CN117674958A - Network resource optimization method and device for air-space-earth integrated network - Google Patents
Network resource optimization method and device for air-space-earth integrated network Download PDFInfo
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Abstract
The invention discloses a network resource optimization method and a device for an air-space-earth integrated network, wherein the method comprises the following steps: constructing an air-space integrated communication network system model; the system comprises a low-orbit satellite, a relay unmanned aerial vehicle and a ground intelligent terminal; based on the constructed communication network system model, the transmission resource allocation model in the uplink of the system is constructed with the aim of maximizing the system capacity; aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, converting the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; and acquiring an optimal scheme of sub-channel selection and transmission power distribution. Therefore, on the basis of ensuring the transmission capacity of the system, the energy loss in the data uploading process is reduced, and the optimization of the space-earth integrated network resource is realized.
Description
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to a method and an apparatus for optimizing network resources for an air-space-ground integrated network.
Background
With the continuous growth of satellite communication services and the continuous development of network systems, the integration of a space network and a ground network becomes an emerging research direction. The space network has extremely strong complementary relation with the ground network in the aspects of coverage, mobile access and the like, so that the research direction has wider application range and prospect. The air-ground integrated network is a typical combination of a space network and a ground network, is based on the ground network, extends by taking the space network as an extension, and can provide service and information guarantee for users such as an air base, a sea base and the like by combining an unmanned aerial vehicle relay technology.
In the space-earth integrated network, three different types of communication equipment including ground users, relay unmanned aerial vehicles (UAV, unmanned Aerial Vehicle) and low-orbit satellites are simultaneously included, and the space-earth integrated network has the characteristics of wide communication coverage range, a large number of access equipment, large communication interference among the equipment, complex regional limiting conditions and the like, and has higher stability and reliability requirements. On the one hand, the air-ground integrated network ground terminal can be forwarded by a relay unmanned aerial vehicle to communicate with a low-orbit satellite; alternatively, the ground terminal may be configured to connect directly to the low-orbit satellite to establish a communication link. Based on different transmission selections, the method relates to the distribution of channel resources and transmission resources between a ground terminal and an unmanned plane and between a low-orbit satellite in a network, requires the network to have higher resource utilization rate, and provides challenges for the existing network resource distribution scheme. In order to improve the stability and reliability of the air-ground integrated network, reduce the communication delay of the equipment and ensure the connection of a communication link, it is highly necessary to propose a network resource allocation optimization algorithm for the air-ground integrated network, so as to realize the efficient utilization of limited bandwidth resources and transmission power.
Disclosure of Invention
The invention provides a network resource optimization method and device for an air-to-ground integrated network, which are used for solving the technical problem that the resource utilization rate of the existing network resource allocation scheme is not high enough.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the invention provides a network resource optimization method for an air-space-earth integrated network, which comprises the following steps:
constructing an air-space integrated communication network system model; wherein, the space-to-ground integrated communication network system model includes: low orbit satellite, relay unmanned aerial vehicle and ground intelligent terminal;
constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and optimization of network resources is realized.
Further, in the space-to-ground integrated communication network system model, all the relay unmanned aerial vehicles and the ground intelligent terminals are in the coverage range of the low-orbit satellite, and all the devices share a channel with the bandwidth W; the frequency band resources occupied by the relay unmanned aerial vehicle and the low-orbit satellite communication are equally divided into M parts, so that each relay unmanned aerial vehicle works in different frequency bands; the frequency band resources occupied by each relay unmanned aerial vehicle are divided into K sub-channels, each relay unmanned aerial vehicle can improve data transmission service for a group of ground intelligent terminals, information from the ground intelligent terminals is forwarded to a low-orbit satellite, and the orbit height of the low-orbit satellite is H S Communication between the relay unmanned aerial vehicle and the low-orbit satellite is regarded as line-of-sight transmission, and channel fading is Rice fading.
Further, the simplified problem is expressed as:
P
C1
C2
C3
C4
C5
C6
C7
C8
wherein,the system capacity after the integer variable is relaxed represents the total bit number successfully transmitted to the low-orbit satellite by the ground intelligent terminal by the relay unmanned aerial vehicle in unit time; />To from the nth ground intelligent terminal to in the kth sub-channelThe transmission power of the mth relay unmanned plane; />Representing the maximum transmission power of the ground intelligent terminal; />The transmission power of communication between the mth relay unmanned aerial vehicle and the low-orbit satellite is calculated; />Hover power for the relay drone; a, a n,m,k Represented as channel selection between the nth ground intelligent terminal and the mth relay unmanned aerial vehicle, when a n,m,k =1 means that the nth ground intelligent terminal occupies the kth sub-channel in the mth relay unmanned network, otherwise a n,m,k ＝0；/>Representing the uplink capacity from the nth ground intelligent terminal to the low-orbit satellite on the kth sub-channel of the mth relay unmanned aerial vehicle; />Representing the maximum transmission capacity that the low-orbit satellite can receive; a represents a channel selection matrix; p represents the power control matrix of all the ground intelligent terminals; m represents the number of relay unmanned aerial vehicles; n represents the number of intelligent terminals on the ground; />Representing a maximum transmit power of the relay drone; />Representing the minimum power required by the relay drone to maintain hover; c min Representing the minimum transmission capacity for guaranteeing the service quality of the ground intelligent terminal; />Representing a low railMaximum capacity of the satellite.
Further, C1 to C5 are limits on transmission power in the communication network system, C7 limits channel selection between the terminal unmanned aerial vehicle and the ground intelligent terminal, so as to ensure that communications between different devices do not interfere with each other, and C6 and C8 limit minimum and maximum values of transmission capacity in the air-ground integrated communication network system.
Further, the solving the problem by using the Lagrangian dual method comprises:
step one: initializing Lagrangian variables, maximum iteration times, channel gain, a power control matrix, a channel selection matrix, a ground intelligent terminal position, a relay unmanned plane position and a low-orbit satellite position, and calculating initial power so that the value of the initial power is larger than 0;
step two: constructing Lagrange dual function and finding out aboutAnd->Based on the KKT condition, updating the optimal values of the ground intelligent terminal and the unmanned aerial vehicle power;
step three: solving for a n,m,k Updating an optimal value of a channel selection scheme in the sub-channel allocation based on the KKT condition;
step four: updating Lagrangian multipliers;
step five: judging whether the iteration times are smaller than the maximum times, if so, repeating the second to fourth steps; and if not, ending the circulation, and outputting the optimal solution of the power of the ground intelligent terminal and the unmanned aerial vehicle and the sub-channel allocation.
Further, the lagrangian dual function is expressed as:
wherein a= { a n,m,k }，μ, ν, ω, ε, η are Lagrangian multipliers associated with the constraints; mu (mu) n ，ν m ，ω n ，ε m,k η is the Lagrangian multiplier; />Representing the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel;
the partial derivative of (2) is calculated as:
wherein, representing channel gain from the nth ground intelligent terminal to the mth relay unmanned aerial vehicle in the kth sub-channel; />Representing low-orbit satellite receiving noise from the mth relay drone;
wherein n, m, k satisfy the following conditions:
wherein,representing the optimal solution of the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel; (ρ) D→U ) * Representing an optimal solution of power control from the ground intelligent terminal to the unmanned aerial vehicle;
the partial derivative of (2) is calculated as:
wherein the method comprises the steps of Representing channel gain from the mth relay drone to the low orbit satellite; />Representing the receiving noise of the relay unmanned aerial vehicle from the nth ground intelligent terminal as additive Gaussian white noise;
wherein m satisfies the following condition:
wherein,representing an optimal solution for power control of the mth drone to the low orbit satellite;
a n,m,k the partial derivative of (2) is calculated as:
wherein,
consider constraint C7, a n,m,k Is the optimal solution (a) n,m,k ) * The conditions are as follows:
in the communication network system model, each relay unmanned aerial vehicle network is required to have at most one ground intelligent terminal to access the same sub-channel in one time slot; in order to maximize the Lagrangian function, there isWhen (a) n,m,k ) * =1 represents the choice of best ground intelligent terminal.
Further, the lagrangian multiplier is updated by adopting a sub-gradient method, and an updating formula is as follows:
wherein,for update step, (i) is an iteration index, x is the order of update steps, x=1, … 5, [ ·] + Equivalent to max {0, }, and->η (i) And the method is a Lagrange multiplier in the ith iteration, wherein m, n and k represent the relay unmanned aerial vehicle, the ground intelligent terminal and the channel sequence number corresponding to the current Lagrange multiplier.
On the other hand, the invention also provides a network resource optimizing device facing the space-air-ground integrated network, which comprises:
the communication model building module is used for building an air-to-ground integrated communication network system model; the space-sky integrated communication network system model comprises: low orbit satellite, relay unmanned aerial vehicle and ground intelligent terminal;
a network resource optimization module for:
constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and optimization of network resources is realized.
In yet another aspect, the present invention also provides an electronic device including a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
aiming at the problems of wide communication coverage, large number of access devices, large communication interference among devices, complex regional restriction conditions and the like in an air-space integrated network formed by a low-orbit satellite, a relay unmanned aerial vehicle and a ground intelligent terminal, the invention establishes a network transmission system capacity model, controls a channel selection constraint range, simplifies the problem solving scale and complexity, adopts a Lagrange dual method to solve the optimal channel allocation and control allocation scheme, thereby reducing the transmission loss of the air-space integrated network, reducing the system energy consumption on the basis of ensuring the transmission capacity of the system, improving the stability and the effectiveness of network transmission and having great application prospect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an execution flow of a network resource optimization method for an air-space-oriented integrated network according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an air-to-ground integrated communication network system provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of solving a problem using Lagrangian dual methods provided by an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings.
First embodiment
Aiming at the problem of space node resource limitation of an air-space-ground integrated network composed of a low-orbit satellite, an unmanned aerial vehicle and intelligent communication equipment, the embodiment provides a network resource optimization method for the air-space-ground integrated network, and designs a multi-layer network transmission architecture; establishing a network resource allocation model according to a communication network consisting of a low-orbit satellite, a relay unmanned aerial vehicle and a ground intelligent terminal; and taking the maximized system capacity as a target, loosening integer variables in integer programming constraint, performing convex approximation on non-convex constraint, converting the non-convex problem into a convex optimization problem, and adopting a Lagrange dual method to finish solving, so that the optimal solution of transmission power and channel allocation between the relay unmanned aerial vehicle and the ground intelligent terminal is realized, the transmission energy consumption of the system is reduced, and the network transmission effectiveness is improved. The method can be implemented by an electronic device, and the execution flow of the method is as shown in fig. 1, including:
s1, constructing an air-space-ground integrated communication network system model;
specifically, in this embodiment, as shown in fig. 2, the air-ground integrated communication network system model includes: the number of the low-orbit satellites can be multiple at the same time, and one embodiment is illustrated as an example. The embodiment considers an air-ground integrated network comprising a low-orbit satellite, M relay unmanned aerial vehicles and N ground intelligent terminals. Wherein all UAVs and ground intelligent terminals are within the coverage of the low orbit satellite, and these devices share a channel with a bandwidth W. The frequency band resources occupied by the UAV and low orbit satellite communications are divided equally into M parts to reduce intra-system interference problems, thereby allowing each UAV to operate in a different frequency band. Dividing the frequency band resources occupied by each UAV into K sub-channels, wherein each UAV can improve data transmission service for a group of ground intelligent terminals, and forwarding information from the terminals to low-orbit satellites with orbit heights of H S Communication between the UAV and the low orbit satellites is known as line of sight (LOS), and channel fading is Rice fading. The channel selection matrix is defined as a N×M×K The power control matrix of all the ground intelligent terminals is P N×M×K 。
S2, constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
specifically, for uplink, in order to make the system obtain the best transmission efficiency and channel allocation scheme, the embodiment constructs a transmission resource allocation model in the uplink of the system as follows:
P
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
wherein, R is the system capacity, represents the total bit number of the ground intelligent terminal transmitted by the successful low orbit satellite of the relay of the unmanned aerial vehicle in unit time, and is expressed as:
wherein c n,m,k For uplink capacity from the nth ground intelligent terminal to the low-orbit satellite on the kth sub-channel of the mth UAV, the link capacity c can be expressed as:
wherein,in the mth UAV network, UAV received signal-to-noise ratio (SNR) from the nth ground intelligent terminal occupying the kth subchannel; />Representing the received signal-to-noise ratio from the low orbit satellite for the mth UAV. />Andthe calculation method of (2) is as follows:
wherein,transmitting power from the nth ground intelligent terminal to the mth UAV in the kth sub-channel;channel gain from the nth ground intelligent terminal to the mth UAV in the kth sub-channel; />Receiving noise for the UAV from the nth ground intelligent terminal, wherein the noise is additive Gaussian white noise; />And->Transmission power and channel gain from the mth UAV to the low orbit satellite, respectively; similarly, a->Noise is received for the low orbit satellite from the mth UAV. Wherein (1)>Andthe expression is as follows:
wherein g 0 Representing the channel power gain at a reference distance of 1 meter.And->Respectively representing physical positions of an mth UAV and an nth ground intelligent terminal, h m Representing the height of the mth UAV hover.
In the constraint part of the model, a total of 11 constraint requirements of C1-C11 are included, wherein C1, C2, C3, C4 and C5 are constraints on signal power; c6 is used for guaranteeing the minimum transmission rate of the ground intelligent terminal; c7 and C8 limit the channel selection between the ground intelligent terminal and the UAV; c9 and C10 limit the position and altitude of the UAV; c11 means that the total transmission capacity per unit time should be less than the transmission capacity limit of the low-orbit satellite.
S3, aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying a non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and network resource optimization is realized.
In order to simplify the problem solving scale and reduce the model solving difficulty, the original problem can be decomposed, and the UAV deployment position D is fixed U Thereby fixingAnd->Based on which the optimal channel allocation and power control scheme is found. While approximating integer programming in problem constraintsBeam C8 relaxation is a continuous convex optimization, i.e. a n,m,k ∈[0,1]The original non-convex optimization problem is converted into a convex optimization problem which is convenient to solve. Further, introducing auxiliary variablesThen c can be n,m,k And R is rewritten as:
on this basis, the optimization problem can be rewritten as:
P
C1
C2
C3
C4
C5
C6
C7
C8
wherein,the system capacity after the integer variable is relaxed represents the total bit number successfully transmitted to the low-orbit satellite by the ground intelligent terminal by the relay unmanned aerial vehicle in unit time; />The transmission power from the nth ground intelligent terminal to the mth relay unmanned aerial vehicle in the kth sub-channel is used; />Representing the maximum transmission power of the ground intelligent terminal; />The transmission power of communication between the mth relay unmanned aerial vehicle and the low-orbit satellite is calculated; />Hover power for the relay drone; a, a n,m,k Represented as channel selection between the nth ground intelligent terminal and the mth relay unmanned aerial vehicle, when a n,m,k =1 means that the nth ground intelligent terminal occupies the kth sub-channel in the mth relay unmanned network, otherwise a n,m,k ＝0；/>Representing the uplink capacity from the nth ground intelligent terminal to the low-orbit satellite on the kth sub-channel of the mth relay unmanned aerial vehicle; />Representing the maximum transmission capacity that the low-orbit satellite can receive; a represents a channel selection matrix; p represents the power control matrix of all the ground intelligent terminals; m represents the number of relay unmanned aerial vehicles; n represents the number of intelligent terminals on the ground; />Representing the maximum transmitting power of the relay unmanned plane;representing the minimum power required by the relay drone to maintain hover; c min Representing the minimum transmission capacity for guaranteeing the service quality of the ground intelligent terminal; />Representing the maximum capacity of the low-orbit satellite.
The simplified optimization problem has 8 constraint conditions in total; wherein, C1-C5 are the restriction to the transmission power in the communication network system, C7 has restricted the channel selection between terminal unmanned aerial vehicle and ground intelligent terminal to ensure that the communication between different equipment can not interfere each other, C6 and C8 have restricted the minimum and the maximum of transmission capacity in the space-earth integration communication network system.
Based on the above, in the scenario facing the space-earth integrated network, the present embodiment models the problem of communication resource and power resource allocation of the network including the low-orbit satellite, the relay UAV and the ground intelligent terminal, simplifies the original problem to a solvable convex optimization problem, then uses the lagrangian dual method to implement the solution, and finally obtains the optimal channel allocation and transmission power allocation scheme, as shown in fig. 3, and the implementation steps are as follows:
step one: initializing Lagrangian variables, the maximum iteration times, channel gain, a power control matrix, a channel selection matrix, a ground intelligent terminal position, a relay unmanned plane position, a low-orbit satellite position and other variables, and calculating initial power so that the value of the initial power is larger than 0;
step two: constructing Lagrange dual function and finding about the changeMeasuring amountAnd->Based on the Karush-Kuhn-Tucker (KKT) condition, update the power +.>And->Is the optimum value of (2);
further, the lagrangian dual function is expressed as:
wherein a= { a n,m,k }，μ, ν, ω, ε, η are Lagrangian multipliers associated with the constraints; mu (mu) n ，ν m ，ω n ，ε m,k η is the Lagrangian multiplier; />Representing the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel;
further, the above formula may be expressed as:
wherein Φ and ψ are represented as:
based on the above-mentioned that,the partial derivative of (2) is calculated as:
wherein,
wherein n, m, k satisfy the following conditions:
wherein,representing the optimal solution of the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel; (ρ) D→U ) * Representing an optimal solution of power control from the ground intelligent terminal to the unmanned aerial vehicle;
in response to this, the control unit,the partial derivative of (2) is calculated as:
wherein the method comprises the steps of
Wherein m satisfies the following condition:
wherein,representing an optimal solution of the mth unmanned aerial vehicle to low-orbit satellite power control;
step three: solving for the variable a n,m,k Based on KKT conditions, updates the channel allocation selection (a n,m,k ) * Is the optimum value of (2);
wherein a is n,m,k The partial derivative of (2) is calculated as:
wherein,
consider constraint C7, a n,m,k Is the optimal solution (a) n,m,k ) * The conditions are as follows:
in the established communication network system model, each relay unmanned aerial vehicle network is required to have at most one ground intelligent terminal to access the same sub-channel in one time slot; in order to maximize the Lagrangian function, there isWhen (a) n,m,k ) * =1 represents the choice of best ground intelligent terminal.
Step four: updating Lagrangian multipliers;
further, the lagrangian multiplier is updated by adopting a sub-gradient method, and an updating formula is as follows:
wherein,for update step, (i) is an iteration index, x is the order of update steps, x=1, … 5, [ ·] + Equivalent to max {0, }, and->η (i) And the method is a Lagrange multiplier in the ith iteration, wherein m, n and k represent the relay unmanned aerial vehicle, the ground intelligent terminal and the channel sequence number corresponding to the current Lagrange multiplier.
Step five: judging whether the iteration times lambda is smaller than omega, if so, repeating the steps II to IV; otherwise, ending the cycle and outputtingAnd (a) n,m,k ) * Is a solution to the optimization of (3).
In summary, the embodiment provides a network resource optimization method for an air-to-ground integrated network, which is based on a wireless network technology, and establishes a communication network model comprising a low-orbit satellite, a relay unmanned aerial vehicle and a ground intelligent terminal for an uplink; modeling a communication resource allocation problem of an aerospace-ground integrated network comprising a low-orbit satellite, a relay UAV and a ground intelligent terminal; according to the established system model, the variable value range is widened, the problem solving scale is reduced, the non-convex constraint is subjected to convex approximation by loosening integer variables in integer programming constraint, the non-convex problem is converted into a convex optimization problem, the problem solving scale and the solving complexity are simplified, the solution is completed by utilizing the Lagrange dual method, the problem of channel resource and power resource allocation can be solved, the optimal channel selection between the ground intelligent terminal and the UAV and the optimal transmission power between the ground terminal and the UAV are obtained, the optimal system energy consumption is finally obtained, the network transmission effectiveness is improved, and the efficient utilization of limited resources is realized. The effect of optimizing the channel resources and the transmission power is achieved.
Second embodiment
The embodiment provides a network resource optimization device for an aerospace-earth integrated network, which comprises:
the communication model building module is used for building an air-to-ground integrated communication network system model; the space-sky integrated communication network system model comprises: low orbit satellite, relay unmanned aerial vehicle and ground intelligent terminal;
a network resource optimization module for:
constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and optimization of network resources is realized.
The network resource optimization device facing the space-time integrated network of the embodiment corresponds to the network resource optimization method facing the space-time integrated network of the first embodiment; the functions realized by the functional modules in the network resource optimization device facing the space-time integrated network are in one-to-one correspondence with the flow steps in the network resource optimization method facing the space-time integrated network; therefore, the description is omitted here.
Third embodiment
The embodiment provides an electronic device, which comprises a processor and a memory; wherein the memory stores at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may vary considerably in configuration or performance and may include one or more processors (central processing units, CPU) and one or more memories having at least one instruction stored therein that is loaded by the processors and performs the methods described above.
Fourth embodiment
The present embodiment provides a computer-readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the first embodiment described above. The computer readable storage medium may be, among other things, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. The instructions stored therein may be loaded by a processor in the terminal and perform the methods described above.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (8)
1. A network resource optimization method for an aerospace-earth integrated network is characterized by comprising the following steps:
constructing an air-space integrated communication network system model; wherein, the space-to-ground integrated communication network system model includes: low orbit satellite, relay unmanned aerial vehicle and ground intelligent terminal;
constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and optimization of network resources is realized.
2. The network resource optimization method for an aerospace integrated network according to claim 1, wherein in the aerospace integrated communication network system model, all relay unmanned aerial vehicles and ground intelligent terminals are in the coverage range of a low-orbit satellite, and all devices share a channel with a bandwidth of W; relayThe frequency band resources occupied by the unmanned aerial vehicle and the low-orbit satellite communication are equally divided into M parts, so that each relay unmanned aerial vehicle works in different frequency bands; the frequency band resources occupied by each relay unmanned aerial vehicle are divided into K sub-channels, each relay unmanned aerial vehicle can improve data transmission service for a group of ground intelligent terminals, information from the ground intelligent terminals is forwarded to a low-orbit satellite, and the orbit height of the low-orbit satellite is H S Communication between the relay unmanned aerial vehicle and the low-orbit satellite is regarded as line-of-sight transmission, and channel fading is Rice fading.
3. The method for optimizing network resources of an integrated space-time network according to claim 2, wherein the simplified problem is expressed as:
P
C1
C2
C3
C4
C5
C6
C7
C8
wherein,the system capacity after the integer variable is relaxed represents the total bit number successfully transmitted to the low-orbit satellite by the ground intelligent terminal by the relay unmanned aerial vehicle in unit time; />The transmission power from the nth ground intelligent terminal to the mth relay unmanned aerial vehicle in the kth sub-channel is used; />Representing the maximum transmission power of the ground intelligent terminal; />The transmission power of communication between the mth relay unmanned aerial vehicle and the low-orbit satellite is calculated; />Hover power for the relay drone; a, a n,m,k Represented as channel selection between the nth ground intelligent terminal and the mth relay unmanned aerial vehicle, when a n,m,k =1 means that the nth ground intelligent terminal occupies the kth sub-channel in the mth relay unmanned network, otherwise a n,m,k ＝0；/>Representing the uplink capacity from the nth ground intelligent terminal to the low-orbit satellite on the kth sub-channel of the mth relay unmanned aerial vehicle; />Representing the maximum transmission capacity that the low-orbit satellite can receive; a represents a channel selection matrix; p represents the power control matrix of all the ground intelligent terminals; m represents the number of relay unmanned aerial vehicles; n represents the number of intelligent terminals on the ground; />Representing the maximum transmitting power of the relay unmanned plane; />Representing the minimum power required by the relay drone to maintain hover; c min Representing the minimum transmission capacity for guaranteeing the service quality of the ground intelligent terminal; />Representing the maximum capacity of the low-orbit satellite.
4. The network resource optimization method for an air-ground integrated network according to claim 3, wherein C1 to C5 are limits of transmission power in the air-ground integrated communication network system, C7 limits channel selection between the terminal unmanned aerial vehicle and the ground intelligent terminal to ensure that communication between different devices does not interfere with each other, and C6 and C8 limit minimum and maximum values of transmission capacity in the air-ground integrated communication network system.
5. The method for optimizing network resources of an aerospace-earth-oriented integrated network according to claim 4, wherein solving the problem using lagrangian pair-wise method comprises:
step one: initializing Lagrangian variables, maximum iteration times, channel gain, a power control matrix, a channel selection matrix, a ground intelligent terminal position, a relay unmanned plane position and a low-orbit satellite position, and calculating initial power so that the value of the initial power is larger than 0;
step two: construction of Lagrangian pairsEven function, find aboutAnd->Based on the KKT condition, updating the optimal values of the ground intelligent terminal and the unmanned aerial vehicle power;
step three: solving for a n,m,k Updating an optimal value of a channel selection scheme in the sub-channel allocation based on the KKT condition;
step four: updating Lagrangian multipliers;
step five: judging whether the iteration times are smaller than the maximum times, if so, repeating the second to fourth steps; and if not, ending the circulation, and outputting the optimal solution of the power of the ground intelligent terminal and the unmanned aerial vehicle and the sub-channel allocation.
6. The method for optimizing network resources of an aerospace-earth integration network according to claim 5, wherein the lagrangian dual function is expressed as:
wherein a= { a n,m,k }，μ, ν, ω, ε, η are Lagrangian multipliers associated with the constraints; mu (mu) n ，ν m ，ω n ，ε m,k η is the Lagrangian multiplier; />Representing the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel;
the partial derivative of (2) is calculated as:
wherein, representing channel gain from the nth ground intelligent terminal to the mth relay unmanned aerial vehicle in the kth sub-channel; />Representing low-orbit satellite receiving noise from the mth relay drone;
wherein n, m, k satisfy the following conditions:
wherein,representing the optimal solution of the transmission power from the nth ground intelligent terminal to the mth unmanned aerial vehicle in the kth sub-channel; (ρ) D→U ) * Representing an optimal solution of power control from the ground intelligent terminal to the unmanned aerial vehicle;
calculation of partial derivatives of (2)The formula is:
wherein the method comprises the steps of Representing channel gain from the mth relay drone to the low orbit satellite; />Representing the receiving noise of the relay unmanned aerial vehicle from the nth ground intelligent terminal as additive Gaussian white noise;
wherein m satisfies the following condition:
wherein,representing an optimal solution for power control of the mth drone to the low orbit satellite;
a n,m,k the partial derivative of (2) is calculated as:
wherein,
consider constraint C7, a n,m,k Is the optimal solution (a) n,m,k ) * The conditions are as follows:
in the communication network system model, each relay unmanned aerial vehicle network is required to have at most one ground intelligent terminal to access the same sub-channel in one time slot; in order to maximize the Lagrangian function, there isWhen (a) n,m,k ) * =1 represents the choice of best ground intelligent terminal.
7. The network resource optimization method for an aerospace-earth integration network according to claim 5, wherein the lagrangian multiplier is updated by a sub-gradient method, and the update formula is as follows:
wherein,for update step, (i) is an iteration index, x is the order of update steps, x=1, … 5, [ ·] + Equivalent to max {0, }, and->η (i) And the method is a Lagrange multiplier in the ith iteration, wherein m, n and k represent the relay unmanned aerial vehicle, the ground intelligent terminal and the channel sequence number corresponding to the current Lagrange multiplier.
8. A network resource optimization device for an aerospace-earth integrated network, comprising:
the communication model building module is used for building an air-to-ground integrated communication network system model; the space-sky integrated communication network system model comprises: low orbit satellite, relay unmanned aerial vehicle and ground intelligent terminal;
a network resource optimization module for:
constructing a transmission resource allocation model in a system uplink with the aim of maximizing the system capacity;
aiming at the constructed transmission resource allocation model, relaxing integer variables in integer programming constraint, performing convex approximation on non-convex constraint, simplifying the non-convex problem into a convex optimization problem, and solving the problem by using a Lagrange dual method; on the basis of guaranteeing the transmission capacity of the system, an optimal scheme of sub-channel selection and transmission power distribution is obtained, so that energy loss in the data uploading process is reduced, and optimization of network resources is realized.
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| CN117955553B (en) * | 2024-03-26 | 2024-06-04 | 成都本原星通科技有限公司 | Terminal time slot allocation method for low-orbit satellite Internet of things |
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