A resource allocation method in a multi-uav rsma-isac system

Abstract

The application provides a resource allocation method in a multi-unmanned aerial vehicle RSMA-ISAC system, comprising the following steps: constructing a multi-unmanned aerial vehicle RSMA-ISAC system model, representing various beams in a spatial angle domain, and extracting beam main lobe direction, beam width and spatial energy distribution characteristic parameters; constructing a beam-to-beam interference metric model to form an interference coefficient matrix; constructing a joint optimization model under the condition of meeting communication performance and sensing performance constraints; performing multi-unmanned aerial vehicle cooperative scheduling according to the beam-to-beam interference degree; when a high spatial conflict beam is detected, interference suppression is performed by adjusting the beam direction, the beam shape or the rate split power ratio to obtain optimal communication and sensing beam configurations of each unmanned aerial vehicle and RSMA resource allocation results. The application can effectively reduce the spatial interference superposition effect in a complex multi-beam parallel scene, improve the communication link stability and the sensing task reliability, and realize the cooperative improvement of the communication and sensing performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and intelligent aerial network technology, specifically to a resource allocation method considering inter-beam interference in a multi-UAV rate split multiple access (RSMA) communication-aware integration (ISAC) system. Background Technology

[0002] With the development of 6G mobile communication technology, communication systems are gradually evolving from simple information transmission to an integrated approach encompassing communication, sensing, and computing. Integrated Sensing and Communication (ISAC) technology, by sharing spectrum resources, hardware platforms, and signal processing procedures, enables high-speed data transmission while simultaneously performing functions such as target detection and environmental perception, and has become a crucial supporting technology for future integrated air-space-ground networks.

[0003] Unmanned Aerial Vehicles (UAVs) have broad application prospects in emergency communication, disaster monitoring, intelligent transportation, and low-altitude security due to their advantages of mobile deployment, flexible networking, and line-of-sight propagation. Aerial networks constructed collaboratively by multiple UAVs can provide enhanced coverage for ground users while simultaneously performing radar sensing tasks against ground targets; therefore, multi-UAV ISAC systems are gradually becoming a research hotspot.

[0004] In multi-user access, Rate-Splitting Multiple Access (RSMA) technology splits user messages into public and private streams for joint transmission, suppressing multi-user interference while partially decoding interference. Compared to traditional spatial division multiple access and power domain multiple access technologies, it has stronger robustness and spectral efficiency. Therefore, RSMA is gradually being introduced into UAV-assisted communication and ISAC systems to improve system performance in multi-user scenarios.

[0005] Existing research on multi-UAV RSMA-ISAC systems mainly focuses on the following aspects: UAV 3D trajectory optimization, transmit power allocation, RSMA common flow and private flow power ratio design, and communication and sensing performance tradeoffs. This type of research typically models multi-user interference at the channel level and uses beamforming techniques to improve signal gain in the desired direction and suppress interference in the undesired direction.

[0006] However, in real-world multi-UAV deployment environments, each UAV typically needs to simultaneously generate multiple communication and sensing beams. Different beams may experience main lobe overlap or side lobe leakage in the spatial angular domain, resulting in significant inter-beam interference. This type of interference differs from traditional inter-user interference caused by channel fading; its essence stems from beam direction distribution conflicts and array energy leakage, exhibiting distinct spatial structural characteristics.

[0007] Especially in RSMA-ISAC systems, the common stream beam often has a wide coverage area, while the private stream beam and sensing beam usually have strong directionality. When the beams of multiple UAVs point to adjacent spatial areas at the same time, it can easily cause: a decrease in the signal-to-interference-plus-noise ratio of the communication user, affecting decoding performance; the main lobe energy of the sensing beam being contaminated by other beams, reducing target detection accuracy; and the RSMA common stream causing additional spatial interference to the private stream, weakening the advantages brought by rate splitting.

[0008] Most existing technologies treat the aforementioned effects as equivalent to channel interference noise processing, and have not yet explicitly established an inter-beam interference model at the spatial beam structure level, nor have they incorporated inter-beam spatial conflict as a schedulable resource dimension into the multi-UAV collaborative resource allocation process. Therefore, in multi-UAV RSMA-ISAC systems, how to characterize the spatial coupling relationship between beams and realize interference-aware resource allocation based on this relationship remains a technical problem that urgently needs to be solved. Summary of the Invention

[0009] To address the issues in existing multi-UAV Rate Split Multiple Access (RSMA-ISAC) systems where interference caused by beam overlap and sidelobe leakage is not explicitly modeled and not incorporated into resource allocation decisions, this invention proposes a resource allocation method for multi-UAV RSMA-ISAC systems to resolve the degradation in communication performance and reduced sensing accuracy caused by multi-beam spatial conflicts. To achieve the above objectives, this invention proposes a resource allocation method for multi-UAV RSMA-ISAC systems that constructs a beam spatial conflict model and combines it with a rate split transmission structure to achieve collaborative optimization of communication and sensing tasks. The technical solution provided by this invention is as follows:

[0010] A resource allocation method in a multi-UAV RSMA-ISAC system includes the following steps:

[0011] Step S1: Construct a multi-UAV RSMA-ISAC system model. Each UAV uses a multi-antenna array to simultaneously form a communication beam and a sensing beam. Downlink communication uses rate split multiple access to transmit common and private stream data. Perform spatial angular domain representation on various beams and extract beam main lobe direction, beamwidth, and spatial energy distribution characteristic parameters.

[0012] Step S2: Based on the degree of directional overlap and sidelobe leakage characteristics of different beams in the spatial angular domain, construct an inter-beam interference measurement model to form an interference coefficient matrix that describes the spatial conflict relationship between each communication beam and sensing beam in the system.

[0013] Step S3: Introduce the inter-beam interference factor into the resource allocation process of the multi-UAV RSMA-ISAC system, and construct a joint optimization model under the constraints of communication performance and perception performance.

[0014] Step S4: Perform multi-UAV cooperative scheduling based on the degree of inter-beam interference. When a high spatial conflict beam is detected, interference is suppressed by adjusting the beam direction, beam shape, or rate splitting power ratio to obtain the optimal communication and sensing beam configuration and RSMA resource allocation results for each UAV, which are then used for system transmission control.

[0015] Preferably, the direction in space is determined by the azimuth angle. and pitch angle Description, for drones Any beam The main lobe direction of the beam is The beamwidth is when The corresponding angular domain range, where For the preset threshold, The normalized spatial gain is expressed as:

[0016]

[0017] in For beam Beamforming vector, For drones Antenna array in direction The array response vector on is represented as:

[0018]

[0019] in: The carrier wavelength; Indicates the first The root antenna is in the direction relative to the reference point. The difference in equivalent propagation distance, The total number of transmitting antennas configured on the drone.

[0020] Preferably, the interference coefficient matrix is ​​as follows:

[0021]

[0022] in: The diagonal elements represent the total number of communication and sensing beams in the system; the diagonal elements represent the beam's own energy term; the off-diagonal elements represent the spatial interference relationship between different beams. Beam The spatial interference power is:

[0023]

[0024] in, Indicates beam The transmission power, The inter-beam spatial overlap coefficient is expressed as:

[0025]

[0026] in, This indicates the spatial angular domain range considered by the system.

[0027] Preferably, for beams from different UAVs, cross-UAV beam interference is represented as:

[0028]

[0029] in, Indicates by the first The beam emitted by the drone; Indicates by the first The beam emitted by the drone For the first The drone and the first Path loss coefficient between drones.

[0030] Preferably, a beam type weighting coefficient is introduced. The interference contribution of common flow beams, private flow beams, and sensing beams is differentiated, and the corrected interference metric is as follows:

[0031]

[0032] Among them, the common stream beam Take the larger value; private flow beam Take the average value; sensing beam The settings are adaptively configured based on the required sensing accuracy.

[0033] Preferably, step S3 specifically includes:

[0034] Step S31: Communication received signal and SINR modeling, assuming ground users By drone The service, whose received signal can be represented as:

[0035]

[0036] in, For drones To users The channel vector; Indicates a common flow signal; Indicates the first Private stream signals for each user; Indicates other users Private stream signals; These are the beamforming vectors for the corresponding signals; This refers to the inter-beam spatial interference power; To interfere with beam signals; It is additive white Gaussian noise;

[0037] The signal-to-interference-plus-noise ratio (SIR) of a private stream is expressed as:

[0038]

[0039] The signal-to-interference-plus-noise ratio (SIR) of the common stream is expressed as:

[0040]

[0041] The variance of communication signal noise;

[0042] Step S32: Perception performance modeling, assuming the UAV Target direction The effective sensing signal power is:

[0043]

[0044] in For the beamforming vector of the sensing signal, the leakage interference from other communication beams or other UAV beams in this direction is:

[0045]

[0046] The perceived signal-to-interference-plus-noise ratio is then expressed as:

[0047]

[0048] To sense the variance of signal noise;

[0049] Step S33: Incorporate the following variable into the joint resource allocation: beamforming vector Rate splitting multiple access power splitting ratio: UAV launch power UAV spatial location ;

[0050] Step S34: Construct the system weighted performance maximization problem:

[0051]

[0052] Among them, the first item The first term represents the user communication rate; the second term represents the perceived performance gain; and the third term represents the inter-beam interference penalty. These are the weighting coefficients.

[0053] Step S35: Constraints include:

[0054] Transmit power constraints:

[0055] Common stream decodeability constraints:

[0056] Perception performance threshold constraints: .

[0057] Preferably, step S4 specifically includes:

[0058] Step S41: Based on the inter-beam interference matrix Define beam The total spatial conflict intensity is:

[0059]

[0060] when ,in When the preset conflict threshold is not met, the beam is determined to be in a state of high spatial interference and needs to be scheduled and adjusted.

[0061] Step S42: For beams identified as having high conflict By fine-tuning its main lobe pointing angle, angular overlap with other beams is reduced; the update rule is:

[0062]

[0063] in: Step size factor; This indicates the gradient direction of the collision intensity with respect to the beam pointing angle;

[0064] Step S43: If the conflict is still significant after directional adjustment, adaptively shrink the beamwidth. Let the beamwidth parameter be... The update rules are as follows:

[0065]

[0066] in, This is the adjustment coefficient; This is the maximum allowed collision reference value by the system;

[0067] Step S44: When rate-split multiple access common current beam causes significant spatial interference, suppress the common current power, assuming the common current power ratio is... Then it will be updated to:

[0068]

[0069] And redistribute the reduced power to the private stream or sensing beam:

[0070]

[0071] in For adjustment coefficients, The proportion of power released from the common flow.

[0072] Step S45: Divide the spatial corner domain into several corner domain resource blocks. And calculate the total beam energy occupancy in each corner domain:

[0073]

[0074] When the load in a certain corner exceeds the threshold At that time, the scheduler restricts new high-power beams from being directed at that area;

[0075] Step S46: After the above scheduling steps, the system outputs: the updated beamforming vector. The updated beam pointing and width parameters, updated rate splitting multiple access power splitting ratio, and final transmit power configuration for each UAV are then distributed to each UAV for execution.

[0076] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0077] 1. Existing technologies typically treat system interference as a uniform equivalent to channel interference or noise, which makes it difficult to accurately reflect the structural interference caused by main lobe overlap and side lobe leakage of multiple beams in the spatial angular domain. This invention decouples inter-beam interference from traditional channel interference by establishing a beam spatial gain function and angular domain overlap model, and constructs an inter-beam interference matrix for quantitative description, extending interference analysis from the "channel level" to the "spatial beam level," significantly improving the refinement and physical interpretability of interference modeling.

[0078] 2. This invention uses the degree of spatial conflict between beams as an important basis for resource allocation. The joint optimization model simultaneously considers communication rate, sensing accuracy, and spatial interference suppression, thereby avoiding the concentrated transmission of multiple high-power beams in adjacent angular domains. Compared to methods that only base scheduling on channel gain or power constraints, this invention can effectively reduce the superposition effect of spatial interference in complex multi-beam parallel scenarios, improve communication link stability and sensing task reliability, and achieve a synergistic improvement in communication and sensing performance.

[0079] 3. Since RSMA common flow beams typically have a wide coverage area, while private flow beams are highly directional, the contribution and sensitivity of different beam types to system interference vary significantly. This invention introduces beam type weights into the interference model and coordinates the adjustment of the common flow power ratio and private flow beam shape during the scheduling phase. This achieves a differentiated interference control strategy for different service beams, thereby reducing spatial interference to private communication and sensing tasks while ensuring reliable broadcasting of public information, and fully leveraging the performance advantages of RSMA in multi-user scenarios.

[0080] 4. In multi-UAV deployment scenarios, spatial conflicts between beams not only exist within a single UAV but also widely exist between different UAVs. This invention achieves cross-UAV beam pointing coordination and conflict avoidance control by unifying spatial corner domain representation and introducing a corner domain resource block partitioning mechanism, enabling the system to have spatial domain-level collaborative scheduling capabilities. This mechanism can be expanded as the number of UAVs increases, avoiding the problem of a sharp increase in spatial interference caused by dense node deployment, thereby improving the system's scalability and network stability.

[0081] 5. This invention not only proposes a joint optimization model incorporating inter-beam interference factors, but also designs a dynamic beam adjustment process based on conflict intensity determination, including control methods such as beam direction fine-tuning, beamwidth compression, and RSMA power splitting ratio reallocation, realizing a closed-loop scheduling mechanism from theoretical modeling to actual transmission control. Therefore, this invention has a clear parameter acquisition method and control output form, making it easy to integrate into existing UAV communication platforms and ISAC system architectures, and possessing good engineering feasibility.

[0082] 6. By suppressing unnecessary beam overlap and energy leakage in space, this invention reduces mutual interference during multi-beam parallel transmission without increasing spectrum resources, allowing more spatial directions to be efficiently reused, thereby improving the overall spectrum utilization efficiency of the system. This is of great significance for low-altitude communication and sensing integration applications where spectrum resources are limited. Attached Figure Description

[0083] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0084] Figure 1 This is the main flowchart of the present invention. Detailed Implementation

[0085] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0086] To make the above-mentioned objectives, features and effects of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0087] Example 1: A resource allocation method in a multi-UAV RSMA-ISAC system, generally comprising the following steps:

[0088] Step S1: Construct a multi-UAV RSMA-ISAC system model. Each UAV uses a multi-antenna array to simultaneously form a communication beam and a sensing beam. Downlink communication uses rate split multiple access to transmit common and private stream data. Perform spatial angular domain representation on various beams and extract beam main lobe direction, beamwidth, and spatial energy distribution characteristic parameters.

[0089] Step S2: Based on the degree of directional overlap and sidelobe leakage characteristics of different beams in the spatial angular domain, construct an inter-beam interference measurement model to form an interference coefficient matrix that describes the spatial conflict relationship between each communication beam and sensing beam in the system.

[0090] Step S3: Introduce the inter-beam interference factor into the resource allocation process of the multi-UAV RSMA-ISAC system, and jointly consider variables such as beam pointing, beamwidth, power splitting ratio of RSMA common flow and private flow, UAV transmit power and UAV spatial position to construct a joint optimization model under the condition of satisfying communication performance and perception performance constraints.

[0091] Step S4: Perform multi-UAV cooperative scheduling based on the degree of inter-beam interference. When a high spatial conflict beam is detected, interference is suppressed by adjusting the beam direction, beam shape, or rate splitting power ratio to obtain the optimal communication and sensing beam configuration and RSMA resource allocation results for each UAV, which are then used for system transmission control.

[0092] The specific implementation steps are as follows:

[0093] Step S1: Consider the following... A communication and sensing integrated system consisting of drones. Each drone is equipped with... One transmitting antenna can simultaneously send communication signals to multiple ground users and actively sense the target area. In downlink communication, the first... The UAV uses Rate Split Multiple Access (RSMA) to transmit signals, and its transmitted signal can be represented as:

[0094]

[0095] in: Indicates a common flow signal; Indicates the first Private stream signals for each user; Indicates the perceived signal; These are the beamforming vectors for the corresponding signals. Therefore, each beamforming vector corresponds to a spatial beam, including a public stream communication beam, a private stream communication beam, and a sensing beam, and satisfies the following conditions:

[0096]

[0097] For the first The total transmit power of the drone. Assume a certain direction in space is determined by the azimuth angle. and pitch angle The array response vector of the UAV antenna array in this direction is described as follows:

[0098]

[0099] in: The carrier wavelength; Indicates the first The root antenna is in the direction relative to the reference point. The equivalent propagation distance difference, the array response vector is used to describe the array gain characteristics in different spatial directions.

[0100] For drones Any emitted beam (corresponding shaping vector) ), its direction The normalized space gain on is defined as:

[0101]

[0102] This function describes the energy distribution of the beam in the spatial angular domain, where: the direction of maximum gain corresponds to the beam's main lobe pointing angle; the angular domain range where the gain exceeds a certain threshold is defined as the beamwidth; and the gain region outside the main lobe corresponds to the sidelobe energy distribution. Therefore, based on the spatial gain function, we can further define:

[0103] Main lobe pointing angle:

[0104] Beamwidth: When The corresponding angular domain range is the main lobe coverage area, in which This is a preset threshold.

[0105] Let the first The position of the drone in three-dimensional space is:

[0106]

[0107] For any spatial direction It can be converted into a three-dimensional unit direction vector:

[0108]

[0109] This allows the beams emitted by different UAVs to be uniformly mapped onto the same three-dimensional spatial orientation system, facilitating subsequent analysis of the spatial orientation overlap relationship between the beams of different UAVs.

[0110] Step S2: Modeling Inter-beam Interference. This step is used to characterize the mutual influence relationship between different communication beams and sensing beams in the spatial angular domain of a multi-UAV RSMA-ISAC system, and to establish an inter-beam interference measurement model to provide a quantitative basis for subsequent resource allocation.

[0111] Based on the beam space angular domain representation established in step S1, the first beam launched by any UAV... A beam, in spatial direction The normalized spatial gain at point can be expressed as:

[0112]

[0113] in, Indicates the first Beamforming vectors for each beam; This represents the array response vector corresponding to the spatial direction; and These represent the azimuth and elevation angles, respectively. This gain function is used to describe the energy distribution characteristics of the beam in different spatial directions, including the main lobe and side lobe regions.

[0114] For any two beams in the system and The degree of spatial interference depends on the energy overlap between the two beams in the angular domain. Therefore, this invention defines an inter-beam spatial overlap coefficient:

[0115]

[0116] in, Indicates the spatial angular domain range considered by the system; This is used to reflect the overlap intensity of two beams in the spatial direction. When the main lobe directions of the two beams are close or their side lobe regions overlap, The value is relatively large; conversely, when the two beams are basically separated in space, the value is close to zero.

[0117] After considering the transmit power factor, define the beam. Beam The generated spatial interference power is:

[0118]

[0119] in, Indicates beam The transmission power; Indicates beam Causes and acts on the beam Spatial interference intensity. When the beam When a beam belongs to the RSMA common stream or sensing beam, its transmit power is usually high, making it more likely to cause significant interference to other beams.

[0120] For beams from different drones, the spatial geometric relationships between the drones also need to be considered. Let the... The drone and the first The path loss coefficient between the drones is Inter-beam interference across UAVs can be expressed as:

[0121]

[0122] in, Indicates by the first The beam emitted by the drone; Indicates by the first The model reflects the combined effects of spatial overlap and the distance between drones on interference intensity.

[0123] Based on the above definition, construct the system-level inter-beam interference matrix:

[0124]

[0125] in: This represents the total number of communication and sensing beams in the system; diagonal elements represent the beam's own energy; and off-diagonal elements represent the spatial interference relationships between different beams. This interference matrix serves as an important input for subsequent joint resource allocation and coordinated scheduling.

[0126] To further consider the structural differences of RSMA, a beam type weighting coefficient is introduced. The interference contribution of common flow beams, private flow beams, and sensing beams is differentiated, and the corrected interference metric is as follows:

[0127]

[0128] Among them, the common stream beam Take the larger value; private flow beam Take the average value; sensing beam The settings are adaptively configured based on the required sensing accuracy.

[0129] Step S3: Interference-Aware Joint Resource Allocation Modeling. Based on the inter-beam interference matrix obtained in Step 2, spatial beam interference factors are introduced into the resource allocation process of the multi-UAV RSMA-ISAC system to construct a communication and sensing collaborative optimization model. Specifically, S3 includes:

[0130] Step S31: Communication received signal and SINR modeling (including inter-beam interference), assuming ground users By drone The service, whose received signal can be represented as:

[0131]

[0132] in, For drones To users The channel vector; and For other users Beamforming vectors and private flow signals; This refers to the inter-beam spatial interference power obtained in step 2; To interfere with beam signals; It is additive white Gaussian noise.

[0133] Under the RSMA mechanism, the user first decodes the public stream, then decodes the private stream. The signal-to-interference-plus-noise ratio (SIR / N) of the private stream is expressed as:

[0134]

[0135] The variance of additive white Gaussian noise is used, and the SINR of the common current is defined similarly, except that the expected signal is replaced by... It can be seen that there is inter-beam interference. It is explicitly included in the SINR expression.

[0136] Step S32: Perception performance modeling (affected by beam interference), assuming the UAV Target direction The effective sensing signal power is:

[0137]

[0138] Leakage interference from other communication beams or other drone beams in this direction is as follows:

[0139]

[0140] The perceived signal-to-interference-plus-noise ratio can then be expressed as:

[0141]

[0142] This quantity directly affects the target detection probability and the accuracy of distance estimation.

[0143] Step S33: Joint optimization variable definition. This invention incorporates the following variables into joint resource allocation: beamforming vector. RSMA power splitting ratio: UAV launch power UAV spatial location .

[0144] Step S34: For interference-aware joint optimization objective function, under the conditions of satisfying communication and sensing constraints, construct the system weighted performance maximization problem:

[0145]

[0146] Among them, the first item The first term represents the user communication rate; the second term represents the perceived performance gain; and the third term represents the inter-beam interference penalty. These are the weighting coefficients.

[0147] Step S35: Constraints:

[0148] Transmit power constraints:

[0149] Common stream decodeability constraints:

[0150] Perception performance threshold constraints: .

[0151] Step S4: Cooperative scheduling output based on spatial conflict suppression. Based on the initial resource allocation results obtained in Step 3, the spatial cooperative scheduling and dynamic control among multiple UAVs are further combined with the inter-beam interference intensity to reduce interference between high-conflict beams and achieve stable communication and sensing performance output.

[0152] Step S41: Determine the spatial conflict intensity. Based on the inter-beam interference matrix constructed in step S2. Define beam The total spatial conflict intensity is:

[0153]

[0154] when

[0155]

[0156] in If the preset conflict threshold is not met, the beam is determined to be in a state of high spatial interference and needs to be scheduled and adjusted.

[0157] Step S42: Beam direction adaptive adjustment. For beams identified as having high conflict... By fine-tuning its main lobe pointing angle, angular overlap with other beams is reduced. The update rule can be expressed as:

[0158]

[0159] in: Step size factor; This represents the gradient direction of the collision intensity with respect to the beam pointing angle. This process gradually moves the beam away from the high collision angle region.

[0160] Step S43: Beamwidth compression control. If the conflict remains significant after directional adjustment, adaptive beamwidth compression is applied. Let the beamwidth parameter be... The update rules are as follows:

[0161]

[0162] in, This is the adjustment coefficient; This is the maximum allowable collision reference value for the system. Reducing the beamwidth can effectively decrease energy leakage from sidelobes to adjacent directions.

[0163] Step S44: RSMA power splitting ratio coordinated adjustment. Considering that the RSMA common current beam usually has a wide coverage area, when it causes significant spatial interference, the common current power needs to be suppressed. Let the common current power ratio be... Then it will be updated to:

[0164]

[0165] And redistribute the reduced power to the private stream or sensing beam:

[0166]

[0167] in For adjustment coefficients, The proportion of power released from the common flow.

[0168] Step S45: Multi-UAV Corner Domain Cooperative Avoidance Mechanism. The spatial corner domain is divided into several corner domain resource blocks. And calculate the total beam energy occupancy in each corner domain:

[0169]

[0170] When the load in a certain corner exceeds the threshold At this time, the scheduler restricts new high-power beams from being directed into the region, thereby enabling angular domain separation control across UAVs.

[0171] Step S46: Output final control parameters. After the above scheduling steps, the system outputs: updated beamforming vector. The updated beam pointing and width parameters, updated RSMA power splitting ratio, and final transmit power configuration for each UAV are then distributed to each UAV for execution, enabling real-time control of the communication and sensing beams.

[0172] Example 2: The computer-readable storage medium of this example stores a computer program that, when executed by a processor, implements the steps in the resource allocation method of a multi-UAV RSMA-ISAC system of Example 1.

[0173] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0174] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0175] Example 3: The computer device of this example includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the resource allocation method of a multi-UAV RSMA-ISAC system in Example 1.

[0176] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The memory can include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0177] Those skilled in the art will clearly understand that each implementation can be achieved using software plus the necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0178] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A resource allocation method in a multi-UAV RSMA-ISAC system, characterized in that, Includes the following steps: Step S1: Construct a multi-UAV RSMA-ISAC system model. Each UAV uses a multi-antenna array to simultaneously form a communication beam and a sensing beam. Downlink communication uses rate split multiple access to transmit common and private stream data. Perform spatial angular domain representation on various beams and extract beam main lobe direction, beamwidth, and spatial energy distribution characteristic parameters. Step S2: Based on the degree of directional overlap and sidelobe leakage characteristics of different beams in the spatial angular domain, construct an inter-beam interference measurement model to form an interference coefficient matrix that describes the spatial conflict relationship between each communication beam and sensing beam in the system. Step S3: Introduce the inter-beam interference factor into the resource allocation process of the multi-UAV RSMA-ISAC system, and construct a joint optimization model under the constraints of communication performance and perception performance. Step S4: Perform multi-UAV cooperative scheduling based on the degree of inter-beam interference. When a high spatial conflict beam is detected, interference is suppressed by adjusting the beam direction, beam shape, or rate splitting power ratio to obtain the optimal communication and sensing beam configuration and RSMA resource allocation results for each UAV, which are then used for system transmission control.

2. The resource allocation method in a multi-UAV RSMA-ISAC system according to claim 1, characterized in that, Direction in space is determined by azimuth. and pitch angle Description, for drones Any beam The main lobe direction of the beam is The beamwidth is when The corresponding angular domain range, where For the preset threshold, The normalized spatial gain is expressed as: ； in For beam Beamforming vector, For drones Antenna array in direction The array response vector on is represented as: ； in: The carrier wavelength; Indicates the first The root antenna is in the direction relative to the reference point The difference in equivalent propagation distance, The total number of transmitting antennas configured on the drone.

3. The resource allocation method in a multi-UAV RSMA-ISAC system according to claim 2, characterized in that, The interference coefficient matrix is ​​as follows: ； in: The diagonal elements represent the total number of communication and sensing beams in the system; the diagonal elements represent the beam's own energy term; the off-diagonal elements represent the spatial interference relationship between different beams. Beam The spatial interference power is: ； in, Indicates beam The transmission power, The inter-beam spatial overlap coefficient is expressed as: ； in, This indicates the spatial angular domain range considered by the system.

4. The resource allocation method in a multi-UAV RSMA-ISAC system according to claim 3, characterized in that, For beams from different UAVs, cross-UAV beam interference is represented as: ； in, Indicates by the first The beam emitted by the drone; Indicates by the first The beam emitted by the drone For the first The drone and the first Path loss coefficient between drones.

5. A resource allocation method in a multi-UAV RSMA-ISAC system according to claim 3, characterized in that, Introducing beam type weighting coefficients The interference contribution of common flow beams, private flow beams, and sensing beams is differentiated, and the corrected interference metric is as follows: ； Among them, the common stream beam Take the larger value; private flow beam Take the average value; sensing beam The settings are adaptively configured based on the required sensing accuracy.

6. The resource allocation method in a multi-UAV RSMA-ISAC system according to claim 3, characterized in that, Step S3 specifically includes: Step S31: Communication received signal and SINR modeling, assuming ground users By drone The service, whose received signal can be represented as: ； in, For drones To users The channel vector; Indicates a common flow signal; Indicates the first Private stream signals for each user; Indicates other users Private stream signals; These are the beamforming vectors for the corresponding signals; This refers to the inter-beam spatial interference power; To interfere with beam signals; It is additive white Gaussian noise; The signal-to-interference-plus-noise ratio (SIR) of a private stream is expressed as: ； The signal-to-interference-plus-noise ratio (SIR) of the common stream is expressed as: ； The variance of communication signal noise; Step S32: Perception performance modeling, assuming the UAV Target direction The effective sensing signal power is: ； in For the beamforming vector of the sensing signal, the leakage interference from other communication beams or other UAV beams in this direction is: ； The perceived signal-to-interference-plus-noise ratio is then expressed as: ； To sense the variance of signal noise; Step S33: Incorporate the following variable into the joint resource allocation: beamforming vector Rate splitting multiple access power splitting ratio: UAV launch power UAV spatial location ; Step S34: Construct the system weighted performance maximization problem: ； Among them, the first item The first term represents the user communication rate; the second term represents the perceived performance gain; and the third term represents the inter-beam interference penalty. These are the weighting coefficients; Step S35: Constraints include: Transmit power constraints: ; Common stream decodeability constraints: ; Perception performance threshold constraints: .

7. A resource allocation method in a multi-UAV RSMA-ISAC system according to claim 6, characterized in that, Step S4 specifically includes: Step S41: Based on the inter-beam interference matrix Define beam The total spatial conflict intensity is: ； when ,in When the preset conflict threshold is not met, the beam is determined to be in a state of high spatial interference and needs to be scheduled and adjusted. Step S42: For beams identified as having high conflict By fine-tuning its main lobe pointing angle, angular overlap with other beams is reduced; the update rule is: ； in: Step size factor; This indicates the gradient direction of the collision intensity with respect to the beam pointing angle; Step S43: If the conflict is still significant after directional adjustment, adaptively shrink the beamwidth. Let the beamwidth parameter be... The update rules are as follows: ； in, This is the adjustment coefficient; This is the maximum allowed collision reference value by the system; Step S44: When rate-split multiple access common current beam causes significant spatial interference, suppress the common current power, assuming the common current power ratio is... Then it will be updated to: ； And redistribute the reduced power to the private stream or sensing beam: ； in For adjustment coefficients, The proportion of power released from the common flow; Step S45: Divide the spatial corner domain into several corner domain resource blocks. And calculate the total beam energy occupancy in each corner domain: ； When the load in a certain corner exceeds the threshold At that time, the scheduler restricts new high-power beams from being directed at that area; Step S46: After the above scheduling steps, the system outputs: the updated beamforming vector. The updated beam pointing and width parameters, updated rate splitting multiple access power splitting ratio, and final transmit power configuration for each UAV are then distributed to each UAV for execution.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the resource allocation method of a multi-UAV RSMA-ISAC system as described in any one of claims 1-7.

9. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the resource allocation method of a multi-UAV RSMA-ISAC system as described in any one of claims 1-7.

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