Base station joint multi-band resource allocation method and device, medium and product

Abstract

The invention discloses a base station joint multi-band resource allocation method and device, a medium and a product. The method comprises the following steps: acquiring spectrum state data of each band; determining demand data of user communication according to the acquired user communication data; and performing global optimization by adopting a preset optimization model according to the spectrum state data and the demand data, and determining a frequency band resource allocation strategy. The scheme of the invention can improve the spectrum utilization rate, improve the network throughput and ensure the service quality.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a base station joint multi-band resource allocation method, device, medium and product. Background Art

[0002] The existing wireless communication network spectrum resource allocation scheme is usually managed based on a single frequency band, using a static or semi-dynamic allocation strategy. This scheme mainly relies on preset spectrum allocation rules and fixed resource reservation mechanisms, such as pre-allocated frequency bands, time slots or codeword resources for specific service types or user groups. For example, in early wireless communication systems such as 3G, 4G / LTE, spectrum allocation is usually based on static configuration at the cell or sector level.

[0003] Existing technical solutions do not consider the real-time status of spectrum resources and the dynamic changes in future user needs, resulting in relatively low spectrum utilization. In addition, due to the lack of unified coordination and optimization across frequency bands, when the network faces high-density user access, sudden traffic growth, or interference problems in complex electromagnetic environments, the resource allocation method of a single frequency band is often unable to effectively respond, thereby limiting the improvement of the overall network throughput and the guarantee of user service quality. Summary of the invention

[0004] Compared with the prior art, the present invention proposes a base station joint multi-band resource allocation method, device, medium and product, which can improve spectrum utilization and network throughput and ensure service quality.

[0005] An embodiment of the present invention provides a base station joint multi-band resource allocation method, the method comprising:

[0006] Obtain spectrum status data of each frequency band;

[0007] Determining user communication demand data based on the acquired user communication data;

[0008] According to the spectrum status data and the demand data, a preset optimization model is used to perform global optimization to determine a frequency band resource allocation strategy.

[0009] Preferably, the method further comprises:

[0010] Control each base station to execute corresponding resource configuration instructions and adjust frequency band parameters according to the frequency band resource allocation strategy;

[0011] The network performance indicators monitored by each base station are obtained, and the frequency band resource allocation strategy is dynamically adjusted according to the monitored network performance indicators.

[0012] As a preferred solution, spectrum status data of each frequency band is obtained, including:

[0013] The spectrum sensing equipment deployed at each base station collects spectrum usage data of a preset frequency band within the coverage area in real time at a preset sampling period;

[0014] The collected spectrum usage data is sorted and analyzed according to different base stations, and a spectrum resource map is generated in combination with geographic information and time information of each base station as the spectrum status data;

[0015] The spectrum usage data includes a timestamp, a frequency band identifier, a spectrum occupancy status, and a channel quality parameter.

[0016] Preferably, determining the user communication demand data according to the acquired user communication data includes:

[0017] Acquire user communication data of user terminals in real time;

[0018] Inputting the user communication data into a pre-built user demand prediction model for prediction to obtain the demand data;

[0019] The user communication data includes signal strength, service type, current rate, QoS parameters and historical data flow requirements;

[0020] The demand data includes bandwidth requirements, delay sensitivity, and reliability requirements.

[0021] Preferably, according to the spectrum status data and the demand data, a preset optimization model is used to perform global optimization to determine a frequency band resource allocation strategy, including:

[0022] Define resource parameters for frequency band resource allocation for different users;

[0023] Determining a constraint condition according to the spectrum status data and the demand data;

[0024] An objective function of the allocation strategy is constructed according to resource parameters defined by different users, and the objective function is solved according to the constraint conditions to determine the optimal resource parameters for different users and obtain the frequency band allocation strategy.

[0025] Further, an objective function of the allocation strategy is constructed based on resource parameters defined by different users, and the objective function is solved according to the constraint conditions to determine the optimal resource parameters for different users, thereby obtaining the frequency band allocation strategy;

[0026] According to different user-defined resource parameters, mixed integer linear programming is used to build different objective functions based on different optimization goals;

[0027] Determine the global optimal target based on different objective functions and corresponding weight factors;

[0028] According to the constraints, the weight factors of different objective functions are dynamically adjusted in combination with a non-cooperative game theory algorithm to determine the optimal resource parameters for different users and obtain the frequency band allocation strategy;

[0029] Among them, different objective functions include a spectrum objective function taking spectrum efficiency as a target, a capacity objective function taking network capacity as a target, and a quality objective function taking user service quality as a target.

[0030] Preferably, the resource parameters include subcarriers of different frequency bands used by different users in different time slots, transmit powers of different users in different frequency bands, expected data rates of different users using different frequency bands in different time slots, and minimum QoS requirements of different users;

[0031] The constraints include a carrier constraint that each user can only use one subcarrier of one frequency band in any time slot, a total power constraint of the sum of transmission powers of different users in different frequency bands, and a user QoS requirement restriction constraint.

[0032] Preferably, controlling each base station to execute a corresponding resource configuration instruction and adjusting a frequency band parameter according to the frequency band resource allocation strategy includes:

[0033] Converting the frequency band resource allocation strategy into resource configuration instructions for different base stations;

[0034] Transmitting the resource configuration instruction to each base station in the form of a data packet to control each base station to adjust the frequency band parameters;

[0035] The resource configuration instruction includes a subcarrier allocation list, a transmit power configuration corresponding to the user equipment, a time schedule, a priority setting, and a switching strategy.

[0036] Preferably, obtaining network performance indicators monitored by each base station, and dynamically adjusting the frequency band resource allocation strategy according to the monitored network performance indicators, includes:

[0037] Real-time recording of network performance indicators monitored by base stations;

[0038] Compare the network performance indicators before and after the base station adjusts the frequency band parameters;

[0039] According to the comparison result of the network performance indicators, the frequency band resource allocation strategy is revised.

[0040] An embodiment of the present invention further provides a base station joint multi-band resource allocation device, the device comprising:

[0041] A data acquisition module, used to acquire spectrum status data of each frequency band;

[0042] A demand analysis module, used to determine the demand data of user communication according to the acquired user communication data;

[0043] The strategy generation module is used to perform global optimization using a preset optimization model according to the spectrum status data and the demand data to determine a frequency band resource allocation strategy.

[0044] An embodiment of the present invention also provides a base station joint multi-band resource allocation device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a base station joint multi-band resource allocation method as described in any of the above embodiments.

[0045] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a base station joint multi-band resource allocation method as described in any of the above embodiments.

[0046] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the method described in any of the above embodiments when executed by a processor.

[0047] Compared with the prior art, the present invention provides a base station joint multi-band resource allocation method, device, medium and product, which obtains spectrum status data of each frequency band; determines user communication demand data according to the acquired user communication data; and uses a preset optimization model to perform global optimization according to the spectrum status data and the demand data to determine the frequency band resource allocation strategy. The application scheme can improve spectrum utilization and network throughput, and ensure service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of a flow chart of a base station joint multi-band resource allocation method provided by an embodiment of the present invention;

[0049] Figure 2 It is a structural diagram of a base station joint multi-band resource allocation device provided by an embodiment of the present invention;

[0050] Figure 3 It is another structural schematic diagram of a base station joint multi-band resource allocation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] 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.

[0052] This application proposes a base station joint multi-band resource allocation method, see Figure 1 , is a flow chart of a base station joint multi-band resource allocation method provided by an embodiment of the present invention, the method comprising steps S1 to S3;

[0053] S1, obtaining spectrum status data of each frequency band;

[0054] S2, determining user communication demand data based on the acquired user communication data;

[0055] S3, performing global optimization using a preset optimization model according to the spectrum status data and the demand data, and determining a frequency band resource allocation strategy.

[0056] In the specific implementation of this embodiment, the method is executed by a central controller, and the central controller can communicate and interact with each distributed base station.

[0057] The base station is equipped with advanced spectrum sensing equipment to obtain spectrum status information of each frequency band in real time, and upload the data to the central controller through wired or wireless links. The central controller conducts quantitative analysis to provide a basis for subsequent decision-making.

[0058] The communication data is collected through two-way communication between mobile phone base stations and user devices, including signal strength, service type and traffic demand data, to determine the user's communication needs.

[0059] The central controller calculates the globally optimal or near-optimal cross-band resource allocation plan based on real-time spectrum information and user demand prediction results, taking into account multiple objectives such as spectrum efficiency, network capacity, and user service quality, and controls the communication frequency band resource allocation between base stations and users based on the generated frequency band resource allocation strategy.

[0060] This case can detect and accurately evaluate the status of multi-band spectrum resources in real time, taking into account multiple goals such as spectrum efficiency, network capacity and user service quality. It introduces a user demand prediction function, which can plan resource allocation in advance according to future needs, improve user experience, ensure the effective implementation of resource allocation plans and real-time feedback adjustments, reduce spectrum resource waste, and can flexibly allocate spectrum resources in real time and accurately according to spectrum occupancy and user needs, thereby improving spectrum utilization and network throughput and ensuring service quality.

[0061] In another embodiment provided by the present invention, after determining the frequency band resource allocation strategy, the solution further includes a frequency band resource allocation strategy execution and a strategy dynamic adjustment process, which specifically includes the following steps:

[0062] Convert the optimal resource allocation plan into specific operation instructions and send them to each base station in the form of data packets, ensuring that the instructions contain detailed parameters such as subcarrier allocation, power allocation, and time slice allocation, so as to achieve refined guidance on base station resource configuration;

[0063] After executing the resource allocation instruction, the network performance index is monitored, and the frequency band resource allocation strategy is dynamically adjusted according to the monitored network performance index.

[0064] By monitoring the network performance indicators after executing the allocation instructions and adjusting the allocation strategy, a closed-loop control is formed to ensure dynamic optimization of resource allocation strategies and efficient and stable operation of the network.

[0065] In another embodiment provided by the present invention, the above step S1 specifically includes:

[0066] Spectrum usage data is collected through spectrum sensing equipment deployed in the base station, and the spectrum usage data includes a timestamp, a frequency band identifier, a spectrum occupancy status, and a channel quality parameter.

[0067] That is, the base station deploys high-performance spectrum sensing equipment, such as spectrum analyzers with ultra-wideband detection capabilities, which can monitor the spectrum usage of all designated frequency bands (for example, multiple frequency bands ranging from 700MHz to 6GHz) in its coverage area in real time, and update the data once per second or shorter. For example, at a certain moment, the actual spectrum occupancy rate of frequency band 1 is 70% (that is, 70% of the time or frequency resources in the total bandwidth of this frequency band are effectively utilized), the signal-to-noise ratio (SNR) of frequency band 2 is 20dB (this is a good signal quality threshold, indicating that the received signal is strong and the noise interference is relatively small), and an intermodulation interference coefficient (Intermodulation Distortion Coefficient) of 0.1 is detected in frequency band 3, which represents a high level of nonlinear interference caused by the interaction of different signals.

[0068] The collected spectrum usage data is sorted and analyzed according to different base stations, that is, the collected spectrum data will be organized in a structured manner by the base station, including timestamp (accurate to milliseconds), frequency band identifier, spectrum occupancy status (expressed as percentage or number of occupied frequency points), channel quality parameters (such as SNR, signal-to-interference ratio C / I, channel fading characteristics, etc.). Then, these data are packaged and uploaded to the central controller through a wired backhaul channel (such as a fiber link) or wireless backhaul (such as a microwave link or LTE backhaul).

[0069] After receiving real-time spectrum information, advanced data processing and analysis technologies, such as cluster analysis and trend forecasting, will be used to quantitatively evaluate the availability of resources in each frequency band, such as the proportion of idle spectrum resources, potential releasable spectrum resources, and applicability, such as the selection of appropriate frequency bands for different business types. It will also combine geographic information, time dimension and other factors to form a spectrum resource map, providing a scientific basis for subsequent resource allocation decisions.

[0070] In another embodiment provided by the present invention, the step S2 specifically includes the following steps:

[0071] Real-time acquisition of user communication data of user terminals, that is, the base station obtains information such as the signal strength (Received Signal Strength Indicator, RSSI), service type (such as VoIP calls, high-definition video streaming, high-speed data downloads, etc.), current rate, QoS parameters, and historical data traffic requirements of user terminals through a two-way communication protocol (such as LTE, NR, etc.) between the base station and the user equipment (UEs).

[0072] Based on historical data and real-time data, with the help of big data analysis platforms and deep learning algorithms, such as the LSTM model based on time series analysis or the Apriori algorithm based on association rule mining, the user's business needs, traffic needs and expected service quality needs in the next 1-2 hours are accurately predicted, and a user demand prediction model is built. The user communication data is input into the pre-built user demand prediction model for prediction to obtain the demand data.

[0073] After the prediction results are integrated by the central controller, a personalized service level requirement profile is generated for each user device. The requirement data includes bandwidth requirements, delay sensitivity, and reliability requirements.

[0074] For example, if user A is expected to require a stable downlink data rate of 10 Mbps, and user B will conduct a high-definition video conference within the next hour, the demand for low latency and high bandwidth is particularly prominent.

[0075] In another embodiment provided by the present invention, the step S3 specifically includes the following steps:

[0076] Define resource parameters for frequency band resource allocation for different users;

[0077] Determining a constraint condition according to the spectrum status data and the demand data;

[0078] The objective function of the allocation strategy is constructed according to the resource parameters defined by different users. The goal of the allocation strategy is to maximize the overall performance of the network. The optimized objective function can be represented by the resource parameters.

[0079] In actual operation, a global optimization model is constructed based on the real-time spectrum resource status and user demand prediction results. The objective function is usually designed to balance spectrum efficiency, network capacity and user service quality. Through iterative calculation, the algorithm finds the optimal solution or approximate optimal solution, that is, a set of cross-band resource allocation schemes that can optimize the use of limited spectrum resources in a complex and changing wireless environment while ensuring that all users can get a satisfactory communication service experience.

[0080] Specifically, in order to achieve the goal of global optimal allocation of cross-band resources, the system relies on real-time spectrum resource status information and forward-looking user demand prediction results.

[0081] Real-time spectrum resource status information, which comes from the spectrum resource detection and evaluation module, covers key parameters such as the real-time occupancy of each frequency band, channel quality, interference level, etc. For example, the central controller needs to know which frequency bands are not fully utilized, which frequency bands are not suitable for high data rate transmission due to interference or other reasons, and when and where spectrum holes may occur.

[0082] Predicted user demand: This part of data comes from the user demand acquisition and prediction module, which collects information such as the signal strength, service type, historical and future traffic demand of user devices, and uses big data analysis and machine learning algorithms for in-depth mining and prediction. The central controller understands each user's requirements for different quality of service (QoS) in the future time period, such as bandwidth requirements, delay sensitivity, reliability requirements, etc.

[0083] In a wireless communication network including three frequency bands (Band 1, Band 2, Band 3), the coverage frequency bands include 700 MHz, 900 MHz and 2.6 GHz.

[0084] There are 100 users in the network, each with specific quality of service (QoS) requirements, including data rate, latency, packet loss rate, HD video streaming, VoIP calls, and web browsing. The central controller's task is to optimize the allocation of spectrum and time resources under a given power budget to maximize network performance.

[0085] The central controller collects the following data from the spectrum monitoring system and user behavior prediction models:

[0086] Real-time spectrum status: 50% of subcarriers are available in band 1, 80% of subcarriers are available in band 2, and 60% of subcarriers are available in band 3; the interference level of each band is mild, moderate, and mild respectively.

[0087] User demand forecast: It is predicted that within the next hour, 50% of users will perform high-definition video streaming, requiring a rate of at least 10Mbps and a latency of less than 100ms; 30% of users will make VoIP calls, requiring a stable rate of 2Mbps and a latency of less than 50ms; the remaining 20% ​​of users will perform regular web browsing, requiring lower rate requirements but attaching importance to connection stability.

[0088] Construct a joint optimization model, and the objective of optimizing the objective function is expressed as:

[0089]

[0090] Among them, N is the total number of users, T is the total number of time slots, and F is the total number of frequency bands.

[0091] The objective function is solved according to the constraints to determine the optimal resource parameters for different users. The final resource allocation plan will ensure that high-definition video streaming users are preferentially allocated to frequency bands with less interference and sufficient bandwidth, such as 900MHz and 2.6GHz. VoIP users are allocated to frequency bands with low latency, such as 700MHz. Web browsing users are dispersed to various frequency bands to maintain a balanced load on the network.

[0092] In another embodiment provided by the present invention, the process of constructing the objective function solution specifically includes:

[0093] This solution integrates the real-time spectrum resource status and predicted user needs, starts a joint optimization engine, and uses mixed integer linear programming (MILP) or non-cooperative game theory algorithms (such as Nash equilibrium solution) to globally optimize the allocation of cross-band resources.

[0094] After obtaining the spectrum status data and demand data, a powerful joint optimization engine is started, which has built-in complex mathematical optimization algorithms, such as Mixed Integer Linear Programming (MILP) or non-cooperative game theory algorithms (such as Nash Equilibrium solutions). Mixed integer linear programming is a mathematical modeling method suitable for solving optimization problems involving integer variables and continuous variables. In the present invention, it can be used to determine how to allocate limited subcarrier resources between different frequency bands, how to reasonably allocate power budgets, and how to arrange different user communication periods on the time axis to maximize spectrum efficiency, improve network capacity and meet user service quality requirements. Non-cooperative game theory algorithms can be applied to resource allocation problems in a multi-user competition environment, in which each user is regarded as a selfish participant trying to maximize his own interests. By finding the Nash equilibrium point, the system can find a relatively stable state, that is, in this state, no user can unilaterally increase revenue by changing his strategy, thereby ensuring the fairness and stability of overall resource allocation.

[0095] During the resource allocation calculation process, the system simultaneously optimizes three objective functions: spectrum efficiency (the amount of information that can be carried per unit spectrum), network capacity (total number of service users or total data traffic), and user service quality (such as average throughput, latency, packet loss rate, etc.).

[0096] In actual operation, the central controller will combine two or more of the above algorithms to construct a global optimization model based on the real-time spectrum resource status and user demand prediction results. The objective function is usually designed to balance spectrum efficiency, network capacity and user service quality. Through iterative calculation, the algorithm finds the optimal solution or a near-optimal solution, that is, a set of cross-band resource allocation solutions that can optimize the use of limited spectrum resources in a complex and changing wireless environment while ensuring that all users can get a satisfactory communication service experience.

[0097] According to different objective functions and corresponding weight factors, the system can balance the conflicting relationships among various objectives and determine the global optimal objective by dynamically adjusting the weight factors.

[0098] When solving the problem, iterative calculations are used to find the optimal solution or approximate optimal solution of the MILP model. For the Nash equilibrium part, multiple rounds of iterations are performed using the response dynamics or best response algorithm until the user strategy no longer changes and the Nash equilibrium state is reached, and the resource allocation solution is finally obtained.

[0099] The solution specifically includes: carriers of different frequency bands divided according to different user needs.

[0100] The specific resource allocation scheme obtained through optimization calculation is detailed to each user and frequency band. For example, to ensure high-quality service for user A, 10 continuous OFDM subcarriers are allocated to it in frequency band 1 to ensure sufficient bandwidth; to meet the large traffic demand of user B, 15 subcarriers are allocated in frequency band 2, and the transmission power of user C in frequency band 3 is set to 20dBm to ensure signal coverage; at the same time, user D is arranged to exclusively use part of the resources of frequency band 1 in the future preset time period to ensure that its service is not interfered with by other users. High-definition video streaming users are preferentially allocated to subcarriers of frequency bands 2 and 3 with less interference and sufficient bandwidth, and power allocation ensures high data rate; VIP users are allocated to the frequency band with the lowest latency to ensure voice quality; regular web browsing users are dispersed to various frequency bands to maintain balanced load on the network. Through this refined joint optimization strategy, the central controller not only maximizes spectrum efficiency and network capacity, but also ensures the service quality of all users, achieving efficient resource utilization and improving user experience.

[0101] In another embodiment provided by the present invention, the resource parameters include subcarriers of different frequency bands used by different users in different time slots, transmit powers of different users in different frequency bands, expected data rates of different users using different frequency bands in different time slots, and minimum QoS requirements of different users;

[0102] Among them, the variable x ijk Indicates that the i-th user uses the subcarrier of the k-th frequency band in the j-th time slot, x ijk is a binary variable. If the i-th user uses the subcarrier of the k-th frequency band in the j-th time slot, then x ijk =1; otherwise x ijk =0.

[0103] p ik is the transmission power of the i-th user in the k-th frequency band.

[0104] R ijk represents the expected data rate of the i-th user when using the k-th frequency band in the j-th time slot.

[0105] C i represents the minimum QoS requirement of the i-th user.

[0106] The constraints include a carrier constraint that each user can only use one subcarrier of one frequency band in any time slot, a total power constraint of the sum of transmission powers of different users in different frequency bands, and a user QoS requirement restriction.

[0107] The carrier constraint condition is that a user can only use one subcarrier of a frequency band in any time slot, that is:

[0108]

[0109] The total power constraint of the sum of the transmit powers of different users in different frequency bands, that is, the total power budget limit, assumes that the total power is Pmax.

[0110] Then we have:

[0111]

[0112] User QoS requirements meet the user QoS requirement constraints, namely:

[0113]

[0114] By solving the objective function within the constraints and determining the optimal frequency band resource allocation strategy, dynamic optimization of network performance is achieved, network throughput is improved, and latency and packet loss rates are reduced.

[0115] In another embodiment of the present invention, each base station is controlled to execute a corresponding resource configuration instruction according to the frequency band resource allocation strategy, and the process of adjusting the frequency band parameters specifically includes:

[0116] Convert the optimal resource allocation scheme into a set of resource matching instructions, each of which contains a detailed subcarrier allocation list, the corresponding transmit power configuration of the user equipment, a time schedule (such as time slice allocation in the TDMA frame structure), and other possible specific configuration rules, such as priority setting and switching strategy;

[0117] The resource matching instructions are distributed to each base station in the form of data packets through a reliable communication link. In addition to basic parameters, the data packets also include necessary check codes to ensure the accuracy of instruction transmission.

[0118] After receiving the instruction, the base station first verifies the integrity and correctness of the instruction, then parses it into a locally executable operation task, prepares an execution plan for the corresponding resource configuration, and controls the base station to adjust the frequency band parameters.

[0119] In another embodiment of the present invention, the process of dynamically adjusting the frequency band resource allocation strategy specifically includes:

[0120] During the resource allocation execution, the base station continuously monitors the network performance, records and counts important performance indicators, such as whether the throughput of user A reaches the predetermined value, whether the service delay of user B is within the specified range, whether the overall packet loss rate of the network is maintained at a low level, and whether the signal-to-noise ratio of each frequency band is improved.

[0121] The base station immediately transmits these real-time monitored network performance data back to the central controller. Based on this closed-loop control mechanism, the central controller analyzes the feedback information in real time and dynamically adjusts the resource allocation strategy to achieve continuous optimization of network performance and ensure that the entire wireless communication network always maintains an efficient and stable operating state. This process not only enhances the network's adaptability, but also greatly improves spectrum utilization and user service quality.

[0122] See also Figure 2 , is a schematic diagram of the structure of a base station joint multi-band resource allocation device provided by an embodiment of the present invention, the device comprising:

[0123] A data acquisition module, used to acquire spectrum status data of each frequency band;

[0124] A demand analysis module, used to determine the demand data of user communication according to the acquired user communication data;

[0125] The strategy generation module is used to perform global optimization using a preset optimization model according to the spectrum status data and the demand data to determine a frequency band resource allocation strategy.

[0126] It should be noted that the base station joint multi-band resource allocation device provided in this embodiment can execute all the steps and functions of the base station joint multi-band resource allocation method provided in any of the above embodiments, and the specific functions of the device are not described in detail here.

[0127] See also Figure 3 , is another structural diagram of a base station joint multi-band resource allocation device provided in an embodiment of the present invention. The base station joint multi-band resource allocation device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a base station joint multi-band resource allocation program. When the processor executes the computer program, the steps in each of the above-mentioned base station joint multi-band resource allocation method embodiments are implemented, such as Figure 1 Alternatively, the processor implements the functions of each module in the above-mentioned device embodiments when executing the computer program.

[0128] Exemplarily, the computer program may be divided into one or more modules, and the one or more modules are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in the base station joint multi-band resource allocation device. For example, the computer program may be divided into several modules, and the specific functions of each module have been described in detail in a base station joint multi-band resource allocation method provided in any of the above embodiments, and the specific functions of the device will not be repeated here.

[0129] The base station joint multi-band resource allocation device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The base station joint multi-band resource allocation device may include, but is not limited to, a processor and a memory. Those skilled in the art may understand that the schematic diagram is only an example of a base station joint multi-band resource allocation device and does not constitute a limitation on a base station joint multi-band resource allocation device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the base station joint multi-band resource allocation device may also include input and output devices, network access devices, buses, etc.

[0130] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the base station joint multi-band resource allocation device, and uses various interfaces and lines to connect the various parts of the entire base station joint multi-band resource allocation device.

[0131] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the base station joint multi-band resource allocation device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0132] Wherein, if the module integrated by the base station joint multi-band resource allocation device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0133] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which implements the steps of the method described in any of the above embodiments when executed by a processor.

[0134] It should be noted that the computer program product provided in this embodiment can execute all the steps and functions of a base station joint multi-band resource allocation method provided in any of the above embodiments, and the specific functions of the device are not described in detail here.

[0135] It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A base station joint multi-band resource allocation method, characterized in that: The method comprises: Obtain spectrum status data of each frequency band; Determining user communication demand data based on the acquired user communication data; According to the spectrum status data and the demand data, a preset optimization model is used to perform global optimization to determine a frequency band resource allocation strategy.

2. A base station joint multi-band resource allocation method according to claim 1, characterized in that: The method further comprises: Control each base station to execute corresponding resource configuration instructions and adjust frequency band parameters according to the frequency band resource allocation strategy; The network performance indicators monitored by each base station are obtained, and the frequency band resource allocation strategy is dynamically adjusted according to the monitored network performance indicators.

3. The method for allocating base station resources in a joint multi-band manner according to claim 1, characterized in that: Get spectrum status data for each frequency band, including: The spectrum sensing equipment deployed at each base station collects spectrum usage data of a preset frequency band within the coverage area in real time at a preset sampling period; The collected spectrum usage data is sorted and analyzed according to different base stations, and a spectrum resource map is generated in combination with geographic information and time information of each base station as the spectrum status data; The spectrum usage data includes a timestamp, a frequency band identifier, a spectrum occupancy status, and a channel quality parameter.

4. The method for allocating base station resources in a joint multi-band manner according to claim 1, characterized in that: Determine the user communication demand data based on the acquired user communication data, including: Acquire user communication data of user terminals in real time; Inputting the user communication data into a pre-built user demand prediction model for prediction to obtain the demand data; The user communication data includes signal strength, service type, current rate, QoS parameters and historical data flow requirements; The demand data includes bandwidth requirements, delay sensitivity, and reliability requirements.

5. The method for allocating base station joint multi-band resources according to claim 1, characterized in that: According to the spectrum status data and the demand data, a preset optimization model is used to perform global optimization to determine a frequency band resource allocation strategy, including: Define resource parameters for frequency band resource allocation for different users; Determining a constraint condition according to the spectrum status data and the demand data; An objective function of the allocation strategy is constructed according to resource parameters defined by different users, and the objective function is solved according to the constraint conditions to determine the optimal resource parameters for different users and obtain the frequency band allocation strategy.

6. A base station joint multi-band resource allocation method according to claim 5, characterized in that: Constructing an objective function of the allocation strategy based on resource parameters defined by different users, and solving the objective function according to the constraint conditions to determine the optimal resource parameters for different users, thereby obtaining the frequency band allocation strategy; According to different user-defined resource parameters, mixed integer linear programming is used to build different objective functions based on different optimization goals; Determine the global optimal target based on different objective functions and corresponding weight factors; According to the constraints, the weight factors of different objective functions are dynamically adjusted in combination with a non-cooperative game theory algorithm to determine the optimal resource parameters for different users and obtain the frequency band allocation strategy; Among them, different objective functions include a spectrum objective function taking spectrum efficiency as a target, a capacity objective function taking network capacity as a target, and a quality objective function taking user service quality as a target.

7. A base station joint multi-band resource allocation method according to claim 5, characterized in that: The resource parameters include subcarriers of different frequency bands used by different users in different time slots, transmit powers of different users in different frequency bands, expected data rates of different users using different frequency bands in different time slots, and minimum QoS requirements of different users; The constraints include a carrier constraint that each user can only use one subcarrier of one frequency band in any time slot, a total power constraint of the sum of transmission powers of different users in different frequency bands, and a user QoS requirement restriction constraint.

8. The method for allocating base station joint multi-band resources according to claim 2, characterized in that: Controlling each base station to execute a corresponding resource configuration instruction and adjusting a frequency band parameter according to the frequency band resource allocation strategy includes: Converting the frequency band resource allocation strategy into resource configuration instructions for different base stations; Transmitting the resource configuration instruction to each base station in the form of a data packet to control each base station to adjust the frequency band parameters; The resource configuration instruction includes a subcarrier allocation list, a transmit power configuration corresponding to the user equipment, a time schedule, a priority setting, and a switching strategy.

9. A base station joint multi-band resource allocation method according to claim 2, characterized in that: Acquiring network performance indicators monitored by each base station, and dynamically adjusting the frequency band resource allocation strategy according to the monitored network performance indicators, including: Real-time recording of network performance indicators monitored by base stations; Compare the network performance indicators before and after the base station adjusts the frequency band parameters; According to the comparison result of the network performance indicators, the frequency band resource allocation strategy is revised.

10. A base station joint multi-band resource allocation device, characterized in that: The device comprises: A data acquisition module, used to acquire spectrum status data of each frequency band; A demand analysis module, used to determine the demand data of user communication according to the acquired user communication data; The strategy generation module is used to perform global optimization using a preset optimization model according to the spectrum status data and the demand data to determine a frequency band resource allocation strategy.

11. A base station joint multi-band resource allocation device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements a base station joint multi-band resource allocation method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a base station joint multi-band resource allocation method as described in any one of claims 1 to 9.

13. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.

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