Calculation Resource Optimization Method and Device for Multi-Carrier NOMA Network Based on Potential Game

Abstract

The present invention discloses a method and device for optimizing computing resources in a multi-carrier NOMA network based on potential games, wherein the method includes: forming a NOMA network with M unmanned aerial vehicle (UAV) nodes, each of which is equipped with a millimeter-wave communication unit and a NOMA transmitter; each UAV node randomly initializes its own sub-carrier allocation and power allocation strategies; the current UAV node obtains the allocation strategies of other nodes to determine its own best response; using the SSO framework to randomly search for and update the allocation strategy variables, and iteratively converging to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice for the best response of other nodes, and at this time the entire network reaches the optimal state of resource utilization. The present invention has the advantages of strong scalability, high robustness, fast response speed and effectively improving information privacy protection.

Description

Technical Field

[0001] The present invention belongs to the field of computer system engineering, and particularly relates to a method and device for optimizing computing resources of a multi-carrier NOMA network based on potential games. Background Art

[0002] Modern activities, whether it is a concert, a sports game or a large public gathering, increasingly rely on technology to enhance the experience. With the emergence of augmented reality applications, live streaming and real-time social media interactions becoming the norm, the demand for high-bandwidth and low-latency communication is constantly growing. Drone-assisted networks can complement existing infrastructure and provide the necessary bandwidth. Heterogeneous QoS considerations ensure smooth transmission of live streams while supporting other types of data traffic.

[0003] Utilizing millimeter-wave (mmWave) frequencies can significantly increase the available bandwidth and provide the potential to support ultra-high data rates for wireless communication systems. The combination of non-orthogonal frequency division multiple access NOMA and mmWave technology has become a phenomenon in the field of wireless communication, providing a promising approach to overcome the limitations of traditional wireless networks.

[0004] On this basis, considering the quality of service (QoS) requirements of heterogeneous user data rates, the present invention formulates the sub-carrier allocation and discrete power allocation problems under multi-drone millimeter-wave cooperation and non-orthogonal frequency division multiple access NOMA networks as a joint optimization problem, which is called discrete power and sub-carrier allocation (DPSA). It is a non-convex and mixed integer non-linear programming (MINP) problem, making it an NP-hard problem. Then, the DPSA is transformed into a sub-carrier and power resource efficient cooperative potential game (SRECPG) based on game theory to facilitate distributed execution. For the SRECPG, most current research uses the best response dynamics based on local exhaustive search (BRLES), resulting in a computational burden due to exhaustive search. Summary of the Invention

[0005] Aiming at the defects existing in the above-mentioned prior art, the present invention provides a method for optimizing computing resources of a multi-carrier NOMA network based on potential games, including the following steps:

[0006] Step S101: Form an NOMA network with M drone nodes, each drone node is equipped with a millimeter-wave communication unit and a NOMA transmitter, and there is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism between each drone node;

[0007] Step S103: Each drone node randomly initializes its own sub-carrier allocation and power allocation strategies;

[0008] Step S105: The current UAV node obtains the allocation strategies of other nodes to determine its own best response;

[0009] Step S107: Use the SSO framework to perform random search and update on the allocation strategy variables, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice for the best response of other nodes. At this time, the entire network reaches the optimal state of resource utilization.

[0010] Among them, the step S103 includes: The UAV node i randomly initializes its own subcarrier allocation vector X i and power allocation vector P i , where the subcarrier allocation X i satisfies the mutual exclusion constraint, and the power allocation P i satisfies the power coupling constraint.

[0011] Among them, the UAV node i randomly initializes its own subcarrier allocation vector X i , where the subcarrier allocation X i satisfies the mutual exclusion constraint, including:

[0012] Assume that there are a total of M subcarriers available in the system,

[0013] The UAV node i randomly generates an M-dimensional 0-1 binary vector X i as the initial subcarrier allocation decision;

[0014] Among them, X i (m)=1 means that node i uses the m-th subcarrier, and X i (m)=0 means not using;

[0015] This ensures that no two nodes will use the same subcarrier, thus satisfying the mutual exclusion constraint.

[0016] Among them, the UAV node i randomly initializes its own power allocation vector P i , where the power allocation P i satisfies the power coupling constraint, including:

[0017] Assume that the UAV node i has a maximum available power

[0018] The UAV node i randomly generates an M-dimensional power allocation vector P i ;

[0019] Among them, P i (m) represents the power allocated by node i on the m-th subcarrier;

[0020] At initialization, P i (m) is within Randomly generated within the interval to ensure that the power allocation meets the total power limit of each node.

[0021] Among them, the step S105 includes: The UAV node i calculates its own best response according to the decision information (X j , P j ) obtained from other nodes, where i≠j, that is, to solve the optimal combination of subcarrier allocation and power allocation to maximize its own performance index.

[0022] Among them, the UAV node i calculates its own best response according to the decision information (X j , P j ) obtained from other nodes, including:

[0023] The UAV node i obtains their subcarrier allocation X j and power allocation P j from its neighboring node j through broadcasting or point-to-point communication;

[0024] The UAV node i substitutes its own decision variables X i and P i , as well as the decision information X j and P j obtained from other nodes into the first optimization model;

[0025] The UAV node i uses the first optimization algorithm to solve the first optimization model to obtain its own optimal subcarrier allocation and power allocation

[0026] The UAV node i takes the calculated optimal decision and as its new decision.

[0027] Among them, the optimization objective of the first optimization model is to maximize the total throughput or total rate of all nodes, and the objective function can be expressed as:

[0028] max∑ j R j , where R j represents the rate of node j;

[0029] Calculate the rate R j of each node j according to the Shannon formula, then

[0030] Among them, W m is the bandwidth of the mth subcarrier; G jj(m) is the channel gain of node j on the m-th subcarrier; G ij (m) is the channel gain of UAV node i to UAV node j on the m-th subcarrier; N0 is the background noise power spectral density;

[0031] The subcarrier allocation constraint is: That is, each node can only use different subcarriers;

[0032] The power allocation constraint is: That is, the total power of each node cannot exceed its maximum power;

[0033] Combining the above objective function and constraints into a non-linear optimization problem, node i uses the gradient descent method or sequential quadratic programming to solve this optimization problem to obtain its own optimal subcarrier allocation and power allocation

[0034] Among them, the step S107 includes:

[0035] UAV node i performs random search near and to generate a new candidate decision X′ i and P′ i ;

[0036] UAV node i substitutes the new candidate decision X′ i and P′ i into the first optimization model to calculate its corresponding total throughput performance R′ i ;

[0037] If R′ i >R i , then accept the new decision X′ i and P′ i .

[0038] Among them, the step S107 also includes:

[0039] Node i broadcasts the updated new decision X′ i and P′ i to its neighboring nodes;

[0040] After receiving it, the neighboring nodes will also re-optimize their decisions based on this new information.

[0041] The present invention also proposes a multi-carrier NOMA network computing resource optimization device based on potential game, characterized in that,

[0042] The devices are distributed among M unmanned aerial vehicles (UAVs), and the M UAV nodes form a non-orthogonal multiple access (NOMA) network. Each UAV node is equipped with a millimeter-wave communication unit and a NOMA transmitter, and there is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism among each UAV node;

[0043] The device includes:

[0044] An initialization module, which is used to randomly initialize the subcarrier allocation and power allocation strategies of each UAV node itself;

[0045] An optimal response module, which is used to determine its own optimal response based on the current UAV node obtaining the allocation strategies of other nodes;

[0046] An optimization module, which is used to perform random search and update on the allocation strategy variables by using the SSO framework, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice for the optimal response of other nodes. At this time, the entire network reaches the optimal state of resource utilization.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] Since each node independently runs the BR-SSO algorithm, the network scale can be easily expanded. The newly added UAV nodes only need to independently run the algorithm without global coordination. This distributed architecture makes the algorithm have good scalability;

[0049] If a certain node fails or goes offline, it will not affect the optimization process of other nodes; each node makes autonomous decisions and runs the algorithm independently; this distributed characteristic makes the algorithm have strong fault tolerance and robustness.

[0050] Distributed optimization means that the computing tasks are executed on each node, which can greatly accelerate the convergence speed of the algorithm and the decision response time, and is especially suitable for dynamic UAV network scenarios with high real-time requirements;

[0051] Each node only needs to interact with adjacent nodes with a small amount of decision information and does not need to expose all its own information to the central coordinator, which is beneficial to protecting the information privacy and data security of the nodes. Description of the Drawings

[0052] By reading the following detailed description with reference to the drawings, the above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become easy to understand. In the drawings, several embodiments of the present disclosure are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0053] Figure 1It is a flowchart showing a method for optimizing computing resources in a multi-carrier NOMA network based on potential games according to an embodiment of the present invention. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0055] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0056] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe..., these... should not be limited to these terms. These terms are only used to distinguish.... For example, without departing from the scope of the embodiments of the present invention, the first... may also be referred to as the second..., and similarly, the second... may also be referred to as the first....

[0057] It should be understood that the term "and / or" used herein is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0058] Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "when...", "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".

[0059] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a commodity or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the presence of additional identical elements in the commodity or device including said element.

[0060] The optional embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0061] Embodiment 1

[0062] As Figure 1 shown, the present invention discloses a method for optimizing computing resources in a multi-carrier NOMA network based on potential games, including the following steps:

[0063] Step S101: Form a NOMA network with M unmanned aerial vehicle (UAV) nodes, each UAV node being equipped with a millimeter-wave communication unit and a NOMA transmitter, and there being a millimeter-wave cooperative transmission and NOMA signal superposition mechanism between each UAV node;

[0064] Step S103: Each UAV node randomly initializes its own sub-carrier allocation and power allocation strategies;

[0065] Step S105: The current UAV node obtains the allocation strategies of other nodes to determine its own best response;

[0066] Step S107: Use the SSO framework to randomly search and update the allocation strategy variables, iteratively converging to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice for the best response of other nodes, and at this time the entire network reaches the optimal state of resource utilization.

[0067] In the present invention, each UAV node is equipped with a communication unit in the millimeter-wave band (30 GHz - 300 GHz). Millimeter-wave communication can provide characteristics such as high bandwidth and low latency, which are very suitable for the high-speed data transmission requirements of UAV networks. These millimeter-wave communication units are responsible for realizing high-speed link connections between nodes.

[0068] In addition to the millimeter-wave communication unit, each UAV node is also equipped with a NOMA transmitter. The NOMA transmitter can transmit signals of multiple nodes on the same time-frequency resource and adopt non-orthogonal power allocation. By dynamically adjusting the transmission power of each node, the NOMA transmitter can achieve efficient utilization of spectrum resources.

[0069] When a UAV node needs to send data to other nodes, it first obtains the channel state information of the target node through the millimeter-wave link. Based on this channel information, the NOMA transmitter of the node will dynamically optimize the power allocation of each node. Then the NOMA transmitter will superimpose the signals of multiple nodes on the same time-frequency resource for transmission.

[0070] The receiving UAV node will use the Successive Interference Cancellation (SIC) technique to separate the received superimposed signals. First, it decodes the signal with the strongest power, and then gradually removes the interference of other node signals until all signals are correctly decoded. This SIC processing depends on the pre-shared channel state information between nodes.

[0071] Through the UAV network of the present invention, the millimeter-wave cooperative transmission and the NOMA (Non-Orthogonal Multiple Access) signal superposition mechanism between nodes can improve the data transmission efficiency and spectrum utilization rate.

[0072] Millimeter-wave cooperative transmission refers to multiple UAV nodes jointly participating in the transmission of a single data packet to improve the transmission efficiency through distributed cooperation. This method can utilize the high bandwidth and directivity of the millimeter-wave band to achieve higher data rates and transmission distances.

[0073] The NOMA signal superposition mechanism allows the signals of different nodes to coexist on the same time-frequency resource, and realizes signal separation and interference removal through power allocation and prior channel information. This can further improve the spectrum utilization rate and increase the network capacity.

[0074] Specifically, the following mechanism can be adopted between UAV nodes:

[0075] Millimeter-wave cooperative transmission

[0076] Multiple UAV nodes cooperate to participate in the transmission of the same data packet to achieve distributed MIMO. Through channel state interaction and joint signal processing, power allocation and beamforming are optimized to improve transmission reliability. Utilizing the high bandwidth and directivity characteristics of the millimeter-wave band, gbit-level data rates can be achieved.

[0077] Among them, the UAV nodes participating in cooperative transmission need to interact with each other's channel state information, including channel gain, phase, time delay, etc. This information can be used for joint signal processing and resource optimization to maximize the performance of the entire transmission link.

[0078] Based on the channel state information between nodes, the transmission power of each node can be dynamically adjusted to achieve global power optimization.

[0079] For example, for nodes with poor channels, higher power can be allocated to compensate for their transmission disadvantages; for nodes with good channels, power can be reduced to reduce interference.

[0080] This dynamic power allocation can significantly improve the overall transmission reliability.

[0081] Similarly, by leveraging the channel state information among nodes, joint beamforming optimization can be achieved.

[0082] Each node can adjust its antenna beam direction and width so that the signal can be more accurately focused on the target receiving node.

[0083] The optimized beamforming can enhance the strength of the received signal and reduce interference and noise at the receiving end.

[0084] The nodes participating in cooperative transmission can perform joint signal encoding, modulation, channel coding, etc.

[0085] Through information interaction and computing resource sharing among nodes, a better joint signal processing strategy can be achieved.

[0086] This can significantly improve the overall transmission reliability and anti-interference ability.

[0087] NOMA signal superposition

[0088] Different UAV nodes send signals on the same time-frequency resource, and NOMA is achieved through power allocation and prior channel information. The receiving end nodes use successive interference cancellation (SIC) technology to separate the signals of each node and remove mutual interference. This can greatly improve the spectrum utilization rate and support more nodes for concurrent communication.

[0089] Among them, on the same time-frequency resource, different UAV nodes can transmit signals using different power levels.

[0090] Based on the prior channel information between nodes, the transmission power of each node can be dynamically optimized.

[0091] For nodes with better channel conditions, lower power can be allocated; for nodes with poorer channels, higher power can be allocated.

[0092] Such non-orthogonal power allocation can improve the overall spectrum utilization rate.

[0093] In the UAV network, nodes can obtain each other's prior channel information through channel state information interaction.

[0094] These channel information can be used for NOMA signal detection and decoding.

[0095] The receiving node can use prior channel information to perform successive interference cancellation (SIC) on the superimposed NOMA signals.

[0096] SIC can gradually strip off the strong signals and finally decode the weak signals.

[0097] In addition to power allocation and SIC, the nodes participating in NOMA transmission can also perform signal processing such as joint encoding, modulation, and channel coding.

[0098] Through cooperation among nodes, a better joint signal processing strategy can be achieved, further improving the performance of NOMA.

[0099] The UAV formation and tasks may change dynamically over time, and the NOMA parameters also need to be adaptively adjusted.

[0100] The nodes can dynamically optimize NOMA-related parameters such as power allocation and SIC order based on real-time channel conditions and service requirements.

[0101] This adaptive mechanism can enable NOMA to maintain high flexibility and transmission efficiency in the UAV network.

[0102] Among them, the receiving end node uses the Successive Interference Cancellation (SIC) technology to separate the signals of each node, thereby removing mutual interference, including:

[0103] Step a: The receiving node first receives the superimposed signals from multiple UAV nodes.

[0104] Based on the previously obtained channel state information, the receiving node will sort the signal powers and place the strongest signal at the front.

[0105] Step b: The receiving node first decodes the signal with the strongest power and completely decodes it. This step is not affected by the interference of other node signals.

[0106] Step c: The receiving node will reconstruct the interference part of the received signal according to the decoded strongest signal. Then subtract this interference component from the original received signal to remove the influence of the strongest signal.

[0107] Step d: After the interference cancellation in the previous step, the receiving node will turn to decode the second-strongest signal. This step is no longer affected by the interference of the previous strongest signal.

[0108] The receiving node will repeat the above steps b-d to gradually perform interference cancellation and decoding on the signals with gradually decreasing powers.

[0109] Until all received signals are successfully decoded.

[0110] Embodiment 2

[0111] A method for optimizing computing resources in a multi-carrier NOMA network based on potential game proposed by the present invention includes the following steps:

[0112] Step S101: Form a NOMA network with M UAV nodes. Each UAV node is equipped with a millimeter-wave communication unit and a NOMA transmitter, and there is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism between each UAV node.

[0113] Step S103: Each UAV node randomly initializes its own sub-carrier allocation and power allocation strategies.

[0114] Step S105: The current UAV node obtains the allocation strategies of other nodes to determine its own best response.

[0115] Step S107: Use the SSO framework to perform random search and update on the allocation strategy variables, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice for the best response of other nodes. At this time, the entire network reaches the optimal state of resource utilization.

[0116] Among them, the step S103 includes: UAV node i randomly initializes its own sub-carrier allocation vector X i and power allocation vector P i , where the sub-carrier allocation X i satisfies the mutual exclusion constraint, and the power allocation P i satisfies the power coupling constraint.

[0117] Among them, the UAV node i randomly initializes its own sub-carrier allocation vector X i , where the sub-carrier allocation X i satisfies the mutual exclusion constraint, including:

[0118] Assume that there are a total of M sub-carriers available in the system.

[0119] UAV node i randomly generates an M-dimensional 0-1 binary vector X i as the initial sub-carrier allocation decision;

[0120] where X i (m)=1 indicates that node i uses the m-th sub-carrier, and X i (m)=0 indicates not to use;

[0121] This ensures that no two nodes will use the same sub-carrier, thus satisfying the mutual exclusion constraint.

[0122] Among them, the UAV node i randomly initializes its power allocation vector P i , where the power allocation P i satisfies the power coupling constraint, including:

[0123] Assume that the UAV node i has a maximum available power

[0124] The UAV node i randomly generates an M-dimensional power allocation vector P i ;

[0125] where P i (m) represents the power allocated by node i on the m-th subcarrier;

[0126] At initialization, P i (m) is randomly generated within the interval to ensure that the power allocation satisfies the total power limit of each node.

[0127] Among them, the step S105 includes: The UAV node i calculates its optimal reaction according to the decision information (X j , P j ) obtained from other nodes, where i≠j, that is, to solve the optimal combination of subcarrier allocation and power allocation to maximize its own performance index.

[0128] Among them, the UAV node i calculates its optimal reaction according to the decision information (X j , P j ) obtained from other nodes, including:

[0129] The UAV node i obtains their subcarrier allocation X j and power allocation P j from its neighboring node j through broadcasting or point-to-point communication;

[0130] The UAV node i substitutes its own decision variables X i and P i as well as the decision information X j and P j obtained from other nodes into the first optimization model;

[0131] The UAV node i uses the first optimization algorithm to solve the first optimization model to obtain its own optimal subcarrier allocation and power allocation

[0132] The UAV node i transmits the calculated optimal decision and as its new decision.

[0133] Among them, the optimization objective of the first optimization model is to maximize the total throughput or total rate of all nodes, and the objective function can be expressed as:

[0134] max∑ j R j , where R j represents the rate of node j;

[0135] Calculate the rate R of each node j according to the Shannon formula j , then

[0136] where, W m is the bandwidth of the m-th subcarrier; G jj (m) is the channel gain of node j on the m-th subcarrier; G ij (m) is the channel gain of UAV node i to UAV node j on the m-th subcarrier; N0 is the background noise power spectral density;

[0137] The subcarrier allocation constraint is: That is, each node can only use different subcarriers;

[0138] The power allocation constraint is: That is, the total power of each node cannot exceed its maximum power;

[0139] Form the above objective function and constraints into a non-linear optimization problem. Node i uses the gradient descent method or sequential quadratic programming to solve this optimization problem to obtain its own optimal subcarrier allocation and power allocation

[0140] Among them, SSO represents Sequential Stochastic Optimization, which is a framework for distributed optimization. It finally converges to a stable Nash equilibrium state through random search and mutual interaction of each node.

[0141] After the best response BR (Best Response) step, each node i has an optimal response and However, this is only a local optimum and needs further optimization. Therefore, the step S107 includes:

[0142] UAV node i performs random search near and to generate a new candidate decision X′ i and P′i ;

[0143] The UAV node i brings the new candidate decision X′ i and P′ i into the first optimization model and calculates its corresponding total throughput performance R′ i ;

[0144] If R′ i >R i , then accept the new decision X′ i and P′ i .

[0145] Wherein, the step S107 further includes:

[0146] The node i broadcasts the updated new decision X′ i and P′ i to its neighboring nodes;

[0147] After receiving, the neighboring nodes will also re-optimize their own decisions based on this new information.

[0148] The above process is iterated repeatedly until the whole system converges to a stable state. This stable state is a Nash equilibrium, at which time the decision of each node is the optimal choice for the optimal response of other nodes. Through such SSO iterative optimization, the entire network will ultimately reach the global optimal state of resource utilization. This is because at the Nash equilibrium state, any node unilaterally changing its decision cannot further improve the overall performance.

[0149] Embodiment III

[0150] The present invention also proposes a computing resource optimization device for a multi-carrier NOMA network based on potential game, characterized in that

[0151] the device is distributed in M UAVs, the M UAV nodes form a NOMA network, each UAV node is equipped with a millimeter-wave communication unit and a NOMA transmitter, and there is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism between each UAV node;

[0152] The device includes:

[0153] An initialization module, which is used to randomly initialize the sub-carrier allocation and power allocation strategies of each UAV node itself;

[0154] An optimal response module, which is used to determine its own optimal response based on the current UAV node obtaining the allocation strategies of other nodes;

[0155] An optimization module, which is used to perform random search and update on the allocation policy variables by using the SSO framework, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the optimal choice that is the best response to other nodes. At this time, the entire network reaches the optimal state of resource utilization.

[0156] Embodiment 4

[0157] The embodiments of the present disclosure provide a non-volatile computer storage medium, and the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the method steps described in the above embodiments.

[0158] It should be noted that the computer-readable medium in the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device. In the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or combined with an instruction execution system, apparatus, or device. The program code included on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0159] The above computer-readable medium may be included in the above electronic device; or may exist separately without being assembled into the electronic device.

[0160] Computer program code for performing the operations of this disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0162] The units described in the embodiments of this disclosure may be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases.

[0163] The above describes the preferred embodiments of the present invention, aiming to make the spirit of the present invention clearer and easier to understand, and is not intended to limit the present invention. Any modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope defined by the appended claims of the present invention.

Claims

1. A computational resource optimization method for a multi-carrier NOMA network based on potential games, comprising the following steps: Step S101: Form a NOMA network with M UAV nodes. Each UAV node is equipped with a millimeter-wave communication unit and a NOMA transmitter. There is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism among the UAV nodes; Step S103: Each UAV node randomly initializes its own sub-carrier allocation and power allocation strategies; Step S105: The current UAV node obtains the allocation strategies of other nodes to determine its own best response; Step S107: Use the Sequential Stochastic Optimization (SSO) framework to perform random search and update on the allocation strategy variables, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the best choice for the best response of other nodes. At this time, the entire network reaches the optimal state of resource utilization; Among them, the step S105 includes: the drone node i uses the subcarrier allocation vector X obtained from other neighboring nodes j j and the power allocation vector P j , identifying them as its decision-making information to calculate its own best response, where i≠j, that is, to solve the optimal combination of the optimal subcarrier allocation vector and the optimal power allocation vector to maximize its own performance metric; Among them, the drone node i calculates its optimal response according to the decision-making information (X j , P j ) obtained from other nodes, including: The UAV node i obtains their subcarrier allocation vectors X from its neighboring nodes j through broadcasting or point-to-point communication j and power allocation vectors P j ; The UAV node i brings its own decision information vector X i and vector P i as well as the decision information vector X j and vector P j obtained from other nodes into the first optimization model; The UAV node i uses the first optimization algorithm to solve the first optimization model and obtains its own optimal subcarrier allocation vector and optimal power allocation vector The UAV node i takes the calculated optimal decision, i.e., the vector and the vector as its new decision; Among them, the optimization objective of the first optimization model is to maximize the total throughput or total rate of all nodes, and the objective function is expressed as: max∑ j R j where R j represents the rate of node j; Calculate the rate R of each node j according to the Shannon formula j , then Where, W m is the bandwidth of the m-th subcarrier; G jj (m) is the channel gain of node j on the m-th subcarrier; G ij (m) is the channel gain of UAV node i to UAV node j on the m-th subcarrier; N0 is the background noise power spectral density; The subcarrier allocation constraint is: ∑ m ∪X i (m) ≤ 1, and each node i can only use different subcarriers. X i (m) represents the subcarrier allocation decision of UAV node i on the m-th subcarrier; The power allocation constraint is as follows: For each node i, the total power of each node cannot exceed its maximum power, denotes the maximum available power of UAV node i, P i (m) denotes the power allocated by UAV node i on the m-th subcarrier, P j (m) denotes the power allocated by UAV node j on the m-th subcarrier; The above objective function and constraints are combined into a non-linear optimization problem. Node i uses the gradient descent method or sequential quadratic programming to solve this optimization problem and obtains its own optimal subcarrier allocation vector and the optimal power allocation vector 2. The method according to claim 1, wherein the step S103 includes: The UAV node i randomly initializes its subcarrier allocation vector X i and power allocation vector P i , where the subcarrier allocation vector X i satisfies the mutual exclusion constraint, and the power allocation vector P i satisfies the power coupling constraint.

3. The method according to claim 2, wherein the drone node i randomly initializes its subcarrier allocation vector X i , where the subcarrier allocation vector X i satisfies the mutual exclusion constraint, including: Assume that there are a total of M sub-carriers available in the network, The UAV node i randomly generates an M - dimensional 0 - 1 binary vector X i as the initial sub - carrier allocation decision; Where X i (m) = 1 indicates that node i uses the m-th subcarrier, X i (m) = 0 indicates non-use; which ensures that no two nodes use the same sub-carrier, thus satisfying the mutual exclusion constraint.

4. The method according to claim 2, wherein the drone node i randomly initializes its power allocation vector P i , where the power allocation vector P i satisfies the power coupling constraint, including: Suppose the UAV node i has a maximum available power The UAV node i randomly generates an M-dimensional power allocation vector P i ; where P i (m) represents the power allocated to node i on the m-th subcarrier; At initialization, P i (m) is randomly generated within the interval, and it is ensured that the power allocation satisfies the total power limit of each node.

5. The method according to claim 1, wherein the step S107 includes: The UAV node i performs random search in the vectors and the vector to generate new candidate decision vectors X′ i and P′ i ; The UAV node i takes the new candidate decision vector X′ i and the vector P′ i into the first optimization model and calculates its corresponding total throughput performance R′ i , where R i is and the corresponding total throughput performance; If R' i > R i , then accept the new candidate decision vectors X' i and P' i .

6. The method according to claim 5, wherein the step S107 further includes: The UAV node i broadcasts the new candidate decision vector X′ i and the vector P′ i to its neighboring nodes; After receiving, neighboring nodes will also re-optimize their decisions based on this new information.

7. A computing resource optimization device for a multi-carrier NOMA network based on potential game, characterized in that, The device is distributed among M UAV nodes. The M UAV nodes form a NOMA network. Each UAV node is equipped with a millimeter-wave communication unit and a NOMA transmitter. There is a millimeter-wave cooperative transmission and NOMA signal superposition mechanism among the UAV nodes; The device includes: An initialization module, which is used to randomly initialize the sub-carrier allocation and power allocation strategies of each UAV node itself; A best response module, which is used to determine its own best response based on the current UAV node obtaining the allocation strategies of other nodes; An optimization module, which is used to perform random search and update on the allocation strategy variables by using the SSO framework, and iteratively converge to a stable Nash equilibrium state, so that the decision of each UAV node is the best choice for the best response of other nodes. At this time, the entire network reaches the optimal state of resource utilization; Among them, the UAV node i uses the subcarrier allocation vector X j and the power allocation vector P j obtained from other neighboring nodes j as its decision information to calculate its own best response, where i≠j, that is, to solve the optimal combination of the subcarrier allocation vector and the optimal power allocation vector to maximize its own performance metric; Among them, the drone node i calculates its optimal response according to the decision-making information (X j , P j ) obtained from other nodes, including: The UAV node i obtains their subcarrier allocation vectors X from its neighboring node j through broadcasting or point-to-point communication j and power allocation vectors P j ; The UAV node i brings its own decision information vector X i and vector P i as well as the decision information vector X j and vector P j obtained from other nodes into the first optimization model; The UAV node i uses the first optimization algorithm to solve the first optimization model and obtains its own optimal subcarrier allocation vector and power allocation vector The UAV node i will use the calculated optimal decision, i.e., the vector and the vector as its new decision; Among them, the optimization objective of the first optimization model is to maximize the total throughput or total rate of all nodes, and the objective function is expressed as: max∑ j R j where R j represents the rate of node j; Calculate the rate R of each node j according to Shannon's formula j , then Among them, W m is the bandwidth of the m-th subcarrier; G jj (m) is the channel gain of node j on the m-th subcarrier; G ij (m) is the channel gain of UAV node i to UAV node j on the m-th subcarrier; N0 is the background noise power spectral density; The subcarrier allocation constraint is: ∑ m X i (m) ≤ 1, and different subcarriers can only be used by each node i. X i (m) represents the subcarrier allocation decision of UAV node i on the m-th subcarrier; The power allocation constraint is as follows: For each node i, the total power of each node cannot exceed its maximum power. denotes the maximum available power of UAV node i, P i (m) denotes the power allocated by UAV node i on the m-th subcarrier, P j (m) denotes the power allocated by UAV node j on the m-th subcarrier; Form the above objective function and constraints into a non - linear optimization problem. Node \(i\) uses the gradient descent method or sequential quadratic programming to solve this optimization problem and obtain its own optimal sub - carrier allocation vector and power allocation vector

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