Communication-aware resource allocation method and apparatus, program product, and device

Abstract

The present disclosure relates to the technical field of low-altitude communication perception, and relates to a resource allocation method and device based on communication perception, a computer program product and an electronic device. The method comprises: after a resource allocation condition is triggered, determining a risk level of a perception target according to current state information, environment information and echo signal quality information of the perception target; determining a reference weight of a perception feature according to resource utilization information and current scene information; determining a beam priority of each perception target according to the risk level of each perception target and the reference weight of the perception feature; determining a capacity indication state information corresponding to each perception target according to at least one of real-time occupation information of a perception resource, the risk level and the beam priority of each perception target, and the number of perception targets in a perception area; and performing resource allocation on each perception target according to the beam priority and the capacity indication state information of each perception target.

Description

Technical Field

[0001] This disclosure relates to the field of low-altitude communication sensing technology, and more specifically, to a resource allocation method and apparatus, computer program product and electronic device based on communication sensing. Background Technology

[0002] Sensing integration (or sensing fusion) is a key research direction in the field of 5G-A / 6G communication and sensing. It aims to achieve collaborative or integrated design of communication and sensing functions (such as radar and environmental monitoring) by sharing hardware, spectrum, and signal processing technologies. This technology can significantly improve system efficiency, reduce costs, and promote the development of emerging applications such as 5G-A / 6G, intelligent transportation, and the low-altitude economy. Currently, due to limitations in hardware resources for processing sensing signals, the number of targets that a sensing network can detect is limited. However, with the development of low-altitude networks and the increasing number of low-altitude aircraft, the possibility of sensing networks reaching the maximum number of detectable targets is also increasing. Therefore, ensuring the performance of sensing networks in capacity-constrained scenarios is one of the key research directions in the sensing field.

[0003] However, in a sensing network, resources for sensing are limited. Sensing base stations typically have a maximum number of sensing targets due to limitations in sensing beam configuration and sensing echo data processing resources. When the number of sensing targets exceeds this maximum, the base station cannot sense all targets. Meanwhile, in communication networks, when resources are limited, service assurance and resource allocation strategies can be implemented based on end-user attributes to prioritize the service experience of specific users. However, in sensing networks, due to the unpredictability of the sensed targets and the lack of bidirectional communication between the sensed targets and the sensing base station, existing service assurance and resource allocation strategies cannot be used. This may result in some targets being unsensitized, impacting the rational scheduling of resources.

[0004] It should be noted that the information in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a resource allocation method and apparatus, computer program product and electronic device based on communication sensing, which can at least to some extent overcome the defects of related technologies, improve the accuracy and rationality of resource allocation in resource-limited scenarios, improve resource utilization, and enhance the perception capability of sensing targets.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a resource allocation method based on communication sensing is provided, applied to a sensing base station, comprising: after a resource allocation condition is triggered, determining the risk level of the sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target; determining the reference weight of sensing features based on resource utilization information and current scene information, wherein the sensing features include at least the risk coefficient of the sensing target, the sensing area attribute, and the sensing beam type; determining the beam priority of each sensing target based on the risk level of each sensing target and the reference weight of the sensing features; determining the capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area, wherein the capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target; and allocating resources to each sensing target based on the beam priority and capacity indication status information of each sensing target.

[0008] According to one aspect of this disclosure, a resource allocation device based on communication sensing is provided, applied to a sensing base station, comprising: a risk prediction module, configured to determine the risk level of a sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target after a resource allocation condition is triggered; a weight adjustment module, configured to determine reference weights of sensing features based on resource utilization information and current scene information, wherein the sensing features include at least a sensing target risk coefficient, a sensing area attribute, and a sensing beam type; a priority determination module, configured to determine the beam priority of each sensing target based on the risk level of each sensing target and the reference weights of the sensing features; a sensing capacity indication module, configured to determine capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area, wherein the capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target; and a resource allocation module, configured to allocate resources to each sensing target based on the beam priority and capacity indication status information of each sensing target.

[0009] According to one aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the above methods.

[0010] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above methods by executing the executable instructions.

[0011] The communication-aware resource allocation method in the exemplary embodiments of this disclosure, after the resource allocation conditions of the sensing base station are triggered, determines the risk level of the sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target; determines the reference weight of the sensing features based on resource utilization information and current scene information; determines the beam priority of each sensing target based on the risk level and reference weight of the sensing features; determines the capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area; and allocates resources to each sensing target based on the beam priority and capacity indication status information of each sensing target. Through risk prediction, dynamic weight adjustment, and capacity indication status, resources can be accurately allocated to sensing targets based on sensing data under conditions of limited or insufficient resources, ensuring the reasonable resource needs of different sensing targets, improving the sensing capability of sensing targets, and enhancing resource utilization.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation.

[0014] Figure 1 A flowchart of a communication-aware resource allocation method according to an exemplary embodiment of the present disclosure is shown.

[0015] Figure 2 A flowchart illustrating an implementation of determining reference weights for perceived features according to an exemplary embodiment of the present disclosure is shown.

[0016] Figure 3 A flowchart illustrating an implementation of determining beam priority for a sensing target according to an exemplary embodiment of the present disclosure is shown.

[0017] Figure 4 A flowchart illustrating an implementation method for determining capacity indication status information corresponding to a sensing target according to an exemplary embodiment of the present disclosure is shown.

[0018] Figure 5 A flowchart illustrating an implementation of resource allocation according to an exemplary embodiment of this disclosure is shown.

[0019] Figure 6 A schematic diagram of the composition of a communication-aware resource allocation device according to an exemplary embodiment of the present disclosure is shown.

[0020] Figure 7 A block diagram of an electronic device according to exemplary embodiments of the present disclosure is shown.

[0021] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0023] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0024] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0025] Currently, in sensing networks, due to the unpredictability of the sensed targets and the lack of bidirectional communication between the sensed targets and the sensing base stations, existing service assurance strategies and resource allocation strategies in the communication network cannot be adopted. This may result in some targets being undetectable, which in turn cannot guarantee the security and resource needs of critical areas, and there is also the problem of insufficient resource utilization.

[0026] To address one or more of the aforementioned issues, exemplary embodiments of this disclosure provide a resource allocation method based on communication sensing, applied to a sensing base station. Facing the limitation of sensing capacity in low-altitude scenarios for integrated sensing and communication networks, and specifically addressing scenarios where the number of sensing targets a sensing base station can detect reaches its maximum, the sensing base station combines risk prediction results, multi-dimensional dynamic weights, and low-altitude scenario characteristics to determine processing priorities. Furthermore, it ensures sensing performance through capacity indication and resource allocation mechanisms, thereby achieving differentiated and highly secure sensing in low-altitude scenarios, ensuring sensing coverage of high-risk targets and key areas, while simultaneously improving the utilization rate of sensing resources.

[0027] refer to Figure 1 The diagram shown is a flowchart of a communication-aware resource allocation method according to an exemplary embodiment of this disclosure. Figure 1 As shown, the method includes steps S110 to S150: Step S110: After the resource allocation conditions are triggered, the risk level of the sensed target is determined based on the current status information, environmental information, and echo signal quality information of the sensed target.

[0028] Step S120: Determine the reference weights of the sensing features based on resource utilization information and current scene information. The sensing features include at least the sensing target risk coefficient, sensing area attributes, and sensing beam type.

[0029] Step S130: Determine the beam priority of each sensing target based on the risk level and reference weight of the sensing features of each sensing target.

[0030] Step S140: Based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area, determine the capacity indication status information corresponding to each sensing target. The capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target.

[0031] Step S150: Allocate resources to each sensing target based on the beam priority and capacity indication status information of each sensing target.

[0032] The communication-aware resource allocation method in the exemplary embodiments of this disclosure, after the resource allocation conditions of the sensing base station are triggered, determines the risk level of the sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target; determines the reference weight of the sensing features based on resource utilization information and current scene information; determines the beam priority of each sensing target based on the risk level and reference weight of the sensing features; determines the capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area; and allocates resources to each sensing target based on the beam priority and capacity indication status information of each sensing target. Through risk prediction, dynamic weight adjustment, and capacity indication status, resources can be accurately allocated to sensing targets based on sensing data under conditions of limited or insufficient resources, ensuring the reasonable resource needs of different sensing targets, improving the sensing capability of sensing targets, and enhancing resource utilization.

[0033] Steps S110 to S150 will be described in more detail below.

[0034] In step S110, after the resource allocation conditions are triggered, the risk level of the sensed target is determined based on the current status information, environmental information, and echo signal quality information of the sensed target.

[0035] In the exemplary embodiments of this disclosure, the resource allocation condition is a preset logical node that triggers the execution of the subsequent resource reallocation process. This condition may include, but is not limited to, periodic triggering, event-based triggering, and non-periodic request triggering (such as a measurement reconfiguration command issued by the upper-layer network management system). If it is an event-based trigger, it may be triggered when a new sensing target is detected, the number of sensing targets changes drastically, or the sensing resource occupancy rate exceeds a preset threshold. The specific triggering condition can be set according to the actual scenario requirements.

[0036] The sensing target is a physical entity that needs to be detected, tracked, or identified by sending sensing beams and receiving their echo signals, such as drones, vehicles, and pedestrians. Current status information can be the real-time status of the sensing target, including but not limited to position, speed, attitude, and device health. Environmental information may include, for example, wind speed, electromagnetic interference intensity, rainfall, and remaining device battery power. Echo signal quality information refers to the characteristic parameters of the uplink physical signal received by the sensing base station and reflected by the sensing target, including echo power and echo signal-to-noise ratio. The risk level characterizes the urgency with which the sensing target urgently needs high-quality sensing resources under the current wireless environment and motion situation.

[0037] In an exemplary embodiment, a model to be trained can be developed based on sample data containing real-time state of the perceived target, environmental factor information, and echo signal quality information, to determine a risk prediction model based on the training results. The model to be trained can be modeled using an LSTM (Long Short-Term Memory) neural network, or constructed using lightweight neural networks such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), and DNN (Deep Neural Networks). Alternatively, a more complex network structure can be used, such as a heavyweight neural network, or even a model with a scale of hundreds of millions of nodes. This disclosure does not limit the specific network structure of the model.

[0038] Based on this, the current state information, environmental information, and echo signal quality information of the perceived target can be obtained, and the risk prediction model can be used to predict the risk level of the perceived target.

[0039] For example, the current location coordinates, rainfall, velocity vector, remaining battery power, ambient wind speed, sensing echo power, and echo signal-to-noise ratio of the perceived target can be input into the risk prediction model to output the risk level of the perceived target, such as high / medium / low-medium. Based on this, the risk level can be accurately predicted for different perceived targets in different sensing scenarios. For example, a drone passing through an airport no-fly zone at a speed exceeding 80 km / h is predicted to have a high risk level; a low-altitude aircraft with equipment health below 20% is predicted to have a medium risk level; and a drone on a regular flight path with normal equipment is predicted to have a low risk level.

[0040] It should be understood that this disclosure may use high / medium / low to represent risk levels, and in some scenarios, other methods may be used, such as risk level 1, risk level 2, etc.

[0041] This disclosure trains the model by introducing samples containing environmental factor information. The model learns to distinguish between the inherent danger of the target and the adverse conditions of the measurement environment, thereby avoiding false alarms and missed alarms caused by traditional threshold methods under complex conditions. This makes the risk prediction results robust in different scenarios, providing accurate risk prediction results for subsequent resource scheduling and improving the accuracy of resource scheduling.

[0042] In step S120, the reference weights of the sensing features are determined based on the resource utilization information and the current scene information. The sensing features include at least the sensing target risk coefficient, the sensing area attribute, and the sensing beam type.

[0043] In an exemplary embodiment of this disclosure, the sensing base station collects statistical data on the actual occupancy ratio and utilization efficiency of the sensing resource pool (including time slot resources, frequency domain physical resource blocks, transmit power margin, beam dwell time, and baseband computing unit load) within a preset time window prior to the current moment (e.g., the past second). This data may include: resource occupancy rate, number of resource conflicts, and average sensing latency compliance rate. Optionally, resource utilization information includes sensing resource utilization rate. Current scene information refers to the current scene to be sensed, such as the sensing time period and sensing area type. Sensing features are abstract attribute dimensions used to comprehensively evaluate the service requirements of the sensing target, and may include at least the sensing target risk coefficient, sensing area attributes, and sensing beam type.

[0044] The target risk coefficient represents the target's urgent need for sensing security and is related to the risk level determined above. For example, the sensing coefficient can be set for high, medium, and low risk levels according to the actual scenario, such as high = 1.0, medium = 0.6, and low = 0.2. The specific setting can be flexibly adjusted according to the scenario, and this disclosure does not impose any restrictions on it. The sensing area attribute represents the functional attributes of the target's spatial location, such as a collision avoidance zone at an intersection, a highway overtaking zone, or a drone no-fly zone. Different areas may have different sensing accuracy and refresh rate baseline requirements. Therefore, corresponding sensing coefficients can also be set for different sensing area attributes according to actual needs, such as key area target = 1.0, high-risk target in regular area = 0.8, and ordinary target in regular area = 0.5. The sensing beam type refers to the type of beam assigned to the sensing target, such as a tracking beam or a scanning beam. Corresponding sensing coefficients can also be set for different beam types, such as tracking beam = 1.2 and scanning beam = 1.0. Correspondingly, the reference weights are applied to each of the aforementioned sensing features to adjust the contribution of different sensing features in the final beam priority calculation at the current moment.

[0045] In one exemplary embodiment, an implementation method for determining reference weights for perceived features is provided. For example... Figure 2 The reference weights for determining the perceived features based on resource utilization information and current scene information can include: Step S210: Determine the initial weights corresponding to the target risk coefficient, the sensing area attribute, and the sensing beam type, respectively.

[0046] The initial weights are preset baseline priority allocation ratios for each sensing feature without considering current real-time load fluctuations. These initial weights can originate from the default configuration predefined by the communication protocol, long-term static policies issued by the network management plane, or statistically optimal values ​​based on historical big data. The appropriate method for determining the initial weights can be selected according to actual needs. It should be understood that the sum of the initial weights or adjusted reference weights of each sensing feature is 100%.

[0047] Step S220: Adjust each initial weight according to the perceived target scene and perceived resource utilization rate to obtain the reference weights corresponding to the perceived target risk coefficient, the perceived area attributes, and the perceived beam type.

[0048] The initial weights can be adjusted based on real-time sensing target scenarios and sensing resource utilization. Optionally, the initial weights can be adjusted using adjustment strategies in a pre-configured file. For example, during busy low-altitude periods (e.g., 7:00-9:00 AM, peak drone delivery times), the weights of the sensing target risk coefficient, sensing area attributes, and sensing beam type can be increased to 40%, 40%, and 20%, respectively. Alternatively, during nighttime periods (e.g., 11:00 PM-6:00 AM), the weights of the sensing area (e.g., key monitoring areas) can be increased to 50%, the sensing target risk coefficient to 30%, and the sensing beam type to 20%, respectively. Regarding sensing resource utilization, when the utilization rate is >80%, the weights of the sensing target risk coefficient, sensing area attributes, and sensing beam type are increased to 50%, 30%, and 20%, respectively; when the utilization rate is <80%, the weights of the sensing target risk coefficient, sensing area attributes, and sensing beam type are increased to 40%, 30%, and 30%, respectively. Of course, these adjustment strategies can all be set in advance in the pre-configured files, and will not be listed one by one.

[0049] Optionally, a model can be pre-trained using sample data containing weight labels, resource utilization information, and current scene information, including the risk coefficient of the perceived target, the attributes of the perceived area, and the type of the perceived beam. This yields a weight-adaptive model, which is then used to adjust the weights of the perceived features of each perceived target to determine the reference weights. This weight-adaptive model can be a CNN, RNN, etc., and there are no specific limitations on its implementation.

[0050] This disclosure uses initial weights as inertial anchors, allowing adjustments to be triggered only when resource utilization information and current scene information meet the characteristic conditions. This makes the weight change curve smooth and controllable, enhances the continuity of perception scheduling in the time domain, avoids frequent jumps, and ensures that each perception feature has a more appropriate weight under different scenarios and resource usage conditions, providing a basis for subsequent priority determination.

[0051] In step S130, the beam priority of each sensing target is determined according to the risk level and reference weight of the sensing features of each sensing target.

[0052] In an exemplary embodiment of this disclosure, beam priority is a scheduling order index assigned to each sensing target to be served within the same sensing frame or sensing scheduling period. This index directly determines the order of subsequent physical layer resource allocation.

[0053] In one exemplary embodiment, an implementation method for determining the beam priority of a sensed target is provided. For example... Figure 3 Determining the beam priority of each sensing target based on its risk level and the reference weight of its sensing features may include: Step S310: Determine the beam priority of each sensing target based on its risk level.

[0054] Step S320: If there are at least two sensing targets with the same beam priority, then for each of the at least two sensing targets, determine the comprehensive score based on the sensing characteristics of the sensing target and the corresponding reference weight, and determine the beam priority of the at least two sensing targets based on the comprehensive score of the at least two sensing targets.

[0055] After determining the risk level of the sensing targets, beam priority can be assigned based on the risk level, with beam priority positively correlated with risk level; that is, higher risk levels correspond to higher beam priority, meaning higher-risk targets will be allocated resources first. However, in determining beam priority based on risk level, conflicts can arise. For example, when multiple sensing targets compete for resources, if they have the same risk level, a resource allocation method cannot be determined. Therefore, this disclosure, in such cases, determines a comprehensive score based on the sensing characteristics of the sensing targets and their corresponding reference weights, thereby further arbitrating beam priority.

[0056] Optionally, determining the comprehensive score based on the perception features of the perception target and the corresponding reference weights may include: for each perception target, determining the perception coefficient of the perception feature of the perception target, the perception coefficient being used to characterize the importance of the perception feature; and then, based on the reference weights of each perception feature, weightedly fusing the perception coefficients corresponding to each perception feature to obtain the comprehensive score of the perception target.

[0057] As mentioned above, the perception coefficients of each perception feature can be set according to the needs of the scenario. After obtaining the perception coefficients, the perception coefficients are weighted and summed according to the perception weights corresponding to each perception feature to determine the comprehensive score of the perception target.

[0058] For example, if the perception coefficients for the target risk level are: High = 1.0, Medium = 0.6, Low = 0.2; the perception benefit coefficients (perception coefficients for perception area attributes) are: Key area targets = 1.0, High-risk targets in regular areas = 0.8, Ordinary targets in regular areas = 0.5; and the resource consumption coefficients (perception coefficients for perception beam types) are: Tracking beam = 1.2, Scanning beam = 1.0. Furthermore, the determined reference weights for each perception feature are: 0.5 for the perception target risk coefficient, 0.3 for the perception area attribute, and 0.2 for the perception beam type. Based on this, during busy low-altitude periods with resource utilization > 80%, and with two high-risk targets, target A (key area, tracking beam) and target B (regular area, scanning beam), then the comprehensive scores for these two perceived targets are: Target A's comprehensive score is 1.0. 0.4 0.5 + 1.0 0.4 0.3 + 1.2 0.2 0.2 = 0.368, therefore, the overall score for target B is 1.0. 0.4 0.5 + 0.8 0.4 0.3 + 1.0 0.2 0.2 = 0.336. Therefore, it can be determined that the beam priority of sensing target A is higher than that of sensing target B.

[0059] This disclosure introduces a decision-making method for priority conflict by incorporating sensing features and their dynamic weights into the determination process. This allows sensing features, scene, and resources to jointly determine beam priority, achieving an effective balance among the three and thus improving the accuracy of beam priority determination.

[0060] In step S140, based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area, capacity indication status information corresponding to each sensing target is determined. The capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target.

[0061] In exemplary embodiments of this disclosure, real-time occupancy information of sensing resources includes, for example, the occupancy rate of sensing resources at the current scheduling moment. The number of sensing targets in the sensing area refers to the total number of sensing targets currently activated and in the tracking list within the current sensing coverage area. The capacity indication status information is structured multi-dimensional control signaling information, which may include the sensing resource status of the sensing targets and beam priority adjustment strategies. For example, current resource occupancy rate, triggering reasons, and suggested priority adjustment direction information.

[0062] In one exemplary embodiment, an implementation method is provided for determining capacity indication state information corresponding to a sensing target. For example... Figure 4 Based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area, capacity indication status information corresponding to each sensing target is determined, including: Step S410: Determine the multi-level capacity thresholds for the capacity indication status. The multi-level capacity thresholds are obtained by dividing the capacity based on resource occupancy information. Each level of capacity threshold has corresponding trigger adjustment conditions and priority adjustment strategies.

[0063] Step S420: Determine the capacity indication status information corresponding to each sensing target based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and heat of the sensing area.

[0064] Step S430: Send the corresponding capacity indication status information to the core network and the sensing target.

[0065] The multi-level capacity threshold for capacity indication status refers to the capacity thresholds set for different layers. This can be understood as dividing the system into multiple layers based on resource occupancy information. Each layer has multiple capacity thresholds, corresponding trigger adjustment conditions, and priority adjustment strategies. Trigger adjustment conditions refer to the conditions that trigger the determination of capacity indication status information, while priority adjustment strategies refer to the specific methods for adjusting beam priority corresponding to that layer.

[0066] For example, a warning threshold can be set: resource occupancy 70%. If this threshold is exceeded, a mild priority adjustment is triggered, only increasing the beam priority of high-risk targets without affecting the perception of medium- and low-risk targets. A flow-limiting threshold can be set: resource occupancy 85%. If this threshold is exceeded, moderate resource control is triggered, suspending non-critical perception of low-risk targets, such as changing periodic scanning to on-demand scanning. An emergency threshold can be set: resource occupancy 95%. If this threshold is exceeded, deep resource scheduling is triggered, only retaining perception resource allocation for high-risk targets and key areas (which can be preset), suspending perception of all low-risk targets. The multi-level capacity thresholds for this capacity indication status and their corresponding related information can be pre-configured, so that in actual implementation, the capacity indication status information corresponding to each perception target can be determined according to the configuration file. This disclosure includes, but is not limited to, the above configuration content.

[0067] After obtaining the capacity indication status information corresponding to each sensing target, the capacity indication status information can be output to the core network and the sensing targets via signaling. Based on this, by pre-setting multi-level capacity thresholds, hierarchical and differentiated processing of resource regulation can be achieved. Each threshold level is bound to specific triggering conditions and adjustment strategies, which makes the system behave stably in different load ranges, effectively preventing frequent priority flipping due to small fluctuations in resource occupancy, thereby improving the stability of the system in resource scheduling.

[0068] Furthermore, considering that the sensing base station of this disclosure only performs the above-mentioned determination process after the resource allocation conditions are triggered, the sensing base station does not need to be in the decision-making process at all times, thus avoiding ineffective resource waste.

[0069] Optionally, based on the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area, the capacity indication status information corresponding to each sensing target is determined, including at least one of the following triggering methods: Method 1: Real-time resource occupancy information is sensed to meet the trigger adjustment conditions in the multi-level capacity threshold of the capacity indication status. Specifically, the resource occupancy-based triggering process can be initiated when resource occupancy reaches the corresponding tiered threshold to determine the capacity indication status information.

[0070] Method 2: The risk level of the perceived target rises to the target risk level. This method is designed to address sudden changes in risk levels. When the risk level of the perceived target suddenly rises to the target risk level, for example, from low risk to high risk, a capacity warning can be triggered even if the warning threshold has not been reached.

[0071] Method 3: Increase the number of sensed targets in the target sensing area to the target number. This method is for handling area heat, that is, when the number of sensed targets in the target sensing area (which can be preset, such as key areas) exceeds the preset number threshold, or the growth rate of the number exceeds the preset threshold, the warning threshold is triggered in advance, for example, the warning threshold is adjusted from 70% to 65%.

[0072] It should be understood that when each triggering condition is met, the current resource occupancy rate, the triggering reason, and the corresponding beam priority adjustment strategy can be obtained. For example, when a capacity warning is triggered, its beam priority needs to be increased by at least one level. This disclosure, by setting multiple triggering mechanisms, enables the capture of the timing of adjustments from multiple perspectives, ensuring the timeliness of determining capacity indication status information, and allowing each sensing target to obtain accurate resource scheduling results accordingly.

[0073] In step S150, resources are allocated to each sensing target based on the beam priority and capacity indication status information of each sensing target.

[0074] In an exemplary embodiment of this disclosure, after determining the beam priority and capacity indication status information of each sensing target, resources can be allocated to each sensing target accordingly.

[0075] In one exemplary embodiment, a method for implementing resource allocation is provided. For example... Figure 5 Resource allocation for each sensing target based on its beam priority and capacity indication status information may include: Step S510: Determine the perception risk level of each perception target based on the beam priority of each perception target, wherein different perception risk levels have their own corresponding resource binding validity period.

[0076] Due to different beam priorities, the real-time resource requirements of each sensing target vary. This disclosure can determine the sensing risk level of each sensing target based on its beam priority. The sensing risk level is positively correlated with the beam priority; that is, a higher beam priority corresponds to a higher sensing risk level. The resource binding validity period refers to the validity period of reserved sensing resources after they are allocated to a sensing target. For example, the validity period of reserved sensing resources for high-risk targets is 5 minutes, and for medium-risk targets, it is 3 minutes. Optionally, the validity period can be dynamically renewed based on the risk level.

[0077] Step S520: Based on the perception risk level of the perception target, determine the reserved perception resources corresponding to each perception target, and allocate the reserved perception resources to the corresponding perception target.

[0078] Pre-set reserved sensing resources for different sensing risk levels. For example, set 10%-20% reserved sensing resources (time domain + frequency domain) for high sensing risk levels.

[0079] Optionally, a target perception risk level can be determined, and reserved perception resources can be allocated to the first target perception target corresponding to the target perception risk level; wherein, the target perception risk level is higher than the first preset level.

[0080] Among them, targets with a perceived risk level higher than the first preset level, such as a high perceived risk level, can have their reserved perception resources allocated only to targets with a high perceived risk level to meet their perception needs.

[0081] Based on this, if the first target perception target does not use the reserved perception resources after the preset time period, the unused reserved perception resources will be allocated to the remaining perception targets other than the first target perception target according to the perception risk level of each perception target.

[0082] This disclosure takes into account that when the reserved sensing resources allocated to the first target sensing target are not used for a long time, it will affect the utilization rate of the reserved sensing resources. Therefore, the unused reserved sensing resources can be temporarily allocated to the remaining sensing targets other than the first target sensing target. Before and after the allocation, the allocation can be based on the sensing risk level of the remaining sensing targets, for example, priority can be given to those with higher risk levels (e.g., medium risk levels).

[0083] Optionally, if the capacity indication status information of the first target sensing target indicates a target triggering state, then the reserved sensing resources allocated to the remaining sensing targets will be reclaimed to the first target sensing target. Specifically, if the target triggering state is, for example, meeting triggering conditions corresponding to a warning threshold, a flow limiting threshold, an emergency threshold, or at least one of the aforementioned triggering methods, the temporarily allocated resources can be reclaimed.

[0084] For example, when a high-risk target is triggered, temporarily allocated resources can be reclaimed in real time. The reclamation adopts a first-come, first-served release method, that is, resources temporarily allocated to low-risk targets are reclaimed first.

[0085] This disclosure combines the beam priority and capacity indication status information of each sensing target to allocate resources to each sensing target. This can ensure the resource needs of high-risk sensing targets even when resources are limited or sufficient. At the same time, through full-domain sensing, it can ensure that resources are fully allocated to each sensing target and improve resource utilization.

[0086] In an exemplary embodiment, multiple second target sensing targets located in the same sensing area may be allocated merged resources. The sensing risk level of the second target sensing targets is lower than a second preset level, and the risk level of the second preset level is lower than a first preset level. The merged resources are scheduled using a time-division multiplexing method and allocated to each second target sensing target in sequence to perform communication sensing on the second target sensing targets based on the allocated resources.

[0087] In this system, the risk level of the second preset level is lower than that of the first preset level. That is, if the first preset level corresponds to a high-risk target, the second target is considered a low-risk target. Resource merging involves the sensing base station bundling and combining physical resources that would otherwise be allocated independently to a single target. Examples include dedicated time slots and fixed frequency domain physical resource blocks. This spatially covers multiple second targets within the same sensing area. Time-division multiplexing divides the merged resources into multiple sub-time slots of equal or unequal length in the time domain. The sensing base station can allocate these sub-time slots sequentially to the second targets within the group, either through polling or based on simple priorities (such as distance). This ensures that at any given time, only one second target exclusively uses the merged resource for echo reception, but all targets within the group are covered once within a complete scheduling cycle. For example, three low-risk UAVs traveling on the same route can reuse the same scanning beam and acquire sensing data sequentially at 10 ms intervals.

[0088] Based on this, by merging sensing tasks, resources can be allocated to multiple low-risk sensing targets in the same area using beam multiplexing and time-division multiplexing, thereby reducing resource consumption.

[0089] Optionally, this disclosure may also determine the resource recovery method of the reserved sensing resources corresponding to the sensing target based on the sensing resource status in the capacity indication status information of the sensing target.

[0090] Different sensing resource states require different resource recovery methods. For example, in an early warning state, pre-allocated resources are not recovered; in a flow-limiting state, temporary resources for low-risk targets are recovered; and in an emergency state, only pre-allocated resources for high-risk targets are retained. This disclosure, by setting resource recovery methods for different sensing resource states, can handle resource recovery differently for different sensing states, making it more flexible, further improving resource utilization, and meeting the resource needs of sensing targets in different states.

[0091] The communication-aware resource allocation method in the exemplary embodiments of this disclosure, after the resource allocation conditions of the sensing base station are triggered, determines the risk level of the sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target; determines the reference weight of the sensing features based on resource utilization information and current scene information; determines the beam priority of each sensing target based on the risk level and reference weight of the sensing features; determines the capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area; and allocates resources to each sensing target based on the beam priority and capacity indication status information of each sensing target. Through risk prediction, dynamic weight adjustment, and capacity indication status, resources can be accurately allocated to sensing targets based on sensing data under conditions of limited or insufficient resources, avoiding resource waste or overload. This ensures reasonable resource needs for different sensing targets, guaranteeing the sensing performance of high-risk targets and key areas while also considering the basic sensing needs of medium- and low-risk targets, achieving a balance in overall sensing and improving resource utilization.

[0092] In an exemplary embodiment of this disclosure, a communication-aware resource allocation apparatus is also provided, applied to a sensing base station. (See reference...) Figure 6 As shown, the device 600 may include a risk prediction module 610, a weight adjustment module 620, a priority determination module 630, a sensing capacity indication module 640, and a resource allocation module 650. Specifically: The risk prediction module 610 is used to determine the risk level of the sensing target based on the current status information, environmental information, and echo signal quality information of the sensing target after the resource allocation conditions are triggered. The weight adjustment module 620 is used to determine the reference weight of the sensing features based on resource utilization information and current scene information. The sensing features include at least the risk coefficient of the sensing target, the attributes of the sensing area, and the sensing beam type. The priority determination module 630 is used to determine the beam priority of each sensing target based on the risk level of each sensing target and the reference weight of the sensing features. The sensing capacity indication module 640 is used to determine the capacity indication status information corresponding to each sensing target based on at least one of the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area. The capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target. The resource allocation module 650 is used to allocate resources to each sensing target based on the beam priority and capacity indication status information of each sensing target.

[0093] Since the details of each functional module of the communication-aware resource allocation apparatus of the exemplary embodiments of this disclosure have been described in the exemplary embodiments of the communication-aware resource allocation method described above, they will not be repeated here.

[0094] It should be noted that although several modules or units of the communication-aware resource allocation device have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0095] Exemplary embodiments of this disclosure also provide a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the communication-aware resource allocation method described above.

[0096] In one implementation, the computer program product can be a tangible product containing a computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium can be a storage medium based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be implemented as a non-volatile storage medium storing a computer program, such as read-only memory, NAND flash memory, etc.

[0097] In one implementation, the computer program product can be an intangible product containing a computer program. For example, the computer program product can be implemented as a virtual digital product, such as an executable file, installation package, or other digital file storing the computer program.

[0098] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0099] Computer programs can be carried or transmitted via signals such as electrical, magnetic, optical, electromagnetic, and infrared rays. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to execute) the method steps of various exemplary embodiments of this disclosure, such as the communication-aware resource allocation method described above.

[0100] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform an exemplary method of any of the above-described methods by executing the executable instructions.

[0101] The following is for reference. Figure 7 The electronic device is illustrated by way of a general-purpose computing device. It should be understood that... Figure 7 The electronic device 700 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0102] like Figure 7 As shown, the electronic device 700 may include: a processor 710, a memory 720, a bus 730, an I / O (input / output) interface 740, and a network adapter 750.

[0103] The memory 720 may include volatile memory, such as RAM 721 and cache unit 722, and may also include non-volatile memory, such as ROM 723. The memory 720 may also include one or more program modules 724, including but not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. For example, program module 724 may include the modules described above.

[0104] The processor 710 may include one or more processing units, such as an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit).

[0105] The processor 710 can be used to execute executable instructions stored in the memory 720 to perform method steps of various embodiments of this disclosure, such as... Figure 1 The steps are shown.

[0106] Bus 730 is used to connect different components of electronic device 700 and may include a data bus, an address bus and a control bus.

[0107] Electronic device 700 can communicate with one or more external devices 800 (such as keyboard, mouse, external controller, etc.) through I / O interface 740.

[0108] Electronic device 700 can communicate with one or more networks via network adapter 750. For example, network adapter 750 can provide mobile communication solutions such as 3G / 4G / 5G, or wireless communication solutions such as wireless LAN, Bluetooth, and near-field communication. Network adapter 750 can communicate with other modules of electronic device 700 via bus 730.

[0109] In one embodiment, the electronic device 700 further includes a display for displaying a graphical user interface.

[0110] although Figure 7 As not shown in the diagram, other hardware and / or software modules may also be configured in the electronic device 700, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems.

[0111] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which may be referred to as "circuit," "module," or "system," respectively.

[0112] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A method for communication-aware resource allocation, characterized in that, Applications to sensing base stations include: Once the resource allocation conditions are triggered, the risk level of the sensed target is determined based on the target's current status information, environmental information, and echo signal quality information. The reference weights of the sensing features are determined based on resource utilization information and current scene information. The sensing features include at least the sensing target risk coefficient, sensing area attributes, and sensing beam type. Based on the risk level and reference weight of the sensing features of each sensing target, the beam priority of each sensing target is determined respectively. Based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area, capacity indication status information corresponding to each sensing target is determined. The capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target. Resource allocation is performed on each of the sensing targets based on their beam priority and capacity indication status information.

2. The method of claim 1, wherein, The method further includes: The model to be trained is trained based on sample data containing real-time status of the perceived target, environmental factor information, and echo signal quality information, so as to determine the risk prediction model based on the training results. Determining the risk level of the sensed target based on its current state information, environmental information, and echo signal quality information includes: The current state information, environmental information, and echo signal quality information of the perceived target are obtained, and the risk prediction model is used to predict the risk level of the perceived target.

3. The method of claim 1, wherein, The step of determining the reference weights of the perceived features based on resource utilization information and current scene information includes: Determine the initial weights corresponding to the risk coefficient of the sensing target, the attribute of the sensing area, and the type of the sensing beam, respectively; Based on the perceived target scene and the perceived resource utilization rate, the initial weights are adjusted respectively to obtain the reference weights corresponding to the perceived target risk coefficient, the perceived area attributes, and the perceived beam type.

4. The method of claim 1, wherein, The step of determining the beam priority of each sensing target based on the risk level and reference weight of the sensing features of each sensing target includes: Based on the risk level of each of the sensing targets, the beam priority of each of the sensing targets is determined; If there are at least two sensing targets with the same beam priority, then for each of the at least two sensing targets, a comprehensive score is determined based on the sensing characteristics of the sensing target and the corresponding reference weight, and the beam priority of the at least two sensing targets is determined based on the comprehensive score of the at least two sensing targets.

5. The method of claim 4, wherein, The determination of the comprehensive score based on the perceptual features of the perceived target and the corresponding reference weights includes: For each sensing target, a sensing coefficient of the sensing feature of the sensing target is determined, and the sensing coefficient is used to characterize the importance of the sensing feature; Based on the reference weights of each of the aforementioned perceptual features, the perceptual coefficients corresponding to each of the aforementioned perceptual features are weighted and fused to obtain the comprehensive score of the perceptual target.

6. The method of claim 1, wherein, The step of determining the capacity indication status information corresponding to each sensing target based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area, includes: Determine the multi-level capacity thresholds for the capacity indication status. The multi-level capacity thresholds are obtained by dividing the data based on resource occupancy information. Each level of capacity threshold has a corresponding trigger adjustment condition and priority adjustment strategy. Based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and heat of the sensing area, determine the capacity indication status information corresponding to each sensing target. Send the corresponding capacity indication status information to the core network and sensing targets.

7. The method of claim 6, wherein, Based on the real-time occupancy information of sensing resources, the risk level and beam priority of each sensing target, and the number of sensing targets in the sensing area, the capacity indication status information corresponding to each sensing target is determined, including at least one of the following triggering methods: The real-time occupancy information of the sensed resources satisfies the corresponding trigger adjustment conditions in the multi-layer capacity threshold of the capacity indication state. The risk level of the perceived target has been raised to the target risk level. The number of perceived targets within the target perception area increases to the target number.

8. The method of claim 1, wherein, The resource allocation for each sensing target based on its beam priority and capacity indication status information includes: Based on the beam priority of each sensing target, the sensing risk level of each sensing target is determined, wherein different sensing risk levels have their own corresponding resource binding validity period; Based on the perception risk level of the perception target, the reserved perception resources corresponding to each perception target are determined, and the reserved perception resources are allocated to the corresponding perception target.

9. The method according to claim 8, characterized in that, The step of determining the reserved sensing resources corresponding to each sensing target based on the sensing risk level of the sensing target, and allocating the reserved sensing resources to the corresponding sensing targets, includes: Determine the target perception risk level and allocate reserved perception resources to the first target perception target corresponding to the target perception risk level; wherein the target perception risk level is higher than the first preset level; If the first target perception target does not use the reserved perception resources after a preset time period, the unused reserved perception resources will be allocated to the remaining perception targets excluding the first target perception target according to the perception risk level of each perception target.

10. The method according to claim 9, characterized in that, The method further includes: If the capacity indication status information of the first target sensing target indicates a target triggering state, then the reserved sensing resources allocated to the remaining sensing targets will be recovered to the first target sensing target.

11. The method of claim 9, wherein, The method further includes: Resources are allocated and merged for multiple second target sensing targets located in the same sensing area. The sensing risk level of the second target sensing targets is lower than the second preset level, and the risk level of the second preset level is lower than the first preset level. The merged resources are scheduled using a time-division multiplexing method and sequentially allocated to each of the second target sensing targets to perform communication sensing on the second target sensing targets based on the allocated resources.

12. The method of claim 8, wherein, The step of determining the reserved sensing resources corresponding to each sensing target based on the sensing risk level of the sensing target further includes: Based on the sensing resource status in the capacity indication status information of the sensing target, determine the resource recovery method for the reserved sensing resources corresponding to the sensing target.

13. A resource allocation device based on communication sensing, characterized in that, The device, applied to a sensing base station, includes: The risk prediction module is used to determine the risk level of the sensed target based on the current status information, environmental information, and echo signal quality information of the sensed target after the resource allocation conditions are triggered. The weight adjustment module is used to determine the reference weight of the perception features based on resource utilization information and current scene information. The perception features include at least the perception target risk coefficient, perception area attribute and perception beam type. The priority determination module is used to determine the beam priority of each sensing target according to the risk level and reference weight of the sensing features of each sensing target. The sensing capacity indication module is used to determine the capacity indication status information corresponding to each sensing target based on at least one of the following: real-time occupancy information of sensing resources, risk level and beam priority of each sensing target, and number of sensing targets in the sensing area. The capacity indication status information is used to indicate the sensing resource status and beam priority adjustment strategy of the sensing target. The resource allocation module is used to allocate resources to each of the sensing targets based on the beam priority and capacity indication status information of each sensing target.

14. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 12.

15. An electronic device, comprising: include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 12 by executing the executable instructions.

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