5g-aeromacs service adaptive common beam power joint allocation method

Abstract

The application discloses a 5G-AeroMACS service adaptive sensing beam power joint allocation method, and belongs to the technical field of aviation mobile communication and radar sensing integration. The method is applied to an airport surface communication and sensing integrated system, and comprises a multi-antenna base station and a single-antenna aircraft. According to the communication service type, sensing task and risk level of each aircraft, the performance weight and constraint threshold of communication and sensing are dynamically controlled; the weighted sum of communication rate and sensing signal-to-jamming noise ratio is maximized as the target, and the base station transmission beamforming, sensing receiving processing and communication / sensing power allocation are jointly optimized under the constraints of power, communication rate and sensing signal-to-jamming noise ratio; and an alternating optimization framework is adopted for solving. The application can adaptively adjust the resource allocation strategy according to the heterogeneous service demand of the airport surface, and cooperatively improves the aircraft communication reliability and target sensing accuracy under the limited spectrum and power, and is suitable for the deployment of the airport surface communication and sensing integrated wireless network.

Description

Technical Field

[0001] This invention belongs to the technical fields of aviation mobile communication, 5G-AeroMACS airport surface wireless network, and integrated wireless communication and radar sensing. Specifically, it relates to a 5G-AeroMACS service adaptive sensing beam power joint allocation method, and more particularly to a method based on the 5G-AeroMACS system that, in the airport surface aircraft operating environment, jointly transmits communication signals and radar sensing signals, and achieves synergistic improvement of airport surface communication performance and target sensing performance by jointly optimizing airport surface base station transmit beamforming, base station sensing and receiving processing, and power allocation of communication signals and sensing signals. Background Technology

[0002] With the growth in air transport demand, the airport surface operating environment is becoming increasingly complex. Aircraft, support vehicles, and jet bridge equipment operate at high density on runways, taxiways, and aprons, placing higher demands on communication, surveillance, and security. Existing airports typically deploy separate mobile airport communication systems and surface surveillance systems: the former ensures data communication between aircraft and ground control systems, while the latter relies on surface surveillance radar, Automatic Dependent Surveillance-Broadcast (ADS-B), and surface multipoint positioning equipment for target detection. This separate deployment of the two systems, with independent hardware platforms, spectrum resources, signal systems, and network architectures, results in high construction costs, significant equipment redundancy, and complex maintenance. Furthermore, limited spectrum resources are insufficient to meet the comprehensive requirements of future 5G-AeroMACS systems for high-speed communication and precise sensing.

[0003] In the complex electromagnetic environment of airports, strong line-of-sight propagation, multipath reflection, and building / aircraft obstruction lead to coverage blind spots and stability degradation in wireless links. Aircraft require highly reliable communication to receive control commands and safety alarms, while the system also needs to perform real-time detection of targets such as aircraft, vehicles, and runway obstructions. Ensuring both communication and detection performance under limited spectrum resources is therefore crucial.

[0004] Integrated communication and sensing technology, through unified hardware, spectrum, and signal design, enables base stations to possess both communication and radar sensing capabilities, potentially overcoming the drawbacks of separate deployment. However, existing solutions have shortcomings in 5G-AeroMACS airport surface scenarios: the superposition of communication and sensing signals can easily lead to inter-functional interference; and aircraft are typically equipped with a single antenna, making it difficult to achieve complex spatial domain interference suppression, necessitating the transfer of sensing reception and processing to the surface base station side equipped with a multi-antenna array.

[0005] In this architecture, the base station's transmit beamforming, sensing and receiving processing, and power allocation between communication and sensing signals are mutually coupled, directly affecting system performance. Power allocation can improve spectral efficiency by superimposing power domains on time-frequency resources to carry two types of signals, but its joint optimization problem with beamforming and receiving processing is non-convex and difficult to solve directly. In addition, airport surface services have differentiated priorities (such as taxiing instructions, collision alarms, and other high real-time services), and fixed weight or fixed power allocation strategies cannot adapt to real-time changes in service load, channel status, and target risk level.

[0006] Therefore, there is an urgent need for a joint resource optimization method for 5G-AeroMACS integrated sensing scenarios, and to introduce a dynamic adjustment mechanism for heterogeneous service requirements, adaptively adjusting the proportion and optimization weight of communication and sensing resources, so as to achieve a dynamic balance between communication reliability, sensing accuracy and resource utilization efficiency. Summary of the Invention

[0007] The purpose of this invention is to address the problems of low spectrum utilization, high hardware redundancy, and insufficient system coordination caused by the separate deployment of existing airport surface communication systems and surveillance and perception systems, as well as the problems of weak electromagnetic wave diffraction capability, high propagation loss, insufficient communication coverage stability, and difficulty in balancing aircraft communication reliability and target perception performance in complex airport surface obstruction, high dynamic targets, and strong multipath propagation environments. The invention proposes an adaptive sensing beam power joint allocation method for 5G-AeroMACS services.

[0008] This invention discloses a 5G-AeroMACS service adaptive sensing beam power joint allocation method, applied to an airport surface communication and sensing integrated system. The system includes an airport surface base station configured with a multi-antenna array and multiple aircraft configured with single antennas. The method includes: The first step is to determine the communication demand intensity and perception demand intensity of each aircraft based on the communication service type, target perception task type and collision risk level of each aircraft in the airport area, and then dynamically adjust the communication performance weight, perception performance weight, minimum communication rate threshold of each aircraft and minimum perception signal-to-noise ratio threshold when each aircraft is a perceived target. The second step, with the goal of maximizing the weighted sum of communication and sensing performance, involves jointly optimizing the following constraints: total transmit power of the airport surface base station, minimum communication rate of each aircraft, minimum sensing signal-to-clutter ratio of each aircraft when it is a sensed target, power allocation factor of communication signal and radar sensing signal, and normalization of sensing and receiving processing vector. The third step is to solve the joint optimization problem using an alternating optimization framework. Under the condition of a fixed sensing and receiving processing vector, the transmit beamforming vector and power allocation factor are solved. Under the condition of a fixed transmit beamforming vector and power allocation factor, the sensing and receiving processing vector is solved. The optimized transmit beamforming vector, sensing and receiving processing vector and power allocation factor are obtained through alternating iteration. The fourth step involves the airport surface base station simultaneously transmitting communication signals and radar sensing signals to each aircraft on the same time and frequency resources according to the optimized transmit beamforming vector and power allocation factor, and processing the sensing echo signals reflected back from the airport surface targets using the optimized sensing and receiving processing vector.

[0009] Optionally, the intensity of communication demand is determined by weighting the service priority, service latency sensitivity, service rate requirement, service packet loss rate sensitivity, and communication link quality degradation of each aircraft; the intensity of perception demand is determined by weighting the collision risk level, motion state change intensity, importance of the airport surface area, perception accuracy requirement, and density of surrounding targets of each aircraft.

[0010] Optionally, the minimum communication rate threshold is dynamically adjusted upwards or downwards based on the basic minimum communication rate requirement of each aircraft, combined with the aircraft's service priority, service latency sensitivity, service packet loss rate sensitivity, and the degree of communication link quality degradation; the minimum perceived signal-to-noise ratio (SNR) threshold is dynamically adjusted upwards or downwards based on the basic minimum perceived SNR requirement of each aircraft, combined with the aircraft's collision risk level, intensity of motion state changes, importance of the area, perception accuracy requirements, and density of surrounding targets.

[0011] Optionally, in the weighted sum of communication performance and perception performance, communication performance is represented by the sum of the achievable communication rates of each aircraft, and perception performance is represented by the sum of the radar perception signal-to-clutter ratios corresponding to each aircraft when it is a perceived target; the weights of communication performance and perception performance are determined by the intensity of communication demand and the intensity of perception demand, and the sum of the two is a normalized value.

[0012] Optionally, when the intensity of communication demand is greater than the intensity of sensing demand, the weight of communication performance is increased and the power allocation range of communication signals is expanded to prioritize the reliability of aircraft downlink communication; when the intensity of sensing demand is greater than the intensity of communication demand, the weight of sensing performance is increased and the power allocation range of radar sensing signals is expanded to prioritize the performance of airport surface target detection.

[0013] Optionally, when solving for the transmit beamforming vector and power allocation factor under the condition of a fixed sensing and receiving processing vector, a transmit beamforming covariance matrix is ​​introduced to transform the rank-one constraint into a penalty term for the difference between the nuclear norm and the spectral norm, and the solution is obtained iteratively through successive convex approximations; when solving for the sensing and receiving processing vector under the condition of a fixed transmit beamforming vector and power allocation factor, a sensing and receiving covariance matrix is ​​introduced, and the solution is obtained iteratively using a sequential rank-one constraint relaxation method.

[0014] Optionally, the transmit beamforming vector, communication signal power allocation factor, and radar sensing signal power allocation factor of the airport surface base station for each aircraft jointly determine the integrated signal transmitted by the base station; wherein, the communication signal and radar sensing signal corresponding to each aircraft are independent and their power is normalized, the communication signal is used to transmit downlink communication data to the aircraft, and the radar sensing signal is used to detect the aircraft and its surrounding targets.

[0015] Another aspect of the present invention discloses a 5G-AeroMACS service adaptive sensing beam power joint allocation system, comprising an airport surface base station configured with a multi-antenna array and multiple aircraft configured with single antennas; The airport surface base station includes: The dynamic control module is used to determine the communication demand intensity and perception demand intensity of each aircraft based on the communication service type, target perception task type and collision risk level of each aircraft, and dynamically adjust the communication performance weight, perception performance weight, minimum communication rate threshold of each aircraft and minimum perception signal-to-noise ratio threshold when each aircraft is a perceived target. The joint optimization module aims to maximize the weighted sum of communication and sensing performance. Under constraints such as the total transmit power of the airport surface base station, the minimum communication rate of each aircraft, the minimum sensing signal-to-clutter ratio of each aircraft when it is a sensed target, the power allocation factor of the communication signal and the radar sensing signal, and the normalization constraint of the sensing and receiving processing vector, the module jointly optimizes the transmit beamforming vector of the airport surface base station for each aircraft, the sensing and receiving processing vector of the airport surface base station for each aircraft when it is a sensed target, the power allocation factor of the communication signal corresponding to each aircraft, and the power allocation factor of the radar sensing signal corresponding to each aircraft. The signal transmission module is used to simultaneously transmit communication signals and radar sensing signals to various aircraft on the same time and frequency resources, according to the optimized transmission beamforming vector and power allocation factor. The echo receiving and processing module is used to receive the sensing echo signal reflected back from the airport surface target, and to process the sensing echo signal using an optimized sensing receiving and processing vector. The aircraft is used to receive communication signals from airport surface base stations via a single antenna and to demodulate communication information.

[0016] Optionally, the multi-antenna array of the airport surface base station is a uniform linear antenna array, in which all antenna elements serve as transmitting antenna elements for communication signals and radar sensing signals, and are also used to receive the sensing echo signals.

[0017] Optionally, the joint optimization module uses an alternating optimization framework for solving: under the condition of a fixed sensing and receiving processing vector, the transmit beamforming vector and the power allocation factor are solved; under the condition of a fixed transmit beamforming vector and the power allocation factor, the sensing and receiving processing vector is solved; and the alternating iteration continues until the objective function converges or reaches the preset maximum number of iterations, and the optimized transmit beamforming vector, sensing and receiving processing vector, and power allocation factor are output.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: First, this invention integrates the airport surface communication function and radar sensing function of 5G-AeroMACS into the same airport surface base station platform, and completes the joint transmission of communication signals and sensing signals in the same frequency band and the same time-frequency resources. This can reduce the spectrum occupation and hardware redundancy caused by the separate deployment of traditional communication systems and surveillance radar systems, and improve the spectrum resource utilization efficiency of 5G-AeroMACS.

[0019] Second, the present invention adopts a system architecture of single antenna on aircraft and multiple antennas on airport surface base station, which avoids configuring complex multi-antenna arrays on the aircraft side, reducing the hardware complexity, power consumption and installation burden on the aircraft side; at the same time, the multi-antenna reception and spatial processing of sensing echoes are concentrated on the 5G-AeroMACS airport surface base station side, which can make full use of the spatial degrees of freedom of the base station multi-antenna array and improve the target detection and interference suppression capabilities.

[0020] Third, by jointly optimizing the 5G-AeroMACS airport surface base station transmit beamforming, base station sensing and receiving processing, and the power allocation of communication signals and sensing signals, this invention can achieve a flexible trade-off between communication rate and radar sensing signal-to-clutter ratio, reduce inter-functional interference between communication signals and sensing signals, and thus simultaneously improve aircraft communication reliability and airport surface target sensing accuracy.

[0021] Fourth, this invention addresses the non-convexity and variable coupling characteristics of the 5G-AeroMACS communication-sensing integrated joint optimization problem by employing a solution method that combines alternating optimization, penalty function, semidefinite relaxation, successive convex approximation, and sequential rank-one constraint relaxation. This transforms the problem, which was originally difficult to solve directly, into an iteratively solvable convex optimization subproblem, thereby improving the algorithm's feasibility and convergence stability.

[0022] Fifth, this invention is applicable to future smart airports, advanced surface activity guidance and control systems, aviation mobile airport communication systems, and 5G-AeroMACS wireless network enhancement deployment scenarios. It can provide integrated technical support for aircraft communication assurance, target detection, situational awareness, and collision risk warning on the airport surface, and serve the safe and efficient collaborative operation of "aircraft-vehicle-airway-facilities" on the airport surface. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly introduced below. The features and advantages of the present invention can be more clearly understood by referring to the accompanying drawings. The accompanying drawings are schematic and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the airport surface communication and sensing integrated system based on 5G-AeroMACS of the present invention. Detailed Implementation

[0025] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0026] The following description, using the 5G-AeroMACS airport surface communication and sensing integrated system as an example, further illustrates the specific implementation method of this invention. It should be understood that the embodiments described in this section are used to explain the technical solutions of this invention and do not constitute a limitation on the scope of protection of this invention. Without departing from the core ideas of this invention, those skilled in the art can adjust the relevant system parameters according to the airport scale, 5G-AeroMACS base station deployment density, number of base station antennas, number of aircraft, airport surface service types, communication service quality requirements, and sensing accuracy requirements.

[0027] A specific embodiment of the present invention, such as Figure 1As shown, a method for adaptive sensing beam power joint allocation for 5G-AeroMACS services is disclosed. This method involves setting up an airport surface base station with dual functions of 5G-AeroMACS communication and radar sensing, and multiple aircraft. The airport surface base station is configured with a multi-antenna array, and each aircraft is equipped with a single antenna. Based on the dedicated 5G-AeroMACS frequency band or its evolved frequency band, the airport surface base station transmits communication signals and radar sensing signals to the aircraft on the same frequency band and the same time-frequency resources, and receives sensing echo signals reflected from aircraft, support vehicles, runway debris, or other airport surface targets. The method specifically includes the following steps: Step 1: Establish an integrated airport surface communication and sensing system model based on 5G-AeroMACS, including one airport surface base station and multiple aircraft.

[0028] Furthermore, the airport surface base station is a ground base station with dual functions of 5G-AeroMACS communication and radar sensing, and can be deployed in control towers, terminals, surface communication stations, runway and taxiway edges, or other high points within the airport. The airport surface base station is equipped with a uniform linear antenna array, which includes... Each antenna element, the Each antenna element serves as a transmitting antenna element on the airport surface base station side, used to transmit 5G-AeroMACS downlink communication signals and radar sensing signals. Simultaneously, these antenna elements can also be used to receive sensing echo signals reflected from aircraft, support vehicles, runway obstructions, or other airport surface targets. Each aircraft is equipped with a single-antenna receiver to receive the 5G-AeroMACS downlink communication signals transmitted by the airport surface base station. The aircraft does not require a complex multi-antenna array; its communication reception, control information demodulation, and status information feedback can all be completed using a single antenna. Spatial reception and processing of sensing echoes are centrally handled on the airport surface base station side.

[0029] Specifically, the uniform linear antenna array of the airport surface base station includes Each antenna element, the All antenna units are transmitting antenna units used for integrated communication and sensing signal transmission on the airport surface base station side; the number of aircraft is... The set of aircraft is represented as: .

[0030] Furthermore, the airport surface base station sets the transmit beamforming vector for the k-th aircraft, as follows: ,in, This represents the transmit beamforming vector of the airport surface base station facing the k-th aircraft. Indicates by A column vector space consisting of complex elements.

[0031] Get the For each aircraft, the two are independent of each other and satisfy the unit power normalization condition. Furthermore, obtain the communication signal corresponding to the kth aircraft. Radar sensing signals The two are independent of each other and satisfy the unity power normalization condition:

[0032] in, This represents the 5G-AeroMACS downlink communication signal directed to the k-th aircraft. This represents the radar sensing signal directed toward the k-th aircraft or its corresponding sensing direction. Represents the mathematical expectation. This indicates the conjugate operation.

[0033] Furthermore, based on the transmitted beamforming vector, communication signal, and radar sensing signal, the airport surface base station generates an integrated communication and sensing transmitted signal vector:

[0034] in, This represents the transmitted signal vector of the airport surface base station; This represents the power allocation factor for the communication signal corresponding to the k-th aircraft; This represents the radar sensing signal power allocation factor for the k-th aircraft or its corresponding sensing task. This represents the transmit beamforming vector directed toward the k-th aircraft.

[0035] The above transmitted signal vector It can be embedded in 5G-AeroMACS downlink transmission frames, and the airport surface base station can dynamically adjust the power allocation factor and transmit beamforming vector according to the different aircraft's service priority, channel state, target perception requirements, and airport surface security level. For aircraft carrying high-priority services such as air traffic control instructions, safety alarms, and conflict resolution instructions, the proportion of their communication signal power can be increased; for targets located in runway and taxiway areas, intersections, densely populated apron areas, or areas with collision risks, the proportion of their corresponding radar perception signal power can be increased to enhance target detection and risk warning capabilities.

[0036] The total transmit power of airport surface base stations is limited to meet the following requirements:

[0037] in, This indicates the maximum transmission power of the airport surface base station; This represents the Euclidean norm of a vector.

[0038] Furthermore, the power allocation factors for communication signals and radar sensing signals satisfy: .in, This is used to limit the power ratio of the communication signal and radar sensing signal corresponding to the kth aircraft to not exceed the allowable range.

[0039] Step 2: Establish a 5G-AeroMACS communication link performance model.

[0040] Specifically, airport surface environments typically include terminals, hangars, jet bridges, ground vehicles, aircraft fuselages, and other large obstacles. The channel environment simultaneously contains line-of-sight propagation paths and multiple non-line-of-sight reflection paths. For 5G-AeroMACS airport surface communication, obstructions near runways, aprons, parking positions, and jet bridges can lead to direct link fading, enhanced reflection paths, and rapid channel changes. Therefore, this invention employs a Ricean channel model to describe the downlink communication channel between airport surface base stations and aircraft, reflecting the coexistence of strong line-of-sight propagation and multipath scattering characteristics of airport surfaces.

[0041] Furthermore, let the downlink communication channel vector from the airport surface base station to the k-th aircraft be: .in, Let represent the downlink communication channel vector from the airport surface base station to the k-th aircraft. Since the k-th aircraft is equipped with a single-antenna receiver, this channel is a multiple-input single-output (MIMO) channel from the airport surface base station's multi-antenna array to the aircraft's single-antenna receiver. Its Ricean channel model is expressed as:

[0042] in, The Rice factor is used to characterize the energy ratio between line-of-sight paths and non-line-of-sight paths. Indicates the line-of-sight channel component; This represents the non-line-of-sight channel component.

[0043] Furthermore, the line-of-sight channel component can be expressed as:

[0044] The non-line-of-sight channel component can be represented as:

[0045] in, Indicates the number of non-line-of-sight propagation paths; Indicates the line-of-sight path complex gain; Indicates the first Complex gain of a non-line-of-sight path; Indicates the airport surface base station to the first The visual path departure angle of an aircraft; Indicates the first The starting angle of a non-line-of-sight path; This represents the directional response vector of the airport surface base station antenna array.

[0046] Furthermore, for those containing A uniform linear array of antenna elements, its directional response vector The expression is:

[0047] in, Indicates the spacing between adjacent antenna elements. Indicates the carrier wavelength; This indicates the departure angle or target direction angle of the signal relative to the reference direction of the uniform linear antenna array of the airport ground station; Represents the imaginary unit; Represents the natural constant.

[0048] Furthermore, the expression for the path complex gain is:

[0049] in, Indicates the first Large-scale fading gain of the path, Indicates the first The propagation delay of each path, Indicates the sampling period.

[0050] Furthermore, the first An aircraft uses a single antenna to receive signals transmitted from an airport surface base station. The expression for the received signal is:

[0051] in, This represents the downlink signal received by the k-th aircraft; This represents the downlink communication channel vector from the airport surface base station to the k-th aircraft; Represents the base station's transmitted signal vector; Indicates the first Communication reception noise at the aircraft location.

[0052] Furthermore, the signals transmitted by the airport surface base station are substituted into the first... The signal received by an aircraft is expressed as follows:

[0053] , in, Indicates a useful signal. This indicates an interference signal. This represents the downlink communication channel vector from the airport surface base station to the k-th aircraft; This represents the power allocation factor for the communication signal corresponding to the k-th aircraft; This represents the radar sensing signal power allocation factor for the k-th aircraft or its corresponding sensing task. This represents the transmit beamforming vector directed towards the k-th aircraft. The communication signal corresponding to the k-th aircraft is... The radar sensing signal is .

[0054] Indicates the first The communication reception noise at the aircraft. The communication reception noise at each aircraft meets the following requirements: .

[0055] in, This indicates that the mean is 0 and the variance is 0. The complex Gaussian distribution; This indicates the noise power received by the aircraft's communications.

[0056] Furthermore, during the aircraft communication demodulation process, the first Each aircraft treats its own sensing signal and the superimposed signals from other aircraft as interference. Therefore, the first... The signal-to-interference-plus-noise ratio (SIR) of an aircraft's communication can be expressed as:

[0057] Accordingly, the first The achievable communication rate for an aircraft is:

[0058] The achievable communication speed of the system is:

[0059] Furthermore, in order to ensure the reliability of aircraft communications, the first The communication rate of each aircraft should meet the minimum communication rate constraint:

[0060] in, This represents the minimum communication rate threshold corresponding to the k-th aircraft, which can be dynamically determined based on the type of 5G-AeroMACS service it carries, communication service quality requirements, latency sensitivity, reliability requirements, and channel status.

[0061] Step 3: Establish a 5G-AeroMACS radar perception performance model.

[0062] Specifically, the 5G-AeroMACS radar sensing performance model includes an airport surface base station receiving sensing echo signal model, a base station-side sensing reception and processing model, and a radar sensing signal-to-clutter-noise ratio (SNR) model. The airport surface base station receiving sensing echo signal model describes the echo signal returned to the airport surface base station after being reflected by aircraft, support vehicles, runway obstructions, or other airport surface targets. The base station-side sensing reception and processing model describes the process by which the airport surface base station uses a multi-antenna array to spatially receive and process the sensing echo signal. The radar sensing SNR model calculates the radar sensing SNR corresponding to the sensed target based on the target echo signal power, communication signal leakage interference, interference from other targets or other beams, airport surface clutter, and base station receiving noise, and uses the radar sensing SNR as an evaluation index for target sensing performance.

[0063] Furthermore, based on the first Target reflectance coefficient corresponding to a perceived target Target direction and base station array directional response vector , obtained the The equivalent matrix of the base station sensing echo corresponding to each sensed target is expressed as follows:

[0064] Furthermore, the expression for the sensed echo signal received by the airport surface base station is as follows:

[0065] in, This indicates airport surface clutter interference. This indicates the noise sensed by the base station.

[0066] Furthermore, based on airport surface base stations, targeting the first The sensing and receiving processing vectors for each target are: , obtained the The sensory reception output corresponding to each target is expressed as:

[0067] In the airport surface base station perception and processing process, the first Sensing signal power corresponding to each target The expression is: .

[0068] Furthermore, the corresponding communication signal leakage interference, interference from other targets or other beams, clutter, and noise power can be expressed as follows:

[0069]

[0070]

[0071] in, This indicates interference from communication signal leakage; This indicates other targets or other beam interference; Indicates clutter and noise power; This represents the covariance matrix of airport surface clutter. This represents the noise power sensed and received by the base station.

[0072] Therefore, the first The expression for the radar sensing signal-to-clutter ratio of a target is:

[0073] Furthermore, in order to ensure the target perception performance of the airport surface, the first The radar sensing signal-to-clutter ratio of each target satisfies:

[0074] in, Indicates the first The minimum perceived signal-to-noise ratio requirement for each target.

[0075] Step 4: Establish a dynamic control model for the heterogeneous business needs of the airport surface.

[0076] Specifically, the dynamic control model is used to adaptively adjust the communication performance weights, sensing performance weights, minimum communication rate threshold, minimum sensing signal-to-clutter ratio threshold, and power allocation ranges of communication signals and radar sensing signals in the subsequent joint optimization problem based on airport surface communication service requirements, target perception task requirements, target risk status, channel status, and interference status.

[0077] Furthermore, the dynamic control model includes the construction of an airport surface service feature set, calculation of communication demand intensity, calculation of sensing demand intensity, determination of dynamic weights, adjustment of constraint thresholds, and adjustment of power allocation range. The airport surface service feature set is the set of input parameters for the dynamic control model, used to characterize the service demands and risk status of different aircraft communication links and their corresponding sensing targets at the airport surface.

[0078] Specifically, the airport surface base station constructs an airport surface service feature set based on the communication service type of the k-th aircraft, surface operation status, target perception task type, collision risk level, historical traffic, channel state information, and interference state information. The airport surface service feature set includes at least one of the following: communication service priority, service latency sensitivity, service rate requirement, service packet loss rate sensitivity, communication link quality degradation degree, target collision risk level, intensity of target motion state changes, importance of the target's location area, perception accuracy requirement, and density of surface targets around the target.

[0079] Furthermore, to ensure that the communication performance weights and perception performance weights can adapt to the heterogeneous service requirements of 5G-AeroMACS airport surface services, this invention constructs a communication demand intensity index for the k-th aircraft based on the aircraft's communication service type, communication service quality requirements, channel state, target risk level, and perception task requirements. and perceived demand intensity index The communication demand intensity index of the k-th aircraft is: ,in, The priority normalization index represents the 5G-AeroMACS service carried by the k-th aircraft. The 5G-AeroMACS service includes at least one of the following: aircraft taxiing instructions, runway entry permission, air traffic control instructions, safety alerts, conflict resolution instructions, periodic status feedback, and surface operation data transmission. This represents a normalized metric indicating business latency sensitivity. This represents a normalized indicator of business rate demand. This represents a normalized metric indicating the sensitivity of packet loss rate to business operations. The normalized index representing the degree of communication link quality degradation can be determined based on the channel gain, communication signal-to-interference-plus-noise ratio, historical throughput, or link interruption probability between the airport surface base station and the k-th aircraft. Let m be the m-th weighting coefficient in the calculation of communication demand intensity, and satisfy the following:

[0080] Where m represents the index number in the communication demand intensity calculation; the upper limit is 5 because this embodiment selects five types of communication demand indicators for quantification: service priority, latency sensitivity, rate requirement, packet loss rate sensitivity, and link quality degradation degree; where the perceived demand intensity index of the k-th aircraft is middle, This represents the normalized index for the collision risk level of the kth aircraft. The normalized index representing the intensity of changes in the target's motion state can be determined by the speed, acceleration, rate of change of heading, or trajectory prediction deviation of the kth aircraft. The normalized index represents the importance of the airport surface area where the kth aircraft is located. Higher values ​​can be set for runway and taxiway intersections, runway-taxi conflict areas, dense apron areas, and areas near jet bridges. A normalized index representing the required accuracy of distance, angle, or velocity estimation for a perception task; This represents a normalized index indicating the density of surface targets around the k-th aircraft. Let m be the m-th weight coefficient in the calculation of perceived demand intensity, and satisfy the following: Where m represents only the index number in the calculation of perception demand intensity; the upper limit is 5 because this embodiment selects five types of perception demand indicators for quantification: collision risk level, intensity of motion state change, regional importance, perception accuracy requirement, and target density.

[0081] Furthermore, based on the obtained communication demand intensity index and perceived demand intensity index, communication performance weights are assigned. and perceived performance weight They are respectively determined as:

[0082]

[0083] in, Indicates communication performance weights. Represents perceived performance weights. It is a stability factor greater than zero, used to avoid instability in weight calculation caused by both communication demand intensity and perception demand intensity being too small at the same time.

[0084] Furthermore, to ensure the normalization relationship between communication performance weights and perception performance weights, the following can be done: and Perform normalization to ensure that it satisfies: .

[0085] Furthermore, the minimum communication rate threshold for the k-th aircraft Dynamically adjust according to business needs middle, This represents the minimum basic communication rate requirement for the kth aircraft, which can be preset according to the 5G-AeroMACS service type. ~ These represent normalized indicators for service priority, service latency sensitivity, service packet loss rate sensitivity, and communication link quality degradation, respectively. ~ It is a non-negative adjustment coefficient.

[0086] For aircraft carrying high-priority services such as taxiing instructions, runway entry clearance, safety alerts, and conflict resolution instructions, the requirements for service priority, latency sensitivity, and reliability are high, and the corresponding minimum communication rate threshold is required. Increase; for aircraft that only carry periodic status feedback, general surface operation data, or low-priority monitoring information, the minimum communication rate threshold can be kept at a low level; when the communication link quality deteriorates or there are obstructions, strong multipath, and increased interference, it can be increased. Raise the communication support threshold for this aircraft.

[0087] Similarly, the minimum perceived signal-to-noise ratio threshold for the k-th target The dynamic adjustment is as follows: middle, This represents the minimum perceived signal-to-clutter ratio requirement for the kth aircraft. ~ These represent the target collision risk level, the intensity of changes in target motion state, the importance of the airport surface area, the perception accuracy requirement, and the normalized index of target density, respectively. ~ It is a non-negative adjustment coefficient.

[0088] For aircraft located near runway or taxiway intersections, densely populated apron areas, runway / taxiway conflict zones, or jet bridges, or aircraft with high speeds, large rates of change of heading, and high collision risks, the minimum sensing signal-to-clutter ratio (SNR) threshold should be increased. For stationary aircraft, aircraft in low-risk areas, or low-precision sensing tasks, the sensing threshold can be appropriately lowered. By dynamically setting communication rate constraints and sensing SNR constraints, the joint optimization problem can implement differentiated communication and sensing resource guarantees for different aircraft based on the actual operational status of the airport surface, thereby avoiding resource waste or insufficient protection of high-risk targets caused by fixed thresholds.

[0089] Furthermore, when the intensity of communication demand is greater than the intensity of sensing demand, the weight of communication performance and the power allocation range of communication signals are increased to prioritize the reliability of aircraft downlink communication; when the intensity of sensing demand is greater than the intensity of communication demand, the weight of sensing performance and the power allocation range of radar sensing signals are increased to prioritize the performance of airport surface target detection and risk warning; when the intensity of communication demand and the intensity of sensing demand are close, the power allocation range of communication signals and radar sensing signals is balanced and adjusted under the condition of meeting the minimum communication rate threshold and the minimum sensing signal-to-clutter ratio threshold.

[0090] Furthermore, the specific calculations of the communication demand intensity, sensing demand intensity, communication performance weight, sensing performance weight, minimum communication rate threshold, and minimum sensing signal-to-clutter ratio threshold can be determined using normalized weighted summation, lookup table mapping, threshold grading, or prediction methods based on historical service data. The normalization, weighted summation, threshold grading, and service prediction methods themselves can employ conventional methods from existing communication resource scheduling and radar mission management. The improvement of this invention lies in applying the above methods to the 5G-AeroMACS airport surface communication and sensing integrated scenario, and uniformly mapping communication service requirements, sensing task requirements, target risk status, and channel status into dynamic weights, dynamic constraint thresholds, and power allocation ranges in a joint optimization problem. This enables airport surface base stations to adaptively adjust communication and sensing resources according to heterogeneous service requirements.

[0091] Step 5: Construct the 5G-AeroMACS airport surface communication and perception integrated joint optimization problem.

[0092] Specifically, based on the aforementioned 5G-AeroMACS communication link performance model, 5G-AeroMACS radar sensing performance model, and dynamic control model for heterogeneous airport surface service requirements, with the goal of maximizing the overall system performance, the system jointly optimizes the airport surface base station transmit beamforming vector, base station sensing and receiving processing vector, and power allocation factors for communication signals and radar sensing signals.

[0093] Furthermore, this invention uses the weighted sum of communication performance and sensing performance as the overall system performance index. Specifically, communication performance is represented by the achievable sum rate of the 5G-AeroMACS communication system, and sensing performance is represented by the radar sensing signal-to-clutter ratio corresponding to each aircraft as the sensed target.

[0094] Furthermore, the joint optimization objective function of the system is expressed as:

[0095] in, This represents the transmit beamforming vector of the airport surface base station facing the k-th aircraft. This represents the sensing and receiving processing vector of the airport surface base station when the kth aircraft is the sensed target. This represents the power allocation factor for the communication signal corresponding to the k-th aircraft; This represents the power allocation factor for the radar sensing signal corresponding to the k-th aircraft; This represents the achievable communication rate of the k-th aircraft; This represents the radar signal-to-clutter ratio when the k-th aircraft is the detected target. Indicates communication performance weight This represents the perceived performance weight.

[0096] Furthermore, communication performance weights and perceived performance weight The dynamic control model in step four dynamically determines the requirements based on the heterogeneous business needs of the airport surface, and satisfies the following: Therefore, when airport surface communication services have a high priority or the quality of aircraft communication links deteriorates, the joint optimization problem increases the proportion of communication performance in the objective function; when the risk level of airport surface targets is high or the requirements for sensing tasks are enhanced, the joint optimization problem increases the proportion of sensing performance in the objective function.

[0097] Furthermore, the joint optimization problem includes the following constraints: First, the total transmit power constraint for airport surface base stations is used to limit the total transmit power consumed by airport surface base stations in transmitting communication signals and radar sensing signals to not exceed a preset maximum transmit power, expressed as: , equivalent to in, This indicates the maximum transmission power of the airport surface base station. This represents the Euclidean norm of a vector.

[0098] Second, the minimum communication rate constraint for aircraft is used to ensure that the achievable communication rate of each aircraft is not lower than the corresponding minimum communication rate threshold, so as to guarantee the reliability of downlink communication for aircraft at the airport surface, and is expressed as: ,in, This represents the minimum communication rate threshold corresponding to the k-th aircraft. The minimum communication rate threshold is dynamically determined by the dynamic control model in step four based on the aircraft's service priority, service rate requirements, latency constraints, reliability requirements, and channel state.

[0099] Third, the minimum target sensing signal-to-clutter ratio constraint is used to ensure that the radar sensing signal-to-clutter ratio corresponding to each aircraft as a sensed target is not lower than the corresponding minimum sensing signal-to-clutter ratio threshold, so as to guarantee the performance of airport surface target detection, situational awareness, and risk warning. It is expressed as:

[0100] in, This represents the minimum sensing signal-to-clutter ratio (SCR) threshold corresponding to the k-th aircraft as the sensed target. The minimum sensing SCR threshold is dynamically determined by the dynamic control model in step four based on the target risk level, the target's location, the target's motion state, the sensing accuracy requirements, and the target density.

[0101] Fourth, the power allocation factor constraint is used to limit the range of power allocation factors for communication signals and radar sensing signals, and to ensure that the power ratio of communication signals and the power ratio of radar sensing signals satisfy a preset allocation relationship, expressed as:

[0102] Fifth, the normalization constraint of the sensing and receiving processing vector is used to avoid distortion in the radar sensing signal-to-clutter ratio calculation caused by arbitrary amplification of the sensing and receiving processing vector. It is expressed as:

[0103] Therefore, the complete 5G-AeroMACS airport surface communication and sensing integrated joint optimization problem table is as follows:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Furthermore, in the above optimization problem, the communication rate With communication signal-to-interference-plus-noise ratio Related to radar sensing signal-to-clutter ratio It is related to the target echo signal power, communication signal leakage interference, other beam interference, clutter power, and noise power; at the same time... and All are related to the beamforming vector transmitted by the airport surface base station. Base station sensing and receiving processing vector Communication signal power allocation factor and radar sensing signal power allocation factor There is a coupling relationship between them. Therefore, the joint optimization problem is a non-convex optimization problem, and it is difficult to obtain the global optimum directly.

[0112] Furthermore, the objective function, optimization variables, and constraints of the joint optimization problem constitute the technical solution constructed by this invention for the 5G-AeroMACS airport surface communication and sensing integrated scenario. The joint optimization problem integrates transmit beamforming, base station sensing and receiving processing, communication / sensing power allocation, dynamic service weights, minimum aircraft communication rate constraints, and minimum target sensing signal-to-clutter-noise ratio constraints into a single optimization framework. This enables the 5G-AeroMACS airport surface communication and sensing integrated system to achieve a synergistic trade-off between aircraft communication reliability, airport surface target sensing performance, and resource utilization efficiency under limited spectrum resources and limited transmit power conditions.

[0113] Sixth, solve the 5G-AeroMACS airport surface communication and sensing integrated joint optimization problem.

[0114] Specifically, regarding the non-convex objective function and constraints in the joint optimization problem constructed in step five, and the airport surface base station transmit beamforming vector... Base station sensing and receiving processing vector Communication signal power allocation factor and radar sensing signal power allocation factor For problems that are mutually coupled, an alternating optimization framework is used for solution.

[0115] Furthermore, the joint optimization problem is decomposed into a transmit beamforming and power allocation optimization sub-problem and a base station sensing and receiving processing optimization sub-problem. Given a base station sensing and receiving processing vector... In this case, optimize the transmit beamforming vector of airport surface base stations. and power allocation factor ; given the transmit beamforming vector and power allocation factor In this case, optimize the base station sensing and receiving processing vector. The objective function is iterated by alternating between the two subproblems until it converges or the preset maximum number of iterations is reached.

[0116] Furthermore, given the base station sensing and receiving processing vector In this case, to facilitate solving the transmit beamforming and power allocation optimization subproblem, a transmit beamforming covariance matrix is ​​introduced: .in, Let represent the transmit beamforming covariance matrix corresponding to the (k)th aircraft, and satisfy: .

[0117] Furthermore, the equivalent communication channel matrix corresponding to the k-th aircraft is defined as follows: ,in, Let represent the equivalent communication channel matrix formed by the downlink communication channel vectors from the airport surface base station to the k-th aircraft. Then, the communication signal power corresponding to the k-th aircraft can be expressed as: in, This represents the matrix trace operation. Furthermore, No. The signal-to-interference-plus-noise ratio (SIR) of an aircraft can be equivalently expressed as:

[0118] Communication rate constraints can be transformed into communication signal-to-interference-plus-noise ratio (SIR / NOT) constraints, let:

[0119] The communication constraints can then be expressed as:

[0120] Similarly, given a base station sensing and receiving processing vector In this case, define the perceptual equivalence matrix:

[0121] but:

[0122] No. The perceived signal-to-clutter ratio of a target can be written as:

[0123] The perceived signal-to-noise ratio constraint can be transformed into:

[0124] Furthermore, to process the transmit beamforming covariance matrix The rank-one constraint is transformed into an equivalence relationship between the nuclear norm and the spectral norm in this invention:

[0125] in, Represents the nuclear norm. Represents the spectral norm.

[0126] Furthermore, the above equation is constrained. Introducing the objective function as a penalty term yields a penalty-form emitter-side optimization problem:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] in, For penalty parameters; This indicates the maximum transmit power of the base station. When... As the value gradually decreases, the penalty effect of the rank-one constraint increases, causing the optimized matrix to gradually approach the rank-one matrix.

[0134] Furthermore, in the At the next iteration, perform a first-order approximation: Let... Shape the covariance matrix of the transmitted beam If the largest eigenvalue corresponds to the unit eigenvector, then:

[0135] Therefore, the penalty problem can be approximated as an iteratively solvable convex optimization problem. After each solution, update... , , and penalty parameters until satisfied:

[0136] in, This is the preset rank-one error threshold.

[0137] Furthermore, when the rank-one constraint is satisfied... Then, the transmitted beamforming vector is recovered through eigenvalue decomposition:

[0138]

[0139] in, for non-zero eigenvalues, This is the corresponding feature vector.

[0140] Given the transmit beamforming vector and power allocation factor In this case, optimize the sensing and receiving processing vector of the airport surface base station. Introducing the sensing reception covariance matrix:

[0141] definition:

[0142]

[0143] in, This represents the equivalent matrix of the useful sensing signal corresponding to the k-th target; This represents the equivalent matrix of interference, clutter, and noise corresponding to the k-th target; Represents the identity matrix that matches the dimensions of the airport surface base station antenna array; Then the first The perceived signal-to-clutter-to-noise ratio for each target can be expressed as:

[0144] The corresponding perceptual constraints are:

[0145] in, Indicates the first The perceived signal-to-noise ratio corresponding to each target; Indicates the first The sensing and receiving covariance matrix corresponding to each target; To ensure normalization of the received processing vector power, the following settings are configured:

[0146] The base station sensing and receiving processing subproblem can be represented as:

[0147] st

[0148]

[0149]

[0150]

[0151] To handle rank-one constraints, this invention employs a sequential rank-one constraint relaxation method. Rank-one constraints can be characterized by the relationship between the largest eigenvalue and the trace:

[0152] In the In the next iteration, a relaxation parameter is introduced. And gradually approximate the rank-one constraint using the following constraints:

[0153] Since the maximum eigenvalue function is still nonlinear, this invention utilizes the eigenvector corresponding to the maximum eigenvalue in the previous iteration. Perform a linear approximation:

[0154] After each solution of the relaxed convex optimization problem, update the relaxation parameters:

[0155] in, For the first The step size of the next iteration.

[0156] Furthermore, when relaxation is not feasible, reduce the step size:

[0157] in, This represents the step size reduction factor, which is used when the relaxation problem is not feasible, by adjusting the step size of the nth iteration. The updated step size is obtained by reducing the step size by a ratio of 1 / J. This is to slow down the magnitude of relaxation parameter updates and improve the feasibility and stability of subsequent iterative solutions.

[0158] The iteration stops when the following convergence condition is met:

[0159]

[0160] in, and The preset convergence threshold is used; Represents the result of the nth iteration. The sensing and receiving covariance matrix corresponding to each target; This represents the result obtained in the (n-1)th iteration. The sensing and receiving covariance matrix corresponding to each target; This represents the relaxation parameter for the nth iteration.

[0161] When the rank-one matrix is ​​obtained Then, the base station sensing and receiving processing vector is recovered through eigenvalue decomposition:

[0162] in, for non-zero eigenvalues, This is the corresponding feature vector.

[0163] Furthermore, this invention employs an alternating optimization framework to jointly solve for the transmit beamforming vector, the base station sensing and receiving processing vector, and the power allocation factor, specifically including the following iterative steps: Step 1: Initialize system parameters, including the number of airport surface base station antennas. Number of aircraft Maximum transmission power of base stations Communication noise power Sensing noise power Communication weight Perceived weights Minimum communication rate threshold Minimum perceived signal-to-noise ratio threshold And the convergence threshold.

[0164] Step two: Based on the airport surface environment and aircraft position, estimate or obtain the communication channel from the airport surface base station to the aircraft. and target reflection-related parameters clutter covariance matrix .

[0165] Step 3: Initialize the transmit beamforming vector Base station sensing and receiving processing vector Communication power allocation factor and perceived power allocation factor .

[0166] Step 4: Given the base station sensing and receiving processing vector In this case, the transmit beamforming and power allocation subproblem is solved to obtain the transmit beamforming vector of the airport surface base station facing the k-th aircraft, the communication signal power allocation factor, and the radar sensing signal power allocation factor at the nth iteration. .

[0167] Step 5: Given the transmit beamforming vector and power allocation factor, solve the base station sensing and receiving processing subproblem to obtain the base station sensing and receiving processing vector for the k-th aircraft as the sensing target at the airport surface base station in the nth iteration. .

[0168] Step 6: Calculate the overall system performance objective function value obtained after the (n+1)th iteration of the current system:

[0169] in, This represents the objective function value of the overall system performance obtained after the (n+1)th outer layer alternation iteration. Represents communication performance weights. K represents the perception performance weight, and K represents the total number of aircraft being served on the airport surface, that is, the number of targets that are simultaneously providing communication support and radar perception. This represents the achievable communication rate of the k-th aircraft after the (n+1)-th iteration; This represents the radar signal-to-clutter ratio when the k-th aircraft is the detected target after the (n+1)-th iteration.

[0170] Step 7: Determine if the alternating optimization has converged. If it satisfies: If the maximum number of iterations is reached, then the iteration stops; where, This represents the system's overall performance objective function value obtained in the nth iteration; This represents the convergence threshold of the objective function; otherwise, let Return to step four and continue iterating. The final output includes the optimized airport surface base station transmit beamforming vector, base station sensing and receiving processing vector, and power allocation factor: After obtaining the optimal or near-optimal solution, the airport surface base station proceeds according to... , and Generate and transmit integrated communication and sensing signals: .in For the optimized integrated signal, The optimized beamforming matrix; This is the optimized power allocation factor.

[0171] Each aircraft uses a single antenna to receive downlink signals from the 5G-AeroMACS airport surface base station and demodulates the corresponding communication information. The airport surface base station simultaneously receives sensing echo signals reflected from aircraft, support vehicles, runway obstructions, or other airport surface targets, and uses an optimized base station sensing reception processing vector. The echo signal is processed to obtain sensing information such as target direction, distance, reflection intensity, or motion state.

[0172] Furthermore, the aforementioned alternating optimization, semidefinite relaxation, penalty function, successive convex approximation, and sequential rank-one constraint relaxation are existing mathematical optimization tools. This invention does not reintroduce these basic optimization tools themselves, but rather combines them and applies them to the 5G-AeroMACS airport surface communication and sensing integrated joint optimization problem constructed in this invention, forming an iterative solution process oriented towards transmit beamforming, base station sensing and receiving processing, and communication / sensing power allocation.

[0173] Through the above process, this invention enables the transmission of communication signals, transmission of sensing signals, and reception and processing of sensing echoes using a 5G-AeroMACS airport surface base station multi-antenna array without increasing the hardware burden on the aircraft. Furthermore, it achieves a dynamic trade-off between communication and sensing performance through joint beamforming and power allocation. This method enhances the communication reliability, spectrum utilization efficiency, and active target sensing capabilities of 5G-AeroMACS in complex multipath environments, strong line-of-sight propagation, non-line-of-sight obstruction, and highly dynamic target environments at airport surfaces. It is suitable for future smart airports, advanced surface activity guidance and control systems, and integrated wireless network deployment scenarios for airport surface communication and sensing.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for adaptive sensing beam power joint allocation in 5G-AeroMACS services, characterized in that, An integrated airport surface communication and sensing system is applied, the system comprising an airport surface base station configured with a multi-antenna array and multiple aircraft configured with single antennas, the method comprising: The first step is to determine the communication demand intensity and perception demand intensity of each aircraft based on the communication service type, target perception task type and collision risk level of each aircraft in the airport area, and then dynamically adjust the communication performance weight, perception performance weight, minimum communication rate threshold of each aircraft and minimum perception signal-to-noise ratio threshold when each aircraft is a perceived target. The second step, with the goal of maximizing the weighted sum of communication and sensing performance, involves jointly optimizing the following constraints: total transmit power of the airport surface base station, minimum communication rate of each aircraft, minimum sensing signal-to-clutter ratio of each aircraft when it is a sensed target, power allocation factor of communication signal and radar sensing signal, and normalization of sensing and receiving processing vector. The third step is to solve the joint optimization problem using an alternating optimization framework. Under the condition of a fixed sensing and receiving processing vector, the transmit beamforming vector and power allocation factor are solved. Under the condition of a fixed transmit beamforming vector and power allocation factor, the sensing and receiving processing vector is solved. The optimized transmit beamforming vector, sensing and receiving processing vector and power allocation factor are obtained through alternating iteration. The fourth step involves the airport surface base station simultaneously transmitting communication signals and radar sensing signals to each aircraft on the same time and frequency resources according to the optimized transmit beamforming vector and power allocation factor, and processing the sensing echo signals reflected back from the airport surface targets using the optimized sensing and receiving processing vector.

2. The method according to claim 1, characterized in that, The intensity of communication demand is determined by weighting the service priority, service latency sensitivity, service rate requirement, service packet loss rate sensitivity, and communication link quality degradation of each aircraft; the intensity of perception demand is determined by weighting the collision risk level, motion state change intensity, importance of the airport surface area, perception accuracy requirement, and density of surrounding targets of each aircraft.

3. The method according to claim 1, characterized in that, The minimum communication rate threshold is dynamically adjusted upwards or downwards based on the basic minimum communication rate requirement of each aircraft, combined with the aircraft's service priority, service latency sensitivity, service packet loss rate sensitivity, and the degree of communication link quality degradation; the minimum perceived signal-to-noise ratio (SNR) threshold is dynamically adjusted upwards or downwards based on the basic minimum perceived SNR requirement of each aircraft, combined with the aircraft's collision risk level, intensity of motion state changes, importance of the area, perception accuracy requirements, and density of surrounding targets.

4. The method according to claim 1, characterized in that, In the weighted sum of communication performance and perception performance, communication performance is represented by the sum of the achievable communication rates of each aircraft, and perception performance is represented by the sum of the radar perception signal-to-clutter ratios corresponding to each aircraft when it is a perceived target; the weights of communication performance and perception performance are determined by the intensity of communication demand and the intensity of perception demand, and the sum of the two is a normalized value.

5. The method according to claim 4, characterized in that, When the intensity of communication demand is greater than the intensity of sensing demand, the weight of communication performance is increased and the power allocation range of communication signals is expanded to prioritize the reliability of aircraft downlink communication; when the intensity of sensing demand is greater than the intensity of communication demand, the weight of sensing performance is increased and the power allocation range of radar sensing signals is expanded to prioritize the performance of airport surface target detection.

6. The method according to claim 1, characterized in that, When solving for the transmit beamforming vector and power allocation factor under the condition of fixed sensing and receiving processing vector, the transmit beamforming covariance matrix is ​​introduced, and the rank-one constraint is transformed into a penalty term for the difference between the nuclear norm and the spectral norm. The solution is then obtained through successive convex approximations. When solving for the sensing and receiving processing vector under the condition of fixed transmit beamforming vector and power allocation factor, the sensing and receiving covariance matrix is ​​introduced, and the sequential rank-one constraint relaxation method is used for iterative solution.

7. The method according to claim 1, characterized in that, The integrated signal transmitted by the airport surface base station for each aircraft is determined by the transmission beamforming vector, communication signal power allocation factor, and radar sensing signal power allocation factor. Among them, the communication signal and radar sensing signal corresponding to each aircraft are independent and their power is normalized. The communication signal is used to transmit downlink communication data to the aircraft, and the radar sensing signal is used to detect the aircraft and its surrounding targets.

8. A 5G-AeroMACS service adaptive sensing beam power joint allocation system, characterized in that, It includes an airport surface base station with a multi-antenna array and multiple aircraft with single antennas; The airport surface base station includes: The dynamic control module is used to determine the communication demand intensity and perception demand intensity of each aircraft based on the communication service type, target perception task type and collision risk level of each aircraft, and dynamically adjust the communication performance weight, perception performance weight, minimum communication rate threshold of each aircraft and minimum perception signal-to-noise ratio threshold when each aircraft is a perceived target. The joint optimization module aims to maximize the weighted sum of communication and sensing performance. Under constraints such as the total transmit power of the airport surface base station, the minimum communication rate of each aircraft, the minimum sensing signal-to-clutter ratio of each aircraft when it is a sensed target, the power allocation factor of the communication signal and the radar sensing signal, and the normalization constraint of the sensing and receiving processing vector, the module jointly optimizes the transmit beamforming vector of the airport surface base station for each aircraft, the sensing and receiving processing vector of the airport surface base station for each aircraft when it is a sensed target, the power allocation factor of the communication signal corresponding to each aircraft, and the power allocation factor of the radar sensing signal corresponding to each aircraft. The signal transmission module is used to simultaneously transmit communication signals and radar sensing signals to various aircraft on the same time and frequency resources, according to the optimized transmission beamforming vector and power allocation factor. The echo receiving and processing module is used to receive the sensing echo signal reflected back from the airport surface target, and to process the sensing echo signal using an optimized sensing receiving and processing vector. The aircraft is used to receive communication signals from airport surface base stations via a single antenna and to demodulate communication information.

9. The system according to claim 8, characterized in that, The multi-antenna array of the airport surface base station is a uniform linear antenna array. All antenna elements of the array serve as transmitting antenna elements for communication signals and radar sensing signals, and are also used to receive the sensing echo signals.

10. The system according to claim 8, characterized in that, The joint optimization module uses an alternating optimization framework for solving: under the condition of a fixed sensing and receiving processing vector, it solves for the transmit beamforming vector and the power allocation factor; under the condition of a fixed transmit beamforming vector and the power allocation factor, it solves for the sensing and receiving processing vector; through alternating iterations until the objective function converges or reaches the preset maximum number of iterations, it outputs the optimized transmit beamforming vector, sensing and receiving processing vector, and power allocation factor.

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