Wireless resource allocation method and apparatus, computer device and storage medium

Abstract

This application discloses a wireless resource allocation method, apparatus, computer device, and storage medium. Belonging to the field of wireless communication technology, the method specifically includes: acquiring historical index parameters of a target device; determining future resource allocation weights based on the historical index parameters and the historical resource allocation weights of the target device; and allocating wireless resources to the target device based on the future resource allocation weights. Compared to existing technologies, this application can achieve dynamic adaptive allocation of wireless resources, improving the phenomenon of target device backhaul bandwidth preemption in high network load scenarios, stabilizing the upload rate of the target device, and ensuring service transmission performance in weak coverage areas such as elevators and basements; simultaneously, it can output allocation weights adapted to the current network state based on the historical operation of the link, avoiding the rigidity of resource allocation under static resource configuration mode, and balancing the service guarantee of the target device with the overall wireless resource utilization efficiency of the cell.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, specifically to a wireless resource allocation method, apparatus, computer device, and storage medium. Background Technology

[0002] In existing mobile communication systems, wireless relay stations, acting as relay nodes between macro stations and user terminals, can improve edge coverage and indoor signal quality. With the application of RedCap (Reduced Capability, 5G lightweight) relay stations in 5G and subsequent 6G networks, their use as lightweight terminals or small relays is increasing, especially in areas with weak coverage such as elevators and basements.

[0003] However, macro base stations have limited wireless time and frequency resources, which are shared by all mobile phones, IoT devices, and RedCap repeaters in the community. During peak periods (e.g., after get off work, when multiple people are using the internet simultaneously in an elevator), network load increases, and a large number of devices compete for resources. This can lead to bandwidth constraints on the backhaul channels allocated to RedCap repeaters, resulting in a significant drop in repeater upload speeds. Summary of the Invention

[0004] Therefore, it is necessary to provide a wireless resource allocation method, apparatus, computer equipment, and storage medium to address the aforementioned technical problems and realize the rational allocation of wireless resources.

[0005] In a first aspect, this application provides a wireless resource allocation method applied to macro stations, the method comprising:

[0006] Obtain historical parameter values ​​of the target device;

[0007] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0008] Based on future resource allocation weights, allocate wireless resources to the target device.

[0009] In one embodiment, the target metrics include at least one of backhaul link rate, transmission latency, and packet loss rate.

[0010] In one embodiment, before obtaining historical metric parameters of the target device, the method further includes:

[0011] Obtain the RFSP value and device identifier of the candidate device;

[0012] If a candidate device is identified as a RedCap relay station based on its RFSP value and device identifier, the candidate device will be used as the target device.

[0013] In one embodiment, the RFSP value is recorded in the session context of the candidate device; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device.

[0014] In one embodiment, the device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is fed back by the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the process of establishing an RRC connection.

[0015] In one embodiment, determining the future resource allocation weight based on historical indicator parameters of the target indicator and historical resource allocation weights of the target device includes:

[0016] Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment.

[0017] Determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0018] In one embodiment, determining the future resource allocation weight based on the future indicator parameters of the target indicator includes:

[0019] Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric;

[0020] Based on the first and second trade-off parameters, the historical metrics and user experience metrics of each target indicator are weighted and summed to obtain the summation result;

[0021] Based on the summation result, determine the weights for future resource allocation.

[0022] Secondly, this application provides a wireless resource allocation device configured in a macro base station, the device comprising:

[0023] The first acquisition module is used to acquire historical index parameters of the target device's target indicators;

[0024] The first determining module is used to determine the future resource allocation weight based on the historical indicator parameters of the target indicator and the historical resource allocation weight of the target equipment.

[0025] The allocation module is used to allocate wireless resources to target devices based on future resource allocation weights.

[0026] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0027] Obtain historical parameter values ​​of the target device;

[0028] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0029] Based on future resource allocation weights, allocate wireless resources to the target device.

[0030] Fourthly, this application also provides a computer-readable storage medium on which a computer program is stored, and when executed by a processor, the computer program performs the following steps:

[0031] Obtain historical parameter values ​​of the target device;

[0032] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0033] Based on future resource allocation weights, allocate wireless resources to the target device.

[0034] Fifthly, this application also provides a computer program product comprising a computer program that, when executed by a processor, performs the following steps:

[0035] Obtain historical parameter values ​​of the target device;

[0036] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0037] Based on future resource allocation weights, allocate wireless resources to the target device.

[0038] The aforementioned wireless resource allocation method, apparatus, computer equipment, and storage medium acquire historical index parameters of the target device's target indicators. Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target device, future resource allocation weights are determined. Wireless resources are allocated to the target device based on these future resource allocation weights. Compared to existing technologies, this application enables dynamic adaptive allocation of wireless resources, improving the situation where backhaul bandwidth is preempted for target devices (e.g., RedCap relay stations) under high network load scenarios, stabilizing the upload rate of target devices, and ensuring service transmission performance in weak coverage areas such as elevators and basements. Simultaneously, it can output allocation weights adapted to the current network state based on historical link operation data, avoiding the rigidity of resource allocation under static resource configuration mode, and balancing target device service assurance with overall cell wireless resource utilization efficiency. Attached Figure Description

[0039] Figure 1 This is an application environment diagram of a wireless resource allocation method provided in this embodiment;

[0040] Figure 2 This is a flowchart illustrating the first wireless resource allocation method provided in this embodiment;

[0041] Figure 3 This is a schematic diagram of the process for determining the target device provided in this embodiment;

[0042] Figure 4 This is a schematic diagram of the process for determining the weights of future resource allocation provided in this embodiment;

[0043] Figure 5 This is a flowchart illustrating the second wireless resource allocation method provided in this embodiment;

[0044] Figure 6 This is a structural block diagram of a wireless resource allocation device provided in this embodiment;

[0045] Figure 7 This is an internal structural diagram of the computer device provided in this embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] In existing mobile communication systems, wireless relay stations, acting as relay nodes between macro stations and user terminals, can improve edge coverage and indoor signal quality. With the application of RedCap (Reduced Capability, 5G lightweight) relay stations in 5G and subsequent 6G networks, their use as lightweight terminals or small relays is increasing, especially in areas with weak coverage such as elevators and basements.

[0048] However, macro base stations have limited wireless time and frequency resources, which are shared by all mobile phones, IoT devices, and RedCap repeaters in the community. During peak periods (e.g., after get off work, when multiple people are using the internet simultaneously in an elevator), network load increases, and a large number of devices compete for resources. This can lead to bandwidth constraints on the backhaul channels allocated to RedCap repeaters, resulting in a significant drop in repeater upload speeds.

[0049] To address the aforementioned problems, the wireless resource allocation method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the macro base station acquires historical indicator parameters of the target device, determines the future resource allocation weight based on the historical indicator parameters and the historical resource allocation weight of the target device, and then allocates wireless resources to the target device according to the future resource allocation weight.

[0050] The target device refers to the device that needs to be dynamically allocated wireless resources. In this application, the target device mainly refers to the RedCap relay station.

[0051] RedCap relay stations refer to wireless relay devices equipped with RedCap (Reduced Capability, lightweight 5G) terminal capabilities.

[0052] A macro base station, or 5G macro base station gNB, is a wide-coverage base station device for mobile communication systems. It can provide wireless access services to various terminals within a cell, has wireless resource scheduling and management capabilities, can obtain the RFSP value and device identifier of candidate devices, and after identifying the target device, executes the wireless resource allocation method of this invention to complete the allocation of wireless resources.

[0053] In one embodiment, Figure 2 This is a flowchart illustrating a wireless resource allocation method according to an embodiment of this application, applied to... Figure 1 The method, specifically taking the scheduler or scheduling module applied in a macro station as an example, includes the following steps:

[0054] S201, Obtain historical index parameters of the target device.

[0055] In this context, "target equipment" refers to equipment requiring dynamic allocation of wireless resources; in this application, it primarily refers to RedCap relay stations. "Target indicators" refer to key indicators that need to be collected or monitored periodically. "Historical indicator parameters" refer to the indicator values ​​of the target indicators over historical periods.

[0056] Optionally, the target metrics in this embodiment include, but are not limited to, backhaul link rate, transmission latency, and packet loss rate. Backhaul link rate refers to the amount of data successfully transmitted per unit time on the backhaul link between the target device and the macro station, reflecting the data transmission capability of the backhaul link. The unit can be bit / s; specifically, it is the actual rate of data transmission and reception completed on the air interface between the macro station and the target device, excluding data that has failed to be retransmitted or has been discarded. When the network load is high, wireless resources are congested, and this rate will decrease; this metric is a core indicator for judging the quality of the target device's backhaul link. Transmission latency refers to the time taken from when a data packet is sent from the sending end to when the receiving end fully receives the data packet. In this application, it specifically refers to the transmission latency of data packets on the target device's backhaul link; latency includes air interface queuing latency, wireless transmission propagation latency, and retransmission processing latency. The higher the cell load, the longer the queuing time, and the greater the transmission latency; increased latency can cause service interruptions in elevator and basement coverage areas. Packet loss rate refers to the ratio of the number of lost data packets to the total number of data packets sent during transmission, usually expressed as a percentage. Packet loss can be caused by factors such as wireless channel fading, insufficient resources leading to scheduling failures, and retransmission failures due to air interface transmission errors. A higher packet loss rate indicates a lower reliability of the backhaul link, which directly increases the risk of service delays and interruptions.

[0057] One possible implementation is that the parameter acquisition is directly completed by the internal scheduling module of the macro station. The macro station performs statistical measurements on the air interface link of the target device in each scheduling cycle, and obtains real-time measurement values ​​of backhaul link rate, transmission delay and packet loss rate. The measurement values ​​in multiple historical scheduling cycles are saved to form historical index parameters of the target device.

[0058] Another optional implementation is that the target device performs the link index measurement. The target device periodically generates measurement reports and reports them to the macro station via air interface RRC (Radio Resource Control) signaling. The measurement reports carry the measurement results of backhaul link rate, transmission delay, and packet loss rate.

[0059] Another alternative implementation involves sampling the indicator using a fixed-duration sliding time window. The macro station continuously collects real-time data of the target indicator, retaining only the indicator samples within the sliding window time range and discarding outdated historical data outside the window. The data stored within the window is the historical indicator parameter of the target indicator.

[0060] S202, determine the future resource allocation weight based on the historical indicator parameters of the target indicator and the historical resource allocation weight of the target equipment.

[0061] The historical resource allocation weight refers to the resource allocation weight that the macro station has calculated and actually used to allocate radio resources to the target device in one or more historical scheduling cycles prior to the current scheduling slot. The magnitude of the resource allocation weight is used to characterize the priority of the macro station in allocating radio time and frequency resources to the target device; the higher the weight, the easier it is for the target device to obtain more radio time and frequency resources during scheduling. Each scheduling cycle outputs a resource allocation weight, and all or part of the weight data used in previous cycles constitutes the historical resource allocation weight of this application.

[0062] One possible implementation is to pre-store an indicator-weight mapping table within the macro site. The mapping table records the recommended weights corresponding to different combinations of historical indicator parameters and historical resource allocation weights. The macro site uses the acquired historical indicator parameters and historical resource allocation weights as lookup inputs, matches the corresponding entries in the mapping table, and outputs the future resource allocation weights.

[0063] Another optional implementation method is to perform statistical fitting on the historical indicator parameters and historical resource allocation weights of the target indicator, establish a fitting function between the historical resource allocation weights and the link indicator, and use the fitting function to estimate the value of the target indicator at the next moment; then, based on the preset weight adjustment strategy, the future resource allocation weight is calculated and output in combination with the estimated indicator value.

[0064] Another optional implementation involves setting a sliding time window to capture historical indicator parameters and historical resource allocation weights, performing weighted statistics on the indicators within the window to obtain a comprehensive link quality assessment value; setting a link quality threshold, comparing the comprehensive assessment value with the threshold, and, in conjunction with the changing trend of historical resource allocation weights, adjusting the weights upwards or downwards according to a preset adjustment step size to obtain future resource allocation weights.

[0065] S203 allocates wireless resources to the target device based on future resource allocation weights.

[0066] Among them, radio resources refer to the air interface time and frequency resources available for scheduling within a macro base station cell. These mainly include time slots and symbols in the time domain, as well as subcarriers and resource blocks in the frequency domain. They are the physical resources used by the macro base station to transmit uplink and downlink data to various terminals within the cell.

[0067] One optional implementation involves maintaining multiple terminal scheduling queues within the macro base station scheduler. The scheduler configures future resource allocation weights for the service scheduling queue corresponding to the target device, and performs round-robin scheduling based on the weight ratio of each terminal queue. The higher the weight, the higher the probability that the target device queue is selected for scheduling, and the more time-frequency resource blocks it receives. This implementation transforms future resource allocation weights into queue scheduling priorities, reuses the existing weighted round-robin scheduling framework of the base station, requires minimal modification, and can achieve balanced resource allocation among the other terminals in the cell.

[0068] Another optional implementation involves mapping future resource allocation weights to a percentage of the total radio resources in the cell, and reserving a corresponding proportion of time-frequency resource blocks for the target device according to this percentage. In each scheduling time slot, radio resources within the reserved range are prioritized for the target device, and the remaining radio resources are then allocated to other terminals within the cell. When the target device's service data volume is small, unused reserved resources can be released to other devices. This implementation ensures a minimum resource limit for the target device, preventing resources from being completely occupied in high-load scenarios, and is suitable for scenarios requiring guaranteed minimum performance for relay backhaul services.

[0069] Another optional implementation involves converting future resource allocation weights into scheduling offset parameters for Downlink Control Information (DCI). The macro base station scheduler adjusts the resource allocation indication for the target device based on these offset parameters, dynamically adjusting the number of resource blocks allocated to the target device when generating DCI scheduling instructions. The macro base station then sends the DCI carrying resource allocation information to the target device via the air interface. The target device then occupies the corresponding radio resources according to the DCI indication to complete data transmission and reception. This implementation achieves resource allocation through physical layer scheduling instructions, providing fine-grained scheduling and allowing for dynamic adjustment of resource allocation size on a time-slot basis.

[0070] The aforementioned wireless resource allocation method obtains historical indicator parameters of the target device. Based on the historical indicator parameters and the historical resource allocation weights of the target device, the future resource allocation weights are determined. Wireless resources are then allocated to the target device according to these future resource allocation weights. Compared to existing technologies, this application enables dynamic adaptive allocation of wireless resources, improving the situation where backhaul bandwidth is preempted for target devices (e.g., RedCap relay stations) under high network load scenarios, stabilizing the upload rate of target devices, and ensuring service transmission performance in weak coverage areas such as elevators and basements. Simultaneously, it can output allocation weights adapted to the current network state based on historical link operation data, avoiding the rigidity of resource allocation under static resource configuration mode, and balancing target device service assurance with overall cell wireless resource utilization efficiency.

[0071] In one embodiment, such as Figure 3As shown, before obtaining historical index parameters of the target device, an optional implementation of a wireless resource allocation method includes:

[0072] S301, obtain the RFSP value and device identifier of the candidate device.

[0073] The RFSP value refers to a specific value that the AMF (Access and Mobility Management Function) network element / SMF (Session Management Function) network element subscribes to with the RedCap relay station according to the policy issued by the Unified Data Management (UDM) function when the target device (RedCap relay station) establishes a registration process with the core network. This RFSP value is set as a priority value that distinguishes it from ordinary UEs; that is, the system reserves a high-priority range and records it in the subscription information of the core network. The device identifier is a unique identifier used to characterize the target device.

[0074] Optionally, in this embodiment, the RFSP value is recorded in the terminal's session context; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device. Specifically, the RFSP value is encapsulated in the session context corresponding to the candidate device, which is generated by the SMF network element. When the candidate device initiates the establishment of a PDU (Protocol Data Unit) session, the SMF network element obtains the RFSP value of the candidate device and carries the RFSP value in the session context, sending the session context to the macro station through the N2 interface; after receiving the session context, the macro station reads the RFSP value from it to determine whether the candidate device is the target device in conjunction with the device identifier.

[0075] Optionally, in this embodiment, the device identifier is recorded in the terminal capability information. The terminal capability information is provided by the terminal to the macro station after the macro station sends a terminal capability query request to the candidate device during the establishment of an RRC (Radio Resource Control) connection. Specifically, the device identifier is carried within the terminal capability information. After the candidate device and the macro station complete the RRC connection establishment, the macro station sends a terminal capability query request to the candidate device. Upon receiving the query request, the candidate device sends its terminal capability information back to the macro station. The macro station extracts the device identifier (i.e., the identifier corresponding to the RedCap flag in the UECapability information) from this terminal capability information to jointly determine whether the candidate device is the target device, in conjunction with the RFSP value.

[0076] S302, if a candidate device is determined to be a RedCap relay station based on the RFSP value and device identifier, the candidate device shall be used as the target device.

[0077] An optional implementation involves the macro station acquiring the RFSP value and device identifier of the candidate device, and performing logic and judgment. When the device identifier indicates that the candidate device has RedCap capability, and the RFSP value is equal to the preset relay station dedicated index value, the candidate device is determined to be a RedCap relay station, and the candidate device is identified as the target device.

[0078] Another optional implementation involves pre-storing an identification mapping table locally on the macro station. The mapping table stores a set of RFSP values ​​that can be identified as RedCap relay stations, as well as the corresponding device identification conditions. After the macro station obtains the RFSP value and device identification of a candidate device, it queries the mapping table. If the device identification of the candidate device meets the RedCap capability conditions and the RFSP value exists in the relay station-specific RFSP value set in the mapping table, then the candidate device is determined to be a RedCap relay station, and the candidate device is used as the target device.

[0079] Another optional implementation method is to use a step-by-step verification approach. First, the device identifier is verified. If the device identifier does not represent RedCap capability, the candidate device is directly determined not to be a RedCap relay station, and the identification process ends. If the device identifier indicates that it has RedCap capability, the RFSP value is further verified to see if it is a relay station-specific RFSP value. Only when both conditions are verified are the candidate device determined as the target device.

[0080] In this embodiment, the RFSP value and device identifier of the candidate device are first obtained. Then, based on the RFSP value and device identifier, it is determined whether the candidate device is a RedCap relay station. The determined RedCap relay station is then used as the target device for subsequent radio resource allocation procedures. Compared with the prior art, this solution uses the RFSP value issued by the core network and the device identifier reported by the terminal for joint identification. It does not rely on new hardware devices and can complete the identification and differentiation of RedCap relay stations within the existing 5G protocol framework. Unlike the single-condition identification method, the dual-verification identification logic can reduce the probability of misidentification and missed identification of devices, and avoid ordinary RedCap terminals being mistakenly treated as relay stations. The corresponding resource allocation mechanism is only activated for RedCap relay stations after they are identified, and it will not affect other ordinary terminals in the cell. While achieving targeted protection for RedCap relay station services, it does not interfere with the normal scheduling of other terminals in the cell.

[0081] In one embodiment, such as Figure 4As shown, the optional implementation methods for determining future resource allocation weights based on historical indicator parameters of the target metric and historical resource allocation weights of the target equipment include:

[0082] S401, based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target indicators of the target equipment.

[0083] One optional implementation involves concatenating historical indicator parameters and historical resource allocation weights of the target indicator stored within a sliding window to construct a time-series input sequence, which is then fed into an LSTM (Long Short-Term Memory) model with an overlay attention mechanism. The LSTM model mines the temporal correlation between historical weights and link indicators, while the attention mechanism assigns higher weights to samples of recent link mutations and abnormal degradation, strengthening the learning of sudden link fluctuations and outputting future indicator parameters corresponding to backhaul link rate, transmission latency, and packet loss rate. This implementation can capture nonlinear changes and sudden degradation trends in links, and can predict the deterioration of backhaul link performance under high-load scenarios in advance, providing forward-looking link prediction results for subsequent weight calculations. In this embodiment, an LSTM model is used as the prediction model. An attention mechanism is added before the output layer of the LSTM model, which can assign differentiated weights to the link state at different historical moments. For example, recent sudden increases in packet loss rate and sharp drops in rate will be given higher weights, making the model pay more attention to key short-term anomalies. The input is:

[0084] Xt={Rs(t),D(t),P(t),Whist(t)};

[0085] Where R_s represents the backhaul link rate of the target device at time t; D represents the transmission delay of the target device at time t; P represents the packet loss rate of the target device at time t; and Whist(t) represents the historical resource allocation weight allocated by the macro station to the target device at time t.

[0086] Another alternative implementation involves using a sliding time window to extract recent historical indicator parameters and historical resource allocation weights. The mean and weighted average of the samples within the window are then calculated, and extrapolation is performed based on the changing trends of historical resource allocation weights to directly predict future indicator parameters. This implementation does not use complex neural network models; it relies on statistical extrapolation of recent historical data to complete the prediction. It requires less computation and has lower demands on base station computing power, making it suitable for macro base station equipment with relatively smooth link changes and limited computing resources.

[0087] Another optional implementation involves using historical resource allocation weights as independent variables and historical indicator parameters as dependent variables, performing linear regression fitting locally at the macro base station to establish the fitting relationship between resource allocation weights and various target indicators; inputting the current weights to be solved into the fitting model, and calculating and outputting the corresponding future indicator parameters. This implementation method features a simple model, convenient parameter tuning, and can reflect the impact of resource allocation weights on link indicators. It is suitable for scenarios with small link channel fluctuations and facilitates engineering deployment.

[0088] S402, determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0089] An optional implementation involves determining initial resource allocation weights based on future indicator parameters of the target indicator. For example, the future indicator parameters of the target indicator can be input into a reinforcement learning (RL) weight adjuster to obtain the initial resource allocation weights. Based on the initial resource allocation weights and the sampling step size, multiple candidate resource allocation weights are obtained; for example, if the initial resource allocation weight is 0.6 and the sampling step size is 0.05, multiple candidate resource allocation weights with values ​​of 0.55, 0.6, and 0.65 can be obtained. For each candidate resource allocation weight other than the initial resource allocation weight, the future indicator parameters of the target indicator of the target device under that candidate resource allocation weight are inferred. Then, the future indicator parameters of the target indicator for each candidate resource allocation weight (the future indicator parameters of the target indicator for the initial resource allocation weight have been determined in the aforementioned steps) are substituted into the objective function to obtain the function value corresponding to that candidate resource allocation weight. The candidate resource allocation weight corresponding to the largest function value is taken as the future resource allocation weight.

[0090] Another optional implementation involves setting weighting coefficients for backhaul link rate, transmission latency, and packet loss rate, performing weighted calculations on future indicator parameters, and calculating a comprehensive link quality score. The comprehensive score is then mapped to a preset weight range to obtain a future resource allocation weight. A lower comprehensive score indicates a worse estimated link quality, and a larger future resource allocation weight is output to improve the scheduling priority of the target device.

[0091] Another optional implementation involves obtaining the first trade-off parameter corresponding to each target metric and the second trade-off parameter for the user experience metric. Based on the first and second trade-off parameters, a weighted sum is calculated of the historical metric parameters and the user experience metric for each target metric. The future resource allocation weights are then determined based on the sum. Specifically, the future resource allocation weights can be determined using the following formula:

[0092] U=α*Rs-β*D-γ*P+δ*QoE

[0093] Where α, β, γ, and δ are trade-off parameters, and QoE is the user experience metric.

[0094] In this embodiment, one optional implementation for determining future resource allocation weights based on the summation result is to use the summation result as the future resource allocation weight. Another optional implementation is to determine the future resource allocation weights based on the summation result and a weight list; wherein the weight list records the mapping relationship between various summation results and resource allocation weights. Yet another optional implementation is to normalize the summation result to obtain the future resource allocation weights.

[0095] It should be noted that the above disclosure can also be used as the objective function in the first embodiment above. When used as the objective function, the value of QoE is a preset fixed value.

[0096] In this embodiment, historical index parameters of the target indicator and historical resource allocation weights of the target device are used to perform time-series prediction to obtain future index parameters of the target device's target indicator. Then, the future resource allocation weights are determined based on these future index parameters. This scheme fully leverages the time-series correlation between historical link indicators and historical resource allocation weights, enabling early prediction of the target device's backhaul link quality change trend. It achieves proactive weight adjustments for bandwidth congestion and sudden link degradation under high network load scenarios. It does not rely solely on the current instantaneous link state for decision-making, reducing the interference of instantaneous noise on scheduling decisions. While ensuring the performance of the target device's backhaul link services, it reasonably constrains resource allocation priorities, considers the overall wireless resource utilization efficiency of the cell, and reduces the negative impact on other terminal services within the cell.

[0097] In one embodiment, such as Figure 5 As shown, an optional implementation of a wireless resource allocation method includes:

[0098] S501, Obtain the RFSP value and device identifier of the candidate device. The RFSP value is recorded in the candidate device's session context; the session context is the device identifier sent by the SMF network element to the macro station during the establishment of the candidate device's PDU session; the device identifier is recorded in the candidate device's terminal capability information; the terminal capability information is the feedback from the candidate device to the macro station after the macro station sends a terminal capability query request during the establishment of the candidate device's RRC connection.

[0099] S502, if a candidate device is determined to be a RedCap relay station based on the RFSP value and device identifier, the candidate device is used as the target device.

[0100] S503: Obtain historical parameter parameters of the target device's target metrics. These target metrics include backhaul link rate, transmission latency, and packet loss rate.

[0101] S504, based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target indicators of the target equipment.

[0102] S505, obtain the first trade-off parameters corresponding to each target indicator and the second trade-off parameters for the user experience indicator.

[0103] S506, based on the first and second trade-off parameters, the historical indicator parameters and user experience indicators of each target indicator are weighted and summed to obtain the summation result.

[0104] S507: Determine the future resource allocation weights based on the summation results.

[0105] S508 allocates wireless resources to target devices based on future resource allocation weights.

[0106] The wireless resource allocation method in this embodiment identifies the target device by acquiring the RFSP value and device identifier of the candidate device and using both. It reuses the existing 5G communication protocol without requiring additional hardware. Dual verification reduces the probability of ordinary RedCap terminals being misidentified as RedCap relay stations. The wireless resource allocation process is only executed on the target device after identification, without interfering with the scheduling of other terminals in the cell. Furthermore, historical indicator parameters and historical resource allocation weights of the target device are collected to predict future indicator parameters. Based on these future indicator parameters, future resource allocation weights are determined. This allows for the discovery of temporal changes in link indicators and resource weights, enabling early prediction of changes in backhaul link quality. It also allows for early weight adjustment to address link degradation caused by resource congestion in high-load network scenarios. This approach ensures the service performance of the RedCap relay station backhaul link, improves the service experience for users in weak coverage areas such as elevators and basements, and balances the overall use of wireless resources in the cell, preventing the target device from excessively seizing resources. This achieves a trade-off between target device service assurance and overall cell resource utilization.

[0107] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0108] Based on the same inventive concept, this application also provides a wireless resource allocation apparatus for implementing the aforementioned wireless resource allocation method. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more wireless resource allocation apparatus embodiments provided below can be found in the limitations of the wireless resource allocation method described above, and will not be repeated here.

[0109] In one embodiment, by Figure 6 A structural block diagram of a wireless resource allocation device in one embodiment is shown. Figure 6 As shown, a wireless resource allocation device 1 is provided, which includes: a first acquisition module 10, a first determination module 20, and an allocation module 30, wherein:

[0110] The first acquisition module 10 is used to acquire historical index parameters of the target indicators of the target device;

[0111] The first determining module 20 is used to determine the future resource allocation weight based on the historical indicator parameters of the target indicator and the historical resource allocation weight of the target equipment.

[0112] The allocation module 30 is used to allocate wireless resources to the target device according to the future resource allocation weight.

[0113] In one embodiment, the target metrics include at least one of backhaul link rate, transmission latency, and packet loss rate.

[0114] In one embodiment, a wireless resource allocation device 1 further includes:

[0115] The second acquisition module is used to acquire the RFSP value and device identifier of the candidate device;

[0116] The second determination module is used to select the candidate device as the target device when the candidate device is determined to be a RedCap relay station based on the RFSP value and the device identifier.

[0117] In one embodiment, the RFSP value is recorded in the session context of the candidate device; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device.

[0118] In one embodiment, the device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is fed back by the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the process of establishing an RRC connection.

[0119] In one embodiment, the first determining module is further specifically used for:

[0120] Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment.

[0121] Determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0122] In one embodiment, the first determining module is further specifically used for:

[0123] Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric;

[0124] Based on the first and second trade-off parameters, the historical metrics and user experience metrics of each target indicator are weighted and summed to obtain the summation result;

[0125] Based on the summation result, determine the weights for future resource allocation.

[0126] Each module in the aforementioned wireless resource allocation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0127] In one embodiment, a computer device is provided, which may be a platform-side device, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores wireless resource allocation information. The network interface communicates with an external user via a network connection. When executed by the processor, the computer program implements a wireless resource allocation method.

[0128] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specifically, the computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0129] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0130] Obtain historical parameter values ​​of the target device;

[0131] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0132] Based on future resource allocation weights, allocate wireless resources to the target device.

[0133] In one embodiment, when the processor executes the computer program, it further performs the following steps: the target metric includes at least one of backhaul link rate, transmission latency, and packet loss rate.

[0134] In one embodiment, the processor, when executing the computer program, further implements the following steps: before obtaining historical indicator parameters of the target indicator of the target device, the method further includes:

[0135] Obtain the RFSP value and device identifier of the candidate device;

[0136] If a candidate device is identified as a RedCap relay station based on its RFSP value and device identifier, the candidate device will be used as the target device.

[0137] In one embodiment, when the processor executes the computer program, it also performs the following steps: the RFSP value is recorded in the session context of the candidate device; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device.

[0138] In one embodiment, when the processor executes the computer program, it further implements the following steps: the device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is the information fed back by the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the process of establishing an RRC connection.

[0139] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the future resource allocation weight based on the historical index parameters of the target index and the historical resource allocation weight of the target device, including:

[0140] Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment.

[0141] Determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0142] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining future resource allocation weights based on future indicator parameters of the target indicator, including:

[0143] Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric;

[0144] Based on the first and second trade-off parameters, the historical metrics and user experience metrics of each target indicator are weighted and summed to obtain the summation result;

[0145] Based on the summation result, determine the weights for future resource allocation.

[0146] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0147] Obtain historical parameter values ​​of the target device;

[0148] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0149] Based on future resource allocation weights, allocate wireless resources to the target device.

[0150] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: the target metric includes at least one of backhaul link rate, transmission latency, and packet loss rate.

[0151] In one embodiment, when the computer program is executed by a processor, it further implements the following steps: before obtaining historical indicator parameters of the target indicator of the target device, the method further includes:

[0152] Obtain the RFSP value and device identifier of the candidate device;

[0153] If a candidate device is identified as a RedCap relay station based on its RFSP value and device identifier, the candidate device will be used as the target device.

[0154] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: the RFSP value is recorded in the session context of the candidate device; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device.

[0155] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is the feedback from the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the establishment of the RRC connection.

[0156] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the future resource allocation weight based on the historical index parameters of the target index and the historical resource allocation weight of the target device, including:

[0157] Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment.

[0158] Determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0159] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining future resource allocation weights based on future indicator parameters of the target indicator, including:

[0160] Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric;

[0161] Based on the first and second trade-off parameters, the historical metrics and user experience metrics of each target indicator are weighted and summed to obtain the summation result;

[0162] Based on the summation result, determine the weights for future resource allocation.

[0163] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0164] Obtain historical parameter values ​​of the target device;

[0165] The future resource allocation weights are determined based on the historical indicator parameters of the target indicators and the historical resource allocation weights of the target equipment.

[0166] Based on future resource allocation weights, allocate wireless resources to the target device.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: the target metric includes at least one of backhaul link rate, transmission latency, and packet loss rate.

[0168] In one embodiment, when the computer program is executed by a processor, it further implements the following steps: before obtaining historical indicator parameters of the target indicator of the target device, the method further includes:

[0169] Obtain the RFSP value and device identifier of the candidate device;

[0170] If a candidate device is identified as a RedCap relay station based on its RFSP value and device identifier, the candidate device will be used as the target device.

[0171] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: the RFSP value is recorded in the session context of the candidate device; the session context is sent by the SMF network element to the macro station during the process of establishing a PDU session by the candidate device.

[0172] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: the device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is the feedback from the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the establishment of the RRC connection.

[0173] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the future resource allocation weight based on the historical index parameters of the target index and the historical resource allocation weight of the target device, including:

[0174] Based on the historical index parameters of the target indicators and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment.

[0175] Determine the future resource allocation weights based on the future indicator parameters of the target indicators.

[0176] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining future resource allocation weights based on future indicator parameters of the target indicator, including:

[0177] Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric;

[0178] Based on the first and second trade-off parameters, the historical metrics and user experience metrics of each target indicator are weighted and summed to obtain the summation result;

[0179] Based on the summation result, determine the weights for future resource allocation.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0181] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0182] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for allocating wireless resources, characterized in that, Applied to macro stations, the method includes: Obtain historical parameter values ​​of the target device; The future resource allocation weight is determined based on the historical index parameters of the target index and the historical resource allocation weight of the target equipment. Based on the future resource allocation weights, wireless resources are allocated to the target device.

2. The method according to claim 1, characterized in that, The target metrics include at least one of backhaul link rate, transmission latency, and packet loss rate.

3. The method according to claim 1, characterized in that, Before obtaining historical parameter parameters of the target device's target metrics, the method further includes: Obtain the radio frequency selection priority RFSP value and device identifier of the candidate devices; If the candidate device is determined to be a 5G lightweight RedCap relay station based on the RFSP value and device identifier, the candidate device will be used as the target device.

4. The method according to claim 3, characterized in that, The RFSP value is recorded in the session context of the candidate device; the session context is sent to the macro station by the session management function (SMF) network element during the establishment of the protocol data unit (PDU) session by the candidate device.

5. The method according to claim 3, characterized in that, The device identifier is recorded in the terminal capability information of the candidate device; the terminal capability information is the feedback from the candidate device to the macro station after the macro station sends a terminal capability query request to the candidate device during the establishment of the RRC connection.

6. The method according to claim 1, characterized in that, The step of determining the future resource allocation weight based on the historical index parameters of the target indicator and the historical resource allocation weight of the target device includes: Based on the historical index parameters of the target index and the historical resource allocation weights of the target equipment, predict the future index parameters of the target equipment. The future resource allocation weights are determined based on the future indicator parameters of the target indicators.

7. The method according to claim 6, characterized in that, The step of determining the future resource allocation weight based on the future indicator parameters of the target indicator includes: Obtain the first trade-off parameters for each target metric and the second trade-off parameters for each user experience metric; Based on the first trade-off parameter and the second trade-off parameter, the historical indicator parameters of each target indicator and the user experience indicator are weighted and summed to obtain the summation result; The future resource allocation weights are determined based on the summation result.

8. A wireless resource allocation device, characterized in that, Configured in a macro station, the device includes: The first acquisition module is used to acquire historical index parameters of the target device's target indicators; The first determining module is used to determine the future resource allocation weight based on the historical indicator parameters of the target indicator and the historical resource allocation weight of the target device. The allocation module is used to allocate wireless resources to the target device according to the future resource allocation weight.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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