A multi-cell coordinated scheduling method, apparatus, device, medium and product

Abstract

This invention discloses a multi-cell collaborative scheduling method, apparatus, device, medium, and product. The method includes: acquiring inter-cell collaborative adaptation information and load statistics data of all cells belonging to the same communication network when a set update cycle is reached; determining the load correlation matrix between cells based on the inter-cell collaborative adaptation information; determining the dynamic load threshold of each cell based on the load correlation matrix and load statistics data; constructing a collaborative sleep optimization model based on the load correlation matrix and dynamic load threshold; and solving the collaborative sleep optimization model to determine the sleep strategy of each cell. By quantifying the load correlation between multiple cells through the load correlation matrix, the sleep threshold is dynamically adjusted in real time according to the resources of neighboring cells to achieve dynamic decision-making. This leads to the construction and solution of the collaborative sleep optimization model, achieving global energy-saving optimization across multiple cells. Based on periodic data updates and model solving, it adapts to the dynamic changes in network load.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a multi-cell collaborative scheduling method, apparatus, equipment, medium, and product. Background Technology

[0002] With the development of mobile communication technology, networks are gradually evolving into heterogeneous networks that combine macro cells and micro cells, and multi-standard co-coverage networks. Multi-cell overlapping coverage has become the mainstream scenario. Traditional localized energy-saving strategies for single cells can no longer adapt to complex network structures, and a global and collaborative energy-saving solution is urgently needed.

[0003] Existing energy-saving solutions typically use a fixed load threshold to determine sleep conditions; or when a base station enters an energy-saving state, energy saving is achieved through a procedural operation of opening the compensation antenna and closing the capacity cell; or small base stations can be put into sleep mode through user association and dynamic thresholds.

[0004] However, fixed load thresholds cannot be dynamically adjusted based on the real-time remaining resources of neighboring cells. Achieving energy savings solely through a streamlined process of "turning on the compensation antenna → turning off the capacity cell" may lead to a decline in coverage quality if the actual load on the compensation antenna exceeds the carrying capacity threshold. Furthermore, the carrier frequency handover process involves multiple user migrations (capacity carrier frequency → basic carrier frequency → compensation carrier frequency), resulting in high operational complexity and a high risk of handover failures or a decline in user experience. User association and dynamic threshold methods are based solely on the load ratio of a single base station and do not consider the load coupling relationships between multiple cells. For example, when a base station goes into sleep mode, its users transferring to an adjacent base station may overload the target base station, actually increasing overall energy consumption. Summary of the Invention

[0005] This invention provides a multi-cell collaborative scheduling method, device, equipment, medium, and product to dynamically adjust the dormancy threshold, realize dynamic decision-making on cell dormancy status, and achieve global energy-saving optimization across multiple cells.

[0006] According to a first aspect of the present invention, a multi-cell cooperative scheduling method is provided, comprising:

[0007] When the set update cycle is reached, obtain the inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network.

[0008] Based on the inter-cell collaborative adaptation information, determine the load correlation matrix between the cells;

[0009] Based on the load correlation matrix and the load statistics, determine the dynamic load threshold for each cell;

[0010] Based on the load correlation matrix and the dynamic load threshold, a collaborative sleep optimization model is constructed.

[0011] The collaborative hibernation optimization model is solved to determine the hibernation strategy for each cell.

[0012] According to a second aspect of the present invention, a multi-cell cooperative scheduling device is provided, comprising:

[0013] The data acquisition module is used to acquire inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network when the set update cycle is reached.

[0014] The matrix determination module is used to determine the load correlation matrix between the cells based on the inter-cell collaborative adaptation information.

[0015] The threshold determination module is used to determine the dynamic load threshold for each cell based on the load correlation matrix and the load statistics.

[0016] The model building module is used to build a collaborative sleep optimization model based on the load correlation matrix and the dynamic load threshold.

[0017] The strategy determination module is used to solve the collaborative hibernation optimization model and determine the hibernation strategy of each cell.

[0018] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor; and

[0020] A memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the multi-cell cooperative scheduling method according to any embodiment of the present invention.

[0022] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the multi-cell cooperative scheduling method according to any embodiment of the present invention.

[0023] According to a fifth aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the multi-cell cooperative scheduling method of any embodiment of the present invention.

[0024] The technical solution of this invention involves acquiring inter-cell collaborative adaptation information and load statistics for all cells belonging to the same communication network when a set update cycle is reached; determining the load correlation matrix between cells based on the inter-cell collaborative adaptation information; determining the dynamic load threshold for each cell based on the load correlation matrix and load statistics; constructing a collaborative sleep optimization model based on the load correlation matrix and dynamic load threshold; and solving the collaborative sleep optimization model to determine the sleep strategy for each cell. By quantifying the load correlation between multiple cells through the load correlation matrix and dynamically adjusting the sleep threshold in real time according to neighboring cell resources, dynamic decision-making is achieved. This leads to the construction and solution of the collaborative sleep optimization model, realizing global energy-saving optimization across multiple cells. Based on periodic data updates and model solving, it adapts to the dynamic changes in network load.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of a multi-cell cooperative scheduling method provided in Embodiment 1 of the present invention;

[0028] Figure 2 This is a flowchart of a multi-cell cooperative scheduling method provided in Embodiment 2 of the present invention;

[0029] Figure 3 This is a schematic diagram of the structure of a multi-cell collaborative scheduling device according to Embodiment 3 of the present invention;

[0030] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] Example 1

[0034] Figure 1 This is a flowchart of a multi-cell cooperative scheduling method provided in Embodiment 1 of the present invention. This embodiment is applicable to cell dormancy control in scenarios with overlapping multi-cell coverage. The method can be executed by a multi-cell cooperative scheduling device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110. When the set update cycle is reached, obtain the inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network.

[0036] In this embodiment, the update cycle can be understood as the cycle used to update the cell sleep policy.

[0037] The update cycle includes a preset basic update cycle or a scenario update cycle corresponding to the communication network. The preset basic update cycle is a general, default update time granularity set for the multi-cell collaborative energy-saving strategy of mobile communication networks. It is a basic update standard applicable to all network scenarios. For example, the preset value of the basic update cycle is 5 minutes. This preset value is set based on the industry characteristic that network load fluctuations within 5 minutes are typically ≤15%. This setting can adapt to network changes caused by user mobility and sudden surges in regular services in a timely manner, while avoiding excessive computational overhead on network resources. It serves as a benchmark reference for setting scenario update cycles and is also the actual execution update cycle in areas where scenario-specific adjustments have not been made. The scenario update cycle corresponding to the communication network can be understood as a differentiated update time granularity tailored to the network area of ​​a specific geographical / business scenario (such as densely populated urban areas, suburbs, and core business districts) divided by the mobile communication network. Different scenarios have different network operation characteristics, corresponding to different scenario update cycles. For example, dense urban areas have frequent user movement and large load fluctuations, corresponding to a 3-minute scenario update cycle; suburban areas have sparse users and stable loads, corresponding to a 10-minute scenario update cycle; core business districts have sudden large traffic transactions, which can trigger real-time scenario update rules. The core purpose is to make the update cycle highly matched with the dynamic network characteristics of the scenario, taking into account both policy adaptability and the economy of computing resources.

[0038] In this embodiment, inter-cell cooperative adaptation information can be understood as core information for measuring the feasibility of inter-cell user migration, comprehensively including the overlap of inter-cell user distribution and signal transmission quality. Load statistics can be understood as real-time load-related data of each cell in the network, including key indicators such as the current cell load, maximum carrying capacity, and the proportion of remaining load capacity in neighboring cells.

[0039] Specifically, after the set update cycle is reached, the processor can collect core data from all cells in the same communication network, including inter-cell coordination and adaptation information and load statistics. This is the basis for all subsequent calculations and modeling. The data must be obtained based on the latest user distribution, signal measurement and load statistics results to ensure the timeliness of the strategy.

[0040] S120. Determine the load correlation matrix between cells based on the inter-cell collaborative adaptation information.

[0041] In this embodiment, the load correlation matrix can be understood as an N×N order quantization matrix, where each element represents the proportion of users in a cell who can be stably transferred to another cell. It is a core tool for quantifying the inter-cell collaboration potential.

[0042] Specifically, the processor can determine the percentage of overlapping users between two cells based on inter-cell coordination and adaptation information, and combine the user signal quality in different cells with the network scenario to calculate the load correlation matrix between each pair of cells, thereby quantifying the inter-cell coordination potential and clarifying the proportion of stable user transfer between each cell, providing accurate numerical basis for subsequent threshold calculation and load allocation.

[0043] S130. Based on the load correlation matrix and load statistics, determine the dynamic load threshold for each cell.

[0044] In this embodiment, the dynamic load threshold can be understood as a sleep trigger determination value calculated independently for each cell, which is jointly determined by the cell's basic threshold, load correlation matrix, and remaining resources of neighboring cells.

[0045] Specifically, the processor can determine the dynamic load threshold for each cell based on the constructed load correlation matrix and the load statistics of neighboring cells of each cell, using a set dynamic load threshold calculation formula.

[0046] S140. Based on the load correlation matrix and dynamic load threshold, construct a collaborative hibernation optimization model.

[0047] In this embodiment, the collaborative hibernation optimization model is a global optimization model constructed by combining load constraints and coverage constraints with the objective of maximizing the number of hibernation cells.

[0048] Specifically, the processor can use the load correlation matrix as the core quantification basis, combined with the candidate cell range selected from the dynamic load thresholds of each cell. The processor can construct a collaborative dormancy optimization model with the objective function of maximizing the number of dormant cells, and construct load constraints and coverage constraints to ensure that the model takes into account both energy efficiency and network operation stability.

[0049] S150. Solve the collaborative hibernation optimization model to determine the hibernation strategy for each cell.

[0050] In this embodiment, the sleep strategy is the final decision result of activation or sleep determined for each cell in the network after solving the optimization model.

[0051] Specifically, the processor can solve the constructed collaborative sleep optimization model by setting an algorithm, verify whether the candidate cells meet the load constraints and coverage constraints in the collaborative sleep optimization model, and finally determine the sleep strategy for each cell in the network to activate or sleep, thus completing the global collaborative energy-saving decision for this update cycle. If the original sleep cell no longer meets the constraints, it will be woken up or replaced in this step to ensure that the network has no overload or coverage hole risks.

[0052] The technical solution of this invention quantifies the load association between multiple cells through a load association matrix, dynamically adjusts the sleep threshold in real time according to the resources of neighboring cells, realizes dynamic decision-making, and then constructs and solves a collaborative sleep optimization model to achieve global energy saving optimization of multiple cells. Based on periodic data updates and model solving, it adapts to the dynamic changes of network load.

[0053] Example 2

[0054] Figure 2 This is a flowchart of a multi-cell cooperative scheduling method provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiment. Figure 2 As shown, the method includes:

[0055] S201. When the set update cycle is reached, obtain the inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network.

[0056] S202. For each cell, based on the inter-cell collaborative adaptation information, determine the number of users overlapping with other cells, as well as the signal quality coefficient of users in other cells.

[0057] In this embodiment, the number of overlapping coverage users refers to the number of users who simultaneously meet the preset access threshold within the coverage areas of one cell and another cell, determined by the reference signal received power. The signal quality coefficient is a value determined based on the signal-to-interference-plus-noise ratio (SIR) of the user in the target cell, reflecting the signal's ability to support communication; a value of 1 is taken if the SIR meets the standard, and 0.3 is taken if it does not.

[0058] Specifically, for each cell in the network, two key quantitative indicators are precisely extracted from the inter-cell cooperative adaptation information. The first is the number of users overlapping coverage between this cell and all other cells in the network, which can be determined by the reference received signal power (RSRP) measured by the user equipment (when a user's RSRP in cell j is greater than or equal to a preset access threshold, it is considered an overlapping user). The second is the signal quality coefficient of the user in these other cells, which can be determined based on the signal-to-interference-plus-noise ratio (SINR) of the user in cell j (if SINR is greater than or equal to a communication quality threshold, the signal quality coefficient S...). i,j =1; otherwise S i,j =0.3, reflecting the signal's ability to support communication. These two types of indicators are the core basic data for constructing the load correlation matrix. Their judgment criteria strictly follow the technical specifications in the disclosure document, and are defined by the reference signal received power and the signal-to-interference-plus-noise ratio, respectively, to ensure the accuracy of the data.

[0059] S203. Determine the weighting coefficients based on the network scenario to which the communication network belongs.

[0060] In this embodiment, a network scenario can be understood as a regional classification and definition of the overall communication network based on its geographical location characteristics, user distribution patterns, service operation characteristics, and network load fluctuation attributes. The weighting coefficient is a coefficient between 0 and 1 set according to the network scenario to which the communication network belongs, used to balance the weights of the overlapping coverage user ratio and signal quality coefficient in the load correlation matrix calculation.

[0061] Specifically, based on the network scenario to which the entire communication network belongs (such as densely populated urban areas, suburbs, or core business districts), corresponding weight coefficients are set according to the scenario characteristics. The value range of the weight coefficient is 0 to 1. For example, if the user distribution in densely populated urban areas fluctuates greatly, the weight coefficient can be set to [0.3, 0.5], giving priority to signal quality stability; if the signal quality in suburbs is unstable, it can be set to [0.6, 0.8], thereby balancing the influence of the two types of core indicators in matrix calculation and making the matrix result more suitable for the network characteristics of the scenario.

[0062] S204. Based on the number of overlapping users, weight coefficients, and the current total number of active users in the cell from the inter-cell collaborative adaptation information, determine the load correlation matrix between the cell and other cells.

[0063] In this embodiment, the current total number of active users refers to the total number of users actually in a communication activity state within a certain cell, which is the basic value for calculating the user transfer ratio between cells.

[0064] Specifically, combining the number of overlapping users, signal quality coefficient, and weight coefficient obtained in the first two steps, and extracting the total number of currently active users in the cell from the inter-cell collaborative adaptation information, the matrix element values ​​between the cell and each other cell are calculated sequentially using a given formula to complete the construction of the matrix row corresponding to a single cell. The above calculation process is repeated for all cells in the network to finally form a complete N×N load correlation matrix, realizing the quantification of the stable transfer ratio of users between all cells, and providing accurate numerical basis for subsequent dynamic load threshold calculation and collaborative sleep optimization.

[0065] The formula for calculating the load correlation matrix is:

[0066]

[0067] Among them, O i,j U represents the number of users whose coverage overlaps with that of cells i and j. i S represents the current total number of active users in community i. i,j α is the signal quality coefficient, and α is the weighting coefficient.

[0068] For example, suppose the network contains 3 cells (macro cell A, micro cell B, and micro cell C), and the relevant parameters are shown in the table below:

[0069] Table 1. Example of relevant parameters for the residential community

[0070]

[0071] Taking α = 0.7, calculate and determine the matrix elements:

[0072] This indicates that 73.75% of users in B can reliably migrate to A.

[0073] This indicates that 50.25% of users in B can reliably migrate to C.

[0074] Similarly, we can conclude that:

[0075]

[0076] The resulting load correlation matrix is:

[0077]

[0078] Matrix M quantifies the inter-cell collaboration potential, providing precise numerical basis for subsequent dynamic threshold calculation and load allocation. For example, it clarifies that the collaboration priority between B and A is higher than that between B and C.

[0079] S205. For each cell, obtain the cell's basic threshold.

[0080] In this embodiment, the basic threshold is a pre-set dormancy determination base value based on the hardware carrying capacity of the cell type (macro cell / micro cell), providing a benchmark for dynamic load threshold calculation.

[0081] S206. Determine the current load and maximum carrying capacity of the neighboring cells of the cell from the load statistics.

[0082] In this embodiment, a neighboring cell can be understood as another cell in the same network that has coverage overlap with the target cell and can accept the transfer of its users. The current load can be understood as the actual load currently carried by the neighboring cell, and the maximum carrying capacity is the maximum load limit that the neighboring cell can carry at the hardware level.

[0083] Specifically, for each cell in the network, the processor can retrieve a pre-set basic threshold based on its type and hardware capacity. The processor can also filter the current load and maximum capacity of all neighboring cells for each cell from the overall network load statistics.

[0084] S207. Determine the remaining load capacity ratio of neighboring cells based on the current load and maximum carrying capacity.

[0085] In this embodiment, the remaining load capacity ratio can be understood as the ratio of the remaining load that the neighboring cell can bear to its maximum bearing capacity, reflecting the degree of resource idleness of the neighboring cell.

[0086] Specifically, the processor can calculate the remaining load capacity ratio according to the formula (remaining load capacity ratio = (maximum carrying capacity - current load) / maximum carrying capacity). The result is between 0 and 1, where 0 represents that the neighboring cell is fully loaded and has no idle resources, and 1 represents that the neighboring cell is unloaded and has completely idle resources. This result directly reflects the resource potential of the neighboring cell to accept the transfer of users from the target cell.

[0087] S208. Determine the dynamic load threshold for each cell based on the basic threshold, the remaining load capacity ratio, and the load correlation matrix.

[0088] Specifically, the processor can combine the basic threshold, the remaining load capacity ratio of all neighboring cells, and the matrix element values ​​of the cell and each neighboring cell in the load correlation matrix to perform comprehensive calculations on various data to obtain the dynamic load threshold corresponding to the cell. The above operation is repeated for all cells in the entire network to complete the independent calculation of the dynamic load threshold of each cell. The final dynamic threshold can accurately match the real-time network environment in which the cell is located, providing a scientific basis for subsequent selection of dormant candidate cells.

[0089] For example, dynamic load threshold The calculation formula is:

[0090]

[0091] in, Based on the threshold, The percentage of remaining load capacity in neighboring cell j. ,in To maximize load capacity, This represents the current load.

[0092] Continuing with the example above, macrocell A has a maximum carrying capacity of 300 and a current load of 100. Therefore, the remaining load capacity ratio is (300-100) / 300≈0.667 (66.7% remaining capacity). Microcell B has a current load of 30 and a basic threshold of 50. Microcell C has a maximum carrying capacity of 150 and a current load of 70. Therefore, the remaining load capacity ratio is (150-70) / 150≈0.533 (53.3% remaining capacity). Using the parameters of microcell B (load correlation matrix M[B][A]=0.7375, M[B][C]=0.5025), we can calculate:

[0093]

[0094] Since the current load (30) of B is lower than the dynamic threshold (88), the hibernation trigger condition is met. The dynamic threshold is adjusted in real time according to the remaining resources and cooperation potential of neighboring cells—the more abundant the resources of neighboring cells and the higher the cooperation potential (the larger M[i][j]), the higher the threshold, and the easier it is for the cell to trigger hibernation, thereby maximizing energy-saving opportunities. This result provides a reasonable range of candidate hibernation cells for subsequent global optimization.

[0095] S209. Based on the dynamic load threshold and load statistics, select the candidate dormant cell range from all cells.

[0096] In this embodiment, the candidate dormant cell range can be understood as a set of cells that meet the basic dormant conditions selected from all cells in the network, and is the decision object for subsequent global dormant optimization.

[0097] Specifically, the processor can compare the real-time load data in the load statistics of each cell with the dynamic load threshold calculated independently by itself, filter out cells whose current load is lower than the dynamic load threshold, form a candidate dormant cell range, and eliminate cells without the basic conditions for dormancy, which greatly reduces the solution range of the subsequent model, reduces the computational complexity, and improves the real-time performance of the project.

[0098] S210. Based on the range of candidate dormant cells, the objective function is to maximize the number of dormant cells.

[0099] In this embodiment, the objective function is a mathematical expression used to define the optimization objective in the cooperative dormancy optimization model.

[0100] Specifically, taking the candidate dormant cells selected in the first step as the decision object, the core objective function of the model is set as maximizing the number of dormant cells. The optimization direction of the model is clearly defined as maximizing energy efficiency under the premise of network stability. The formula is as follows:

[0101]

[0102] Where, x i Indicates the status of the cell, x i =1 indicates that cell i is in sleep mode, x i =0 indicates that cell i is active.

[0103] S211. The total load transferred from the dormant cell to the neighboring cell shall not exceed the remaining load capacity of the neighboring cell as a load constraint.

[0104] In this embodiment, the load constraint can be understood as a model constraint condition set to avoid overload of neighboring cells, requiring that the total load transferred from the dormant cell to the neighboring cell does not exceed the remaining carrying capacity of the neighboring cell.

[0105] Specifically, to prevent user migration from dormant cells from causing overload in neighboring cells, load constraints are set. The core requirement is that the total load from a dormant cell to any neighboring cell must not exceed the remaining load capacity of that neighboring cell. This constraint is calculated based on the remaining capacity of neighboring cells in the load statistics and the user migration ratio in the load correlation matrix, and is a core hard requirement to ensure network load balancing.

[0106] For example, load constraints can be expressed by the formula:

[0107]

[0108] in, This indicates the current load of cell i.

[0109] S212. Construct coverage constraints based on the load correlation matrix and the coverage area of ​​dormant cells.

[0110] In this embodiment, the coverage constraint is a model constraint condition set to prevent coverage holes in the network.

[0111] Specifically, based on the quantified inter-cell collaborative potential in the load correlation matrix and combined with the coverage area characteristics of dormant cells, coverage constraints are constructed. The core requirement is that at least 80% of the coverage area of ​​a dormant cell can be taken over by surrounding activated neighboring cells (the threshold can be dynamically adjusted according to the network scenario, for example, 90% for the core area and 70% for the edge area). The coverage take-over capacity is determined by summing the elements of the load correlation matrix to ensure that there are no obvious coverage gaps in the network after dormancy, thus meeting the coverage gap rate requirements in the energy-saving standards.

[0112] For example, a coverage constraint can be expressed by the formula:

[0113]

[0114] S213. Based on load constraints, coverage constraints, and objective functions, construct a collaborative sleep optimization model.

[0115] Specifically, the processor can use the objective function of maximizing the number of dormant cells as its core, with load constraints and coverage constraints as the two major hard limitations of the model. The overall construction of the collaborative dormancy optimization model is completed based on an integer programming framework. The decision variables of the model are the dormant / active states of the candidate cells. All constraints are quantified through the load correlation matrix and load statistics to ensure the scientific validity and solvability of the model, providing a complete mathematical framework for determining the optimal dormancy strategy.

[0116] S214. Solve the collaborative hibernation optimization model based on the branch and bound method to determine the hibernation strategy of each cell.

[0117] In this embodiment, the branch and bound method can be understood as a classic algorithm for solving integer programming problems. It efficiently finds the optimal solution by decomposing the problem through branching and pruning the invalid solution space through bounding.

[0118] Specifically, the processor can take the objective function, load constraints, coverage constraints, and candidate dormant cell range of the collaborative dormancy optimization model as input to the algorithm, and solve it using the standard branch-and-bound method: first, it enumerates the dormant / active combinations of candidate cells by branching, then verifies whether each combination satisfies the dual constraints, and at the same time, it gradually narrows the solution space by retaining combinations that are closer to the optimal goal and pruning combinations that violate the constraints or have worse objective values. After the algorithm is solved, it obtains the global optimal solution that satisfies all constraints and achieves the goal of "maximizing the number of dormant cells". This solution corresponds to the final state of each cell in the network (0 for active, 1 for dormant). This state result is then transformed into the dormancy strategy of each cell, that is, it explicitly specifies which cells enter the dormant mode and which cells remain in the active mode to take over the transferred load.

[0119] For example, following the example above, B satisfies the dormancy condition, and decision x... B =1 (B hibernation), x A =0, x C =0

[0120] Load constraint: The load of B, 30, is transferred proportionally according to the matrix, i.e., 30 × 0.7375 = 22.125 (to A) and 30 × 0.5025 = 22.125 (to C), neither of which exceeds the remaining capacity of A and C (200 and 80); Coverage constraint: The coverage of B is taken over by A and C, and the following applies: The coverage requirements are met. Conclusion: B can hibernate without the risk of overload or coverage gaps. The branch and bound method is used to solve the integer programming problem, and the optimal solution can be obtained within 1 minute in a network with 10 cells or less, meeting the real-time requirements of the project.

[0121] For example, if the update cycle is 5 minutes, and C's remaining capacity drops to 15 after 5 minutes, the load transferred from B to C (15.075) slightly exceeds its remaining capacity, failing to meet the above load constraint. Therefore, B needs to be woken up to prevent C from overloading. Simultaneously, scenario-based cycle adjustment rules can be adopted: for example, "set the update cycle to 3 minutes in densely populated urban areas (frequent user movement), 10 minutes in suburban areas (small load fluctuations), and real-time updates can be triggered in core business districts (during sudden business disruptions)."

[0122] The technical solution of this invention quantifies the inter-cell load transfer capability by using the overlap user ratio and signal quality coefficient, achieving global load balancing. It adjusts the hibernation threshold in real time based on the remaining capacity of neighboring cells and the load correlation matrix, enabling dynamic decision-making, improved response speed, and timely capture of energy-saving opportunities. Using integer programming as a framework, combined with load and coverage constraints, it achieves global energy-saving optimization across multiple cells. The resulting hibernation strategy is the optimal decision balancing energy efficiency and network stability. It maximizes the number of hibernating cells to achieve energy-saving goals while strictly adhering to load and coverage constraints, avoiding problems such as neighboring cell overload and network coverage gaps. If some cells are removed from the hibernation list due to non-compliance with constraints, they remain active, ensuring that the communication quality of the mobile communication network is not affected, achieving a balance between energy saving and network performance. It achieves deep multi-cell collaboration, maximizing energy efficiency while overcoming the coverage insufficiency and load imbalance problems caused by local decision-making and static thresholds in existing technologies.

[0123] Example 3

[0124] Figure 3 This is a schematic diagram of a multi-cell collaborative scheduling device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0125] The data acquisition module 31 is used to acquire inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network when the set update cycle is reached.

[0126] The matrix determination module 32 is used to determine the load correlation matrix between the cells based on the inter-cell collaborative adaptation information.

[0127] The threshold determination module 33 is used to determine the dynamic load threshold of each cell based on the load correlation matrix and the load statistics.

[0128] The model building module 34 is used to build a collaborative sleep optimization model based on the load correlation matrix and the dynamic load threshold.

[0129] The strategy determination module 35 is used to solve the collaborative hibernation optimization model and determine the hibernation strategy of each cell.

[0130] The technical solution of this invention quantifies the load association between multiple cells through a load association matrix, dynamically adjusts the sleep threshold in real time according to the resources of neighboring cells, realizes dynamic decision-making, and then constructs and solves a collaborative sleep optimization model to achieve global energy saving optimization of multiple cells. Based on periodic data updates and model solving, it adapts to the dynamic changes of network load.

[0131] The update cycle includes: a preset basic update cycle or a scenario update cycle corresponding to the communication network.

[0132] Furthermore, the matrix determination module 32 is specifically used for:

[0133] For each cell, based on the inter-cell cooperative adaptation information, the number of users overlapping coverage between the cell and other cells, and the signal quality coefficient of the user in the other cells are determined;

[0134] The weighting coefficients are determined based on the network scenario to which the communication network belongs;

[0135] Based on the number of overlapping users, the weighting coefficient, and the current total number of active users in the cell in the inter-cell collaborative adaptation information, the load correlation matrix between the cell and the other cells is determined.

[0136] Furthermore, the threshold determination module 33 is specifically used for:

[0137] For each of the cells, obtain the basic threshold for that cell;

[0138] The current load and maximum carrying capacity of the neighboring cells of the cell are determined from the load statistics.

[0139] Based on the current load and the maximum carrying capacity, determine the remaining load capacity ratio of the neighboring cells;

[0140] The dynamic load threshold for each cell is determined based on the basic threshold, the remaining load capacity ratio, and the load correlation matrix.

[0141] Furthermore, the model building module 34 is specifically used for:

[0142] Based on the dynamic load threshold and the load statistics, a range of candidate dormant cells is selected from all the cells;

[0143] Based on the range of candidate dormant cells, the objective function is to maximize the number of dormant cells.

[0144] The total load transferred from a dormant cell to a neighboring cell shall not exceed the remaining load capacity of the neighboring cell as a load constraint.

[0145] Based on the load correlation matrix and the coverage area of ​​the dormant cell, coverage constraints are constructed;

[0146] Based on the load constraint, the coverage constraint, and the objective function, a collaborative sleep optimization model is constructed.

[0147] Furthermore, the strategy determination module 35 is specifically used for:

[0148] The branch-and-bound method is used to solve the cooperative hibernation optimization model to determine the hibernation strategy of each cell.

[0149] The multi-cell collaborative scheduling device provided in the embodiments of the present invention can execute the multi-cell collaborative scheduling method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0150] Example 4

[0151] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0152] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0153] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0154] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as multi-cell cooperative scheduling methods.

[0155] In some embodiments, the multi-cell cooperative scheduling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the multi-cell cooperative scheduling method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the multi-cell cooperative scheduling method by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0160] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0161] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0162] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the multi-cell cooperative scheduling method of any embodiment of the present invention.

[0163] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0164] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0165] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A multi-cell collaborative scheduling method, characterized in that, include: When the set update cycle is reached, obtain the inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network. Based on the inter-cell collaborative adaptation information, determine the load correlation matrix between the cells; Based on the load correlation matrix and the load statistics, determine the dynamic load threshold for each cell; Based on the load correlation matrix and the dynamic load threshold, a collaborative sleep optimization model is constructed. The collaborative hibernation optimization model is solved to determine the hibernation strategy for each cell.

2. The method according to claim 1, characterized in that, The step of determining the load correlation matrix between cells based on the inter-cell collaborative adaptation information includes: For each cell, based on the inter-cell cooperative adaptation information, the number of users overlapping coverage between the cell and other cells, and the signal quality coefficient of the user in the other cells are determined; The weighting coefficients are determined based on the network scenario to which the communication network belongs; Based on the number of overlapping users, the weighting coefficient, and the current total number of active users in the cell in the inter-cell collaborative adaptation information, the load correlation matrix between the cell and the other cells is determined.

3. The method according to claim 1, characterized in that, The step of determining the dynamic load threshold for each cell based on the load correlation matrix and the load statistics includes: For each of the cells, obtain the basic threshold for that cell; The current load and maximum carrying capacity of the neighboring cells of the cell are determined from the load statistics. Based on the current load and the maximum carrying capacity, determine the remaining load capacity ratio of the neighboring cells; The dynamic load threshold for each cell is determined based on the basic threshold, the remaining load capacity ratio, and the load correlation matrix.

4. The method according to claim 1, characterized in that, The step of constructing a collaborative sleep optimization model based on the load correlation matrix and the dynamic load threshold includes: Based on the dynamic load threshold and the load statistics, a range of candidate dormant cells is selected from all the cells; Based on the range of candidate dormant cells, the objective function is to maximize the number of dormant cells. The total load transferred from a dormant cell to a neighboring cell shall not exceed the remaining load capacity of the neighboring cell as a load constraint. Based on the load correlation matrix and the coverage area of ​​the dormant cell, coverage constraints are constructed; Based on the load constraint, the coverage constraint, and the objective function, a collaborative sleep optimization model is constructed.

5. The method according to claim 1, characterized in that, The update cycle includes: a preset basic update cycle or a scenario update cycle corresponding to the communication network.

6. The method according to claim 1, characterized in that, Solving the collaborative sleep optimization model to determine the sleep strategy for each cell includes: The branch-and-bound method is used to solve the cooperative hibernation optimization model to determine the hibernation strategy of each cell.

7. A multi-cell collaborative scheduling device, characterized in that, include: The data acquisition module is used to acquire inter-cell collaborative adaptation information and load statistics of all cells belonging to the same communication network when the set update cycle is reached. The matrix determination module is used to determine the load correlation matrix between the cells based on the inter-cell collaborative adaptation information. The threshold determination module is used to determine the dynamic load threshold for each cell based on the load correlation matrix and the load statistics. The model building module is used to build a collaborative sleep optimization model based on the load correlation matrix and the dynamic load threshold. The strategy determination module is used to solve the collaborative hibernation optimization model and determine the hibernation strategy of each cell.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the multi-cell cooperative scheduling method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multi-cell cooperative scheduling method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the multi-cell cooperative scheduling method according to any one of claims 1-6.

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