A resource scheduling method and device, electronic equipment, storage medium and product

Abstract

This application discloses a resource scheduling method, apparatus, electronic device, storage medium, and product. It relates to the field of wireless communication technology. The method includes: collecting user-side parameters, network-side parameters, and environmental parameters; generating a resource scheduling scheme based on a multi-objective optimization model according to the multi-dimensional input parameters. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience across different services, and minimizing base station power consumption; and jointly scheduling time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme. This technical solution, considering the coupling relationship and mutual influence between different types of resources, utilizes a multi-objective optimization model to find an optimal compromise scheduling scheme with high resource utilization, balanced user experience, and low base station power consumption. It achieves joint scheduling of time-frequency resources, spatial resources, and power resources, improving resource utilization and overall communication performance.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a resource scheduling method, apparatus, electronic device, storage medium, and product. Background Technology

[0002] New Radio (NR) requires scheduling various types of resources, such as time-frequency resources, spatial resources (beams), and power resources. Different types of resources require different scheduling methods and models, necessitating independent optimization across these dimensions. In such cases, neglecting the coupling relationships between different resource types can lead to low resource utilization or overall communication performance.

[0003] For example, when scheduling time-frequency resources, beam coverage is not considered. The frequency points corresponding to the resource blocks (RBs) allocated to user equipment (UE) may not cover the UE's current location, requiring additional beam switching and increasing scheduling latency (measured latency can reach 5-10ms). Similarly, when adjusting beams, power parameters are not considered. When switching to a narrow beam to improve UE signal quality, if the transmit power of adjacent beams is not simultaneously reduced, interference in the overlapping area of ​​the narrow beam and adjacent beams will increase (measured interference power increase of 2-3dB), thus reducing the UE's signal-to-interference-plus-noise ratio (SINR). Furthermore, when adjusting power, time-frequency resource load is not considered. In areas with scarce time-frequency resources (e.g., RB occupancy > 90%), simply increasing power cannot increase the effective rate, but instead wastes energy (measured power utilization rate decrease of 15%-20%). Furthermore, the optimization cycles and parameter updates for different types of resources may not be synchronized. For example, the time-frequency scheduling cycle (e.g., 1ms) may be shorter than the beam adjustment cycle (e.g., 5-10ms). When the UE moves rapidly (e.g., in a high-speed rail scenario, where the speed is >300km / h), the time-frequency resources have been allocated, but the beam has not kept up in time, leading to frequent UE disconnections (measured disconnection rate increases by 8%-12%). Also, if power adjustment relies solely on the TPC feedback of a single UE without considering the priority of multiple services, then for high-priority services (e.g., Ultra-reliable & Low-latency Communication, URLLC), if the power of its corresponding beam area is reduced by low-priority services (e.g., Enhanced Mobile Broadband), the UE may experience a significant drop in connection. Broadband (eMBB) service occupancy can lead to high-priority service latency exceeding requirements (actual latency failure rate increases by 10%-15%). For example, blindly increasing base station transmit power to improve edge UE coverage can increase edge UE speed by 5%-8%, but increases overall base station power consumption by 20%-25%. Furthermore, time-frequency resource scheduling is based solely on short-term channel quality without considering long-term beam and power efficiency; some RB resources, due to poor beam coverage or power interference, have an actual utilization rate of only 60%-70%, far below the theoretical upper limit (above 90%).

[0004] In summary, how to jointly schedule different types of resources to improve resource utilization and overall communication performance is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a resource scheduling method, apparatus, electronic device, storage medium, and product to improve resource utilization and overall communication performance.

[0006] In a first aspect, embodiments of this application provide a resource scheduling method, including:

[0007] Collect multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters;

[0008] Based on the multi-dimensional input parameters, a resource scheduling scheme is generated using a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption.

[0009] According to the resource scheduling scheme, time-frequency resources, spatial resources, and power resources are jointly scheduled. Secondly, embodiments of this application also provide a resource scheduling apparatus, including:

[0010] The acquisition module is used to acquire multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters.

[0011] The optimization module is used to generate a resource scheduling scheme based on the multi-dimensional input parameters and a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption.

[0012] A scheduling module is used to jointly schedule time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme. Thirdly, embodiments of this application provide an electronic device, including:

[0013] One or more processors;

[0014] Storage device for storing one or more programs;

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the resource scheduling method as described in the first aspect.

[0016] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the resource scheduling method as described in the first aspect.

[0017] Fifthly, embodiments of this application also provide a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the resource scheduling method as described in any of the above embodiments.

[0018] This application provides a resource scheduling method, apparatus, electronic device, storage medium, and product. The resource scheduling method includes: collecting multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters; generating a resource scheduling scheme based on a multi-objective optimization model according to the multi-dimensional input parameters, wherein the optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption; and jointly scheduling time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme. The above technical solution, considering the coupling relationship and mutual influence between different types of resources, utilizes a multi-objective optimization model to find an optimal compromise scheduling scheme with high resource utilization, balanced user experience, and low base station power consumption, achieving joint scheduling of time-frequency resources, spatial resources, and power resources, thereby improving resource utilization and overall communication performance. Attached Figure Description

[0019] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0020] Figure 1 A flowchart illustrating a resource scheduling method provided in an embodiment of this application;

[0021] Figure 2 A schematic diagram of the structure of a resource scheduling system provided in one embodiment;

[0022] Figure 3 A schematic diagram illustrating a resource scheduling process as provided in one embodiment;

[0023] Figure 4 This is a schematic diagram of the structure of a resource scheduling device provided in an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present application, not the entire structure.

[0026] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of these steps can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the steps can be rearranged. The process can be terminated when its operation is complete, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc.

[0027] It should be noted that the concepts of "first" and "second" mentioned in the embodiments of this application are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order or interdependence of the functions performed by these devices, modules, units or other objects.

[0028] Furthermore, the embodiments and features described in this application may be combined with each other, unless otherwise specified.

[0029] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0030] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the relevant content of the solution.

[0031] In existing technologies, the scheduling of time-frequency resources, spatial resources, and power resources is optimized independently in each dimension.

[0032] For time-frequency resource scheduling, the core is RB allocation, with mainstream algorithms including Proportional Fairness (PF), Round-Robin (RR), and Maximum Carrier-to-Interference Ratio (Max-C / I). For example, according to the basic scheduling framework defined in the protocol, the Media Access Control (MAC) layer scheduler on the base station side allocates subframes or symbols in the time domain or RB resources in the frequency domain to the UE within each scheduling period (e.g., 1ms) based on parameters such as the User Equipment's (UE) Channel Quality Indication (CQI), Buffer Status Reports (BSR), and service priority. This process does not involve dynamic adjustments to spatial beam and power parameters.

[0033] For spatial resource scheduling, relying on beamforming technology, it is divided into two categories: static beam configuration and semi-dynamic beam adjustment. In the static scheme, the base station pre-configures a fixed beam set (such as a wide beam covering different sectors of the cell). When the UE accesses the network, it selects a beam based on the initially measured Reference Signal Receiving Power (RSRP). Subsequently, beam switching is triggered only when the beam quality is below a threshold (such as RSRP < -105dBm). The switching process is independent of the time and frequency resource allocation at that time. Although the semi-dynamic scheme supports beam prediction based on the UE's movement trajectory (such as adjusting the beam direction through historical location information), the prediction model only relies on spatial dimension data (such as the UE's azimuth and elevation angles) and does not take into account the time and frequency resource load (such as the RB occupancy rate of the target beam coverage area) and power loss. This can easily lead to problems such as insufficient time and frequency resources or power interference in beam overlap areas after beam switching.

[0034] For power scheduling, a hierarchical strategy of open-loop power control + closed-loop power adjustment is adopted. In the open-loop phase, the base station calculates the initial transmit power according to the formula in the protocol based on parameters such as UE path loss (PL) and shadow fading. In the closed-loop phase, the base station fine-tunes the power through the power control (TPC) command fed back by the UE, but the adjustment range is limited to ±3dB, and it only optimizes the signal quality (such as signal-to-noise ratio, SINR) of a single UE, without considering the power coupling between multiple UEs. For example, when two UEs share the same time-frequency resource block and their beams overlap, increasing the power of one UE alone will lead to increased interference to the other UE, and the existing scheme cannot coordinate the power allocation between the two UEs simultaneously.

[0035] Figure 1 This is a flowchart illustrating a resource scheduling method provided in an embodiment of this application. This embodiment is applicable to the acquisition of eye images. Specifically, the resource scheduling method can be executed by a resource scheduling device, which can be implemented through software and / or hardware and integrated into an electronic device. The electronic device includes, but is not limited to, devices with control functions such as computers, smartphones, host computers, or servers.

[0036] like Figure 1 As shown, the method specifically includes the following steps:

[0037] S110. Collect multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters;

[0038] In this embodiment, multi-dimensional input parameters can be used as input data for joint scheduling, that is, input data for a multi-objective optimization model.

[0039] For example, user-side parameters may include the UE's CQI, BSR, service type (such as URLLC, eMBB, or Massive Machine Type Communications (mMTC), location information (such as latitude and longitude and / or distance relative to the base station), and / or movement speed. Network-side parameters may include the coverage area of ​​each base station beam (such as azimuth, elevation, and / or half-power angle), interference power in beam overlap areas, beam overlap interference coefficient, resource (such as RB) occupancy rate, and / or the base station's current total transmit power. Environmental parameters may include path loss, shadow fading coefficient, and / or noise power spectral density. By using user movement speed, beam overlap interference coefficient, and service priority weight as collected parameters, comprehensive data support can be provided for joint optimization. Optionally, multi-dimensional input parameters can be collected periodically, for example, with a collection period of 1ms.

[0040] S120. Based on the multi-dimensional input parameters, a resource scheduling scheme is generated according to a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption.

[0041] In this embodiment, the multi-objective optimization model can be used to handle multiple conflicting optimization objectives at the same time. These optimization objectives usually cannot be optimal at the same time. The solution process mainly involves finding the best compromise solution, i.e., the resource scheduling scheme, among different optimization objectives.

[0042] For example, the optimization objectives of a multi-objective optimization model include:

[0043] Maximize resource utilization, such as allocating as many RBs as possible and maximizing the number of UEs that the beam effectively covers;

[0044] Maximize the balance of user experience across different services. For example, ensure that the latency requirements of users are met as much as possible for low-latency services and that the rate requirements of users are met as much as possible for edge UEs. Furthermore, the corresponding weights can be adjusted according to the priority of different services.

[0045] Minimizing base station power consumption can be achieved by maximizing the total transmit power of the beam and minimizing the difference between the total transmit power of the base station and the total transmit power of the beam.

[0046] Based on this, the best compromise resource scheduling solution can be found.

[0047] S130. According to the resource scheduling scheme, the time-frequency resources, spatial resources and power resources are jointly scheduled.

[0048] In this embodiment, the joint scheduling of time-frequency resources, spatial resources, and power resources can be achieved according to the resource scheduling scheme. For example, the MAC layer scheduler can determine the RB allocation instruction according to the resource scheduling scheme, and allocate time-domain symbols and frequency-domain RBs to the UE accordingly; the beamforming module can determine the beam index and weight according to the resource scheduling scheme, and adjust the phase and amplitude of the antenna array accordingly to generate the target beam; the power amplifier can determine the power instruction according to the resource scheduling scheme, adjust the transmit power of the corresponding beam, and avoid exceeding the total power constraint.

[0049] The resource scheduling method in this application eliminates coupling conflicts between dimensions by establishing a multi-objective joint optimization model of time-frequency resources, spatial beams, and power parameters; it can improve the scheduling response speed in dynamic scenarios (such as high-speed movement or service tidal events) and balance the experience of users with different priorities; while ensuring user rate and coverage, it reduces base station power consumption and improves resource utilization and overall communication performance.

[0050] In one embodiment, based on the multi-dimensional input parameters and a multi-objective optimization model, a resource scheduling scheme is generated, including:

[0051] Based on the multi-dimensional input parameters, the optimization variables of the multi-objective optimization model are determined;

[0052] The multi-objective optimization model is solved using the non-dominated sorting genetic algorithm (NSGA) to obtain a resource scheduling scheme.

[0053] For example, the NSGA-III algorithm is used to iteratively solve a multi-objective function. The number of iterations is set to 50, and 100 candidate solutions are generated in each iteration. Finally, the Pareto optimal solution is output, finding the optimal balance among the three optimization objectives, and resource allocation instructions are generated accordingly.

[0054] Based on this, multi-objective optimization problems can be established and solved efficiently and accurately, taking into account various optimization objectives to find the best compromise resource scheduling scheme.

[0055] In one embodiment, the optimization variables of the multi-objective optimization model include time-frequency variables, spatial variables, and power variables.

[0056] The time-frequency variables include the number of RB allocations and the number of scheduling time-domain symbols. The number of RB allocations is, for example, 0-100, which can be determined based on the NR band bandwidth. The number of scheduling time-domain symbols (or subframes) can be 1-14.

[0057] Spatial variables include beam selection index and beamforming weight. The beam selection index is, for example, the index of one beam selected from 32 beams preset by the base station. The beamforming weight is a real number with a value range of [0,1].

[0058] The power variable includes the beam's transmit power, with a value range of, for example, 10-43 dBm, and a step size of 0.1 dBm.

[0059] Resource utilization is represented by the product of a first ratio and a second ratio. The first ratio is the ratio of the number of allocated RBs to the total number of RBs, and the second ratio is the ratio of the number of UEs effectively covered by the beam to the total number of UEs in the beam coverage area. For example, resource utilization can be expressed as U = (number of allocated RBs / total number of RBs) × (number of UEs effectively covered by the beam / total number of UEs in the beam coverage area).

[0060] The balance of user experience across different services is represented by a weighted sum of the latency compliance rate and the edge UE rate compliance rate for services with latency requirements. For example, the balance of user experience can be expressed as: E = (latency compliance rate of high-priority services × 0.6) + (rate compliance rate of edge UEs × 0.4), where the latency requirements for high-priority services are, for example, URLLC ≤ 5ms and eMBB ≤ 100ms, and the rate requirements for edge UEs are, for example, UE ≥ 100Mbps.

[0061] Base station power consumption is represented by the difference between the total base station transmit power and the total beam transmit power. The total beam transmit power is the sum of the actual transmit power of each beam. The actual transmit power is the product of the initial transmit power of the corresponding beam and the corresponding beam interference coefficient. For example, base station power consumption can be expressed as P = total base station transmit power - ∑(each beam transmit power × beam interference coefficient). The interference coefficient can be calculated based on the area of ​​the beam overlap region. For example, the larger the overlap region, the larger the interference coefficient. The value range can be 0.1-0.8.

[0062] Based on this, resource utilization, user experience balance, and base station power consumption can be quantified, providing a basis for solving multi-objective optimization problems.

[0063] In one embodiment, the constraints of the multi-objective optimization model include:

[0064] Time-frequency constraint: The number of RBs allocated to a single UE is less than or equal to a set proportion of the total number of RBs, for example, less than or equal to 10% of the total number of RBs, so as to avoid resource monopoly;

[0065] Spatial constraints: Beam switching delay does not exceed a delay threshold, for example, beam switching delay is less than or equal to 2ms, thus adapting to high-speed scenarios;

[0066] Power constraints: The total transmit power of the base station shall not exceed the power threshold, for example, less than or equal to 43dBm, so as to meet the power standard of NR base stations in practical applications; and the signal-to-interference-plus-noise ratio (SINR) of the UE shall not be lower than the set value, for example, greater than or equal to -3dB, so as to ensure signal quality.

[0067] Based on this, it can be ensured that the multi-objective optimization model conforms to the actual application scenarios and needs, and an effective resource scheduling scheme can be obtained.

[0068] In one embodiment, after jointly scheduling time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme, the method further includes:

[0069] S140. Obtain monitoring values ​​of UE performance after joint scheduling;

[0070] S150. Generate a correction instruction based on the deviation between the monitored value and the target value;

[0071] S160. Update the multi-objective optimization model according to the correction instruction.

[0072] In this embodiment, based on the feedback mechanism, the multi-objective optimization model can be modified according to the deviation between the UE performance and the expected performance. This can be done by reconstructing the multi-objective optimization model, adjusting its parameters, or updating the collected input parameters, and then resolving the problem to obtain a resource scheduling scheme that better meets actual needs. For example, when the latency deviation of high-priority services is large, the latency weight can be increased; when the rate deviation of edge UEs is large, the rate weight can be increased.

[0073] Optionally, the effectiveness of resource allocation can be monitored periodically, with a feedback period of, for example, 1ms.

[0074] In one embodiment, the monitored values ​​include actual latency, actual data rate, and actual SINR, as well as the actual power consumption and RB utilization of the base station; generating correction instructions based on the deviation between the monitored values ​​and the target values ​​includes: generating correction instructions if the deviation between any monitored value and the corresponding target value exceeds a preset threshold.

[0075] For example, by comparing the monitored value with a preset target value based on the objective function (e.g., a target value for latency compliance of 99%), the deviation between the monitored value and the target value can be calculated by subtracting the target value from the monitored value. If the deviation exceeds a preset threshold, such as greater than 5%, a correction instruction can be generated. This correction instruction can be used to instruct adjustments to the weights of the objective function, updates to constraints, and / or adjustments to the algorithm parameters, thereby triggering the next round of optimization. Based on this, the multi-objective optimization model can be updated in a timely manner to meet the latest practical needs.

[0076] This application also provides a resource scheduling system, such as... Figure 2 As shown, the resource scheduling system includes an information acquisition layer, a (time-frequency-space-power) joint optimization layer, and a resource execution layer. The layers can achieve synchronous scheduling of multi-dimensional resources through bidirectional or unidirectional data interaction at a set period (such as 1ms).

[0077] For example, the information acquisition layer can input the collected multi-dimensional input parameters to the joint optimization layer; the joint optimization layer can solve the resource scheduling scheme based on the multi-objective optimization model and send a one-way control signal to the resource execution layer, that is, output resource allocation instructions according to the resource scheduling scheme; the resource execution layer can execute resource allocation.

[0078] In addition, the resource scheduling system may also include a dynamic feedback layer. The resource execution layer can transmit actual performance data (such as UE rate and / or interference value) after resource allocation to the dynamic feedback layer so that the dynamic feedback layer can determine whether to trigger feedback correction. If feedback correction is triggered, the dynamic feedback layer can issue correction instructions to the joint optimization layer according to the performance deviation. The joint optimization layer can generate correction instructions to update the multi-objective optimization model and also feed back parameter update requirements to the acquisition layer to update the type of acquired parameters.

[0079] Figure 3 This is a schematic diagram illustrating a resource scheduling process as provided in one embodiment. For example... Figure 3 As shown, dynamic adaptive joint resource scheduling can be achieved through parameter acquisition, parameter input, model construction, algorithm solution, resource execution, performance feedback, and deviation judgment.

[0080] The resource scheduling method is illustrated below through some specific examples.

[0081] In one embodiment, the resource scheduling scenario is as follows:

[0082] User density: 500UE / km², of which URLLC services account for 10% (such as industrial control) and eMBB services account for 90% (such as high-definition video).

[0083] Network parameters: The base station uses a 64-antenna array with 32 preset beams, a total of 100 RBs, and a total transmit power of 43dBm;

[0084] Environmental parameters: Path loss PL = 120dB, shadowing fading factor = 8dB, noise power spectral density = -174dBm / Hz. The scheduling process is as follows:

[0085] Step 1: The information acquisition layer collected the following data: UE1 (URLLC service, located 500m northeast of the base station, SINR=-2dB), UE2 (eMBB service, located 300m southeast of the base station, SINR=5dB), the current RB utilization rate is 80%, and the interference power in the overlapping area of ​​beam 1 and beam 2 is 3dBm.

[0086] Step 2: Constructing the model for the joint optimization layer:

[0087] Optimization variables: Allocate 10 RBs and 14 symbols in the time domain to UE1, select beam 1 (covering the northeast direction), and power 35dBm; allocate 8 RBs and 12 symbols in the time domain to UE2, select beam 2 (covering the southeast direction), and power 30dBm.

[0088] Objective function: U = (18 / 100) × (2 / 2) = 18% (simplified calculation here), E = (URLLC latency compliance rate 99% × 0.6) + (edge ​​UE rate compliance rate 98% × 0.4) = 98.6%, P = 43 - (35 × 0.3 + 30 × 0.3) = 43 - 19.5 = 23.5 dBm;

[0089] The constraints include: RB allocation not exceeding 10%, beam switching delay not exceeding 1.5ms, and total power not exceeding 43dBm; Step 3: After the execution layer allocates resources, the feedback layer monitors: UE1 latency 3ms (meets the standard), UE2 rate 150Mbps (meets the standard), RB utilization 88%, and power consumption 38dBm (below the target).

[0090] Step 4: Calculate the deviation value: δ=(88%-85%)=3%, which is less than the preset threshold of 5%, and proceed to the next round of scheduling.

[0091] Based on this, in dense urban scenarios, RB utilization can be increased from 70% to 88%, URLLC latency compliance rate can be increased from 85% to 99%, and base station power consumption can be reduced from 43dBm to 38dBm.

[0092] In one embodiment, the resource scheduling scenario is as follows:

[0093] User density: 100 UE / km² (all eMBB services, such as video calls for high-speed rail passengers), mobile speed 350km / h;

[0094] Network parameters: The base station uses a 32-antenna array with 16 preset beams, a total of 50 RBs, and a total transmit power of 40dBm;

[0095] Environmental parameters: path loss PL = 130dB (high-speed rail carriages have high penetration loss), shadow fading coefficient = 10dB, noise power spectral density is -174dBm / Hz.

[0096] The scheduling process is as follows:

[0097] Step 1: The information acquisition layer collected the following data: UE3 (located 1km north of the base station, moving towards the base station, speed 350km / h, CQI=6), UE4 (located 1.2km north of the base station, moving away from the base station, CQI=4), RB utilization rate 60%, and interference power of 2dBm in the overlapping area of ​​beam 3 (covering the north direction) and beam 4 (covering the northeast direction).

[0098] Step 2: Constructing the model for the joint optimization layer:

[0099] Optimization variables: Allocate 5 RBs and 10 symbols in the time domain to UE3, select beam 3, and power 38dBm (power is appropriately reduced because the UE is close to the base station); allocate 6 RBs and 12 symbols in the time domain to UE4, select beam 3 (to avoid handover delay), and power 40dBm (power is increased because the UE is far away and has high penetration loss).

[0100] Objective function: U = (11 / 50) × (2 / 2) = 22%, E = (eMBB delay compliance rate 98% × 0.6) + (edge ​​UE rate compliance rate 95% × 0.4) = 97.2%, P = 40 - (38 × 0.2 + 40 × 0.2) = 40 - 15.6 = 24.4 dBm; Constraints satisfied: RB allocation not exceeding 10%, beam switching delay 1.8 ms, total power 40 dBm;

[0101] Step 3: After the execution layer allocates resources, the feedback layer monitors: UE3 rate 120Mbps (meets the standard), UE4 rate 105Mbps (meets the standard), RB utilization 72%, power consumption 39dBm;

[0102] Step 4: Calculate the deviation value: δ=(72%-70%)=2%≤5%, proceed to the next round of scheduling.

[0103] Based on this, in the high-speed rail scenario, the UE disconnection rate decreased from 10% to 2%, the edge UE rate compliance rate increased from 75% to 95%, and the beam switching latency decreased from 5ms to 1.8ms.

[0104] The resource scheduling method of this application realizes overall optimization of time-frequency space-power, breaking through dimensional isolation. By constructing a multi-objective optimization model, time-frequency resources, spatial beams, and power parameters are treated as unified variables, and the coupling relationship among the three is considered simultaneously (such as the matching between beam coverage and RB allocation, and the correlation between power and interference). This can avoid mismatch between time-frequency resource allocation and beam coverage, and RB allocation will not be wasted due to beam non-coverage, thereby improving RB utilization by 15%-20%. At the same time, by calculating the correlation between power and beam interference coefficient, the invalid power consumption in beam overlap areas can be reduced, thereby improving power utilization by 25%-30%.

[0105] This method is adaptable to dynamic scenarios: for example, by using a 1ms acquisition and feedback cycle and a beam switching latency of no more than 2ms, it can monitor user movement and service changes in real time, controlling the beam switching latency within 2ms. This meets the real-time requirements of high-speed mobility and service tidal scenarios, reducing UE disconnection rate by 6-9 percentage points in high-speed mobility scenarios. Simultaneously, based on the deviation correction mechanism of the feedback layer, the objective function weights can be adjusted in a timely manner. For example, when the latency deviation of high-priority services is large, increasing the latency weight can improve the latency compliance rate of URLLC services by 8-13 percentage points. Furthermore, this method can balance multiple objective requirements: through multi-objective optimization algorithms and weighted objective functions, it finds the optimal balance between resource utilization, user experience, and power consumption, eliminating dimensional coupling and avoiding phenomena such as blindly increasing power to improve experience. While improving edge UE rate by 15%-20%, power consumption increases by 5%-8%. At the same time, through Pareto optimal solution selection, it finds a balance between resource utilization, experience, and power consumption, reducing the overall base station power consumption by 18%-22% without sacrificing user experience, ultimately improving resource utilization and overall communication performance.

[0106] Figure 4 This is a schematic diagram of a resource scheduling device provided in an embodiment of this application. Figure 4 As shown, the resource scheduling device provided in this embodiment includes:

[0107] The acquisition module 210 is used to acquire multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters.

[0108] The optimization module 220 is used to generate a resource scheduling scheme based on the multi-dimensional input parameters and a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption.

[0109] The scheduling module 230 is used to jointly schedule time-frequency resources, spatial resources and power resources according to the resource scheduling scheme.

[0110] Based on the coupling relationship and mutual influence between different types of resources, this device uses a multi-objective optimization model to find the optimal compromise scheduling scheme with high resource utilization, balanced user experience and low base station power consumption. It realizes the joint scheduling of time and frequency resources, spatial resources and power resources, thereby improving resource utilization and overall communication performance.

[0111] Based on any of the above embodiments, the optimization module 220 is specifically used to: determine the optimization variables of the multi-objective optimization model according to the multi-dimensional input parameters; and solve the multi-objective optimization model based on the non-dominated sorting genetic algorithm to obtain a resource scheduling scheme.

[0112] Based on any of the above embodiments, the optimization variables include:

[0113] Time-frequency variables include the number of resource block (RB) allocations and the number of scheduling time-domain symbols;

[0114] Spatial variables, including beam selection index and beamforming weights;

[0115] Power variables, including the beam's transmit power;

[0116] The resource utilization rate is represented by the product of a first ratio and a second ratio, where the first ratio is the ratio of the number of allocated RBs to the total number of RBs, and the second ratio is the ratio of the number of UEs effectively covered by the beam to the total number of UEs in the beam coverage area.

[0117] The balance of user experience across different services is represented by a weighted sum of the latency compliance rate and the edge UE rate compliance rate for services with latency requirements.

[0118] The power consumption of the base station is represented by the difference between the total transmit power of the base station and the total transmit power of the beam. The total transmit power of the beam is the sum of the actual transmit power of each beam, and the actual transmit power is the product of the initial transmit power of the corresponding beam and the interference coefficient of the corresponding beam.

[0119] Based on any of the above embodiments, the constraints of the multi-objective optimization model include:

[0120] Time-frequency constraint: The number of RBs allocated to a single UE is less than or equal to a set proportion of the total number of RBs;

[0121] Spatial constraints: Beam switching delay does not exceed the delay threshold;

[0122] Power constraints: The total transmit power of the base station shall not exceed the power threshold, and the signal-to-interference-plus-noise ratio (SINR) of the UE shall not be lower than the set value.

[0123] Based on any of the above embodiments, the device further includes: a correction module, configured to, after jointly scheduling time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme, obtain a monitoring value of the UE performance after joint scheduling; generate a correction instruction based on the deviation between the monitoring value and the target value; and update the multi-objective optimization model according to the correction instruction.

[0124] Based on any of the above embodiments, the monitored values ​​include actual latency, actual data rate, and actual SINR, as well as the actual power consumption and RB utilization of the base station; the correction module includes a generation unit, used to generate a correction instruction if the deviation between any monitored value and the corresponding target value exceeds a preset threshold. The resource scheduling device provided in this application embodiment can be used to execute the resource scheduling method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0125] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of this application, is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0126] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0127] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks and wireless networks.

[0128] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above.

[0129] In some embodiments, the methods described above can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the methods of any of the embodiments described above by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device 10. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0135] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0136] This application also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the resource scheduling method as described in any of the above embodiments.

[0137] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0138] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A resource scheduling method, characterized in that, include: Collect multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters; Based on the multi-dimensional input parameters, a resource scheduling scheme is generated using a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption. According to the resource scheduling scheme, time-frequency resources, spatial resources, and power resources are jointly scheduled. The optimization variables of the multi-objective optimization model include: Time-frequency variables include the number of resource block (RB) allocations and the number of scheduling time-domain symbols; Spatial variables, including beam selection index and beamforming weights; Power variables, including the beam's transmit power; The resource utilization rate is represented by the product of a first ratio and a second ratio, where the first ratio is the ratio of the number of allocated RBs to the total number of RBs, and the second ratio is the ratio of the number of UEs effectively covered by the beam to the total number of UEs in the beam coverage area. The balance of user experience across different services is represented by a weighted sum of the latency compliance rate and the edge UE rate compliance rate for services with latency requirements. The power consumption of the base station is represented by the difference between the total transmit power of the base station and the total transmit power of the beam. The total transmit power of the beam is the sum of the actual transmit power of each beam, and the actual transmit power is the product of the initial transmit power of the corresponding beam and the interference coefficient of the corresponding beam.

2. The method according to claim 1, characterized in that, Based on the multi-dimensional input parameters and a multi-objective optimization model, a resource scheduling scheme is generated, including: Based on the multi-dimensional input parameters, the optimization variables of the multi-objective optimization model are determined; The multi-objective optimization model is solved using a non-dominated sorting genetic algorithm to obtain a resource scheduling scheme.

3. The method according to claim 1, characterized in that, The constraints of the multi-objective optimization model include: Time-frequency constraint: The number of RBs allocated to a single UE is less than or equal to a set proportion of the total number of RBs; Spatial constraints: Beam switching delay does not exceed the delay threshold; Power constraints: The total transmit power of the base station shall not exceed the power threshold, and the signal-to-interference-plus-noise ratio (SINR) of the UE shall not be lower than the set value.

4. The method according to claim 1, characterized in that, After jointly scheduling time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme, the method further includes: Obtain monitoring values ​​of UE performance after joint scheduling; A correction instruction is generated based on the deviation between the monitored value and the target value; The multi-objective optimization model is updated according to the correction instructions.

5. The method according to claim 4, characterized in that, The monitored values ​​include actual latency, actual data rate, and actual SINR, as well as the actual power consumption and RB utilization of the base station; Based on the deviation between the monitored value and the target value, a correction instruction is generated, including: If any monitored value deviates from the corresponding target value by more than a preset threshold, a correction instruction will be generated.

6. A resource scheduling device, characterized in that, include: The acquisition module is used to acquire multi-dimensional input parameters, including user-side parameters, network-side parameters, and environmental parameters. The optimization module is used to generate a resource scheduling scheme based on the multi-dimensional input parameters and a multi-objective optimization model. The optimization objectives of the multi-objective optimization model include: maximizing resource utilization, maximizing the balance of user experience for different services, and minimizing base station power consumption. The scheduling module is used to jointly schedule time-frequency resources, spatial resources, and power resources according to the resource scheduling scheme. The optimization variables of the multi-objective optimization model include: Time-frequency variables include the number of resource block (RB) allocations and the number of scheduling time-domain symbols; Spatial variables, including beam selection index and beamforming weights; Power variables, including the beam's transmit power; The resource utilization rate is represented by the product of a first ratio and a second ratio, where the first ratio is the ratio of the number of allocated RBs to the total number of RBs, and the second ratio is the ratio of the number of UEs effectively covered by the beam to the total number of UEs in the beam coverage area. The balance of user experience across different services is represented by a weighted sum of the latency compliance rate and the edge UE rate compliance rate for services with latency requirements. The power consumption of the base station is represented by the difference between the total transmit power of the base station and the total transmit power of the beam. The total transmit power of the beam is the sum of the actual transmit power of each beam, and the actual transmit power is the product of the initial transmit power of the corresponding beam and the interference coefficient of the corresponding beam.

7. An electronic device, characterized in that, include: At least one processor; A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the resource scheduling method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the resource scheduling method as described in any one of claims 1-5.

9. A computer program product, comprising a computer program and / or instructions, characterized in that, When the computer program and / or instructions are executed by the processor, they implement the resource scheduling method as described in any one of claims 1-5.

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