CN112995924B - Inter-cluster communication-oriented U2U centralized dynamic resource allocation method - Google Patents

Abstract

The invention provides a U2U centralized dynamic resource allocation method for inter-cluster communication, which comprises the following steps: constructing IrS-U2U communication scene models and communication flows; constructing IrS-U2U communication objective functions to obtain a IrS-U2U communication multi-objective optimization problem; converting the multi-objective optimization problem and adopting an initial solution generation algorithm, namely generating a starting point of the converted optimization problem by using an ISG algorithm; and solving the transformed optimization problem by stages and iteration by adopting an SCA algorithm according to the starting point, realizing IrS-U2U route optimization according to a solving result, and finishing the distribution of U2U centralized dynamic resources. The invention provides a U2U centralized dynamic resource allocation method facing inter-cluster communication, and provides a centralized U2U communication flow, which realizes IrS-U2U transmission in an off-line state, overcomes the design defect that the existing U2U literature ignores UAV (unmanned aerial vehicle) propulsion energy consumption, jointly considers the transmission quantity and propulsion energy consumption targets of UAV clusters, and realizes effective optimization of cluster routes.

Description

Inter-cluster communication-oriented U2U centralized dynamic resource allocation method

Technical Field

The invention relates to the technical field of communication, in particular to a U2U centralized dynamic resource allocation method for inter-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. Among them, Qiu et al [1] C.Qiu, Z.Wei, Z.Feng, and P.Zhang, "Backhau-aware transmission optimization of fixed-wing UAV-mounted base station for coherent available wireless service," IEEEAccess, vol.8, pp.60940-60950,2020, doi:10.1109/ACCESS 2020.2983516, quote the propulsion energy consumption model of fixed wing UAVs, and construct the problem of minimum propulsion energy consumption under the constraint of considering the Quality of service (QoS) of UAV assisted wireless communication, jointly optimizing the UAV trajectory and the launch power. 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. 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. 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 to deliver short, ultra-reliable and low-latency (URLLC) instruction packets between Internet of things (IoT) devices on the ground, and used a nonlinear 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-UAVnetworks," 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, the scheme only considers the trajectory optimization of one U2U pair, and does not analyze the propulsion energy consumption problem in the flight process.

Disclosure of Invention

The invention provides a U2U centralized dynamic resource allocation method for inter-cluster communication, aiming at overcoming the technical defect that the transmission quantity and the propulsion energy consumption cannot be considered simultaneously in the existing cluster U2U communication scene.

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

a U2U centralized dynamic resource allocation method facing inter-cluster communication comprises the following steps:

s1: constructing IrS-U2U communication scene models and communication flows;

s2: constructing IrS-U2U communication objective functions to obtain a IrS-U2U communication multi-objective optimization problem;

s3: converting the multi-objective optimization problem and adopting an initial solution generation algorithm, namely generating a starting point of the converted optimization problem by using an ISG algorithm;

s4: and solving the transformed optimization problem by stages and iteration by adopting an SCA algorithm according to the starting point, realizing IrS-U2U route optimization according to a solving result, and finishing the distribution of U2U centralized dynamic resources.

In the scheme, the invention constructs a multi-objective optimization problem of transmission quantity and propulsion energy consumption, and optimizes the route of the UAV cluster. Aiming at the characteristics of the scene, the invention provides a centralized communication process of the UAVs among the clusters, and provides a centralized algorithm based on continuous convex approximation (SCA). The algorithm is divided into two stages:

the first stage is as follows: constructing a maximum transmission quantity problem, and obtaining a feasible solution meeting the original problem constraint by adopting an SCA algorithm;

and a second stage: and taking the feasible solution obtained in the first stage as a starting point, and obtaining the local optimal solution of the original problem by adopting an SCA algorithm.

In the above solution, the present invention provides an SCA-based cluster U2U Route Optimization (SARO) solution. The contributions of this scheme mainly include: IrS-U2U communication scene is considered, and a centralized U2U communication flow is proposed, wherein a central UAV (central UAV, C-UAV) positioned in a cluster Center serves as a centralized node to realize IrS-U2U transmission in an Out-of-Coverage (OOC) state; the design defect that the existing U2U literature ignores UAV (unmanned aerial vehicle) propulsion energy consumption is overcome, the transmission quantity and the propulsion energy consumption target of a UAV cluster are considered in a combined manner, a multi-objective optimization problem is constructed, and effective optimization of a cluster route is realized; the problem of maximum transmission quantity is constructed and solved, and a starting point is provided for the multi-target optimization problem by judging whether the multi-target problem has a feasible solution, so that the problem of obtaining the starting point of the SCA algorithm is solved; 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 centralized dynamic resource allocation method facing inter-cluster communication, and provides a centralized U2U communication flow, which realizes IrS-U2U transmission in an off-line state, overcomes the design defect that the existing U2U literature ignores UAV (unmanned aerial vehicle) propulsion energy consumption, jointly considers the transmission quantity and propulsion energy consumption targets of UAV clusters, and realizes effective optimization of cluster routes.

Drawings

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

FIG. 2 is a diagram illustrating a communication scenario of IrS-U2U according to an embodiment of the present invention;

FIG. 3 is a flow chart of communications between IrS-U2U according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of communication schemes IrS-U2U according to an embodiment of the present invention;

FIG. 5 is a TS/RS chart of communications IrS-U2U in accordance with an embodiment of the present invention;

FIG. 6 shows the transmission amount and the propulsive energy consumption according to the preference factor theta_sA varying performance profile;

FIG. 7 is a graph of propulsion power consumption per unit time and propulsion power consumption per unit distance as a function of minimum throughput;

fig. 8 is a graph of energy efficiency per timeslot, and total system propulsion power as a function of timeslot superscript.

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 method for centralized dynamic resource allocation of U2U for inter-cluster communication is characterized by comprising the following steps:

s1: constructing IrS-U2U communication scene models and communication flows;

s2: constructing IrS-U2U communication objective functions to obtain a IrS-U2U communication multi-objective optimization problem;

s3: converting the multi-objective optimization problem and adopting an initial solution generation algorithm, namely generating a starting point of the converted optimization problem by using an ISG algorithm;

s4: and solving the transformed optimization problem by stages and iteration by adopting an SCA algorithm according to the starting point, realizing IrS-U2U route optimization according to a solving result, and finishing the distribution of U2U centralized 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 considers the Inter-cluster U2U (Inter-Swarm U2U, IrS-U2U) communication scene, jointly considers the UAV cluster propulsion energy consumption, and designs the corresponding U2U communication resource scheduling algorithm.

In the specific implementation process, the multi-objective optimization problem of the transmission quantity and the propulsion energy consumption is constructed, and the route of the UAV cluster is optimized. Aiming at the characteristics of the scene, the invention provides a centralized communication process of the UAVs among the clusters, and provides a centralized algorithm based on continuous convex approximation (SCA). The algorithm is divided into two stages:

the first stage is as follows: constructing a maximum transmission quantity problem, and obtaining a feasible solution meeting the original problem constraint by adopting an SCA algorithm;

and a second stage: and taking the feasible solution obtained in the first stage as a starting point, and obtaining the local optimal solution of the original problem by adopting an SCA algorithm.

In a specific implementation process, the invention provides an SCA-based cluster U2U Route Optimization (SARO) scheme. The contributions of this scheme mainly include: IrS-U2U communication scene is considered, and a centralized U2U communication flow is proposed, wherein a central UAV (central UAV, C-UAV) positioned in a cluster Center serves as a centralized node to realize IrS-U2U transmission in an Out-of-Coverage (OOC) state; the design defect that the existing U2U literature ignores UAV (unmanned aerial vehicle) propulsion energy consumption is overcome, the transmission quantity and the propulsion energy consumption target of a UAV cluster are considered in a combined manner, a multi-objective optimization problem is constructed, and effective optimization of a cluster route is realized; the problem of maximum transmission quantity is constructed and solved, and a starting point is provided for the multi-target optimization problem by judging whether the multi-target problem has a feasible solution, so that the problem of obtaining the starting point of the SCA algorithm is solved; 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, two kinds of clusters are considered in the IrS-U2U communication scenario model in step S1: a transmitting cluster (TS) that sends packets and a receiving cluster (RS) that receives packets. Inside the same cluster, there are two types of UAVs, C-UAVs and conventional UAVs (General UAVs, G-UAVs).

The following conditions are assumed:

the distribution of different clusters is sparse, the distance between the clusters TS and RS is much larger than the cluster radius, so the interference between the clusters is not considered;

before route optimization, the cluster distance is far, so that the U2U communication link quality is poor, and the G-UAV is difficult to directly perform the U2U discovery process [9]3GPP TS 24.334 Proximaty-services (ProSe) User Equipment (UE) to ProSe function protocol transactions; stage 3(Release 16). https:// www.3gpp.org/ftp/Specs/archive/24_ series/24.334/,2020. and U2U communication procedures;

the C-UAV communication capability is stronger than that of the G-UAV, the G-UAV communication device has auxiliary means such as satellite communication and the like, is positioned in a cluster center, can collect the U2U communication request of the G-UAV in the cluster, is in charge of the U2U discovery process, and performs signaling interaction with nodes outside the cluster;

G-UAV in RS (RS-G-UAV) needs to acquire data packets from outside the cluster for some purpose, while the buffer space of G-UAV in TS (TS-G-UAV) stores data packets of the type required by RS-G-UAV;

consider that if traffic data for G-UAVs of different clusters are all relayed via the C-UAV of each cluster, overloading of the C-UAV is likely to result. Therefore, the IrS-U2U communication process of the scheme requires that after the flight path optimization is successful, the TS-G-UAV and the RS-G-UAV which are successfully paired directly complete the transmission of service data;

the relative position between the UAVs inside the cluster remains unchanged, the C-UAV is always located in the center of the cluster;

within each time slot, only one G-UAV for TS and RS participates in U2U transmission, and the transmission power of each time slot is equal, denoted as P_tx. Thus, when IrS-U2U communications are implemented, interference from the remaining G-UAVs is not considered.

To summarize the assumptions, the IrS-U2U communication scenario, i.e., U2U communication between G-UAVs belonging to two clusters, can be approximately equivalent to a point-to-point communication scenario between C-UAVs, in order to simplify the problem.

For convenience of description, the set S ═ { TS, RS } is defined, and n is defined_s,

Indicating the number of UAVs of all types in cluster s. We divide the entire flight into T lengths

Time slot of (2), approximating a course of a cluster by T line segments][8]. Wherein the time slot

The superscript T in (a) constitutes a set T of slot numbers, 1,2, T.

In addition, let

Represents the vector of the central coordinates of cluster s at the beginning of the 1 st slot,

represents the vector of the center coordinates of cluster s at the end of the t-th slot, and assumes that all UAVs in TS and RS are at the same altitude, where

For convenience of description, further define

Wherein

Thus, { W } may be used_TSΛ and { W_RSAnd Λ represents routes optimized by the cluster TS and the cluster RS respectively. Notably, the collective course defined herein is a common course for all types of UAVs in the cluster.

The present solution focuses on the application scenario shown in fig. 2: the TS and RS perform specific tasks, planned in advance from their respective origins

To respective destinations

Of a flight line

Namely, it is

And

respectively representing the routes before TS and RS optimization. In this process, inter-cluster U2U communication needs to be done during flight based on certain requirements. For this reason, the cluster needs to temporarily adjust local routes to achieve IrS-U2U communication performance improvement.

In the IrS-U2U communication scenario, it is assumed that the communication links between UAVs are all LOS channels, and the effects of Doppler effect can be completely cancelled [6]][10]M.C.Erturk,J.Haque,W.A.Moreno,and H.Arslan,"Doppler mitigation in OFDM-based aeronaThe clinical communications, "IEEE Trans. Thus, the IrS-U2U communication channel model may employ a free space path loss model [7]][6][8]. Under the communication scene of IrS-U2U, define

And the distance from the center of the cluster i to the center of the cluster j is represented when the tth time slot is ended, wherein i, j belongs to S, and | | · | | is a two-norm operator. Thus, at the t-th time slot, the channel gain of cluster i to cluster j can be expressed as:

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

Furthermore, rotorcraft UAVs are more flexible than fixed-wing UAVs [2]Therefore, the scheme takes a propulsion power model of the rotor UAV as an example for optimization. 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. 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 the step S1, the C-UAV in the cluster has a function of interacting with the node outside the cluster. Therefore, in the scheme, the U2U connection establishment process is carried out by the signaling interaction of a C-UAV (RS-C-UAV) in RS and a C-UAV (TS-C-UAV) in TS, and the connection of RS-G-UAV and TS-G-UAV with U2U is established in an auxiliary mode. In addition, the cluster s, is,

planning the route in advance

And all UAVs in the same cluster fly from the same origin, therefore, it is assumed that all UAVs in the cluster know the original route before the start of the procedure

And the content of all G-UAVs in the C-UAV known cluster is classified, the communication flow of the IrS-U2U communication scenario model is shown in fig. 3, specifically:

s11: each RS-G-UAV initiates a U2U communication request to the RS-C-UAV [9], which forwards the U2U communication request to the TS-C-UAV, either directly or via a satellite communication link;

s12: the TS-C-UAV sends the U2U communication request to each TS-G-UAV, and each TS-G-UAV responds to the TS-C-UAV according to the self state (such as indexes of residual capacity) to judge whether to accept the U2U request [9 ];

s13: the TS-C-UAV integrates the responses of all TS-G-UAVs, and responds to the RS-C-UAV U2U request [11]3GPP TS 23.303Proximity-based services (ProSe) directly or through a satellite communication link; stage 2(Release 16). https:// www.3gpp.org/ftp/Specs/archive/23_ services/23.303/, 2020;

s14: if the connection of U2U is refused in response, the optimization is regarded as failure, and TS and RS implement cluster movement according to the original route; if the response accepts the connection of U2U, go through steps S15-S18;

s15: the RS-C-UAV notifies the TS-C-UAV of content classification information and airline optimization related constraint information; such as departure, destination, maximum flight time, propulsion energy consumption preferences, etc.;

s16: TS-C-UAV calculates content similarity [12] S.Niwattanakul, J.Singthrongchai, E.Naenuron, and S.Wanapu, "Using of Jaccard coefficients for keywords similarity," in Proc.int.Multi conf.Eng.Comp.Sci., Mar.2013, vol.I, pp.380-384, according to the obtained content classification information, and performs centralized U2U pairing [13] Kuhn H.the Hungarian method for the assignment protocol, Naval research qua 1955,2(1-2): 83-97;

s17: then, the TS-C-UAV optimizes by adopting an SCA-based route optimization algorithm, namely an SARO algorithm, according to the relevant constraint information of route optimization;

s18: if the optimization is successful: the TS-C-UAV notifies the RS-C-UAV of the pairing result and the route optimization result; the TS-C-UAV and the RS-C-UAVs broadcast the optimized air route to all G-UAVs in respective clusters, cluster movement is carried out according to the optimized air route result by the TS and the RS, and in the cluster movement process, the C-UAVs organize each pair of G-UAVs to carry out inter-cluster U2U communication in a concurrent or polling mode according to the availability of system bandwidth resources; otherwise, TS and RS implement cluster movement according to the original route.

More specifically, since the IrS-U2U communication scenario model includes a communication process in a period of time composed of a plurality of time slots, in the step S2, in the communication process in the step S1, an objective function of IrS-U2U communication is constructed jointly in consideration of the transmission amount and the propulsion energy consumption of the cluster in the plurality of time slots, specifically:

according to equation (2), the propulsion energy consumption of any UAV in the cluster s at the t-th slot is expressed as:

wherein:

representing the propelling power of the cluster s at the t time slot;

representing the displacement vector of cluster s at the t-th time slot. In addition, let

Indicating the distance vector between the clusters TS and RS in the t-th slot.

And

the definition is as follows:

because the distance from the starting point to the destination of the cluster is long, the flight time of the cluster is often long, and the distance of the cluster moving in a single time slot can be assumed

Much less than the total distance traveled by T slots

Thus, the transmission rate of the cluster TS to the cluster RS in the t-th slot can be approximately replaced by the transmission rate of TS to RS at the end of the slot, denoted as [8]]：

Wherein the content of the first and second substances,

in addition, since it is assumed that clusters are sparsely distributed and distances between respective origins and respective destinations of the clusters TS and RS are far, it can be generally considered that if the clusters TS and RS transmit near the respective origins and destinations, their transmission rates cannot satisfy QoS requirements, that is, their transmission rates are close to the respective origins and destinations

Where L is the number of silent slots, which indicates the number of slots in which the cluster is located in the area near the origin or destination and no U2U communication is performed. Thus, it may be assumed that the cluster does not perform U2U communication tasks near the origin and destination. A set T' ═ L + 1.., T-L } is defined, the elements of which represent the subscripts of the time slots in which communication tasks can be conductedThe communication time of the whole flight process can be defined as

Further, for convenience of expression, according to the formulas (3) and (5), the following variables are defined:

·C_tot(W_TS,W_RSΛ) represents the transmission capacity of the IrS-U2U communication system:

·E_tot(W_TS,W_RSΛ) represents the propulsive energy consumption of the IrS-U2U communication system:

·

representing the energy efficiency per time slot of the IrS-U2U communication system, which may be denoted as [14]]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：

Wherein the content of the first and second substances,

representing the total propulsion power of the t-th time slot.

·E_UTThe energy consumption of the propulsion per unit time can be expressed as:

·E_UDthe unit distance propulsion energy consumption can be expressed as:

since the scheme aims to construct and solve the multi-objective optimization problem of the transmission quantity and the propulsion energy consumption of the cluster, the transmission quantity and the propulsion energy consumption are combined, and an objective function of IrS-U2U communication is defined as follows:

wherein: theta_s∈(0,1],

And the preference factor of the cluster s to the propulsion energy consumption is represented, and is an average value of the propulsion energy consumption preference of each UAV in the cluster, and is used for the cluster s to adjust the proportion of the propulsion energy consumption in the multi-objective problem. Theta_sThe higher the cluster s, the more important the propulsion energy consumption problem is. In addition, positive real numbers

In units of Mbit/J, for adjusting the propulsion energy consumption E_tot(W_TS,W_RSA value of Λ) is set to be equal to the system traffic C_tot(W_TS,W_RSΛ) is in the same order of magnitude and keeps the adjusted term in the same dimension as the amount of transmission.

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

limited by:

wherein:

c1 is a traffic constraint indicating that the traffic between TS and RS cannot be less than the minimum traffic C after the end of the communication time_min；

C2 is a QoS constraint indicating that the transmission rate between the TS and RS clusters cannot be less than the minimum transmission rate at any time slot within the communication time

C3 is the flight speed constraint, meaning that the cluster TS and RS have a speed no higher than the UAV maximum flight speed V during flight time_max；

C4 is the maximum flight distance per time slot constraint, indicating that the flight distance per time slot of cluster s cannot be higher than Δ during communication time_max. The constraint condition can prevent that the transmission rate is greatly changed in a single time slot due to the fact that the flight distance of the clusters in the single time slot is too large, and therefore the inter-cluster transmission rate of the t-th time slot cannot be effectively approximated by the formula (5);

c5 is a cluster collision avoidance constraint that indicates that the separation between cluster TS and cluster RS cannot be less than the cluster protection distance during time of flight

C6 and C7 are time slot constraints, indicating that the time of flight cannot exceed T_maxAnd the value of each time slot length is positive number;

c8 and C9 indicate the initial positions of the clusters TS and RS, respectively

And

the last position is respectively

And

more specifically, in the step S3, it is noticed that the concavity and the convexity of the objective function of P1 are difficult to determine, and the constraint conditions C1, C2, C3, and C5 are all non-convex constraints, so that the calculation difficulty for directly solving the problem is large, and therefore, the optimization problem P1 needs to be relaxed and approximated, and a centralized algorithm solution is proposed, in which the multi-objective optimization problem is first transformed, specifically:

to effectively solve for P1, the objective function and the non-convex constraint are relaxed. Inspired by [2], the following relaxation variables were introduced:

further, inspired by [14], the channel model of cluster U2U communication according to equation (1) is further defined:

relaxation variables defined by equation (14)

Equation (3) can be approximated as:

in view of the above, it is noted that,

and

are all convex functions [2]]，

And

is an affine function, therefore

Is a convex function [15]]S.boyd and l.vandenberghe, convention optimization. Cambridge, u.k.: Cambridge univ.press, 2004. Furthermore, according to equation (5), equation (6) can be expanded as:

by reusing formula (16), it is possible to obtain:

finally, the combination of formula (19) with formula (15) has:

substitution of formula (20) for C1 in formula (13), C1 may relax to:

further, for ease of description, the following variables are defined based on the relaxation variables introduced by C10-C12:

further, using equations (17) and (20), the objective function of equation (11) can be approximated as follows:

therefore, the optimization problem P1 given by equation (12) can be transformed into the following optimization problem:

limited by: C2-C13.

Based on the above, the following properties are present:

properties 1: the optimization problem P2 is equivalent to P1.

And (3) proving that: according to the definitions of P1 and P2, P2 lacks constraint C1 and adds new constraint C10-C13 compared with P1. Wherein, C10-C12 are the constraint of the introduced relaxation variable, and C13 is the constraint of C1 after relaxation. It is important to demonstrate that P2 is equivalent to P1, namely: when P2 is optimal, P2 may be degenerated to P1[15 ].

When the C10-C12 equal sign is not true, to maximize the objective function of P2:

1) on the one hand, according to equation (17), reduce

Can be reduced by the value of (1)

Thereby increasing the objective function value of P2. When in use

When C10 of equation (14) is satisfied, the following steps are performed:

substitution of formula (25) for formula (17) can give:

2) on the other hand, as is clear from C11 of formula (15) and C12 of formula (16), ζ is reduced^＜t＞Can increase xi^＜t＞The upper bound of (c). At the same time, xi is increased^＜t＞Is a value of (1), then

And also increases, the objective function value of P2 may be increased. When xi^＜t＞Continues to increase and ζ^＜t＞Continuing to decrease to the point where the C11 and C12 equal signs hold, the following holds:

two formulae are available in the vertical combination (27):

in summary, when P2 is optimized, the equal sign of constraints C10-C12 holds, and thus equations (26) and (28) hold. Subsequently:

1) by substituting formula (26) and formula (28) for formula (23), it is possible to obtain:

i.e., P2 and P1 reached the optimum, the target function of P2 degenerates to the target function of P1.

2) Substituting equation (28) into constraint C13, i.e., equation (21), C13 degenerates to C1.

In conclusion of the two-step operation, the relaxation variable { Ω ] in P2_TS,Ω_RSPhi, psi is eliminated and P2 degenerates to P1, property 1 is verified.

Note that the objective function of P2, i.e. in equation (23)

Is a non-concave function and constrains the stripPieces C2, C3, C5, C10, C11 and C13 are non-convex constraints. Therefore, further processing is required to solve for P2.

1) Processing of the P2 objective function:

first, refer to function x²Where x is x^(k)The way of performing the first-order taylor expansion,

in xi^＜t＞＝ξ^＜t＞,(k)After the first order taylor expansion, it can be approximated as the following affine function:

wherein ξ^＜t＞,(k)Indicating ξ for the kth iteration^＜t＞. According to [2]]The value formula of the relaxation variable in (1) and the formula (4)

Definition of (1), xi^＜t＞,(k)Can be calculated from the following formula:

wherein the content of the first and second substances,

and

respectively calculated for the k-th iteration

And

next, by substituting equation (29) for equation (23), the objective function of P2 can be approximated as:

as can be seen from the formula (17),

as a convex function, i.e. in equation (31)

Is a concave function, so equation (31) is a non-negative weighted sum of the affine function and the concave function, i.e., equation (31) is a concave function [15]]。

2) Treatment of C2:

first, according to the equation

In conjunction with formula (5), C2 can be represented as:

secondly, after the term shift of equation (32), the equation e is followed^lnaA, one can get:

finally, after shifting the term of equation (33), C2 may be equivalent to the following convex constraint:

wherein the content of the first and second substances,

3) treatment of C3:

due to the fact that

C3, after being shifted, can be equivalent to the following convex constraint:

4) treatment of C5:

firstly, by using | | a | | | b | | | | > or more than a · b, it is possible to obtain:

where · is the vector inner product operator.

Further, using equation (36), C5 may relax into the following convex constraint:

5) treatment of C10:

first, the two sides of C10 in equation (14) are squared and inverted to obtain:

after the two equations in equation (38) are added and shifted, C10 can be equivalently constrained as follows:

since formula (39) is a non-convex constraint, the

And

are respectively at

And

is subjected to a first order Taylor expansion, wherein

And

respectively representing the k-th iteration

And

then it is possible to obtain:

the two equations in equation (40) are added to obtain:

according to [2]]The value formula of the relaxation variable in (1) and the formula (4)

In the definition of (a) is,

and

can be calculated from the following equations:

thus, by placing formula (41) under formula (39), C17 can relax into the following convex constraint:

6) treatment of C11:

because of the fact that

Is a convex function with respect to x (x > 0), so

And with it in x ═ x₀The first order Taylor expansion of (A) satisfies the following relationship:

similar to equation (44), right side of C11 for equation (15) is at

Is subjected to first-order Taylor expansion to obtain [8]：

Subsequently, substituting equation (45) for equation (15) and transposing, C11 can be relaxed as the following convex constraint:

7) treatment of C13:

according to inequality x²≥2x^(k)x-(x^(k))²C13 may relax into the following convex constraint:

combining the above processing of the objective function and the non-convex constraint, P2 can be transformed into the convex problem as follows:

limited by: c4, C6-C9, C12, C14-C16 and C18-C20.

Therefore, P2 can be solved by using SCA algorithm [15], and the specific method is to iteratively solve the convex sub-problem P3 until the convergence condition is reached. Since P3 is a typical convex problem, each iterative solution to it can be done using the CVX tool [15 ].

More specifically, in the step S3, for convenience of description, the following definitions are given:

when the P3 is solved by iteration for the first time, the starting point of P3 needs to be found

Since the original problem of P3 is P2, according to the nature of SCA algorithm [15]]The starting point needs to satisfy the constraint of P2. Further, according to property 1, P2 is equivalent to P1, so that

The constraint of P1 needs to be satisfied. However, due to the existence of the transmission quantity constraint C1 of P1, whether a feasible solution exists in P1 cannot be judged, so that the starting point is difficult to directly obtain. Therefore, in order to determine whether P1 has a feasible solution, it is necessary to determine the maximum transmission amount C between clusters_maxWhether the traffic constraint C1 is satisfied. To obtain the maximum transfer capacity C between clusters_maxTemporarily ignoring propulsion energy consumption in the P2 objective function and ignoring traffic constraints C1, so that P1 degradesBecomes a single target optimization problem of maximizing IrS-U2U communication transmission quantity:

limited by: C2-C9. Since P4 is degenerated by P1, P4 can use a similar transformation method from P2 to P3:

1) first, slack variables and associated constraints C11 and C12 are introduced according to equations (15) and (16);

2) secondly, the conclusion of property 1, i.e. the objective function of equivalent P4 according to equation (28);

3) thirdly, the equivalent target function of P4 is approximated according to equation (29);

4) finally, the same operations described above are performed for non-convex constraints C2, C3, C5, and C11, namely:

i. c2 is equivalent to C14 according to equation (34);

equating C3 to C15 according to formula (35);

relaxing C5 to C16 according to formula (37);

relaxing C11 to C19 according to formula (46);

in summary, P4 can be transformed into the following convex problem P5:

limited by: c4, C6-C9, C12, C14-C16, C19, wherein Γ ═ W_TS,W_RSΛ, Φ, Ψ }. Therefore, by iteratively solving P5 through the SCA algorithm, a locally optimal solution of P4 can be obtained

And maximum transmission capacity C_max. If C_max≥C_minThen, a feasible solution of P1 exists; further, since P1 and P2 are equivalent, it is known that P2 has a feasible solution, that is, P3 has a starting point, and P3 can be solved iteratively by using the SCA algorithm.

More specifically, in the step S3It is noted that in the process of solving P5 through the SCA algorithm, a starting point needs to be provided for P5 in the first iteration. Since the original problem of P5 is P4, it is necessary to find any feasible solution that satisfies the optimization problem P4

For this purpose, algorithm 1 of table 1 is proposed as follows:

TABLE 1 IrS-U2U Initial Solution Generation (ISG) Algorithm

Then, let

And calculating phi from equation (30) and equation (49)⁽⁰⁾Thus obtaining the starting point of P5

More specifically, in the step S4, based on the starting point of the optimization problem P5 obtained in the step S3, the maximum transmission amount C between the clusters TS and RS can be obtained by iteratively solving P5 through the SCA algorithm_maxAnd a locally optimal solution of P4

If C is present_max≥C_minIt is shown that a feasible solution exists in the optimization problem P1, that is, the P3 can be solved by iteration through an SCA algorithm. If C_max＜C_minThen P1 has no feasible solution and the cluster should be made to fly according to the pre-optimization route, i.e.

More specifically, in the step S4, the convex problems P5 and P3 are iteratively solved in stages by using the SARO algorithm, so that the route optimization of IrS-U2U can be completed.

In the implementation, to solve for P3, it is necessary toTo solve the local optimum of P4

For constructing a starting point for the P3 optimization process. Since the constraints of P3 and P5 are different, the starting point is also different. Because there are some variables with the same meaning in P3 and P5, when constructing the starting point of P3, the corresponding starting point of these variables needs to be updated. The specific construction method comprises the following steps:

1) first, the local optimum solution of P4 is determined

For updating

Instant game

2) Then, the calculation is performed based on the equations (42) and (49)

3) Finally, due to

Update of (phi)⁽⁰⁾The values of (a) are also updated according to equations (30) and (49).

After the above-mentioned construction method, the

As a starting point of P3, iteratively solving P3 by adopting an SCA algorithm to obtain a final optimized route

In summary, the SARO algorithm proposed by the present solution is summarized in table 2. It is worth noting that whether the algorithm can optimize success depends on whether there is a feasible solution for P1, which in turn depends on the number of UAV clusters that are to communicate IrS-U2UWhether the basic conditions for communication are met. Thus, if P1 does not have a feasible solution, C is obtained after stage one execution of the SARO algorithm_max＜C_minThen it indicates that the current environment is not sufficient to support at T_maxThe transmission data quantity completed in time is C_minIrS-U2U communication process. In this case, increasing T may be considered_maxOr lower C_minLet P1 become a viable problem.

Table 2: SARO algorithm

In the specific implementation process, the design concept of the IrS-U2U communication system designed by the present scheme is summarized in fig. 4, which is briefly described as follows:

first, since the P1 objective function is complex and non-convex, the constraint is non-convex and difficult to solve. Therefore, according to property 1, P1 can be equivalent to P2, the objective function is relaxed, and the constraint conditions are equivalent and relaxed, resulting in problem P3;

secondly, when P3 is solved iteratively by SCA, a starting point is needed. Since the original problem of P3 is P1, it is necessary to find the starting point of P3 starting from a feasible solution of P1. Due to the existence of the traffic constraint C1, it is difficult to judge whether a feasible solution exists for P1. Therefore, neglecting the propulsion energy consumption in the P1 objective function for the moment, neglecting C1 for the moment, constructing the maximum transmission quantum problem P4, and obtaining P5 by adopting the equivalent and relaxation method of P1-P3. Since the P5 is solved by iteration through the SCA algorithm, the starting point of generating P5 by the ISG algorithm is designed.

And finally, iteratively solving convex problems P5 and P3 in stages to complete the route optimization of IrS-U2U.

Example 3

More specifically, on the basis of embodiments 1 and 2, 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 an optimal time Minimization (CTM) scheme [8] is selected for performance comparison.

The simulation parameters of the present example are shown in table 3, 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 video service as an example, and the minimum transmission quantity among clusters is obtained

Set to 9Mbit/s for supporting 1080p high-resolution video applications.

Table 3: simulation parameter list

In the implementation process, the performance of the SARO scheme oriented to the IrS-U2U communication system proposed by the scheme is evaluated. An existing CTM scheme [8] was selected]A performance comparison is made, which scheme minimizes the time of flight T_totAs an objective function. For the sake of fairness comparison, all the schemes adopt the iterative solution after the initial point is generated by the stage 1 of the algorithm 2.

As shown in FIG. 5, this example simulates the course traces of TS and RS in the IrS-U2U communication scenario when the SARO scheme and the CTM contrast scheme proposed herein are employed. The main simulation parameters are given in Table 3, where the propulsion energy consumption preference factors for TS and RS are set to θ_TS＝θ_RS0.5. The shortest time flight path in the graph corresponds to the flight path generated by the CTM scheme, and the maximum transmission flight path and the combined optimal flight path respectively correspond to the flight paths generated by the stage 1 and the stage 2 of the SARO algorithm

From FIG. 5, it can be seen that the distance of the TS lane after the optimization of the SARO scheme is larger than that of the RS lane. This is because: in simulation, n_TS＜n_RSI.e. the number of UAVs in the cluster RS is higher. From equation (11), the cluster propulsion energy consumption is proportional to the number of UAVs in the cluster. Therefore, the propulsion energy consumption per unit distance traveled by the cluster RS is higher, so the optimization algorithm will try to reduce the flight distance of the cluster RS, while tending to extend the flight distance of the cluster TS.

Further, the transmission amount C is simulated in FIG. 6 under different transmission amount limits_totAnd propulsion energy E_totFactor theta dependent on preference_sCurve of change, where S ∈ S. Setting the preference factors for TS and RS to the same value, i.e. θ_TS＝θ_RS. As can be seen from the figure, the transmission amount and the propulsion energy consumption are both dependent on the preference factor theta_sIncrease and decrease, and all receive the minimum transmission amount C_minThe limit of (2). Furthermore, the results show that the transmission capacity and the propulsion energy consumption are a compromise: theta_sThe higher the transmission quantity, the more important the algorithm execution subject is on the propulsion energy consumption of the UAV cluster, so that the propulsion energy consumption is saved by sacrificing the transmission quantity; but when the transmission amount is reduced to C_minWhile continuously increasing theta_sThe value of (a) will not affect the final optimization result any more.

Further, the preference factor is set to θ_TS＝θ _RS1, and at different C_minUnder the constraint, the unit time of the SARO scheme and the CTM scheme is pushed to consume E_UTPropelling energy consumption per unit distance E_UDAnd energy efficiency

The comparison was made as shown in fig. 7(a), 7(b) and 8, respectively.

As can be seen from fig. 7(a) and 7(b), C is constrained at different transmission amounts_minNext, E of the present scheme_UTAnd E_UDAre superior to the comparative scheme. Therefore, when long-time and long-distance cluster flight is carried out, the scheme consumes lower propulsion energy consumption, and is favorable for promotionDuration of the cluster. Second, fig. 8(a) shows that the energy efficiency of both schemes increases with the timeslot superscript t, and then decreases, and the maximum energy efficiency μ can be maintained for a period of time_max. On the one hand, referring back to fig. 5, in the routes of the SARO scheme and the CTM scheme, TS and RS are close to each other and then gradually far away from each other, i.e. the distance between TS and RS

As the time slot superscript t increases, it decreases and then increases. Therefore, according to equation (5), the transmission rate between clusters can be found

As the slot superscript t increases and then decreases. On the other hand, as can be seen from FIG. 8(b), the total propulsion power of the two schemes

All decreasing and then increasing as the time slot superscript t increases. In summary, the energy efficiency of the two schemes increases and then decreases as the timeslot superscript t increases, as can be seen from the definition of energy efficiency in equation (8). In particular, it can be seen from FIG. 8 that at different C_minUnder the constraints, the energy efficiency of the SARO scheme is always better than the CTM scheme, and the total propulsive power is always lower than the CTM scheme.

As can be seen from fig. 7 and 8, the energy consumption per unit time and the energy consumption per unit distance for the SARO scheme are lower than those of the CTM scheme, and the energy efficiency is higher than that of the CTM scheme.

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 centralized dynamic resource allocation method facing inter-cluster communication is characterized by comprising the following steps:

s1: constructing IrS-U2U communication scene models and communication flows;

s2: constructing IrS-U2U communication objective functions to obtain a IrS-U2U communication multi-objective optimization problem;

s3: converting the multi-objective optimization problem and adopting an initial solution generation algorithm, namely generating a starting point of the converted optimization problem by using an ISG algorithm;

s4: and solving the transformed optimization problem by stages and iteration by adopting an SCA algorithm according to the starting point, realizing IrS-U2U route optimization according to a solving result, and finishing the distribution of U2U centralized dynamic resources.

2. The method of claim 1, wherein two types of clusters are considered in the IrS-U2U communication scenario model in step S1, including a transmitting cluster TS for transmitting packets and a receiving cluster RS for receiving packets; inside the same cluster, a central UAV, namely C-UAV, and a conventional UAV, namely G-UAV, are divided in the central position of the cluster; meanwhile, the present model assumes the following conditions:

the distribution of different clusters is sparse, the distance between the clusters TS and RS is much larger than the cluster radius, so the interference between the clusters is not considered;

before route optimization, the cluster distance is far, so that the U2U communication link quality is poor, and the G-UAV is difficult to directly carry out the U2U discovery process and the U2U communication process;

the UAV communication capability is stronger than that of a G-UAV, the UAV communication capability is provided with auxiliary means such as satellite communication and the like, the UAV is located in a cluster center, a U2U communication request of the G-UAV in the cluster can be collected, the UAV is responsible for a U2U discovery process, and signaling interaction is carried out with nodes outside the cluster;

G-UAV in RS, i.e. RS-G-UAV, needs to acquire data packets from outside the cluster for some purpose, while the buffer space of G-UAV in TS, i.e. TS-G-UAV, stores data packets of the type required by RS-G-UAV;

if the service data of the G-UAV of different clusters are relayed through the C-UAV of each cluster, the C-UAV is easily overloaded, and therefore, the communication process of IrS-U2U requires that after the flight path optimization is successful, the TS-G-UAV and the RS-G-UAV which are successfully paired directly complete the transmission of the service data;

the relative position between the UAVs inside the cluster remains unchanged, the C-UAV is always located in the center of the cluster;

within each time slot, only one G-UAV for TS and RS participates in U2U transmission, and the transmission power of each time slot is equal, denoted as P_tx(ii) a Interference from the remaining G-UAVs is not considered when IrS-U2U communications are implemented;

therefore, the IrS-U2U communication scenario, namely U2U communication between G-UAVs belonging to two clusters, is approximately equivalent to a point-to-point communication scenario between C-UAVs;

define set S ═ { TS, RS }, and define

Represents the number of UAVs of all types in cluster s; divide the whole flight into T lengths

The time slot of (1) is approximately used for representing the route of the cluster by T line segments; wherein the time slot

The superscript T in (a) constitutes a set T of slot numbers, 1,2, T.

In addition, let

Represents the vector of the central coordinates of cluster s at the beginning of the 1 st slot,

represents the vector of the center coordinates of cluster s at the end of the t-th slot, and assumes that all UAVs in TS and RS are at the same altitude, where

Then, further define

Wherein

Thus, using { W_TSΛ and { W_RSΛ represents routes after optimization of the cluster TS and the cluster RS respectively; wherein, the defined cluster route is a common route of all types of UAVs in the cluster;

in the IrS-U2U communication scenario model, the TS and RS perform specific tasks, and are planned in advance from respective origins

To respective destinations

Of a flight line

Namely, it is

And

respectively representing routes before TS and RS optimization; in this process, based on certain requirements, inter-cluster U2U communication needs to be completed during flight; for this reason, the cluster needs to be temporaryAdjusting local routes to obtain IrS-U2U communication performance improvement;

under the IrS-U2U communication scenario, it is assumed that all communication links between UAVs are LOS channels, and the influence caused by Doppler effect can be completely counteracted; therefore, the IrS-U2U communication channel model employs a free space path loss model; under the communication scene of IrS-U2U, define

Representing the distance from the center of the cluster i to the center of the cluster j when the tth time slot is finished, wherein i, j belongs to S, and | | · | | is a two-norm operator; at this time, at the t-th time slot, the channel gain of cluster i to cluster j is expressed as:

wherein, beta and alpha respectively represent a channel coefficient and a path loss index; furthermore, rotorcraft UAV is more flexible than fixed-wing UAV, and therefore optimized with a model of propulsion power for rotorcraft; propulsive power P of 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; since the propulsion power of a single UAV is typically in the order of hundreds of watts, i.e., the launch power is assumed to be much less than the propulsion power.

3. The method of claim 2, wherein in step S1, C in the cluster isUAVs have the function of interactive signaling with nodes outside the cluster, so the U2U connection establishment procedure is signaled by C-UAV in RS, i.e. RS-C-UAV and C-UAV in TS, i.e. TS-C-UAV, assisting in establishing the U2U connection of RS-G-UAV and TS-G-UAV; in addition, clusters

Planning the route in advance

And all UAVs in the same cluster fly from the same origin, therefore, it is assumed that all UAVs in the cluster know the original route before the start of the procedure

And the content of all G-UAVs in the known cluster of the C-UAV is classified, the communication flow of the IrS-U2U communication scene model is specifically as follows:

s11: each RS-G-UAV initiates a U2U communication request to the RS-C-UAV, which forwards the U2U communication request to the TS-C-UAV, either directly or via a satellite communication link;

s12: the TS-C-UAV sends the U2U communication request to each TS-G-UAV, and each TS-G-UAV responds to the TS-C-UAV according to the state of the TS-C-UAV whether to accept the U2U request;

s13: the TS-C-UAV integrates the responses of all TS-G-UAVs, and responds to the request of U2U to the RS-C-UAV directly or through a satellite communication link;

s14: if the connection of U2U is refused in response, the optimization is regarded as failure, and TS and RS implement cluster movement according to the original route; if the response accepts the connection of U2U, go through steps S15-S18;

s15: the RS-C-UAV notifies the TS-C-UAV of content classification information and airline optimization related constraint information;

s16: the TS-C-UAV calculates content similarity by adopting a Jaccard coefficient according to the acquired content classification information, and performs centralized U2U pairing by adopting a classical Hungarian pairing algorithm;

s17: then, the TS-C-UAV optimizes by adopting an SCA-based route optimization algorithm, namely an SARO algorithm, according to the relevant constraint information of route optimization;

s18: if the optimization is successful: the TS-C-UAV notifies the RS-C-UAV of the pairing result and the route optimization result; the TS-C-UAV and the RS-C-UAVs broadcast the optimized air route to all G-UAVs in respective clusters, cluster movement is carried out according to the optimized air route result by the TS and the RS, and in the cluster movement process, the C-UAVs organize each pair of G-UAVs to carry out inter-cluster U2U communication in a concurrent or polling mode according to the availability of system bandwidth resources; otherwise, TS and RS implement cluster movement according to the original route.

4. The method according to claim 3, wherein a IrS-U2U communication scenario model includes a communication process in a period of time that is composed of a plurality of time slots, so in step S2, in the communication process in step S1, an objective function of IrS-U2U communication is constructed jointly, specifically:

according to equation (2), the propulsion energy consumption of any UAV in the cluster s at the t-th slot is expressed as:

wherein:

representing the propelling power of the cluster s at the t time slot;

a displacement vector representing the cluster s at the t-th time slot; in addition, let

Representing a distance vector between the TS and the RS in the t-th time slot;

and

the definition is as follows:

because the distance from the starting point to the destination of the cluster is long, the flight time of the cluster is often long, and the distance of the cluster moving in a single time slot is assumed

Much less than the total distance traveled by T slots

Thus, the transmission rate of the cluster TS to the cluster RS in the t-th slot, approximately replaced by the transmission rate of the TS to the RS at the end of the slot, is expressed as:

wherein the content of the first and second substances,

in addition, since the clusters are assumed to be sparsely distributed and the distances between the respective origins and the respective destinations of the clusters TS and RS are far, that is, if the clusters TS and RS transmit near the respective origins and destinations, the transmission rates thereof cannot satisfy the QoS requirements, that is, the clusters TS and RS transmit near the respective origins and destinations, that is, the transmission rates thereof cannot satisfy the QoS requirements

Wherein, L is the number of silent time slots, which indicates the number of time slots when the cluster is in the area near the departure place or the destination and does not carry out U2U communication; therefore, assume that the cluster does not perform the U2U communication task near the origin and destination; a set of definitions T' ═ { L + 1., T-L }, the elements of which represent that a communication task can be performedThe subscript of the time slot, the communication time of the whole flight process is defined as

According to equations (3) and (5), the following variables are defined:

·C_tot(W_TS,W_RSΛ) represents the transmission capacity of the IrS-U2U communication system:

·E_tot(W_TS,W_RSΛ) represents the propulsive energy consumption of the IrS-U2U communication system:

·

representing the energy efficiency per time slot of the IrS-U2U communication system, as:

wherein the content of the first and second substances,

representing the total propulsion power of the t time slot;

·E_UTexpressing the energy consumption of propulsion per unit time, expressed as:

·E_UDthe unit distance propulsion energy consumption can be expressed as:

the transmission quantity and the propulsion energy consumption are combined, and an objective function of IrS-U2U communication is defined as follows:

wherein:

the preference factor of the cluster s for the propulsion energy consumption is an average value of the propulsion energy consumption preference of each UAV in the cluster, and is used for the cluster s to adjust the proportion of the propulsion energy consumption in the multi-target problem; theta_sThe higher the cluster s, the more important the problem of propulsion energy consumption is; in addition, positive real numbers

Unit of Mbit/J, for adjusting the propulsive energy consumption E_tot(W_TS,W_RSA value of Λ) is set to be equal to the system traffic C_tot(W_TS,W_RSΛ) is in the same order of magnitude and keeps the adjusted terms and the transmission quantity in the same dimension;

thus, the multi-objective optimization problem for IrS-U2U communication is represented as:

limited by:

wherein:

c1 is a traffic volume constraint indicating that after the communication time is over,the transmission quantity between TS and RS of cluster can not be less than the minimum transmission quantity C_min；

C2 is a QoS constraint indicating that the transmission rate between the TS and RS clusters cannot be less than the minimum transmission rate at any time slot within the communication time

C3 is the flight speed constraint, meaning that the cluster TS and RS have a speed no higher than the UAV maximum flight speed V during flight time_max；

C4 is the maximum flight distance per time slot constraint, indicating that the flight distance per time slot of cluster s cannot be higher than Δ during communication time_max(ii) a The constraint condition can prevent that the transmission rate is greatly changed in a single time slot due to the fact that the flight distance of the clusters in the single time slot is too large, and therefore the inter-cluster transmission rate of the t-th time slot cannot be effectively approximated by the formula (5);

c5 is a cluster collision avoidance constraint that indicates that the separation between cluster TS and cluster RS cannot be less than the cluster protection distance during time of flight

C6 and C7 are time slot constraints, indicating that the time of flight cannot exceed T_maxAnd the value of each time slot length is positive number;

c8 and C9 indicate the initial positions of the clusters TS and RS, respectively

And

the last position is respectively

And

5. the method according to claim 4, wherein in step S3, it is noticed that the target function concavity and convexity of P1 are difficult to determine, and the constraints C1, C2, C3, and C5 are all non-convex constraints, so that the calculation difficulty for directly solving the problem is large, and therefore, it is necessary to relax and approximate the optimization problem P1, and a centralized algorithm solution is proposed, and first, a multi-objective optimization problem is transformed, specifically:

the objective function and the non-convex constraint are relaxed first, and the following relaxation variables are introduced:

and further defines the channel model of cluster U2U communication according to equation (1):

relaxation variables defined by equation (14)

Approximating equation (3) as:

in view of the above, it is noted that,

and

all of which are convex functions, are provided with a convex function,

and

is an affine function, therefore

Is a convex function; furthermore, according to equation (5), equation (6) is developed as:

by reusing formula (16), the following can be obtained:

finally, the combination of formula (19) with formula (15) has:

substitution of formula (20) for C1 in formula (13), C1 relaxes to:

further, the following variables are defined based on the relaxation variables introduced at C10-C12:

further, using equations (17) and (20), the objective function of equation (11) is approximated as follows:

therefore, the optimization problem P1 given by equation (12) translates into the following optimization problem:

limited by: C2-C13; based on this, the following properties are present:

properties 1: optimization problem P2 is equivalent to P1:

according to the definitions of P1 and P2, P2 lacks constraint C1 and adds new constraint C10-C13 compared with P1; wherein, C10-C12 are the constraint of the introduced relaxation variable, and C13 is the constraint of C1 after relaxation; it is important to demonstrate that P2 is equivalent to P1, namely: when P2 reaches optimum, P2 may revert to P1;

when the C10-C12 equal sign is not true, to maximize the objective function of P2:

1) on the one hand, according to equation (17), reduce

Is taken as a value of (1) or in the formula (23)

Thereby increasing the objective function value of P2; when in use

When C10 of equation (14) is satisfied, the following steps are performed:

substituting formula (25) for formula (17) to obtain:

2) on the other hand, as is clear from C11 of formula (15) and C12 of formula (16), ζ is reduced^＜t＞Can increase xi^＜t＞The upper bound of (c); at the same time, xi is increased^＜t＞Is a value of (1), then

The target function value of P2 can be improved by increasing the value; when xi^＜t＞Continues to increase and ζ^＜t＞Continuing to decrease to the point where the C11 and C12 equal signs hold, the following holds:

two formulae are available in the vertical combination (27):

in summary, when P2 is optimized, the equal sign of constraint C10-C12 is established, and thus equations (26) and (28) are established; subsequently:

substituting formula (26) and formula (28) for formula (23) yields:

when P2 and P1 reach the optimum, the target function of P2 degenerates to the target function of P1;

substituting equation (28) into constraint C13, i.e., equation (21), C13 degenerates to C1;

in conclusion of the two-step operation, the relaxation variable { Ω ] in P2_TS,Ω_RSPhi, psi is eliminated, P2 degenerates to P1, then property 1 holds;

note that the objective function of P2, i.e. in equation (23)

Is a non-concave function, and constraints C2, C3, C5, C10, C11, and C13 are non-convex constraints; therefore, to solve for P2, further processing is required;

1) processing of the P2 objective function:

first, refer to function x²Where x is x^(k)The way of performing the first-order taylor expansion,

in xi^＜t＞＝ξ^＜t＞,(k)After the first order taylor expansion, the following affine function is approximated:

wherein ξ^＜t＞,(k)Indicating ξ for the kth iteration^＜t＞；ξ^＜t＞,(k)Calculated from the following formula:

wherein the content of the first and second substances,

and

respectively calculated for the k-th iteration

And

next, formula (29) is substituted for formula (23), and the objective function of P2 is approximated as:

as can be seen from the formula (17),

as a convex function, i.e. in equation (31)

Is a concave function, so equation (31) is a non-negative weighted sum of the affine function and the concave function, i.e., equation (31) is a concave function;

2) treatment of C2:

first, according to the equation

Binding formula (5), C2 is represented as:

secondly, after the term shift of equation (32), the equation e is followed^lnaA, yielding:

finally, after shifting the term of equation (33), C2 is equivalent to the following convex constraint:

wherein the content of the first and second substances,

3) treatment of C3:

due to the fact that

C3 is equivalent to the following convex constraint after shifting terms:

4) treatment of C5:

firstly, the method utilizes | | a | | | b | | | | | > a · b, and obtains:

where · is the vector inner product operator; further, using equation (36), C5 may relax into the following convex constraint:

5) treatment of C10:

first, the two sides of C10 in equation (14) are squared and inverted to obtain:

after the two equations in equation (38) are added and shifted, C10 is equivalent to the following constraint:

since formula (39) is a non-convex constraint, the

And

are respectively at

And

is subjected to a first order Taylor expansion, wherein

And

respectively representing the k-th iteration

And

then the following results are obtained:

the two equations in equation (40) are added to obtain:

wherein the content of the first and second substances,

and

are calculated by the following formulas, respectively:

thus, formula (41) is under formula (39), C17 relaxes to the following convex constraint:

6) treatment of C11:

because of the fact that

Is a convex function with respect to x (x > 0), so

And with it in x ═ x₀The first order Taylor expansion of (A) satisfies the following relationship:

similar to equation (44), right side of C11 for equation (15) is at

The first order taylor expansion is performed to obtain:

subsequently, formula (45) is substituted for formula (15) and transposed, relaxing C11 to the convex constraint as follows:

7) treatment of C13:

according to inequality x²≥2x^(k)x-(x^(k))²C13 relaxes to the following convex constraint:

combining the above processing of the objective function and the non-convex constraint, P2 is transformed into the convex problem as follows:

limited by: c4, C6-C9, C12, C14-C16 and C18-C20; up to this point, the SCA algorithm is used to solve P2, and the specific method is to iteratively solve the convex sub-problem P3 until a convergence condition is reached, and since P3 is a typical convex problem, each iterative solution is completed using a CVX tool.

6. The method of claim 5, wherein in the step S3, for convenience of description, the following definitions are given:

when the P3 is solved by iteration for the first time, the starting point of P3 needs to be found

Since the original problem of P3 is P2, the starting point needs to satisfy the constraint condition of P2 according to the nature of SCA algorithm; also, according to property 1, P2 is equivalent to P1, so

The constraint of P1 needs to be satisfied; however, due to the existence of the transmission quantity constraint C1 of P1, whether P1 is present or not cannot be judgedThere is a feasible solution, so this starting point is difficult to obtain directly; therefore, in order to determine whether P1 has a feasible solution, it is necessary to determine the maximum transmission amount C between clusters_maxWhether the transmission amount constraint C1 is satisfied; to obtain the maximum transfer capacity C between clusters_maxTemporarily ignoring the propulsive energy consumption in the P2 objective function and ignoring the traffic constraint C1, so that P1 degenerates to a single-objective optimization problem that maximizes IrS-U2U traffic:

limited by: C2-C9; since P4 is degenerated by P1, P4 can use a similar transformation method from P2 to P3:

1) first, slack variables and associated constraints C11 and C12 are introduced according to equations (15) and (16);

2) secondly, the conclusion of property 1, i.e. the objective function of equivalent P4 according to equation (28);

3) thirdly, the equivalent target function of P4 is approximated according to equation (29);

4) finally, the same operations described above are performed for non-convex constraints C2, C3, C5, and C11, namely:

i. c2 is equivalent to C14 according to equation (34);

equating C3 to C15 according to formula (35);

relaxing C5 to C16 according to formula (37);

relaxing C11 to C19 according to formula (46);

in summary, P4 translates into the following convex problem P5:

limited by: c4, C6-C9, C12, C14-C16, C19, wherein Γ ═ W_TS,W_RSΛ, Φ, Ψ }; therefore, the P5 is solved iteratively through the SCA algorithm, and the local optimal solution of P4 is obtained

And maximum transmission capacity C_max(ii) a If C_max≥C_minThen, a feasible solution of P1 exists; further, since P1 and P2 are equivalent, there is a feasible solution for P2, that is, there is a starting point for P3, and then P3 is solved iteratively by using the SCA algorithm.

7. The method as claimed in claim 6, wherein in step S3, it is noted that, in the process of solving P5 by SCA algorithm, a starting point needs to be provided for P5 in the first iteration, and since the original problem of P5 is P4, any feasible solution meeting the optimization problem P4 needs to be found

For this purpose, an ISG algorithm is used to generate a starting point for the optimization problem P5, specifically:

1) inputting: v_max,B,α,β,ε²,P_tx,

T,L,T_max,Δ_max,

2) In that

And

on the determined line segment, any two position points satisfying C4 and C14 are taken:

and

respectively doThe position points are the communication starting time points of the TS and the RS of the cluster;

3) in that

And

on the determined line segment, any two position points satisfying C4 and C14 are taken:

and

respectively serving as position points of the cluster TS and RS communication ending time;

4) in that

And

inserting the remaining position points on the sequentially formed folding lines so that each position point satisfies C4 to obtain

5) In that

And

inserting the remaining position points on the formed broken line so that each position point satisfies C4 to obtain

6) Will T_maxAllocating T time slots to make each time slot satisfy C3 to obtain lambda^init；

7) And (3) outputting:

then, let

And calculating phi from equation (30) and equation (49)⁽⁰⁾Thus obtaining the starting point of P5

8. The method according to claim 7, wherein in step S4, based on the starting point of the optimization problem P5 obtained in step S3, the P5 is iteratively solved through the SCA algorithm to obtain the maximum transmission capacity C between the TS and the RS of the cluster_maxAnd a locally optimal solution of P4

If C is present_max≥C_minThe optimization problem P1 is proved to have a feasible solution, namely, an SCA algorithm is adopted to solve the P3 in an iterative manner; if C_max＜C_minThen P1 has no feasible solution and the cluster should be made to fly according to the pre-optimization route, i.e.

9. The method according to claim 8, wherein in step S4, convex problems P5 and P3 are solved iteratively in stages by using a SARO algorithm, so that a route optimization of IrS-U2U can be completed.

10. The method of claim 9, wherein in the step S4,to solve P3, a locally optimal solution of P4 is required

A starting point for constructing a P3 optimization process; the starting point of P3 is different from that of P5 due to different constraints; because there are some variables with the same meaning in P3 and P5, when constructing a starting point of P3, the starting point corresponding to the some variables needs to be updated, and the specific construction method is as follows:

1) first, the local optimum solution of P4 is determined

For updating

Instant game

2) Then, the calculation is performed based on the equations (42) and (49)

3) Finally, due to

Update of (phi)⁽⁰⁾The value of (a) is also updated according to the equations (30) and (49);

after the above-mentioned construction method, the

As a starting point of P3, iteratively solving P3 by adopting an SCA algorithm to obtain a final optimized route

In summary, the SARO algorithm is used to solve for P5 and P3, and it is worth pointing out whether the algorithm can be successfully optimized or not, depending on whether P1 is stored or notA feasible solution, which in turn depends on whether the basic conditions for communication are present between two UAV clusters that are to communicate IrS-U2U; thus, if P1 does not have a feasible solution, C is obtained after stage one execution of the SARO algorithm_max＜C_minThen it indicates that the current environment is not sufficient to support at T_maxThe transmission data quantity completed in time is C_minIrS-U2U communication procedures; in this case, increasing T may be considered_maxOr lower C_minLet P1 become a viable problem.

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