U2U distributed dynamic resource allocation method facing intra-cluster communication, provides a distributed U2U communication flow, is suitable for IaS-U2U transmission in an off-line state, and overcomes the defects that actual conditions such as UAV propulsion power neglected, UAV interference neglected and QoS constraint neglected in the existing U2U document.

Drawings

FIG. 1 is a schematic flow diagram of the process of the present invention;

fig. 2 is a schematic view of an IaS-U2U communication scenario in an embodiment of the present invention;

fig. 3 is a schematic diagram of a communication flow of IaS-U2U according to an embodiment of the present invention;

fig. 4 is a schematic diagram of design idea of IaS-U2U communication scheme in an embodiment of the present invention;

FIG. 5 is a schematic diagram of a comparison of positions before and after IaS-U2U communication optimization;

FIG. 6 is a schematic diagram of the impact of different cluster speeds and UAV protection distances on system performance;

fig. 7 is a graph comparing energy efficiency performance for different schemes.

Detailed Description

The drawings are for illustrative purposes only and are not to be construed as limiting the patent;

for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;

it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.

The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.

Example 1

As shown in fig. 1, a U2U distributed dynamic resource allocation method for intra-cluster communication is provided, which includes the following steps:

s1: constructing an IaS-U2U communication scenario model and communication flow, wherein three kinds of UAVs are considered, including a central UAV at a cluster center location, i.e., a C-UAV, an assistor UAV that is actively providing U2U communication service, i.e., an H-UAV, and a requester UAV that requires U2U service, i.e., an R-UAV;

s2: constructing an objective function of IaS-U2U communication to obtain a multi-objective optimization problem of IaS-U2U communication;

s3: decomposing the multi-objective optimization problem, and carrying out a UAV matching process by using the H-UAV and the R-UAV by adopting a classical GS algorithm to complete UAV pairing;

s4: according to the UAV pairing result, completing the position optimization of the R-UAV by adopting an SPO algorithm, and completing the solution of a multi-objective optimization problem;

s5: and according to the solving result, completing the distribution of the U2U distributed dynamic resources.

In the specific implementation process, the invention focuses on the resource allocation scheme design of a cluster U2U communication (Swarm U2U, S-U2U) system, and particularly takes air communication as a main application scenario. Specifically, the invention focuses on the communication scene of U2U (Intra-Swarm U2U, IaS-U2U) in the cluster, and designs a corresponding U2U communication resource scheduling algorithm by jointly considering the propulsion power of the UAV cluster.

In the specific implementation process, the method constructs a multi-objective optimization problem of transmission rate and propulsion power, and jointly optimizes parameters such as UAV matching and position. In order to solve the multi-objective optimization problem, the design provides a distributed communication process of the UAVs in the cluster, and the optimization problem is decomposed into a UAV pairing and position optimization process:

firstly, forming an effective U2U pair based on the similarity of content features between UAVs;

secondly, in order to solve the position optimization problem, the design provides a distributed algorithm based on Sequential Convex Approximation (SCA). The algorithm is divided into two stages, wherein the first stage constructs the problem of the maximum transmission rate, and a feasible solution meeting the constraint of the original problem is obtained by adopting an SCA algorithm according to the initial position of the UAV as a starting point; and in the second stage, the feasible solution obtained in the first stage is used as a starting point, and the local optimal solution of the original problem is obtained by adopting an SCA algorithm.

In a specific implementation process, the invention provides an SCA-based Position Optimization (SPO) scheme based on SCA in a cluster; the contributions of this scheme mainly include: aiming at an IaS-U2U communication scene which is considered less, different from a mechanism that the existing U2U document mainly depends on a centralized node, a distributed U2U communication flow is provided, and the distributed U2U communication flow is suitable for IaS-U2U transmission in an off-line (Out-of-Coverage, OOC) state; the defects that actual conditions such as UAV propelling power, interference among UAVs, QoS constraint and the like are ignored in the existing U2U literature are overcome, the influence of UAV mobility inside a cluster on performance is concerned, the transmission rate and the propelling power of the cluster UAV are considered in a combined mode, a multi-objective optimization problem with QoS constraint is constructed, and UAV matching and position are optimized in a combined mode; the maximum transmission rate problem is constructed and solved, and a starting point is provided for iterative solution of the multi-objective optimization problem by judging whether the multi-objective optimization problem has a feasible solution, so that a feasible starting point can be provided for an SCA algorithm; simulation results show that compared with other existing schemes, the scheme can obtain remarkable performance gain.

Example 2

More specifically, on the basis of embodiment 1, as shown in fig. 2, the present invention studies the resource allocation problem of IaS-U2U communication, and in the IaS-U2U communication scenario model of step S1, three types of UAVs are considered, namely, a central UAV (Center-UAV, C-UAV) at a cluster Center position, an assistant UAV (Helper-UAV, H-UAV) that actively provides U2U communication service, and a Requester UAV (request-UAV, R-UAV) that requires U2U service. Wherein the C-UAV is always at a radius of r_swFor coordinating and governing movement of the remaining UAVs within the cluster. The rest UAVs are randomly distributed in the cluster range at the beginning, and the distance between the UAVs is not less than the preset protection distance

Considering the general case in an aviation scenario, we assume that the UAV cluster is operating in the OOC state, but when necessary, the C-UAV may access the satellites to acquire the routing information. In this scenario, the R-UAV needs to obtain the data packet from the H-UAV buffer space, and in order to obtain better transmission conditions, it needs to complete the optimization of the relative position and movement trajectory within the cluster.

It is assumed that the distance between clusters is long and thus the interference between clusters is small and negligible. The UAVs in the same cluster reuse the same frequency band, and mutual interference exists. Furthermore, due to the close distance between the UAVs within the cluster, it is assumed that the R-UAVs can each be in one slot δ_intraThe movement of the inner completion position. Assume that there are M H-UAVs and N R-UAVs in the UAV cluster, with their numbers forming the sets M {1,2,..,. M } and Ν {1,2,..,. N }, respectively. Furthermore, let w_i＝[x_i,y_i]^TDenotes the coordinate vector of UAVi relative to C-UAV, and let W_H-UAV＝[w_m]_m∈MAnd W_R-UAV＝[w_n]_n∈NRepresenting matrices formed by coordinate vectors of the H-UAV and R-UAV, respectively. Assume that all H-UAVs and R-UAVs within the cluster can acquire δ from the intended flight path or C-UAV's temporary notification message_intraEnd of time slotAbsolute coordinates of rear C-UAV

Then any UAV i can be based on its relative coordinates w within the cluster_iTo calculate the corresponding absolute coordinates

Namely, the method comprises the following steps:

for simplicity of derivation, the present solution assumes that all UAVs inside the cluster are at the same flight altitude. Therefore, for

And i ≠ j, available d_i,j＝||w_i-w_jAnd | | represents the distance from the UAVi to the UAVj, wherein | · | | is a two-norm operator.

In the IaS-U2U communication scenario, it is assumed that there is an LOS channel between UAVs, and the Doppler shift can be accurately estimated, and the effect caused by Doppler can be completely cancelled [6][9]M.c. erturk, j.haque, w.a. moreno, and h.arslan, "Doppler assignment in OFDM-based electronic communications," IEEE trans.aerosp.electron.syst., vol.50, No.1, pp.120-129,2014. Therefore, the channel model of the design adopts a free space path loss model [6]][7][8]. Then H-UAvm and R-UAVn are in slot delta_intraThe inner channel gain can be expressed as:

where β and α represent the channel coefficient and the path loss index, respectively.

Furthermore, as previously mentioned, rotorcraft UAVs are more flexible than fixed-wing UAVs [2]Hence, the optimization is presented herein using a model of propulsion power for a rotorcraft UAV as an example. Propulsive power P of rotorcraft UAV_pIs a function of the magnitude of the velocity V of the UAV, denoted as [2]：

Wherein: p_blaRepresenting blade power; u shape_tipRepresenting tip speed; d_ratRepresenting fuselage drag ratio; ρ represents an air density; s_rotRepresents the rotor volume; a represents the rotor disk area; p_indRepresents the induced power; v. of_rotMean rotor induced velocity is indicated. According to [2]]It can be seen that the propulsive power of the rotorcraft UAV consists of three parts: blade power, induced power, and parasitic power. The blade power and the induced power need to overcome the blade resistance and the fuselage resistance and are respectively in direct proportion to the square and the third power of the speed V; induced power is the power required to overcome the blade induced drag, which decreases as the speed V increases. Therefore, the propulsive power of the rotorcraft UAV exhibits the characteristic of decreasing with increasing speed V and then increasing with increasing speed V. Thus, there is a speed value, denoted V, of the rotorcraft UAV that minimizes the propulsive power_MP. Since the propulsion power of a single UAV is typically on the order of hundreds of watts [2]]It can be assumed that the transmit power is much less than the propulsion power.

More specifically, in step S1, as shown in fig. 3, the IaS-U2U communication flow is briefly described as follows:

first, each R-UAV broadcasts a U2U request [10]3GPP TS 24.334 Proximaty-services (ProSe) User Equipment (UE) to ProSe function protocol actions to each H-UAV; stage 3(Release 16). https:// www.3gpp.org/ftp/Specs/archive/24_ series/24.334/,2020. and its own content feature information. Each H-UAV broadcasts its own content characteristic information upon receiving the request. Thereafter, each R-UAV performs a distributed pairing with each H-UAV based on a default H-UAV transmit power (e.g., assumed to be full power) and content characteristics published by the H-UAV;

subsequently, each R-UAV successfully paired initiates a position optimization application to the C-UAV, and the C-UAV starts a position optimization process for the R-UAV in a polling mode, specifically:

when polling to a particular R-UAV, the C-UAV sends a list of location information to the R-UAV, the list containing location information for all UAVs in the cluster;

the R-UAV executes an SPO algorithm according to the position information list to carry out position optimization;

then, if the optimization is successful, the R-UAV feeds back an optimization result to the H-UAV and the C-UAV which are matched with the R-UAV, and then a position moving process is executed; if the optimization fails, feeding back an optimization failure identifier to the H-UAV, and waiting for the next round of matching by the R-UAV and the H-UAV;

the R-UAV calculates the absolute coordinates thereof according to the optimization result and carries out position movement;

the R-UAV informs the H-UAV paired with it that the position movement process is over.

Finally, each R-UAV establishes a U2U connection with its respective paired H-UAV and implements U2U communications.

More specifically, based on the IaS-U2U communication scenario,

more specifically, in the step S2, according to the communication process in the step S1, considering joint optimization of two aspects of the transmission rate and the propulsion power of the trunking system, an objective function of IaS-U2U communication is constructed, specifically:

the pairing of the R-UAV and the H-UAV needs to be done before the R-UAV changes its own position and establishes a connection with the H-UAV. Defining a binary matrix X_M×NElement x thereof _m,n1 denotes H-UAVm paired with R-UAVn, x_m,n0 then indicates unpaired, where m, n Ν. Assuming that each H-UAV serves at most 1R-UAV, each R-UAV is served by at most one H-UAV, i.e.:

in addition, let

Denotes the initial coordinates, w, of the R-UAVn relative to the C-UAV_nIndicating the coordinates of the next slot of the R-UAVn relative to the C-UAV. Since the present solution only optimizes the position of the R-UAV, it can be assumed that the velocities of the C-UAV and H-UAV are the same and defined as the velocity of the cluster, expressed as a vector

Wherein

Representing the velocity components of the cluster in the x-direction and y-direction, respectively. In summary, the velocity magnitude of R-UAV n can be represented by its two-norm velocity vector as:

wherein,

representing the relative speed of R-UAV n with respect to the cluster. Based on equation (2), the propulsion power of R-UAV n can be represented by w_nExpressed as:

suppose each H-UAV transmits at full power, and the transmit power of H-UAV m is recorded as

The transmission rate of H-UAV m to R-UAV n may then be expressed as:

wherein B and ε²Representing the channel bandwidth and noise power spectral density, respectively.

Based on the above analysis, the objective function of IaS-U2U communication can be constructed by combining equations (5) and (6), that is:

wherein: theta_n∈(0,1]Representing a preference factor of the R-UAVn for propulsion power, used for adjusting the proportion of the propulsion power in the multi-objective problem, theta_nThe higher the ratio is, the more important the R-UAVn is to the propulsion power problem; positive real number

Has the unit of Mbit s^-1·W^-1For imparting propulsion power P_p(w_n) Is adjusted to be equal to the transmission rate R_intra(w_n) The same order of magnitude and keeping the adjusted terms and transmission rate the same dimension.

Further, for the convenience of expression, the following variables are defined based on equations (5) and (6):

·

represents the sum of all R-UAV propulsion powers in the cluster:

·R^totrepresents the sum of all H-UAV transmission rates in the cluster:

·μ_intrarepresenting the energy efficiency of the IaS-U2U communication System [11]A.Zappone and E.Jorswieck,"Energy efficiency in wireless networks via fractional programming theory,"Found Trends Commun.Inf.Theory,vol.11,nos.3-4,pp.185-396,2015：

In summary, the multi-objective optimization problem of IaS-U2U communication can be expressed as:

P1:

limited by:

wherein:

constraints C1 and C2 are pairing constraints for H-UAV and R-UAV;

constraint C3 indicates that the flight speed of the R-UAV should not be higher than the UAV maximum flight speed V_max；

Constraint C4 is the anti-separation Cluster constraint that indicates that the optimized relative coordinates of any R-UAV are still at radius R_swWithin the cluster formation range of (1);

constraint C5 is a QoS constraint indicating that the transmission rate between any successfully paired H-UAV and R-UAV is not below a given

Constraints C6 and C7 are collision avoidance constraints, indicating that the distance between any R-UAV and any H-UAV and C-UAV (whose position is the origin of coordinates) should be greater than

In the concrete implementation process, it is noted that the concavity and the convexity of the objective function of P1 are difficult to determine, the optimized variable X is a discrete variable, and the constraints C1-C2 and C5-C7 are non-convex constraints. It can be seen that P1 is a Mixed-Integer Non-Linear Programming (MINLP) problem, and the computational difficulty of directly solving the problem is large. Therefore, the original problem needs to be decomposed, and a distributed algorithm is designed to solve the problem.

More specifically, in order to effectively solve P1, the present solution decomposes the solving process of P1 into the following two steps: UAV matching and position optimization. The UAV matching process is realized by adopting a classical GS algorithm [12] D.Gusfield and R.Irving.the stable margin protocol, structure and algorithms.MIT press,1989 by an H-UAV and an R-UAV, and the position optimization algorithm is executed in a distributed mode by the R-UAV.

Before the pairing process is started, the H-UAV and the R-UAV acquire content classification information of the opposite party in a broadcasting mode. Subsequently, each UAV calculates content similarity [13 ] from Jaccard coefficients]Niwattanaakul, j.singthongchai, e.naenudorn, and s.wanapu, "Using of Jaccard coefficients for keywords similarity," in proc.int.multi conf.eng.com.sci., mar.2013, vol.i, pp.380-384, and generate respective preference lists. In order to enable the paired position optimization result to satisfy the constraint condition C5, candidate pairs need to be screened. The specific method comprises the following steps: the R-UAV n measures the received Signal-to-Noise-and-Interference Ratio (SINR) for each H-UAV, which is reported as

If it is

Then R-UAV n deletes H-UAV m from its list of preferences, where

Representing the SINR threshold of R-UAV n, the expression is as follows:

after each R-UAV modifies the preference list according to the SINR threshold, the classical GS algorithm [18 ]]Namely, one-to-one matching between the R-UAV and the H-UAV can be realized, and a pairing matrix X is obtained^*. And respectively forming the serial numbers of the H-UAV and the R-UAV which are successfully paired into a new set M 'and N'. And after the matching is finished, optimizing the position of the R-UAV.

More specifically, the step S4 specifically includes the following steps:

s41: converting P1 into an optimization problem P2 according to the pairing result;

s42: according to the property 1, the optimization problem P2 is equivalent to the optimization problem P3, and P3 is relaxed and approximated to obtain a convex problem P5;

s43: constructing a maximum transmission rate problem P6 according to the optimization problem P2, and obtaining an optimization problem P7 by adopting the same relaxation and approximation methods of P3-P5;

s44: and solving P7 and P5 by stages according to the SPO algorithm to complete the position optimization of the R-UAV, and solving the multi-objective optimization problem.

In the specific implementation, in the step S41, for convenience of description, the transmission power of each H-UAV in m' is formed into a vector

Each R-UAV that is successfully paired will be location optimized based on the power value. Based on equation (6), for a successfully paired R-UAV n, the achievable transmission rate is:

where m represents the H-UAV Serial number paired with R-UAV n, i.e., x _m,n1. Further, when UAV pairing is complete, the objective function in equation (7) will degenerate as:

wherein,

representing the objective function of R-UAV n. Therefore, for each R-UAVn, the following optimization problem needs to be solved:

P2:

limited by:

wherein C8-C12 are P1 bound in C3-C7 and w respectively_nThe associated constraints.

In the specific implementation process, by paying attention to step S41, the objective function of the optimization problem P2 is non-concave, and the constraints C10-C12 are non-convex constraints, which makes the original problem difficult to solve; therefore, in the step S42, the objective function and the constraint condition of P2 need to be relaxed, and then the SCA algorithm is adopted to solve the problem; the SCA algorithm solves the original problem in an iterative mode, and the optimal solution obtained by the last iteration is needed in each iterative solution; definition K ═ 0,1,2_max-1 represents the number of iterations of the SCA algorithm, where K_maxRepresenting the maximum number of iterations allowed by the algorithm; particularly, when the first iteration solution is carried out, a starting point needs to be provided for an SCA algorithm; the initial point is any feasible solution which meets the constraint condition of the original problem, and the index k of the initial point is 0;

to this end, a relaxation variable [2] is first introduced:

using equation (18), equation (5) can be approximated as follows:

it is noted that | | v_n||²And v_n||³Are all convex functions, tau is an affine function, thus

Is a convex function [20 ]]. Will be provided with

After substituting P2 and introducing the relaxation variable τ, P2 can be transformed into the following optimization problem:

P3:

limited by: C8-C13, wherein:

based on the above analysis, the following properties were present:

properties 1: the optimization problem P3 is equivalent to P2.

And (3) proving that: when in use

In order to maximize the objective function of P3, the value of τ may be reduced, i.e. the propulsion power in equation (21) is reduced

Thereby increasing the value of the objective function P3. When τ continues to decrease to C13, i.e., when the equal sign of equation (18) holds, equation (19) degenerates to equation (5), i.e., there is now

At this time, the optimization problem P3 is equivalent to P2, which is verified.

Since C13 in equation (18) is still a non-convex constraint, further operation is required. Squaring both sides of equation (18) and taking the reciprocal to obtain:

the two equations in equation (22) add, which can equate C13 as the constraint:

C14：

since C14 is still a non-convex constraint, the left end of C14 may be relaxed. For C14, i.e. τ in formula (23)²And

at τ ═ τ respectively^(k)And

performs a first-order Taylor expansion, wherein (·)^(k)Representing the value obtained for the k iteration of a certain variable, there are:

where · is the vector inner product operator. The two equations in equation (24) are added to obtain:

according to [2]]The relaxation variable value formula (4) is used to give any value

After, τ^(k)And

can be calculated from the following formula:

based on the above processing, non-convex constraint C14 may relax to convex constraint C15:

C15:

further, by using | | a | | | b | | | | ≧ a · b, the left sides of the inequalities of C11, C12 can be relaxed as:

recouple (28), C11, and C12, which can relax two of the equations (28) to the following convex constraints, respectively:

C16:

C17:

thus, optimization problem P3 may be transformed into optimization problem P4:

P4:

limited by: C8-C10 and C15-C17. Note that the objective function of P4

And the constraint C10 includes a non-concave function R_intra(w_n). To effectively solve P4, R also needs to be solved_intra(w_n) An approximation is made. Similar to a first order Taylor expansion for log (a + y) at y-0, R_intra(w_n) Can be approximated by [5]]：

Wherein,

due to the function g in equation (32)_l(w_n) And g_j(w_n) The unevenness of (A) is difficult to determine, is described in [5]]Inspiring, can be right

One-step approximation is performed.

Specifically, according to formula (1), first, g is treated_l(w_n) In that

Performing a first-order Taylor expansion to obtain:

in the formula (33), | w_n-w_lI is about w_nAnd, in general, the path loss index alpha of the line-of-sight channel is > 1[14 ]]Thus- | | w_n-w_l||^αIs about w_nConcave function of [14]]S.Boyd and L.Vandenberghe, Convex optimization. Cambridge, U.K.: Cambridge Univ.Press,2004, i.e., 2004

Is about w_nA concave function of (a). In the same way, pair

The first-order Taylor expansion is processed to obtain:

it is clear that,

is about w_nAn affine function of, thus

Is about w_nConcave function of [14]]。

Due to the fact that

And

are all about w_nSo that the non-negatively weighted sum of the two is still with respect to w_nConcave function of [14]]. Thus, in the formula (32)

Can be approximated by the following concave function:

in summary, the formula (21)

Can be approximated as the expression:

further, according to the formula (19)

As a convex function, i.e. in equation (36)

Is a concave function, equation (36) is a non-negatively weighted sum of two concave functions, i.e.

Is a concave function [14]。

Similarly, since the non-concave function R is also contained in C10_intra(w_n) According to equation (32) -equation (35), C10 may be approximated as the following convex constraint:

C18:

therefore, according to equations (36) and (37), the optimization problem P4 can be transformed into the following convex problem P5:

P5:

limited by: C8-C9 and C15-C18. To this end, the optimization problem P2 can be transformed into a convex problem P5 after being approximated and relaxed by P3-P5, so that the solution can be iteratively solved by SCA algorithm using CVX tool [14 ].

More specifically, when solving for P5 for the first time, the starting point of P5 needs to be found

Since the original problem of P5 is P3, the starting point needs to satisfy the constraint condition of P3 of the optimization problem [14] according to the nature of SCA algorithm]. Further, according to property 1, P2 is equivalent to P3, so that

The constraint of P2 needs to be satisfied. However, due to the existence of the constraint condition C10 of P2, whether a feasible solution exists in P2 cannot be judged, so that the starting point

It is difficult to obtain directly. To determine whether a feasible solution exists for P2, the maximum transmission rate of R-UAV n needs to be determined

Whether QoS constraints can be met. Therefore, in the step S43, to obtain the maximum transmission rate of R-UAV n

The propulsion power in the P2 objective function may be temporarily ignored and the QoS constraint, C10, temporarily ignored, so that P2 degenerates to the single objective problem of maximizing transmission rate P6:

P6：

limited by: C8-C9 and C11-C12. Since P6 is degenerated by P2, P6 can use a similar transformation method from P2 to P5:

first, an objective function of P6 is approximated according to equation (32) -equation (35);

next, non-convex constraints C11 and C12 were relaxed to C16 and C17, respectively, according to equations (29) and (30).

In summary, P6 can be transformed into the following convex problem:

P7:

limited by: C8-C9 and C16-C17.

Initial position of R-UAV n

As a starting point for the optimization problem P7, i.e.

Subsequently, by iteratively solving P7 through SCA algorithm, the maximum transmission rate of H-UAV m to R-UAV n can be obtained

And optimized position of R-UAV

If it is not

It indicates that a feasible solution exists for P2. Since P2 and P3 are equivalent, a feasible solution exists in P3, that is, a starting point exists in P5, and an SCA algorithm can be adopted for solving. Next, can order

And calculating τ from equation (26)⁽⁰⁾Then is followed by

As a starting point for P5, P5 is iteratively solved again using the SCA algorithm.

In the specific implementation process, the process of solving P7 and P5 by stages by the SPO algorithm described in step S44 is shown in table 1:

TABLE 1 SPO Algorithm flow

Wherein,

indicating the convergence accuracy of the algorithm. The algorithm needs to be performed for each R-UAV that is successfully paired. When R-UAV n obtains an optimized position

Later, the absolute coordinates of the C-UAV may be based

Calculating its own absolute coordinates, i.e.

Wait for R-UAV n to move to

After the position, the U2U communication is performed again. So far, the processing flow of the SPO algorithm proposed by the present invention has been introduced. Finally, the design idea of the IaS-U2U communication system designed by the present invention is summarized in fig. 4, and briefly described as follows:

first, P1 is the MINLP problem, which is difficult to solve directly. Therefore, the solving process of P1 is divided into two processes of UAV pairing and R-UAV position optimization. Wherein the position optimization process requires an R-UAV distributed solution P2.

Since the P2 objective function is complex and non-concave, and the constraint condition is non-convex, P2 can be equivalent to P3 according to property 1, and P3 can be relaxed and approximated to obtain the convex problem P5. When the P5 is solved iteratively by using the SCA algorithm, a starting point is needed. Since the original problem of P5 is P2, it is necessary to find the starting point of P5 from a feasible solution of P2. Due to the existence of the QoS constraint C10, it is difficult to determine whether P2 has a feasible solution. Therefore, we neglect the propulsion power in the P2 objective function for the moment and neglect C10 for the moment, construct the maximum transmission rate problem P6, and get P7 using the same relaxation and approximation method from P3 to P5.

And finally, solving P7 and P5 in stages according to the SPO algorithm to complete the position optimization of the R-UAV.

Example 3

More specifically, in addition to examples 1 and 2, the following describes a specific embodiment of the present invention by taking a software simulation as an example.

In this embodiment, a simulation platform developed based on MATLAB 2014a is adopted to perform simulation performance evaluation on the novel cluster U2U communication resource scheduling algorithm, and a plurality of schemes are constructed and compared. In particular, to minimize the R-UAV overall propulsive power

To this end, based on the prior document [2]]The scheme modifies a channel model and a transmission rate model, replaces the model adopted by the design, adds QoS constraints, and constructs a Propulsion Power Minimization (PPM) scheme, so that the scheme can be applied to the application scenarios described herein. Furthermore, in order to evaluate the gain due to considering UAV propulsive power, due to the lack of existing solutions that can be directly contrasted to maximize the overall H-UAV transmission rate R^totTo this end, a Transmission Rate Maximization (TRM) scheme is constructed. For the sake of comparative fairness, the UAV pairing process proposed by the design is adopted for all three schemes, namely SPO, PPM and TRM, and the schemes are all subjected to the processing flow of stage one of the SPO algorithm. It should be noted that, after going through the SPO algorithm stage one, the TRM scheme reserves only U2U pairs that satisfy the QoS constraint, i.e., only statistically satisfies the transmission rate of U2U pair of C5 in equation (12); and the PPM scheme optimizes the propulsion power of the R-UAV after the SPO algorithm stage I. In summary, both the TRM scheme and the PPM scheme can be regarded as simplified versions of the SPO scheme.

The simulation parameters of the present example are shown in table 2, unless otherwise specified below. Wherein, the parameters related to the UAV propulsion power in the formula (2) are all related to [2]Is consistent with (1). UAV maximum flight velocity V_maxAccording to the provisions in 3GPP TR36.777, set to 45 m/s. The simulation takes the video service as an example, and the most important of the clustersSmall transmission volume

With a setting of 4Mbit/s, 720p high-resolution video applications can be supported. Reference [15]Zhu, Y.Liang and M.Yan, "A flexible compliance assembly trajectory for the format of multiple unamended atmospheric vehicles," IEEE Access, vol.7, pp.140743-140754,2019, doi:10.1109/ACCESS.2019.2944160, and set the protection distance of the UAV

2m, cluster UAV control with an accuracy of 2m can be achieved at a cluster speed of 45 m/s.

Table 2: simulation parameter list

In the implementation, fig. 5 simulates UAV position comparison before and after IaS-U2U communication optimization. As shown, under the UAV position constraints, the R-UAV will not collide with the H-UAV and the C-UAV, nor will it depart from the cluster scope. Notably, partial pairing after optimization through stage 1 of algorithm 1 results in a maximum achievable rate

QoS requirements are not met resulting in failure of the final location optimization. Thus, not all H-UAVs are able to provide U2U transmission service in the same sub-optimal process. The UAV that failed the match or failed the optimization may proceed to the next round of optimization.

Different UAV protection distances are simulated by adopting a Monte Carlo method

Next, R-UAV Overall propulsive Power for the SPO scheme

H-UAV Overall Transmission Rate R^totAnd energy efficiency mu_intraV with cluster speed | | v^swThe variation relationship of | is shown in fig. 6(a), 6(b), and 6(c), respectively. It should be noted that the propulsion power preference factors for each R-UAV are set to the same value and are all 0.5, i.e., θ₁＝θ₂＝...＝θ_N＝0.5。

In the specific implementation, as can be seen from fig. 6(a), when the cluster speed changes, it is different

The lower propelling power change trend is that the propelling power is decreased gradually and then increased gradually, and when the cluster speed | | v^swAnd | | is more than or equal to 10m/s, and the propelling power of the devices is gradually increased along with the increase rate of the cluster speed. This is because, on the one hand, the present solution optimizes the relative position of the R-UAV, and as can be seen from equation (4), the position optimization only affects the relative speed of the R-UAV

According to the formulas (4) and (5), the propulsion power of the R-UAV is also related to the cluster speed | | v^swAnd | | is related. Therefore, the temperature of the molten metal is controlled,

the variation trend of (C) is also influenced by the cluster speed | | | v^swThe effect of | l. On the other hand, according to equation (2), the rotorcraft UAV presents a flight speed V corresponding to the lowest propulsive power_MPThe trend of the propulsion power is therefore first decreasing and then increasing, this trend being associated with [2]]The conclusions are consistent.

In addition, when the cluster speed | | | v^swWhen the value of | is small,

with following

Is increased and decreased. This is because, as shown in FIG. 6(a), when

And | | | v^swWhen the value of | is small,

smaller, R^totIs relatively large. In combination with equation (7) -equation (9), the proportion of propulsion power in the multi-objective problem is small, and the optimization algorithm is based on the transmission rate objective. However, with

The combination of the formula (1) and the formula (6) shows that the transmission rate of the R-UAV is gradually reduced, so that the occupation ratio of the propulsion power in the multi-objective problem is gradually increased, at the moment, the optimization algorithm gradually emphasizes the propulsion power target, and the combination of the formula (7) and the formula (9) shows that,

will be reduced. Overall analysis, H-UAV Overall Transmission Rate R^totAnd R-UAV Overall Propulsion Power

There is a trade-off relationship.

However, when the cluster speed | | | v^swAs the l continues to increase in size,

enter the growth interval and the growth trend follows

Is increased and accelerated. This is because, on the one hand, when

When the growth interval is entered, the data is transmitted,

with | | | v^swThe increase in | increases. According to R^totAnd

the trade-off relationship of (1) shows that the optimization algorithm tends to decrease

To maximize the objective function in equation (7). On the other hand, however, according to C6 and C7 in the formula (12), as follows

With the increase in the number of possible solutions for the R-UAV, the set of feasible solutions for the R-UAV will decrease, and the distance of the H-UAV to which the R-UAV is optimized to mate will increase, thus the H-UAV transmission rate decreases, resulting in that after SPO algorithm stage one, the number of R-UAVs that satisfy the QoS condition, i.e., that satisfy C5 in equation (12), will decrease, and the R-UAV that fails to be optimized will remain in place. This illustrates that an increase in the R-UAV movement limit may result in a decrease in the number of R-UAVs that may ultimately perform a position movement. In combination with equation (8), the optimization algorithm is implemented by changing the position of the R-UAV

The effect of the decrease will also gradually diminish. Combining the above two factors, when

When the size of the pipe is increased, the pipe is enlarged,

with | | | v^swThe growth of | will increase rapidly.

From the H-UAV overall transmission rate performance curve given in FIG. 6(b), it can be observed that the H-UAV overall transmission rate R of the present scheme^totV with cluster speed | | v^swThe increase in | decreases. According to C3 in the formula (12), | | v^swAn increase in | may increase the movement limit of the R-UAV; referring again to the conclusion of FIG. 6(a), it can be seen that the number of R-UAVs that eventually undergo position movement is reduced. Thus, according to formula (9), R^totV with cluster speed | | v^swThe increase in | decreases. Furthermore, R^totWith following

Is increased and decreased. This is because when

When the distance between the R-UAV and the H-UAV matched with the R-UAV is increased after optimization, according to the formula (1) and the formula (6), the transmission rate of the H-UAV is reduced, so that the R-UAV is caused to have a reduced transmission rate^totAnd decreases. Finally, FIG. 6(b) shows the results when

When increasing, with | | v^swVariation of | R^totThe variation range of (a) is reduced and gradually becomes gentle. According to C3 in the formula (12), when | | | v^swWhen | l changes within a certain range, the optimization algorithm can be based on | v |^swAdjusting the position w of the R-UAV after optimization_nI.e. w_nWill vary within a feasible solution. And in combination with formula (9), R^totIs about w_nA function of (1), thus R^totWill also vary within certain limits. However, with

From C6 and C7 in equation (12), the mobile location limit of the R-UAV increases, resulting in a decreasing set of feasible solutions. This means that w_nThe adjustable range is also reduced, and R is shown in formula (9)^totWill also gradually narrow. At this time, on one hand, the limitation on the mobile position of the R-UAV is increased, which increases the difficulty of meeting the QoS constraint after the R-UAV is optimized, so that the number of U2U pairs for which the optimization is successful is reduced; on the other hand, the interference term in equation (6)

And the QoS requirement can be more easily met after the R-UAV is optimized. In summary, the number of successfully optimized U2U pairs tends to be stable after being reduced to a certain degree, and R is stable^totThe attenuation does not continue but gradually approaches a certain minimum value, as shown in fig. 6 (b).

FIG. 6(c) shows the difference

Energy efficiency of the system, μ_intraPerformance comparison of (2). It can be seen that the energy efficiency all shows a trend of increasing first and then decreasing with the cluster speed. As can be seen from FIG. 6(a), the energy efficiencies are all

The lowest reaches the peak point. In addition, referring back to FIGS. 6(a) and 6(b), when

At increasing overall transmission rate R^totWill be greatly reduced and the total propulsion power will be reduced

The decrease is not significant, and thus the energy efficiency mu_intraTo R^totInfluence following

Increases and decreases as shown in fig. 6 (c). However, as future UAV technology develops, the control accuracy is expected to be improved continuously, and the protection distance of the UAV may be kept at a smaller value, which may provide better implementation conditions and basis for implementation of the present solution.

More specifically, the energy efficiencies of the three schemes, SPO, TRM and PPM, were compared. Combining the definition of the TRM scheme and the PPM scheme, the SPO scheme is the weighted sum of the target functions of the TRM scheme and the PPM scheme, and the weight distribution is subject to the preference factor theta according to the formula (7)_nThe influence of (c). Thus, theta_nWill be applied to the objective function U of the IaS-U2U system_intraAnd R^totAnd

all bring influence, and as can be seen from equation (10), this will further influence the energy efficiency μ of the system_intraAs shown in fig. 7. This phenomenon illustrates that at different cluster speeds | | | v^swUnder | |)The preference factor theta of the R-UAV can be adjusted by_nTo obtain energy efficiency mu_intraGain in performance.

In the specific implementation process, as shown in fig. 7, when the cluster speed is in the middle-low speed interval, i.e., | | v^swWhen | | | is less than or equal to 32.5m/s, the energy efficiency performance of the SPO scheme is superior to that of the TRM scheme and the PPM scheme, and the preference factor theta can be increased_nTo improve the gain in energy efficiency. When the cluster speed is in a high-speed interval, namely | | | v^swWhen | | ≧ 32.5m/s, although the SPO scheme is at θ_nThe performance is lower than that of the TRM scheme when the preference factor theta is larger than or equal to 0.5, but the preference factor theta can be reduced_nTo gain in energy efficiency performance.

In table 3, the average number of iterations of the SPO algorithm at different convergence accuracies is simulated. The simulated hardware environment is a Windows 7.0 operating system with an Intel i7-6700 CPU. Here, the preference factor for each R-UAV is set to 0.5. Since the complexity of the position optimization algorithm is higher than that of other functional modules, the complexity of the whole algorithm is mainly determined by the complexity of the position optimization algorithm. The position optimization algorithm adopts a CVX tool interior point method to solve, and the complexity of the CVX tool interior point method can be approximate to O (a)^3.5)log₂(1/κ)[8]. Where a and κ represent the number of variables and the accuracy of convergence of the interior point method, respectively. The complexity and the convergence precision k of the interior point method can be understood as the precision used by the SPO algorithm when the CVX tool is called each time to solve the convex problem, and this parameter is determined by the CVX tool. Thus, the complexity of stage 1 and stage 2 of the SPO algorithm are O (n), respectively_var1)^3.5K₁ log₂(1/κ) and O (n)_var2)^3.5K₂ log₂(1/κ) wherein n is_var1And n_var2The number of variables for stage 1 and stage 2 are shown separately. For phase 1, the optimization variable is w_n＝[x_n,y_n]^TThus n is_var12; for stage 2, the optimization variable is w_n＝[x_n,y_n]^TAnd τ, thus n _var23. Furthermore, K₁And K₂The number of iterations for phase 1 and phase 2 are shown separately. K₁、K₂Can take on the value ofSee table 3. As can be seen from Table 3, this value is consistent with the convergence accuracy of the SPO algorithm

In connection with, among others, convergence accuracy

Parameters to determine when phases 1,2 converge. With following

The number of iterations of both stage 1 and stage 2 of the SPO algorithm increases.

Table 3: average iteration number required by SPO algorithm under different convergence precision targets

It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A U2U distributed dynamic resource allocation method facing intra-cluster communication is characterized by comprising the following steps:

s1: constructing an IaS-U2U communication scenario model and communication flow, wherein three kinds of UAVs are considered, including a central UAV at a cluster center location, i.e., a C-UAV, an assistor UAV that is actively providing U2U communication service, i.e., an H-UAV, and a requester UAV that requires U2U service, i.e., an R-UAV;

s2: constructing an objective function of IaS-U2U communication to obtain a multi-objective optimization problem of IaS-U2U communication;

s3: decomposing the multi-objective optimization problem, and carrying out a UAV matching process by using the H-UAV and the R-UAV by adopting a classical GS algorithm to complete UAV pairing;

s4: according to the UAV pairing result, completing the position optimization of the R-UAV by adopting an SPO algorithm, and completing the solution of a multi-objective optimization problem;

s5: and according to the solving result, completing the distribution of the U2U distributed dynamic resources.

2. The method of claim 1, wherein in the IaS-U2U communication scenario model of step S1, the C-UAV is always located at a radius r of the communication scenario model of U2U distributed dynamic resource allocation for intra-cluster communication_swThe circular cluster area center is used for coordinating and controlling the movement of the rest UAVs in the cluster; the rest UAVs are randomly distributed in the cluster range at the beginning, and the distance between the UAVs is not less than the preset protection distance

Considering the general situation in an aviation scene, it is assumed that the UAV cluster works in an OOC state, but when necessary, the C-UAV may access a satellite to acquire route planning information; in this scenario, the R-UAV needs to obtain a data packet from the H-UAV buffer space, and in order to obtain better transmission conditions, it needs to complete optimization of the relative position and movement trajectory of the R-UAV in the cluster;

the distance between clusters is assumed to be long, so that the interference between the clusters is small and can be ignored; the UAVs in the same cluster reuse the same frequency band, and mutual interference exists; furthermore, due to the close distance between the UAVs within the cluster, it is assumed that the R-UAVs can each be in one slot δ_intraMovement of the inner completion location; assume that there are M H-UAVs and N R-UAVs in the UAV cluster, with their numbers forming the sets M {1,2,..,. M } and N {1,2,..,. N }; furthermore, let w_i＝[x_i,y_i]^TDenotes the coordinate vector of UAVi relative to C-UAV, and let W_H-UAV＝[w_m]_m∈MAnd W_R-UAV＝[w_n]_n∈NRespectively H-UAV anda matrix formed by coordinate vectors of the R-UAV; assume that all H-UAVs and R-UAVs within the cluster can acquire δ from the intended flight path or C-UAV's temporary notification message_intraAbsolute coordinates of C-UAV after slot completion

Then any UAV i can be based on its relative coordinates w within the cluster_iTo calculate the corresponding absolute coordinates

Namely, the method comprises the following steps:

assuming all UAVs inside the cluster are at the same flight altitude, then for

And i ≠ j, d_i,j＝||w_i-w_j| l represents the distance from the UAV i to the UAVj, wherein | · | | is a two-norm operator;

under the IaS-U2U communication scene, the UAVs are all assumed to be LOS channels, the Doppler frequency shift can be accurately estimated, and the influence caused by Doppler can be completely offset; therefore, the channel model of the model adopts a free space path loss model, and H-UAVm and R-UAVn are in a time slot delta_intraThe channel gain in is expressed as:

wherein, beta and alpha respectively represent a channel coefficient and a path loss index; furthermore, the rotorcraft UAV is more flexible than a fixed-wing UAV, and is therefore optimized with a model of the propulsion power P of the rotorcraft UAV_pIs a function of the velocity magnitude V of the UAV, written as:

wherein: p_blaRepresenting blade power; u shape_tipRepresenting tip speed; d_ratRepresenting fuselage drag ratio; ρ represents an air density; s_rotRepresents the rotor volume; a represents the rotor disk area; p_indRepresents the induced power; v. of_rotRepresents the average rotor induction speed; wherein, the propulsive power of rotor UAV comprises three parts: blade power, induced power, and parasitic power; the blade power and the induced power need to overcome the blade resistance and the fuselage resistance and are respectively in direct proportion to the square and the third power of the speed V; induced power is the power required to overcome the blade induced drag, which decreases as the speed V increases; thus, the propulsion power of the rotorcraft is characterized by first decreasing with increasing speed V and then increasing with increasing speed V, there being a speed value, denoted V, at which the propulsion power is at a minimum_MP(ii) a Since the propulsion power of a single UAV is typically on the order of hundreds of watts, in the IaS-U2U communication scenario model, the transmit power is assumed to be much less than the propulsion power.

3. The intra-cluster-communication-oriented U2U distributed dynamic resource allocation method according to claim 2, wherein in the step S1, a communication flow of the IaS-U2U communication scenario model specifically includes:

s11: each R-UAV broadcasts a U2U request to each H-UAV and its own content characteristic information; after each H-UAV receives the request, broadcasting the content characteristic information of the H-UAV; then, each R-UAV performs distributed pairing with each H-UAV based on the default H-UAV transmitting power and the content characteristics issued by the H-UAV;

s12: each R-UAV successfully paired initiates a position optimization application to the C-UAV, and the C-UAV starts a position optimization process for the R-UAV in a polling mode, specifically:

i. when polling to a particular R-UAV, the C-UAV sends a list of location information to the R-UAV, the list containing location information for all UAVs in the cluster;

the R-UAV executes an SPO algorithm according to the position information list for position optimization;

if the optimization is successful, the R-UAV feeds back an optimization result to the H-UAV and the C-UAV which are matched with the R-UAV, and then a position moving process is executed; if the optimization fails, feeding back an optimization failure identifier to the H-UAV, and waiting for the next round of matching by the R-UAV and the H-UAV;

calculating the absolute coordinates of the R-UAV according to the optimization result, and implementing position movement;

v. the R-UAV tells the H-UAV paired with it that the position movement process is over;

s13: each R-UAV establishes a U2U connection with its respective paired H-UAV and implements U2U communications.

4. The intra-cluster communication-oriented U2U distributed dynamic resource allocation method according to claim 3, wherein in the step S2, according to the communication process in the step S1, an objective function of IaS-U2U communication is constructed in consideration of joint optimization of two aspects of the transmission rate and the propulsion power of the cluster system, and specifically:

the R-UAV and H-UAV need to be paired before the R-UAV changes its own position and establishes a connection with the H-UAV; defining a binary matrix X_M×NElement x thereof_m,n1 denotes H-UAVm paired with R-UAVn, x_m,n0 then represents unpaired, where m ∈ m, n ∈ Ν; assuming that each H-UAV serves at most 1R-UAV, each R-UAV is served by at most one H-UAV, i.e.:

in addition, let

Denotes the initial coordinates, w, of R-UAV n relative to C-UAV_nRepresenting coordinates of the R-UAV n next time slot relative to the C-UAV; since only the position of the R-UAV is optimized, i.e., assuming that the velocities of the C-UAV and H-UAV are the same, and defining the velocity as the velocity of the cluster, expressed as a vector

Wherein

Representing the velocity components of the cluster in the x-direction and the y-direction, respectively; in summary, the velocity magnitude of R-UAV n is represented by its two-norm velocity vector as:

wherein,

representing the relative velocity of R-UAV n with respect to the cluster; using w as the propulsion power of R-UAV n based on formula (2)_nExpressed as:

suppose each H-UAV transmits at full power, and the transmit power of H-UAV m is recorded as

The transmission rate of H-UAV m to R-UAV n is then expressed as:

wherein B and ε²Respectively representing the channel bandwidth and the noise power spectral density; based on the analysis, the objective function of IaS-U2U communication is constructed by combining the equations (5) and (6), namely:

wherein: theta_n∈(0,1]Representing a preference factor of the R-UAVn for propulsion power, used for adjusting the proportion of the propulsion power in the multi-objective problem, theta_nThe higher the ratio is, the more important the R-UAVn is to the propulsion power problem; positive real number

Has the unit of Mbit s^-1·W^-1For applying propulsive power P_p(w_n) Is adjusted to be equal to the transmission rate R_intra(w_n) The same order of magnitude and keeping the adjusted terms and transmission rate the same dimension.

5. The intra-cluster-communication-oriented U2U distributed dynamic resource allocation method according to claim 4, wherein in the step S2, for convenience of description, the following variables are defined based on the following equations (5) and (6):

·

represents the sum of all R-UAV propulsion powers in the cluster:

·R^totrepresents the sum of all H-UAV transmission rates in the cluster:

·μ_intrarepresenting the energy efficiency of the IaS-U2U communication system:

to sum up, the multi-objective optimization problem of IaS-U2U communication is expressed as:

limited by:

wherein:

constraints C1 and C2 are pairing constraints for H-UAV and R-UAV;

constraint C3 indicates that the flight speed of the R-UAV should not be higher than the UAV maximum flight speed V_max；

Constraint C4 is the anti-separation Cluster constraint that indicates that the optimized relative coordinates of any R-UAV are still at radius R_swWithin the cluster formation range of (1);

constraint C5 is a QoS constraint indicating that the transmission rate between any successfully paired H-UAV and R-UAV is not below a given

Constraints C6 and C7 are collision avoidance constraints, indicating that the distance between any R-UAV and any H-UAV and C-UAV (whose position is the origin of coordinates) should be greater than

6. The method according to claim 5, wherein in the step S3, it is noticed that the concavity and convexity of the objective function of P1 are difficult to determine, the optimized variable X is a discrete variable, and the constraints C1-C2 and C5-C7 are non-convex constraints, so that P1 is a mixed integer nonlinear problem, and the calculation difficulty in directly solving the problem is high, therefore, the solving process of P1 needs to be decomposed into the following two steps: UAV matching and position optimization; wherein, the UAV matching process specifically comprises:

before a pairing process is started, acquiring content classification information of an opposite party by an H-UAV and an R-UAV in a broadcasting mode; then, each UAV calculates content similarity according to the Jaccard coefficient and generates a respective preference list; in order to enable the paired position optimization result to satisfy the constraint condition C5, candidate pairs need to be screened, specifically: R-UAV n measures the received Signal to noise ratio, SINR, for each H-UAV, noted

If it is

Then R-UAV n deletes H-UAV m from its list of preferences, where

Representing the SINR threshold of R-UAV n, the expression is as follows:

after each R-UAV modifies the preference list according to the SINR threshold, the classical GS algorithm is used for realizing one-to-one matching between the R-UAV and the H-UAV, and a pairing matrix X is obtained^*(ii) a And meanwhile, the serial numbers of the H-UAV and the R-UAV which are successfully paired are respectively formed into a new set of M 'and N', and the UAV pairing is completed.

7. The intra-cluster-communication-oriented U2U distributed dynamic resource allocation method according to claim 6, wherein the step S4 specifically includes the following steps:

s41: converting P1 into an optimization problem P2 according to the pairing result;

s42: according to the property 1, the optimization problem P2 is equivalent to the optimization problem P3, and P3 is relaxed and approximated to obtain a convex problem P5;

s43: constructing a maximum transmission rate problem P6 according to the optimization problem P2, and obtaining an optimization problem P7 by adopting the same relaxation and approximation methods of P3-P5;

s44: and solving P7 and P5 by stages according to the SPO algorithm to complete the position optimization of the R-UAV, and solving the multi-objective optimization problem.

8. The intra-cluster-communication-oriented U2U distributed dynamic resource allocation method according to claim 7, wherein the step S41 specifically is:

forming the transmitting power of each H-UAV in the M' into a vector

Each R-UAV successfully paired will be position optimized based on the power value; based on equation (6), for the successfully paired R-UAV n, the obtained transmission rate is:

where m represents the H-UAV Serial number paired with R-UAV n, i.e., x_m,n1 is ═ 1; further, when UAV pairing is complete, the objective function in equation (7) will degenerate as:

wherein,

representing an objective function of R-UAV n; therefore, for each R-UAVn, the following optimization problem needs to be solved:

limited by:

wherein C8-C12 are P1 bound in C3-C7 and w respectively_nThe associated constraints.

9. The intra-cluster-communication-oriented U2U distributed dynamic resource allocation method according to claim 8, wherein the step S42 specifically is:

it is noted in step S41 that the objective function of the optimization problem P2 is non-concave, and the constraints C10-C12 are non-convex constraints, which makes the original problem difficult to solve; therefore, the objective function and the constraint condition of the P2 need to be relaxed, and then the SCA algorithm is adopted for solving; the SCA algorithm solves the original problem in an iterative mode, and the optimal solution obtained by the last iteration is needed in each iterative solution; definition K ═ 0,1,2_max-1 represents the number of iterations of the SCA algorithm, where K_maxRepresenting the maximum number of iterations allowed by the algorithm; particularly, when the first iteration solution is carried out, a starting point needs to be provided for an SCA algorithm; the initial point is any feasible solution which meets the constraint condition of the original problem, and the index k of the initial point is 0;

to this end, a relaxation variable is first introduced:

using equation (18), equation (5) is approximated as follows:

it is noted that | | v_n||²And v_n||³Are all convex functions, tau is an affine function, thus

Is a convex function;

will be provided with

After substituting P2 and introducing the relaxation variable τ, P2 translates into the following optimization problem:

limited by: C8-C13, wherein:

based on the above analysis, the following properties were present:

properties 1: optimization problem P3 is equivalent to P2:

when in use

In order to maximize the objective function of P3, the propulsion power in equation (21) is reduced by reducing the value of τ

Thereby improving the value of the objective function P3; when τ continues to decrease to C13, i.e., when the equal sign of equation (18) holds, equation (19) degenerates to equation (5), i.e., there is now

At this time, optimization problem P3 is equivalent to P2;

since C13 in equation (18) is still a non-convex constraint, further operation is required; squaring both sides of the formula (18) and taking the reciprocal to obtain:

the two equations in equation (22) add, equating C13 to the constraint:

since C14 is still a non-convex constraint, the left end of C14 is relaxed; for C14, i.e. τ in formula (23)²And

at τ ═ τ respectively^(k)And

performs a first-order Taylor expansion, wherein (·)^(k)Representing the value obtained for the k iteration of a certain variable, there are:

where · is the vector inner product operator; the two equations in equation (24) are added to obtain:

wherein, tau^(k)And

calculated from the following formula:

after the above processing, non-convex constraint C14 is relaxed to convex constraint C15:

further, with | | a | | | | b | | | | ≧ a · b, the left sides of the inequalities of C11, C12 are relaxed respectively as:

recouple (28), C11 and C12, relaxing two of the equations (28) into the following convex constraints, respectively:

therefore, the optimization problem P3 is transformed into the optimization problem P4:

limited by: C8-C10, C15-C17; note that the objective function of P4

And the constraint C10 includes a non-concave function R_intra(w_n) (ii) a To effectively solve P4, R also needs to be solved_intra(w_n) Making an approximation; similar to a first order Taylor expansion for log (a + y) at y-0, R_intra(w_n) The approximation is:

wherein,

due to the function g in equation (32)_l(w_n) And g_j(w_n) Is difficult to determine, therefore, it is suitable for

Performing one-step approximation;

specifically, according to formula (1), first, g is treated_l(w_n) In that

Performing first-order Taylor expansion to obtain:

in the formula (33), | w_n-w_lI is about w_nAnd in general the road loss index alpha of the line-of-sight channel is > 1, thus- | w_n-w_l||^αIs about w_nConcave function of, i.e.

Is about w_nA concave function of (d); in the same way, for g_j(w_n) In that

Performing a first-order Taylor expansion:

it is clear that,

is about w_nAn affine function of, thus

Is about w_nA concave function of (d); due to the fact that

And

are all about w_nSo that the non-negatively weighted sum of the two is still with respect to w_nA concave function of (d); thus, in the formula (32)

Approximated by the concave function:

in summary, the formula (21)

Approximated by the expression:

further, according to the formula (19)

As a convex function, i.e. in equation (36)

Is a concave function, equation (36) is a non-negatively weighted sum of two concave functions, i.e.

Is a concave function;

similarly, since the non-concave function R is also contained in C10_intra(w_n) According to the formula (32) -formula (35), C10 is approximated as the following convex constraint：

Therefore, according to equations (36) and (37), the optimization problem P4 can be transformed into the following convex problem P5:

limited by: C8-C9, C15-C18; to this end, the optimization problem P2 is transformed into a convex problem P5 after the approximation and relaxation of P3-P5, so that the solution is iterated through an SCA algorithm by using a CVX tool.

10. The intra-cluster communication-oriented U2U distributed dynamic resource allocation method as claimed in claim 9, wherein when solving for P5, a starting point of P5 needs to be found

Since the original problem of P5 is P3, the starting point needs to satisfy the constraint condition of the optimization problem P3 according to the nature of SCA algorithm; further, according to property 1, P2 is equivalent to P3, so that

The constraint condition of P2 needs to be satisfied; however, due to the existence of the constraint condition C10 of P2, whether a feasible solution exists in P2 cannot be judged, so that the starting point

Difficult to obtain directly; to determine whether a feasible solution exists for P2, the maximum transmission rate of R-UAV n needs to be determined

Whether QoS constraints can be met; therefore, in the step S43, to obtain the maximum transmission rate of R-UAV n

Ignoring the push power in the P2 objective function for the moment and ignoring the QoS constraint, C10, for the moment, so that P2 degenerates to the single objective problem of maximizing transmission rate P6:

limited by: C8-C9, C11-C12; since P6 is degenerated by P2, P6 can use a similar transformation method from P2 to P5:

first, the objective function of P6 is approximated according to equation (32) -equation (35);

secondly, relaxing non-convex constraints C11 and C12 to C16 and C17, respectively, according to equations (29) and (30);

in summary, P6 is transformed into the following convex problem:

limited by: C8-C9, C16-C17;

initial position of R-UAV n

As a starting point for the optimization problem P7, i.e.

Then, iteratively solving P7 through an SCA algorithm to obtain the maximum transmission rate from H-UAV m to R-UAV n

And optimized position of R-UAV

If it is not

Then a feasible solution exists for P2; because P2 and P3 are equivalent, P3 has a feasible solution, namely P5 has a starting point, and an SCA algorithm is adopted for solving; next, let

And calculating τ from equation (26)⁽⁰⁾Then is followed by

As a starting point for P5, P5 is iteratively solved again using the SCA algorithm.

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