Resource allocation method for LTE (Long Term Evolution) wireless communication

Abstract

The invention discloses a resource allocation method for LTE (Long Term Evolution) wireless communication, relates to the technical field of wireless communication, and is used for solving the problem that wireless communication resource allocation is not clear. According to the method, the future position of the user is predicted by acquiring the historical position and speed data of the user and combining with the time step length, the channel quality is predicted through the path loss and interference model, the target cell is identified, data is provided for resource allocation, and the resource occupation state of the target cell is evaluated. The resource reservation proportion is dynamically adjusted in combination with user service requirements and channel quality, the communication quality and service continuity of high-speed users in the switching process are guaranteed, then, a shared resource pool is constructed, and low-interference resource blocks are preferentially allocated to high-priority users through an interference coordination strategy in combination with an interference thermodynamic diagram generated in real time, so that the high-priority users are subjected to resource allocation. The interference influence is reduced, the priority and the resource allocation strategy are dynamically adjusted, user migration is triggered in combination with the load balancing index, and global optimization of resource allocation and improvement of network performance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and more specifically, to a resource allocation method for LTE wireless communications. Background Art

[0002] LTE (Long Term Evolution) is a technical standard designed for wireless mobile communications. It is a long-term evolution project defined by the 3GPP (3rd Generation Partnership Project). LTE is an important stage in the evolution from 3G networks to 4G networks. It is one of the current mainstream wireless communication technologies and provides technical support for emerging fields such as the Internet of Things, real-time communications and intelligent transportation.

[0003] Deficiencies in the existing technology: When high-speed mobile users (such as users on high-speed trains and in vehicles) switch across cells, the resource allocation strategy in the switching area is relatively passive, and resources usually need to be reallocated only after the user enters the target cell, resulting in a large delay in the resource allocation process. In addition, in high-speed mobile scenarios, the channel state changes rapidly, and the traditional channel quality feedback mechanism is difficult to keep up with the dynamic needs of users in a timely manner, further exacerbating the untimely nature of resource allocation. Especially in the switching area, high-speed mobile users may face a high risk of disconnection or a significant decrease in data throughput, and cannot meet the quality requirements of real-time services (such as video calls and online meetings). That is, the resource allocation strategy fails to combine the mobility characteristics of high-speed users for advance perception and predictive optimization, and thus cannot effectively support smooth switching and efficient communication in high-speed mobile scenarios. Summary of the invention

[0004] In order to overcome the above defects of the prior art, there is a solution as follows to solve the problem of unclear allocation of wireless communication resources in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A resource allocation method for LTE wireless communication comprises the following steps:

[0007] Obtain the user's historical location and speed data, combine the time step to predict the user's future location, identify the target cell by calculating the distance between the user and the cell base station, and predict the channel quality based on the path loss and interference from the user to the target cell;

[0008] Evaluate the resource occupancy status of the target cell, dynamically adjust the resource reservation ratio based on user service requirements and channel quality, and allocate resources according to resource allocation priority;

[0009] Build a shared resource pool, coordinate the scheduling of the serving cell and the target cell, monitor the user signal-to-interference ratio in real time to generate an interference heat map, and prioritize the allocation of low-interference resource blocks to high-priority users through interference coordination optimization strategies;

[0010] Collect user communication quality and cell resource status in real time, generate performance snapshots, analyze feedback data to adjust user priority and resource allocation strategies, optimize cell resource distribution, calculate cell load balancing indicators and trigger user migration strategies.

[0011] In a preferred embodiment, the user's historical location and speed data are obtained, the user's future location is predicted in combination with the time step, and the target cell is identified by calculating the distance between the user and the cell base station. The specific steps are as follows:

[0012] Predict user trajectory and obtain the user's current location and historical movement information. The user's current location includes the user's current coordinates and speed vector, and the historical movement information includes the historical location.

[0013] Based on the user's current location and velocity vector, combined with the time step, predict the user's geographic location at the next moment;

[0014] Based on the predicted geographic location, the distance between the user and all cell base stations in the coverage area is calculated, and the cell with the shortest distance is selected as the target cell.

[0015] In a preferred embodiment, the channel quality is predicted based on the path loss and interference from the user to the target cell, and the specific steps include:

[0016] Obtain the distance between the user and the target cell and the user's current channel quality index to predict the channel status;

[0017] Predict the channel quality prediction value at the next time t+1 based on path loss and interference: Among them, CQI t is the channel quality index at the current time t, is the distance between the user and the target cell i, β is the path loss factor, which controls the degree of channel attenuation with distance; γ is the gain factor, which simulates the channel's recovery ability at short distances; ∈ t is the fast fading disturbance term and obeys the normal distribution.

[0018] In a preferred implementation, the resource occupancy status of the target cell is evaluated, the resource reservation ratio is dynamically adjusted in combination with the user service type and the channel quality, and resources are allocated according to the resource allocation priority. The specific steps are as follows:

[0019] The resource occupancy rate of the cell is obtained in real time through the cell scheduler. The resource occupancy rate represents the ratio of the number of allocated resource blocks to the total number of resource blocks. The current resource occupancy rate of the cell is calculated and used as the resource status of the target cell.

[0020] According to the user's service type and channel quality prediction value CQI t+1 Calculate user priority weight ω u , the user priority weight calculation formula is: Among them, CQI max is the maximum value of channel quality, and α is the nonlinear weight adjustment factor of channel quality, which is used to amplify or compress the impact of channel quality on priority;

[0021] Business types include real-time business and non-real-time business. Real-time business includes real-time video, voice communication, and online games. Non-real-time business includes email, web browsing, and video on demand.

[0022] The resource reservation ratio is dynamically adjusted according to the resource status of the target cell and the user priority weight.

[0023] In a preferred embodiment, dynamically adjusting the resource reservation ratio according to the resource status of the target cell and the user priority weight includes the following steps:

[0024] When the resource status of the target cell is less than the low load threshold, resources are reserved for high-speed users first;

[0025] When the resource status of the target cell is less than the high load threshold, the resources are released to make room for high-speed users. The release strategy is based on the following rules:

[0026] Prioritize the release of resources for non-real-time service users, and the number of released resource blocks meets the minimum requirements of high-speed users;

[0027] Calculate user resource requirements based on user service type and channel quality. User resource requirements are obtained based on service data rate and data rate supported by a single resource block. The data rate of a single resource block is determined by channel quality. The modulation and coding scheme mapping table corresponding to each channel quality indicator in the LTE network provides rate information.

[0028] When a high-speed user accesses the target cell, dynamic resource allocation is performed based on the resource reservation results and user needs. The resource allocation priority Φ is calculated based on the user priority weight and channel quality prediction value. u :Φ u =ω u ·f(CQI t+1 ), where f(CQI t+1 ) is the mapping function between channel quality and resource allocation priority, ω u is the user priority weight;

[0029] Allocate user resource requirements based on resource allocation priorities.

[0030] In a preferred embodiment, a shared resource pool is constructed, the serving cell and the target cell are coordinated and scheduled, the user signal-to-interference ratio is monitored in real time to generate an interference heat map, and low-interference resource blocks are preferentially allocated to high-priority users through an interference coordination optimization strategy, including the following steps:

[0031] A shared pool is constructed based on the available resources of the current cell and the available resources of the target cell, and the capacity of the shared resource pool is calculated;

[0032] Monitor the user's signal-to-interference ratio in real time, evaluate the interference level, and generate an interference heat map based on the real-time signal-to-interference ratio. The interference heat map shows the interference distribution of the target cell and neighboring cells on different resource blocks.

[0033] Based on the dynamically perceived interference heat map, the resource allocation and power control strategy of the interference area are adjusted in real time, interference-sensitive users are allocated preferentially, and an interference resource block allocation plan is generated.

[0034] In a preferred embodiment, user communication quality and cell resource status are collected in real time, performance snapshots are generated, feedback is analyzed to adjust user priority and resource allocation strategies, cell resource distribution is optimized, cell load balancing indicators are calculated, and user migration strategies are triggered, including:

[0035] Real-time performance monitoring of users’ service quality indicators and network resource status;

[0036] Performance monitoring includes monitoring user-side data and network-side data. User-side data includes regular feedback from user equipment on its communication status, including throughput, latency, and channel quality.

[0037] Network-side data includes cell-level resource status information collected in real time by base stations and schedulers, including resource utilization, load change trends, and interference levels;

[0038] Introducing a dynamic data aggregation mechanism, the user-side data and the network-side data are integrated through edge computing nodes to generate an overall performance snapshot across users and cells. The performance snapshot includes user throughput distribution, cell load status, and interference heat map.

[0039] Analyze user feedback and network status, and perform user-level optimization and cell-level optimization based on the results of feedback data analysis;

[0040] The user priority adjustment amount is calculated according to the user's service requirements and actual performance, the cell load balancing index is calculated according to the resource occupancy rate and user distribution of each cell, and whether the user needs to be migrated is determined based on the user priority adjustment amount and the cell load balancing index.

[0041] Technical effects and advantages of a resource allocation method for LTE wireless communication of the present invention:

[0042] The present invention dynamically obtains the user's historical location and speed data, combines the time step to predict the user's future location, accurately identifies the target cell based on the distance minimization principle, and further predicts the channel quality based on path loss and interference, providing accurate basic data for resource allocation, thereby improving the foresight and efficiency of resource scheduling. By evaluating the resource occupancy status of the target cell, the resource reservation ratio is dynamically adjusted in combination with the user's business needs and channel quality, and resources are flexibly allocated according to the resource allocation priority, effectively ensuring the communication quality and service continuity of high-speed users during the cell switching process;

[0043] By building a shared resource pool, coordinated scheduling of service cells and target cells is achieved, interference heat maps are generated in real time, interference intensity and distribution are accurately identified, and low-interference resource blocks are allocated to high-priority users in priority through interference coordination strategies, which greatly reduces the interference risk at the cell edge and improves resource utilization efficiency in heterogeneous networks. By collecting user communication quality and cell resource status in real time, performance snapshots are generated, user priority and resource allocation strategies are dynamically adjusted, and cell resource distribution is optimized. At the same time, load balancing indicators are calculated and user migration strategies are triggered, achieving global optimization and dynamic adjustment of resource allocation, significantly improving the overall performance of the network and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a flow chart of a resource allocation method for LTE wireless communication according to the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In order to achieve the above objectives, Figure 1 A structural schematic diagram of a resource allocation method for LTE wireless communication of the present invention is provided, which specifically includes the following steps:

[0047] Obtain the user's historical location and speed data, combine the time step to predict the user's future location, identify the target cell by calculating the distance between the user and the cell base station, and predict the channel quality based on the path loss and interference from the user to the target cell;

[0048] Evaluate the resource occupancy status of the target cell, dynamically adjust the resource reservation ratio based on user service requirements and channel quality, and allocate resources according to resource allocation priority;

[0049] Build a shared resource pool, coordinate the scheduling of the serving cell and the target cell, monitor the user signal-to-interference ratio in real time to generate an interference heat map, and prioritize the allocation of low-interference resource blocks to high-priority users through interference coordination optimization strategies;

[0050] Collect user communication quality and cell resource status in real time, generate performance snapshots, analyze feedback data to adjust user priority and resource allocation strategies, optimize cell resource distribution, calculate cell load balancing indicators and trigger user migration strategies.

[0051] Step 1: Jointly predict user trajectory and channel status. In LTE networks, the trajectory and channel status of high-speed mobile users change dynamically. By jointly predicting the user's mobile trajectory and channel status, the target cell to which the user is about to switch can be identified in advance, and its channel quality can be estimated to provide basic data for resource allocation. The specific steps are as follows:

[0052] User trajectory prediction is used to determine the user's future location and movement direction. The user's geographical location at the next moment is predicted by combining the historical location sequence and speed sequence with the time step. The user's historical location data and speed data are obtained by the terminal device (such as a smartphone or vehicle-mounted device) through GPS or high-precision RTK (real-time kinematic positioning technology) and periodically fed back to the network.

[0053] The user's current location and historical movement information are regularly reported by the terminal device through GPS or RTK. These data include the user's current coordinates and velocity vector as well as historical location. The user's current location includes the user's current coordinates and velocity vector, and historical movement information includes historical location. Based on the location and velocity, based on the user's current location and velocity, combined with the time step, the user's geographic location at the next moment is predicted. In order to improve the adaptability to non-uniform speed scenarios (such as lane changes and turns), position trend correction can also be introduced;

[0054] The historical location is the user's current location coordinates at time t (x t ,y t ) and the position coordinates (x t-1 ,y t-1), the location coordinates are reported periodically by the terminal device, and the accuracy depends on the positioning technology (such as GPS provides 10-meter accuracy and RTK provides centimeter accuracy);

[0055] Assume that the user is on a highway, and the base stations of the current cell are located on both sides of the road. The location information reported by the terminal device is combined with the time interval to estimate the location that the user will arrive at in the future. By analyzing the location trend, it is possible to predict whether the user will accelerate or turn in a non-straight path, and adjust the predicted geographic coordinates.

[0056] The user's velocity vector (v x , v y ) is calculated by position change: Where Δt is the time interval between position reports;

[0057] Based on the user’s current position and velocity vector, predict the next moment’s position (x t+1 ,y t+1 ), the calculation formula for the specific position is: x t+1 =x t +v x Δt,y t+1 =y t +v y Δt;

[0058] The above predicted geographic location is based on the assumption that the user maintains the current speed and direction for a short period of time, which is suitable for uniform linear movement scenarios. When the trajectory changes nonlinearly (such as turning or changing lanes), the momentum term (x t -x t-1 ) to correct the predicted position to enhance adaptability to complex movement patterns. The user’s predicted position at the next moment (x t+1 ,y t+1 ) and speed (v x , v y ), as the input for target cell identification.

[0059] In high-speed mobile scenarios, the rapid change of user (high-speed user) location leads to frequent cell switching. To ensure channel quality and service continuity during the switching process, it is necessary to identify the user's target cell in advance. The identification of the target cell depends on the distance between the user and the adjacent cell base station and the coverage characteristics of the base station. High-speed users refer to user devices (such as smartphones, vehicle-mounted devices, etc.) that move at high speeds in wireless communication networks. These users usually cross the coverage area of ​​the base station at a relatively fast speed. Typical scenarios include users taking high-speed trains, cars, high-speed subways or other fast-moving vehicles;

[0060] Based on the predicted geographic location, the distance between the user and all cell base stations in the coverage area is calculated, and the cell with the shortest distance is selected as the target cell. The base station location is provided by the topological structure configured in the LTE network, and the location accuracy is guaranteed by the planning system;

[0061] The user's future location is determined through trajectory prediction. Combined with the static location data of the cell base station, the distance between the user and each base station is calculated, and the base station with the shortest distance is selected as the target cell. Assuming that the user is about to drive out of the current cell coverage, the target cell that the user is about to access needs to be determined based on the distance minimization principle.

[0062] The base station location information is composed of the coordinates (x i ,y i ) is a static parameter determined during network deployment, and the user's predicted position (x t+1 ,y t+1 ) is obtained through trajectory prediction;

[0063] The distance d between the user and cell i i Calculation formula: Among them, C is the set of all cells in the current area. By comparing d i The size of the cell i is selected * : Target cell number i * Indicates the target cell to which the user is about to switch. The user's arrival time is based on the trajectory prediction time;

[0064] Get the distance from the user to the target cell Channel status prediction based on the user’s current channel quality indicator (periodically reported through the physical layer feedback of the terminal device);

[0065] Channel state prediction is used to estimate the channel quality index between the user and the target cell, providing a basis for resource allocation priority. The channel state is affected by path loss, interference and rapid fading, and needs to be predicted by combining multiple factors, that is, combining path loss and random disturbance to predict the channel quality at the next time t+1: Among them, CQI is the channel quality index at the current time t, is the distance between the user and the target cell i, β is the path loss factor, is the degree of attenuation of the control channel with distance, the gain factor, γ is the recovery ability of the analog channel at a short distance, ∈ t is a fast fading disturbance term, which obeys normal distribution. If the distance between the user and the target cell is close, the path loss is small and the channel quality is good; otherwise, the quality decreases.

[0066] In wireless communications, a cell refers to the wireless coverage area of ​​a base station. A cell is a design structure of a cellular network. The entire network is composed of many cells like a honeycomb. Each cell has a base station responsible for providing wireless communication services. Based on the coverage range and deployment scenario, cells are usually divided into the following types:

[0067] Macro cell: a larger area covered by a high-power base station (such as a tower base station), usually with a diameter of several kilometers to tens of kilometers; micro cell: a medium-range area covered by a medium-power base station, usually with a coverage diameter of several hundred meters; pico cell: a small area covered by a low-power base station, usually with a diameter of tens to hundreds of meters; in a cell, the boundary area of ​​the signal coverage range is called the cell edge.

[0068] This step realizes the identification of the user's target cell and the advance estimation of the channel quality through the combination of trajectory prediction and channel state prediction, and provides basic data for resource reservation and scheduling according to the prediction results.

[0069] Step 2: Dynamic resource reservation and allocation. After completing the joint prediction of user trajectory and channel status, the resource allocation strategy of the target cell is dynamically adjusted based on the target cell number, user arrival time, distance between the user and the target cell, and predicted channel quality. Resources are reserved for high-speed mobile users first to ensure communication quality and service continuity during switching. The specific steps are as follows:

[0070] Conduct cell resource status evaluation, that is, evaluate the current resource usage status of the target cell. The cell resources are mainly composed of resource blocks (RBs). Each subframe in the LTE network consists of multiple resource blocks, which are responsible for transmitting data and signaling.

[0071] The resource occupancy rate of cell i is obtained in real time by the cell scheduler, which represents the ratio of the number of allocated RBs to the total number of RBs. The current resource occupancy rate ρ of cell i is calculated. i (t) and serves as the resource status of the target cell;

[0072] According to the user's service type (such as real-time video, voice communication, online games, etc. in real-time services, email, web browsing, video on demand, etc. in non-real-time services) and the channel quality prediction value CQI t+1 Calculate user priority weight ω u , the weight of real-time services is greater than that of non-real-time services. The higher the channel quality, the higher the priority. The formula for calculating user priority weight is: Among them, CQI max is the maximum value of channel quality, α is the nonlinear weight adjustment factor of channel quality, which is used to amplify or compress the impact of channel quality on priority. If α>1, the priority of users with higher channel quality is significantly improved. If α<1, users with poor channel quality can also be allocated certain resources.

[0073] According to the resource status of the target cell ρ i (t) and user priority weight ω u , dynamically adjust the resource reservation ratio to ensure that the resource needs of high-speed mobile users are met first. The specific resource reservation rules are as follows:

[0074] Low load scenario: When the target cell load is low (ρ i (t)<ρ low , such as 50%), ρ low For low load thresholds, more resources (e.g., 30%-40% of total RBs) are reserved for high-speed users first;

[0075] High load scenario: When the target cell load is high (ρ i (t)<ρ high , such as 80%), ρ high High load threshold, by releasing some low-priority user resources to make room for high-speed users. The release strategy is based on the following rules: 1. Prioritize the release of resources for non-real-time service users; 2. The number of released RBs meets the minimum demand of high-speed users;

[0076] The resource requirements are calculated based on the user service type and channel quality. Real-time services (such as video calls) require more resource blocks, while low-priority services (such as file downloads) require fewer resource blocks. The user resource requirement R u

[0077] It is obtained based on the service data rate and the data rate supported by a single resource block. The data rate of a single resource block is determined by the channel quality. The modulation and coding scheme (MCS) mapping table corresponding to each channel quality indicator in the LTE network provides rate information;

[0078] When a high-speed user accesses the target cell, dynamic resource allocation is performed based on the resource reservation results and user needs to ensure communication continuity during the switching process. The resource allocation priority Φ is calculated based on the user priority weight and channel quality prediction value. u :Φ u =ω u ·f(CQI t+1 ), where f(CQI t+1 ) is a mapping function between channel quality and resource allocation priority (non-linear mapping improves the priority of high-quality channel users);

[0079] It should be noted that f(CQI t+1 ) The mapping function should follow the CQI t+1The mapping function increases with the increase of the channel quality, ensuring that users with good channel quality have higher priority. According to the scenario requirements, the mapping function can be linear or nonlinear. The nonlinear function can more flexibly adjust the priority difference between high-quality users and low-quality users, and the priority range is usually limited to [0, 1] or other standardized intervals, which is convenient for combining with other weights; when the channel quality prediction value is high, the priority change is smoothed to avoid the priority being too concentrated in a few high-quality users. In the scenario, the mapping function can be expressed as: f(CQI t+1 )=log(1+CQI t+1 ) / log(1+CQI max ), which is used when the initial growth is fast and the channel quality prediction value is high. The growth rate is reduced, but it is still increasing. For example, when there are a large number of medium channel quality users in the network, basic services for low-quality users must also be guaranteed.

[0080] Prioritize high Φ u The resource demand of the value user is determined by the number of allocated resource blocks, and the number of allocated resource blocks is limited by the remaining resources of the target cell and the total amount of reserved resources;

[0081] After resource allocation is completed, in order to ensure that the service quality meets the requirements, it is necessary to feedback and monitor the allocation results, and dynamically adjust resource allocation through optimization strategies. Through dynamic resource reservation and allocation, necessary resources can be reserved for users in advance based on the resource status of the target cell and the priority requirements of high-speed users.

[0082] Step 3: Perform cross-cell coordinated scheduling and interference management. In LTE networks, users at the cell edge are usually affected by interference from neighboring cells. Especially during cross-cell handover, the signal quality may drop sharply. Therefore, cross-cell coordinated scheduling and interference management strategies are used to ensure that users can obtain stable communication quality before and after handover. The specific steps are as follows:

[0083] When a user approaches a cell boundary, the current serving cell and the target cell need to collaborate to provide resource support for the user to reduce the risk of communication interruption due to signal fading or resource competition during the handover process. Coordinated scheduling requires not only sharing of user information between the two cells, but also reasonable allocation of resources to ensure service continuity for the user.

[0084] Cross-cell collaborative scheduling manages resource allocation in the handover area by building a shared resource pool. The resources in the shared resource pool are jointly allocated by the current cell and the target cell. The specific allocation ratio is dynamically adjusted according to the real-time load and user priority.

[0085] The current cell and the target cell reserve a portion of resources to enter the shared pool. The reserved ratio is determined by the real-time resource occupancy rate of each cell. The capacity of the shared resource pool is calculated as follows: R shared =α·R current+β·R target , where R shared is the amount of available resources in the current cell, R target is the number of available resources in the target cell, α and β are weight factors, reflecting the resource allocation intention of each cell; thus, the number of resource blocks allocated to high-speed users in the shared resource pool is obtained;

[0086] For example, a user switches from cell A to cell B. Currently, the resource occupancy rate of cell A is 70%, and the resource occupancy rate of cell B is 50%. Through coordinated scheduling, the two cells add 10% and 15% of their own available resources to the shared resource pool respectively to provide resource support for the user who is about to switch. In addition, in order to reduce the delay in resource scheduling, the scheduling control between cells is centrally coordinated by the main base station, and the shared pool status is updated in real time.

[0087] In the cell edge area, high-speed users are usually interfered by multiple neighboring cells. The main sources of interference include neighboring cells using resource blocks of the same frequency band or too high transmission power. Dynamic interference perception evaluates the interference level by real-time monitoring of the user's signal-to-interference ratio (SIR). Through the physical layer feedback of the user equipment, the signal-to-interference ratio is obtained in real time and an interference heat map is generated. The interference heat map shows the interference distribution of the target cell and the neighboring cells on different resource blocks, guiding the scheduler to allocate resource blocks with low interference. The signal-to-interference ratio expression fed back by the user equipment is: Among them, P signal is the serving cell signal power received by the user, P interference is the interference power of the adjacent cell received by the user;

[0088] Based on the dynamically perceived interference heat map, the resource allocation and power control strategy of the interference area are adjusted in real time, interference-sensitive users are preferentially allocated to resource blocks with lower interference, and a low-interference resource block allocation plan is generated. The interference heat map is dynamically generated based on the signal-to-interference ratio fed back by the user equipment and the spectrum usage of the network.

[0089] For example, a user is at the cell edge, and the overlapping spectrum resource blocks of two adjacent cells cause severe interference. The user device feedback shows that the current SIR value is -3dB, indicating poor signal quality. Through interference perception, the system finds that there is high interference in the frequency band 1-10RB, while the interference in the frequency band 11-20RB is low. The scheduler then adjusts the allocation, switches the user's resource demand to the low-interference frequency band, and notifies the adjacent cells to reduce the transmission power of the interference source.

[0090] When a high-speed mobile user approaches the edge of a cell, the signal quality may drop significantly due to path loss and interference. Through joint beamforming technology, the current cell and the target cell cooperate to transmit data for the user, forming joint coverage and improving signal strength. Joint beamforming concentrates transmission energy in the direction of the user through the collaborative work of multiple base stations. The antenna arrays of the current cell and the target cell dynamically adjust the beam direction and power allocation according to the user's location and direction.

[0091] User location (x u ,y u ) is obtained through trajectory prediction, combined with the location of the cell base station, to calculate the relative position of the user and each base station. The channel state information (CSI) is reported by the user equipment, describing the amplitude and phase characteristics of the channel, and is used to adjust the beam direction;

[0092] Based on the joint beam transmission of the current cell and the target cell, dual-source signals are provided to users, reducing the impact of interference and path loss on communication quality, and combining beam direction and power allocation schemes;

[0093] Furthermore, in heterogeneous network scenarios (coexistence of macro cells and small cells), interference coordination is particularly important, especially when the coverage areas of macro cells and small cells overlap, unoptimized spectrum usage will cause serious co-channel interference. Through enhanced interference coordination technology (eICIC), macro cells dynamically adjust transmission time slots in high-interference areas to allow small cells to use specific resource blocks first. This strategy can effectively reduce the interference of high-speed users at the edge of the cell. Therefore, the resource allocation priority is dynamically adjusted based on the conflict matrix to ensure that high-priority users get low-interference resources first, and resource conflicts and interference are avoided according to the adjusted resource allocation plan.

[0094] Through cross-cell collaborative scheduling and interference management, continuous optimization of resources and signals is achieved before and after user switching. The shared resource pool provides buffer space for users to access the target cell. Dynamic interference perception and joint beamforming improve signal quality, while enhanced interference coordination further reduces the interference risk in heterogeneous networks.

[0095] Step 4: Real-time optimization and feedback adjustment. After completing cross-cell coordinated scheduling and interference management, in the actual network, due to user trajectory changes, channel dynamic fluctuations and resource competition, the performance may still not be optimal. This step dynamically improves resource allocation efficiency and user experience through real-time performance monitoring, feedback data collection and optimization adjustment. The specific steps are as follows:

[0096] In LTE networks, real-time monitoring is a key step to ensure the effectiveness of resource allocation strategies. By monitoring the user's quality of service (QoS) and network resource status in real time, abnormal situations (such as channel quality degradation and fierce resource competition) can be discovered. Performance monitoring is performed through two types of data sources:

[0097] User-side data, including regular feedback from user equipment (UE) on its communication status, including throughput, latency, channel quality, etc. The terminal uploads real-time measurement values ​​to the base station through the monitoring modules of the physical layer and MAC layer;

[0098] Network-side data, including base stations and schedulers collecting cell-level resource status information in real time, including resource utilization, load change trends, and interference levels;

[0099] A dynamic data aggregation mechanism is introduced to fuse user-side and network-side data through edge computing nodes (EdgeNode) to generate an overall performance snapshot across users and cells for rapid response. The performance snapshot includes user throughput distribution, cell load status, interference heat map, etc.

[0100] By analyzing user feedback and network status, the following issues are diagnosed:

[0101] Compare the actual channel quality with the predicted value to determine whether there is a significant deviation; detect whether the usage density of certain resource blocks is too high, so as to infer whether the resource allocation is reasonable; identify users whose throughput or latency is lower than the target value, and give priority to adjusting the resource allocation strategy for these users;

[0102] Based on the results of feedback analysis, resource allocation and scheduling strategies need to be adjusted dynamically to solve existing problems and optimize overall performance. The optimization strategy includes two parts:

[0103] User-level optimization: For users whose channel quality does not meet the standard, adjust the user priority weight or increase resource allocation, and reallocate low-interference resource blocks according to channel quality deviation;

[0104] Cell-level optimization: For high-load cells, the load balancing mechanism is triggered to migrate some users to adjacent cells and dynamically adjust the transmission power of the interference source cell.

[0105] The user priority adjustment amount is calculated according to the user service demand and actual performance, and the cell load balancing index is calculated according to the resource occupancy rate and user distribution of each cell to guide user migration, thereby obtaining the optimized user resource allocation plan and cell-level load balancing adjustment plan. The user priority adjustment amount W u Calculation formula: Among them, TP target is the target throughput of the user, TP uis determined by the service quality requirement of the service type, is the actual throughput of the user, reported by the user equipment in real time, is the adjustment coefficient, used to control the sensitivity of the adjustment, and has a value range of [0, 1];

[0106] The service quality requirements of the business type, for example, the target throughput for real-time video is 4 Mbps and the target throughput for file download is 1 Mbps;

[0107] According to the resource occupancy rate and user distribution of each cell, the load balancing index of the cell is calculated to evaluate whether the cell is overloaded or unloaded, providing a basis for user migration decision-making. It represents the balance between the resource utilization and load distribution of cell i. The goal is to adjust the load balancing index of each cell to a value close to the global balance value B. avg , the calculation formula of cell load balancing index is: Among them, ρ i (t) is the real-time resource occupancy rate of cell i, which indicates the proportion of allocated resource blocks to the total resource blocks, N i is the number of users in the current cell i, counted through the core user information table; N max The maximum user capacity of a single cell in the current network, usually set by the operator;

[0108] Adjust the amount ΔW based on user priority u and cell load balancing index B i , determine whether the user needs to migrate and the specific target cell to migrate to. If the load balancing index of the cell where the user is located is significantly higher than the global average, the user migration strategy is triggered and the user is migrated.

[0109] After implementing the migration strategy, it is necessary to verify whether the migration adjustment effect meets expectations. If not, further corrections can be made.

[0110] It should be noted that the threshold information in this embodiment is pre-set by professionals and will not be explained in detail here. Some parameter English letters in the embodiments have the same situation, but different meanings are explained when used, which will not be explained one by one here.

[0111] The present invention dynamically obtains the user's historical location and speed data, combines the time step to predict the user's future location, accurately identifies the target cell based on the distance minimization principle, and further predicts the channel quality based on path loss and interference, providing accurate basic data for resource allocation, thereby improving the foresight and efficiency of resource scheduling. By evaluating the resource occupancy status of the target cell, the resource reservation ratio is dynamically adjusted in combination with the user's business needs and channel quality, and resources are flexibly allocated according to the resource allocation priority, effectively ensuring the communication quality and service continuity of high-speed users during the cell switching process;

[0112] By building a shared resource pool, coordinated scheduling of service cells and target cells is achieved, interference heat maps are generated in real time, interference intensity and distribution are accurately identified, and low-interference resource blocks are allocated to high-priority users in priority through interference coordination strategies, which greatly reduces the interference risk at the cell edge and improves resource utilization efficiency in heterogeneous networks. By collecting user communication quality and cell resource status in real time, performance snapshots are generated, user priority and resource allocation strategies are dynamically adjusted, and cell resource distribution is optimized. At the same time, load balancing indicators are calculated and user migration strategies are triggered, achieving global optimization and dynamic adjustment of resource allocation, significantly improving the overall performance of the network and user experience.

[0113] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0114] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0115] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0118] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A resource allocation method for LTE wireless communication, characterized in that: The steps include: Obtain the user's historical location and speed data, combine the time step to predict the user's future location, identify the target cell by calculating the distance between the user and the cell base station, and predict the channel quality based on the path loss and interference from the user to the target cell; Evaluate the resource occupancy status of the target cell, dynamically adjust the resource reservation ratio based on user service requirements and channel quality, and allocate resources according to resource allocation priority; Build a shared resource pool, coordinate the scheduling of the serving cell and the target cell, monitor the user signal-to-interference ratio in real time to generate an interference heat map, and prioritize the allocation of low-interference resource blocks to high-priority users through interference coordination optimization strategies; Collect user communication quality and cell resource status in real time, generate performance snapshots, analyze feedback data to adjust user priority and resource allocation strategies, optimize cell resource distribution, calculate cell load balancing indicators and trigger user migration strategies.

2. A resource allocation method for LTE wireless communication according to claim 1, characterized in that: Obtain the user's historical location and speed data, combine the time step to predict the user's future location, and identify the target cell by calculating the distance between the user and the cell base station. The specific steps are as follows: Predict user trajectory and obtain the user's current location and historical movement information. The user's current location includes the user's current coordinates and speed vector, and the historical movement information includes the historical location. Based on the user's current location and velocity vector, combined with the time step, predict the user's geographic location at the next moment; Based on the predicted geographic location, the distance between the user and all cell base stations in the coverage area is calculated, and the cell with the shortest distance is selected as the target cell.

3. A resource allocation method for LTE wireless communication according to claim 2, characterized in that: The channel quality is predicted based on the path loss and interference from the user to the target cell. The specific steps include: Obtain the distance between the user and the target cell and the user's current channel quality index to predict the channel status; The channel quality prediction value at the next time t+1 is predicted based on the path loss and interference: Among them, CQI t is the channel quality index at the current time t, is the distance between the user and the target cell i, β is the path loss factor, which controls the degree of channel attenuation with distance; γ is the gain factor, which simulates the channel's recovery ability at short distances; ∈ t is the fast fading disturbance term and obeys the normal distribution.

4. The resource allocation method for LTE wireless communication according to claim 3, characterized in that: Evaluate the resource occupancy status of the target cell, dynamically adjust the resource reservation ratio based on the user service type and channel quality, and allocate resources according to resource allocation priority. The specific steps are as follows: The resource occupancy rate of the cell is obtained in real time through the cell scheduler. The resource occupancy rate represents the ratio of the number of allocated resource blocks to the total number of resource blocks. The current resource occupancy rate of the cell is calculated and used as the resource status of the target cell. According to the user's service type and channel quality prediction value CQI t+1 Calculate user priority weight ω u , the user priority weight calculation formula is: Among them, CQI max is the maximum value of channel quality, and α is the nonlinear weight adjustment factor of channel quality, which is used to amplify or compress the impact of channel quality on priority; Business types include real-time business and non-real-time business. Real-time business includes real-time video, voice communication, and online games. Non-real-time business includes email, web browsing, and video on demand. The resource reservation ratio is dynamically adjusted according to the resource status of the target cell and the user priority weight.

5. The resource allocation method for LTE wireless communication according to claim 4, characterized in that: Dynamically adjust the resource reservation ratio according to the resource status of the target cell and the user priority weight, including the following steps: When the resource status of the target cell is less than the low load threshold, resources are reserved for high-speed users first; When the resource status of the target cell is less than the high load threshold, the resources are released to make room for high-speed users. The release strategy is based on the following rules: Prioritize the release of resources for non-real-time service users, and the number of released resource blocks meets the minimum requirements of high-speed users; Calculate user resource requirements based on user service type and channel quality. User resource requirements are obtained based on service data rate and data rate supported by a single resource block. The data rate of a single resource block is determined by channel quality. The modulation and coding scheme mapping table corresponding to each channel quality indicator in the LTE network provides rate information. When a high-speed user accesses the target cell, dynamic resource allocation is performed based on the resource reservation results and user needs. The resource allocation priority Φ is calculated based on the user priority weight and channel quality prediction value. u :Φ u =ω u ·f(CQI t+1 ), where f(CQI t+1 ) is the mapping function between channel quality and resource allocation priority, ω u is the user priority weight; Allocate user resource requirements based on resource allocation priorities.

6. A resource allocation method for LTE wireless communication according to claim 5, characterized in that: Build a shared resource pool, coordinate the scheduling of the serving cell and the target cell, monitor the user signal-to-interference ratio in real time to generate an interference heat map, and allocate low-interference resource blocks to high-priority users through interference coordination optimization strategies, including the following steps: A shared pool is constructed based on the available resources of the current cell and the available resources of the target cell, and the capacity of the shared resource pool is calculated; Monitor the user's signal-to-interference ratio in real time, evaluate the interference level, and generate an interference heat map based on the real-time signal-to-interference ratio. The interference heat map shows the interference distribution of the target cell and neighboring cells on different resource blocks. Based on the dynamically perceived interference heat map, the resource allocation and power control strategy of the interference area are adjusted in real time, interference-sensitive users are allocated preferentially, and an interference resource block allocation plan is generated.

7. A resource allocation method for LTE wireless communication according to claim 6, characterized in that: Collect user communication quality and cell resource status in real time, generate performance snapshots, analyze feedback to adjust user priority and resource allocation strategies, optimize cell resource distribution, calculate cell load balancing indicators and trigger user migration strategies, including: Real-time performance monitoring of users’ service quality indicators and network resource status; Performance monitoring includes monitoring user-side data and network-side data. User-side data includes regular feedback from user equipment on its communication status, including throughput, latency, and channel quality. Network-side data includes cell-level resource status information collected in real time by base stations and schedulers, including resource utilization, load change trends, and interference levels; Introducing a dynamic data aggregation mechanism to fuse user-side data and network-side data through edge computing nodes to generate an overall performance snapshot across users and cells. The performance snapshot includes user throughput distribution, cell load status, and interference heat map. Analyze user feedback and network status, and perform user-level optimization and cell-level optimization based on the results of feedback data analysis; The user priority adjustment amount is calculated according to the user's service requirements and actual performance, the cell load balancing index is calculated according to the resource occupancy rate and user distribution of each cell, and whether the user needs to be migrated is determined based on the user priority adjustment amount and the cell load balancing index.

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