A method, system, device and medium for high-concurrency data simulation

Abstract

This invention provides a high-concurrency data simulation method, system, device, and medium, belonging to the field of 5G fronthaul network technology. The method constructs a DMT transmission co-simulation model, collecting power data of 5Gbps subcarrier groups under different modulation levels; it designs an ILP control algorithm to minimize total power while satisfying fixed bandwidth and target bit rate constraints. The optimization algorithm improves power calculation by using multi-subcarrier group simulation data instead of single-group summation, and adds mutual exclusion constraints. In low-bit-rate static networks, the basic algorithm is used to determine the modulation combination through power summation to reduce transmission power; in high-bit-rate complex networks or scenarios with time-varying user loads, the optimization algorithm is applied in conjunction with FlexE time slot dynamic adjustment to respond to bit-rate changes in real time and maintain optimal efficiency, improving the reliability and user experience of the 5G fronthaul network.

Description

Technical Field

[0001] This invention belongs to the field of 5G fronthaul network technology, specifically relating to a high-concurrency data simulation method, system, device and medium for flexible bit rate control in 5G digital fronthaul. Background Technology

[0002] The 5G fronthaul market is developing rapidly, and as 5G applications expand to fields such as industrial internet and telemedicine, the energy efficiency requirements for fronthaul networks are becoming increasingly stringent.

[0003] In related technologies, high-complexity optimization algorithms consume more computing power than basic algorithms in low-bit-rate static scenarios, resulting in wasted computing power. On the other hand, greedy algorithms, in high-bit-rate complex scenarios, have a large number of subcarrier groups and many constraints, making it impossible to traverse all feasible combinations and easily missing the optimal solution.

[0004] When acquiring power data, related technologies use MATLAB to simulate optical links or adopt theoretical power values ​​from device datasheets. However, in actual transmission, the EVM of this power exceeds the 3GPP standard and needs to be increased to 0.012mW to meet the standard. Existing technologies ignore this difference and directly use 0.01mW for calculation, resulting in the combined output of the algorithm failing to meet the signal quality standards in actual transmission. This necessitates additional power increases to offset the optimization effect.

[0005] The EAM component of the related optical link supports a maximum of 12 5Gbps subcarrier groups for parallel modulation. While selecting a combination of 15 groups might meet bandwidth and bit rate requirements, actual transmission may suffer from signal distortion due to EAM overload. Furthermore, the related technology does not explicitly define the constraint of selecting only one bit rate subcarrier group at the same modulation level. If both 5Gbps and 10Gbps subcarrier groups are selected simultaneously at the same modulation level, the difference in symbol rate will lead to difficulties in adapting optical link parameters, potentially causing transmission failures. Summary of the Invention

[0006] This invention provides a high-concurrency data simulation method. It obtains power data by constructing a discrete multi-tone (DMT) transmission simulation model, automatically solves the optimal modulation combination using an integer linear programming (ILP) algorithm, and optimizes the power calculation logic based on the simulation data, thereby minimizing the transmit power while ensuring transmission performance.

[0007] The methods include:

[0008] S101: Construct a DMT transmission joint simulation model, including a transmitter, optical link and receiver. The transmitter is used to generate DMT signals for 5Gbps subcarrier groups, the optical link is used to simulate the physical transmission process, and the receiver is used to recover the signal and verify whether the signal meets the preset EVM standard, so as to collect power data of 5Gbps subcarrier groups under different modulation levels.

[0009] S102: By using the DMT transmission co-simulation model, adjust the gain of the electrical preamplifier in the optical link, record the minimum DMT signal input power when the preset EVM standard is met, and obtain the basic input power data.

[0010] S103: Design an ILP control algorithm based on integer linear programming ILP. The ILP control algorithm takes the basic input power data obtained in step S102 as input, minimizes the total power as the objective function, and selects the modulation level combination of 5Gbps subcarrier groups under fixed bandwidth constraints and target bit rate constraints.

[0011] S104: In low bit rate and static fronthaul network scenarios, the ILP control algorithm is adopted to calculate the total power by summing the basic power and output the modulation level combination under the premise of satisfying the bandwidth constraint and bit rate constraint.

[0012] S105: In a high bit rate complex fronthaul network scenario, the ILP control algorithm is called. The simulated power data of parallel transmission of multiple subcarrier groups with the same modulation level in step S101, the target bit rate of the scenario and the fixed bandwidth are input. The ILP control algorithm outputs the optimal combination of total power based on the modified power calculation logic and mutual exclusion constraints.

[0013] Alternatively, in fronthaul network scenarios where user load varies over time, the ILP control algorithm can be used in conjunction with FlexE time slot dynamic adjustment to trigger the ILP control algorithm in real time to update the modulation level combination of the 5Gbps subcarrier group, so as to respond to and maintain optimal power efficiency when the bit rate changes.

[0014] This application also provides a high-concurrency data simulation system, the system comprising:

[0015] The simulation model building module is used to build a DMT transmission joint simulation model, which includes a transmitter, an optical link, and a receiver. The transmitter is used to generate the DMT signal of the 5Gbps subcarrier group, the optical link is used to simulate the physical transmission process, and the receiver is used to recover the signal and verify whether the signal meets the preset EVM standard, so as to collect the power data of the 5Gbps subcarrier group under different modulation levels.

[0016] The power data acquisition module is used to adjust the gain of the electrical preamplifier in the optical link through the DMT transmission co-simulation model, record the minimum DMT signal input power when the preset EVM standard is met, and obtain the basic input power data.

[0017] The control algorithm design module is based on integer linear programming (ILP) to design the ILP control algorithm. The ILP control algorithm takes the basic input power data as input and minimizes the total power as the objective function. Under the constraints of fixed bandwidth and target bit rate, it selects the modulation level combination of 5Gbps subcarrier groups.

[0018] The scenario application module is used in low bit rate and static fronthaul network scenarios. It employs the ILP control algorithm to calculate the total power by summing the base power and outputs the modulation level combination under the premise of satisfying bandwidth and bit rate constraints.

[0019] The scenario application module is used to call the ILP control algorithm in high bit rate complex fronthaul network scenarios. The input is the simulated power data of modulation level multi-subcarrier group parallel transmission, the target bit rate of the scenario and the fixed bandwidth. The ILP control algorithm outputs the optimal combination of total power based on the modified power calculation logic and mutual exclusion constraints.

[0020] Alternatively, in fronthaul network scenarios where user load varies over time, the ILP control algorithm can be used in conjunction with FlexE time slot dynamic adjustment to trigger the ILP control algorithm in real time to update the modulation level combination of the 5Gbps subcarrier group, so as to respond to and maintain optimal power efficiency when the bit rate changes.

[0021] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the high-concurrency data simulation method.

[0022] According to another embodiment of this application, a storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the high-concurrency data simulation method.

[0023] As can be seen from the above technical solutions, the present invention has the following advantages:

[0024] The high-concurrency data simulation method provided by this invention selects the combination with the minimum total power through mathematical optimization, which reduces power in low bit rate static scenarios. The optimized ILP algorithm improves the total power calculation method and reduces power estimation error.

[0025] For low-bit-rate static scenarios, a low-complexity basic algorithm is adopted to adapt to low-computing-power devices at edge nodes; for high-bit-rate scenarios of 50-100Gbps, an optimized algorithm is used to improve the accuracy of power calculation. For scenarios with time-varying user loads, the FlexE time slot mechanism is combined to respond to changes in bit rate and ensure that optimal power efficiency is maintained in different scenarios.

[0026] Using MATLAB and VPI co-simulation, the physical characteristics and signal processing of the optical link are simulated with high precision. The minimum input power is obtained by adjusting the gain of the electrical preamplifier, and the data is closer to the actual transmission than the theoretical calculation. Environmental adaptation correction is introduced to change the power data from the static value of the standard environment to the dynamic value of the actual environment, reducing the optimization failure caused by data deviation.

[0027] EVM verification ensures signal quality meets standards; constraints on the total number of subcarriers prevent signal distortion caused by exceeding the optical link's carrying capacity; and a real-time update method for time-varying scenarios ensures EVM complies with 3GPP standards during transmission. This invention can be directly applied to the large-scale deployment of 5G fronthaul networks, providing technical support for 5G network operation. Attached Figure Description

[0028] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description 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.

[0029] Figure 1 Flowchart of a high-concurrency data simulation method;

[0030] Figure 2 Flowchart of an embodiment of a high-concurrency data simulation method;

[0031] Figure 3 Flowchart of another embodiment of a high-concurrency data simulation method;

[0032] Figure 4 This is a schematic diagram of a high-concurrency data simulation system;

[0033] Figure 5 This is a schematic diagram of an electronic device. Detailed Implementation

[0034] The high-concurrency data simulation method provided by this invention uses the Integer Linear Programming (ILP) algorithm to construct a linear objective function (minimizing total input power) and constraints (fixed bandwidth, target bit rate), and solves the optimal solution under integer decision variables (number of subcarrier groups). This solves the problem that manual calculation of modulation level combinations easily exceeds bandwidth limits and cannot guarantee optimal power consumption. It realizes the automated optimization of modulation level combinations under different data rates within a fixed bandwidth, and reduces the total power consumption of the fronthaul link.

[0035] This invention employs a power calculation method based on simulation data. For multiple subcarrier groups with the same modulation level, it directly uses the total power data obtained from simulation instead of summing the power of a single group, thus solving the problem of discrepancies between traditional power estimation and actual transmission characteristics and improving the accuracy of power consumption calculation.

[0036] Based on the transmission architecture combining DMT and FlexE, DMT technology is used to achieve multi-rate transmission through subcarrier groups, and FlexE is combined to dynamically allocate time slots in increments of 5Gbps. This solves the problem that traditional fixed-rate fronthaul links cannot adapt to variable data rate requirements, and realizes flexible bandwidth utilization and dynamic adjustment of data rate.

[0037] This invention, based on the technical framework of combining FlexE and DMT, employs a dynamic modulation level selection mechanism with 5Gbps subcarrier groups as the unit. Leveraging the 5Gbps time slot allocation characteristics of FlexE technology and the subcarrier group transmission capability of DMT modulation, it constructs a subcarrier group transmission architecture with a granularity of 5Gbps, allowing each subcarrier group to independently select its modulation level (e.g., 4-QAM, 16-QAM, 64-QAM, 256-QAM). Addressing fixed bandwidth constraints and target data rates, it constructs an ILP model with the objective function of minimizing the total input power of the DMT signal and constraints of "bandwidth not exceeding limits" and "total rate matching target." This replaces the traditional manual calculation method, achieving automated and optimal solution for modulation level combinations under different data rates, and resolving the problems of calculations easily exceeding bandwidth limits and inaccurate power consumption optimization.

[0038] The high-concurrency data simulation method involved in this application will be described in detail below. Specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0039] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0041] Please see Figure 1 The diagram shows a flowchart of a high-concurrency data simulation method in a specific embodiment. The method includes:

[0042] Step S101: Construct a DMT transmission joint simulation model, which includes a transmitter, an optical link, and a receiver. The transmitter is used to generate the DMT signal of the 5Gbps subcarrier group, the optical link is used to simulate the physical transmission process, and the receiver is used to recover the signal and verify whether the signal meets the preset EVM standard, so as to collect the power data of the 5Gbps subcarrier group under different modulation levels.

[0043] In some embodiments, the constructed DMT transmission co-simulation model is based on a co-simulation architecture of MATLAB and VPI, specifically including a transmitter module, predefined parameters for a 5Gbps subcarrier group, including modulation level and number of subcarriers, with modulation levels covering four common levels: 4-QAM, 16-QAM, 64-QAM, and 256-QAM. Subsequently, an IFFT transform is performed on the generated frequency domain DMT signal, and a cyclic prefix is ​​inserted to output a transmittable time domain DMT signal.

[0044] The optical link module can simulate a real fronthaul optical transmission environment. It includes components such as an electrical preamplifier, an EAM (electro-optic modulator, which uses the transmission polynomial of the real device to describe the signal modulation characteristics, and introduces a chirp factor to simulate the frequency shift during the modulation process), and single-mode fiber. It performs physical transmission simulation of the time-domain signal output by the transmitter and outputs the signal after attenuation and distortion through the optical link.

[0045] The receiver module performs preamble equalization on the received time-domain signal. It can use a preset preamble sequence to compensate for amplitude and phase distortions introduced by the optical link, ensuring signal synchronization. Then, it uses FFT transformation to restore the time-domain signal to the frequency-domain signal. Finally, it calculates the error vector magnitude (EVM) and compares it with the threshold in the 3GPP standard to verify whether the signal transmission quality meets the standard.

[0046] Step S102: Using the DMT transmission co-simulation model, adjust the gain of the electrical preamplifier in the optical link, record the minimum DMT signal input power when the preset EVM standard is met, and obtain the basic input power data.

[0047] In some embodiments, based on the joint simulation model constructed in step S101, simulation tests are conducted separately for each modulation level predefined in step S101.

[0048] The gain of the electrical preamplifier in the optical link module is adjusted. Each gain adjustment triggers the receiver module to calculate the EVM value of the current transmitted signal in real time. When the EVM value first reaches the preset 3GPP standard threshold in step S101, the minimum input power of the DMT signal output by the transmitter is recorded. The minimum input power corresponding to each modulation level is associated with the bandwidth parameters of the 5Gbps subcarrier group under that modulation level and stored to form basic input power data. Data is obtained through simulation and can be repeatedly verified to ensure data reliability.

[0049] Step S102 in this embodiment also includes the following specific implementation method.

[0050] Step S1021: Collect the operating environment parameters of the electrical preamplifier in the optical link module, including the ambient temperature gradient, fiber optic link loss distribution, and power supply voltage stability, to form an environmental feature dataset containing timestamps.

[0051] Step S1022: Based on the environmental feature dataset collected in step S1021, construct a gain-distortion correlation model for the electrical preamplifier.

[0052] Step S1023: Under the EVM threshold that meets the 3GPP standard, gradually increase the gain of the electrical preamplifier in 0.05dB increments; each time the gain is adjusted, record the current environmental parameters, the amplifier output optical power, the EVM value of the transmitted signal, and the input power of the subcarrier group; stop adjusting when the EVM reaches the threshold for the first time, and record the input power at this time as the initial minimum power.

[0053] Step S1024: For multiple adjustment records under the same modulation level, remove abnormal data points; perform polynomial fitting on the remaining valid data to obtain the minimum input power curve of the modulation level under different environmental conditions.

[0054] Step S1025: Associate the minimum input power curve fitted in step S1024 with the inherent parameters of the corresponding modulation level to generate an environment-adaptive minimum power database; each entry in the database contains a modulation level identifier, an environmental parameter range, a corresponding minimum input power value, and a fitting confidence level.

[0055] This embodiment employs a fixed-step gain adjustment. By collecting key environmental parameters, adjusting the gain in small steps of 0.05 dB, and eliminating outlier data, the deviation between the obtained minimum input power and the actual transmission scenario is reduced. By fitting the environmental parameter-minimum power curve and establishing a database, the power data for the corresponding environmental range can be directly retrieved from the database when deploying fronthaul networks in different environments. This data can be used as input for the ILP algorithm in step S103 and the optimized ILP algorithm in step S104, reducing the computational power consumption of the algorithm and improving the actual power optimization effect of the algorithm output combination due to the reliability of the data.

[0056] Step S103: Design an ILP control algorithm based on integer linear programming ILP. The ILP control algorithm takes the basic input power data obtained in step S102 as input, minimizes the total power as the objective function, and selects the modulation level combination of 5Gbps subcarrier groups under fixed bandwidth constraints and target bit rate constraints.

[0057] In some embodiments, the basic input power data obtained in step S102 is used as the core input to design an ILP control algorithm. The objective function of the algorithm is defined as minimizing the total power, that is, the total power is equal to the basic input power corresponding to each modulation level × the sum of the number of 5Gbps subcarrier groups used under that modulation level.

[0058] This embodiment also sets constraints: a bandwidth constraint, namely, the sum of the subcarrier group bandwidth corresponding to each modulation level and the number of subcarrier groups used at that level must be less than or equal to the fixed bandwidth resource limit of the fronthaul network; and a bit rate constraint, namely, the sum of the subcarrier group bit rate corresponding to each modulation level and the number of subcarrier groups used at that level must be equal to the target bit rate required by the service. Integer solutions are obtained using an integer linear programming solution tool to ensure the practical operability of the solution. The algorithm output is the 5Gbps subcarrier group modulation level combination that satisfies both constraints and has the minimum total power. The output result is directly applicable to practical fronthaul networks, as the number of subcarrier groups is an integer, requiring no additional adjustments.

[0059] Step S104: In a low bit rate and static fronthaul network scenario, the ILP control algorithm is adopted. Based on the basic input power data from step S102, the total power is calculated by summing the basic power. Under the premise of satisfying bandwidth and bit rate constraints, the modulation level combination is output. Here, the basic power summation is the sum of the power of a single group multiplied by the number of corresponding subcarrier groups.

[0060] In some embodiments, the target bit rate and fixed bandwidth upper limit of the scenario are determined; the ILP control algorithm designed in step S103 is called, and the basic input power data obtained in step S102, the target bit rate of the scenario and the fixed bandwidth are input; the algorithm calculates the total power by summing the basic power, and outputs the modulation level combination under the premise of satisfying the bandwidth constraint and bit rate constraint. The combination is applied to the actual fronthaul network to achieve a reduction in transmission power.

[0061] Step S105: In the high bit rate complex fronthaul network scenario, the ILP control algorithm is called. The simulated power data of parallel transmission of multiple subcarrier groups with the same modulation level in step S101, the target bit rate of the scenario and the fixed bandwidth are input. The ILP control algorithm outputs the optimal combination of total power based on the corrected power calculation logic and mutual exclusion constraints.

[0062] Alternatively, in fronthaul network scenarios where user load varies over time, the ILP control algorithm can be used in conjunction with FlexE time slot dynamic adjustment to trigger the ILP control algorithm in real time to update the modulation level combination of the 5Gbps subcarrier group, so as to respond to and maintain optimal power efficiency when the bit rate changes.

[0063] In some embodiments, for complex fronthaul network scenarios with high bit rates of 50-100Gbps, the specific operation includes: calling the ILP control algorithm, inputting the simulated power data of parallel transmission of multiple subcarrier groups with the same modulation level in step S101, the target bit rate of the scenario, and the fixed bandwidth, and the algorithm outputs the optimal combination of total power based on the corrected power calculation logic and mutual exclusion constraints.

[0064] In this embodiment, the fronthaul network scenario with time-varying user load can also monitor user load in real time, adjust FlexE time slot allocation according to load changes, and thus determine the real-time target bit rate. When a change in bit rate demand is detected, the ILP control algorithm is triggered in real time. Based on the bandwidth and bit rate corresponding to the current time slot allocation, the modulation level combination is updated to ensure that the algorithm completes the combination update within 100ms, maintaining optimal power efficiency. This embodiment uses the optimized algorithm for both scenarios, ensuring that optimal power efficiency can be achieved in different scenarios.

[0065] In one embodiment of the present invention, based on step S105, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. For example... Figure 2 As shown, step S105 further includes the following steps:

[0066] Step S1051: Obtain environmental parameters of a high bit rate complex fronthaul network, including the nonlinear loss coefficient of the link, the degree of equipment aging, and the burst characteristics of service traffic; the parameters are collected in real time by intelligent sensors deployed at key nodes of the link to form a dynamic environmental feature library.

[0067] It should be noted that the sensors are located at the transmitter output, the midpoint of the optical link, and the receiver input. The nonlinear loss coefficient is obtained by measuring the spectral broadening at different power levels using a spectral analyzer and fitting the relationship curve between the loss coefficient and power.

[0068] The degree of equipment aging is determined by recording the difference between the light output power of the optical module at the time of manufacture and the current measured power.

[0069] Traffic burst characteristics can be used to statistically analyze hourly traffic peaks and troughs, and calculate variance.

[0070] Step S1052: Based on the multi-subcarrier group simulation power data and environmental parameters obtained in step S102, reconstruct the constraints of the ILP control algorithm;

[0071] Adjust the bit rate of the mutual exclusion constraint and embed the nonlinear loss coefficient and equipment aging degree as correction factors into the objective function.

[0072] The objective function in this embodiment is based on minimizing the total power in S103, and is modified by combining the nonlinear loss coefficient, the degree of equipment aging, and the bit error rate corresponding to the transmission reliability constraints.

[0073] Objective function expression:

[0074]

[0075] N represents the number of modulation levels, such as 4-QAM, 16-QAM, 64-QAM, and 256-QAM. The single-group basic input power of modulation level i obtained in step S102; The correction factor for the nonlinear loss coefficient corresponding to modulation level i is determined by the nonlinear loss coefficient curve fitted in step S1051. If the value is greater than or equal to 0, the loss is greater. The larger; The device aging correction factor corresponding to modulation level i is obtained by normalizing the aging power difference recorded in step S1051. ≥1 indicates more severe aging. The larger; This is the reliability compensation term for modulation level i, which is related to the bit error rate constraint. =k×(1−BER i -1), where k is a constant, BERi is the theoretical bit error rate of modulation level i, and BERi must satisfy 1e-12. i The number of subcarrier groups for modulation level i.

[0076] In this embodiment, the objective function is set by considering the nonlinear loss coefficient α. i The larger the loss coefficient fitted by spectral broadening, the higher the input power required in actual transmission. Therefore, (1+α) i Amplify base power. Equipment aging level. This is because aging of the optical module leads to a decrease in emitted power, requiring an increase in input power to maintain signal quality. Therefore, [the following is used:] (≥1) Corrected base power. Reliability compensation item. This is based on the requirement for power based on a bit error rate ≤1e-12, such as higher-order modulation requiring higher power to ensure EVM, through... The bit error rate constraint is transformed into a power cost, ensuring that combinations that meet reliability requirements are prioritized during optimization.

[0077] Step S1053: Define the transmission reliability target and the quality of service target in the ILP control algorithm; optionally, the transmission reliability target is constrained by a bit error rate ≤ 1e-12. The quality of service target is constrained by a transmission delay of ≤ 10μs for 95% of the traffic flow.

[0078] Step S1054: Train a neural network model using historical traffic data, and adjust the power allocation of subcarrier groups based on the prediction results to avoid temporary power overload caused by sudden load.

[0079] Specifically, the transmission reliability target is constrained by a bit error rate (BER) ≤ 1e-12. For each 5Gbps subcarrier group, the actual BER at the selected modulation level and power must not exceed this threshold. The BER is related to the signal-to-noise ratio (SNR) of the subcarrier, and the SNR is determined by the power Pi and the nonlinear loss αi. η represents the receiver efficiency, and N0 represents the noise power spectral density. Different modulation levels correspond to different SNR-Bit Error Rate curves.

[0080] The service quality objective is to achieve a transmission delay of ≤10μs for 95% of traffic flows. Transmission delay includes signal transmission time, queuing time, and processing time, with queuing time related to the bandwidth allocation of subcarrier groups. The specific implementation process involves pre-generating an SNR-Bit Error Rate mapping table for each modulation level, which can be obtained through simulation or actual measurement. For each subcarrier group i, based on the target Bit Error Rate 1e-12, the minimum required SNR threshold SNR is looked up from the mapping table. min,i SNR min,i Converted to power P i The constraints are used as inequality constraints in the ILP algorithm.

[0081] As can be seen, this embodiment transforms transmission reliability and service quality indicators into quantifiable power and bandwidth limits, which are then embedded into the ILP control algorithm. This allows the algorithm to optimize total power while forcibly meeting the service's hard requirements for bit error rate and latency, thus avoiding transmission failure or service degradation due to excessive pursuit of power minimization.

[0082] In this embodiment, step S1054, which predicts traffic flow and adjusts power allocation using a neural network model, can specifically employ LSTM (Long Short-Term Memory) network to handle the temporal dependencies of traffic data, such as weekday / weekend traffic patterns and sudden peak-hour characteristics.

[0083] The input features include the peak traffic volume of the past hour, the lowest traffic volume of the past hour, and the current traffic rate, totaling three features, corresponding to three neurons in the input layer. The output target is the probability of a traffic peak occurring within the next 10 minutes. The output layer has one neuron with a value between 0 and 1. The training data uses minute-level traffic data from the past 30 days, divided into a training set (80%) and a validation set (20%). When the predicted probability of a peak traffic volume within the next 10 minutes is ≥70%, it is determined that a sudden load may occur, triggering a power reservation strategy. The power reservation strategy can reserve 15% of the current total power as redundancy.

[0084] The specific training method for the model is as follows: Initialize an LSTM network with 8 neurons in the hidden layer and use the ReLU activation function; use the mean squared error (MSE) as the loss function and iteratively train using the Adam optimizer (learning rate 0.001) for 50-100 epochs until the validation set loss converges; save the trained model for real-time prediction. Collect current traffic characteristics (peak, valley, rate) every 5 minutes and input them into the model to obtain the peak probability for the next 10 minutes; if the probability is ≥70%, add 15% redundant power (i.e., multiply Pi in the objective function by 1.15) to the power allocation of the ILP control algorithm, while keeping the bit rate and bandwidth constraints unchanged; if the probability is <70%, maintain the original power allocation strategy to avoid wasting redundant power.

[0085] As can be seen, the traffic prediction model in this embodiment uses an LSTM neural network, with 3 neurons in the input layer (representing the peak and trough traffic values ​​of the past hour and the current traffic rate), 8 neurons in the hidden layer, and 1 neuron in the output layer (representing the peak probability in the next 10 minutes). The training data consists of historical traffic over a preset time period, and the loss function is the mean squared error. Power reservation is triggered when the predicted peak probability in the next 10 minutes is ≥70%. This embodiment avoids temporary power overload caused by sudden load fluctuations.

[0086] Step S1055: In scenarios where user load varies over time, the optimized ILP control algorithm is updated in real time by combining the FlexE time slot adjustment mechanism with the power reservation strategy in step S1054.

[0087] Specifically, this includes: reallocating the time slot positions of subcarrier groups based on the resource occupancy rate of FlexE time slots, migrating high-power-demand subcarrier groups to low-loss time slots, and synchronously updating the modulation level combination to ensure that optimal power efficiency is maintained after the time slot resources are reallocated.

[0088] This embodiment explicitly defines transmission reliability and quality of service objectives in the ILP algorithm, ensuring reliable data transmission while reducing power. Predictive power reservation can handle sudden loads and avoid power overload. Through an LSTM neural network, power reservation is triggered when the peak probability is ≥70%, adapting to load changes in advance and maintaining stable power output. Combining the FlexE time slot adjustment mechanism and power reservation strategy, high-power-demand subcarrier groups are migrated to low-loss time slots based on time slot resource occupancy, and modulation level combinations are updated synchronously to ensure optimal power efficiency is maintained after time slot reallocation, improving the resource utilization efficiency of the fronthaul network.

[0089] In one embodiment of the present invention, based on step S105, the following will provide a possible embodiment and its specific implementation will be described in a non-limiting manner. For example... Figure 3 As shown, step S105 specifically includes the following steps:

[0090] Step S2051: In a low bit rate and static fronthaul network scenario, determine the correspondence between the basic input power of a 5Gbps subcarrier group and the active proportion of subcarriers within that subcarrier group;

[0091] In this embodiment, in a low bit-rate static scenario, although the modulation level of the 5Gbps subcarrier group is fixed, not all subcarriers continuously carry data. This subcarrier active ratio causes a difference between the actual base power consumed by the subcarrier group and the theoretical power when all subcarriers are active. Therefore, simulation or testing is used to clarify the correlation between the subcarrier active ratio and the base power under different modulation levels.

[0092] Step S2052: Based on the correspondence determined in step S2051, adjust the basic input power of each 5Gbps subcarrier group to be selected;

[0093] Considering that step S2051 has clarified the relationship between the active subcarrier ratio and the base power, and that the active subcarrier ratio of each 5Gbps subcarrier group differs under different transmission requirements, the base input power obtained in step S102 is corrected based on the expected active subcarrier ratio of each subcarrier group during actual transmission, so that the power data more closely reflects the actual energy consumption of transmission.

[0094] Step S2053: Based on the basic input power of each 5Gbps subcarrier group after the correction in step S2052, and according to the objective function of the basic integer linear programming control algorithm designed in step S103, candidate modulation level combinations are initially screened.

[0095] In this embodiment, the basic integer linear programming control algorithm in step S103 aims to minimize the total power. After correcting the basic power of each subcarrier group in step S2052, a power value closer to the actual energy consumption is substituted into the algorithm's objective function and bandwidth and bit rate constraints, thereby selecting candidate modulation level combinations with lower total power from the perspective of actual power consumption.

[0096] Step S2054: For the modulation level combinations initially selected in step S2053, verify the power stability of the 5Gbps subcarrier group in each combination under low bit rate and static scenarios.

[0097] In this embodiment, the modulation level combinations initially selected in step S2053 are placed in a simulated static low bit rate transmission environment to detect whether the power output of the subcarrier groups under each combination can be maintained within a preset minimum fluctuation range within a preset time period, so as to ensure that the combination not only has low total power but also operates stably.

[0098] Step S2055: Select the combination with the smallest total power from the modulation level combinations that have passed the power stability verification in step S2054, and use it as the final 5Gbps subcarrier group modulation level combination to reduce transmission power.

[0099] After stability verification in step S2054, this embodiment selects the combination with the smallest sum of corrected base power × quantity among all combinations that meet the stability requirements for power fluctuations. This combines the principles of minimum total power and power stability to achieve optimal application requirements. It ensures both reduced transmission power and meets the power stability requirements in static scenarios, resulting in more reliable low-power transmission.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0101] The following are embodiments of the high-concurrency data simulation system provided in this disclosure. This system and the high-concurrency data simulation methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the high-concurrency data simulation system, please refer to the embodiments of the high-concurrency data simulation methods described above.

[0102] like Figure 4 As shown, the system includes:

[0103] The simulation model construction module 201 is used to construct a DMT transmission joint simulation model, which includes a transmitter, an optical link, and a receiver. The transmitter is used to generate DMT signals for 5Gbps subcarrier groups, the optical link is used to simulate the physical transmission process, and the receiver is used to recover the signal and verify whether the signal meets the preset EVM standard, so as to collect power data of 5Gbps subcarrier groups under different modulation levels.

[0104] The power data acquisition module 202 is used to adjust the gain of the electrical preamplifier in the optical link through the DMT transmission co-simulation model, record the minimum DMT signal input power when the preset EVM standard is met, and obtain the basic input power data.

[0105] The control algorithm design module 203 designs an ILP control algorithm based on integer linear programming (ILP). The ILP control algorithm takes the basic input power data as input and minimizes the total power as the objective function. Under the constraints of fixed bandwidth and target bit rate, it selects the modulation level combination of 5Gbps subcarrier groups.

[0106] The scenario application module 204 is used in low bit rate and static fronthaul network scenarios to use the ILP control algorithm to calculate the total power by summing the basic power and output the modulation level combination under the premise of satisfying bandwidth constraints and bit rate constraints.

[0107] The scenario application module 205 is used to call the ILP control algorithm in high bit rate complex fronthaul network scenarios. The input is the simulated power data of the modulation level multi-subcarrier group parallel transmission, the target bit rate of the scenario and the fixed bandwidth. The ILP control algorithm outputs the optimal combination of total power based on the modified power calculation logic and mutual exclusion constraints.

[0108] Alternatively, in fronthaul network scenarios where user load varies over time, the ILP control algorithm can be used in conjunction with FlexE time slot dynamic adjustment to trigger the ILP control algorithm in real time to update the modulation level combination of the 5Gbps subcarrier group, so as to respond to and maintain optimal power efficiency when the bit rate changes.

[0109] like Figure 5 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, and a computer program stored in the memory and executable on the processor 101, wherein the processor 101 executes the program to implement the steps of a high-concurrency data simulation method.

[0110] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0111] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0112] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0113] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0114] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the high-concurrency data simulation method.

[0115] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0116] In a storage medium, a readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0117] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high concurrency data simulation method, the method comprising: receiving a plurality of data streams; and processing the plurality of data streams in parallel. The method comprises the following steps: S101: Constructing a DMT transmission joint simulation model, including a transmitter, an optical link and a receiver, the transmitter being used to generate a DMT signal of a 5Gbps subcarrier group, the optical link being used to simulate a physical transmission process, and the receiver being used to recover the signal and verify whether the signal meets a preset EVM standard, so as to collect power data of the 5Gbps subcarrier group under different modulation levels; S102: Adjusting the gain of an electrical preamplifier in the optical link through the DMT transmission joint simulation model, recording the minimum DMT signal input power when the preset EVM standard is met, and obtaining basic input power data; S103: Designing an ILP control algorithm based on integer linear programming (ILP), taking the basic input power data obtained in step S102 as input, taking minimization of total power as an objective function, and selecting a modulation level combination of the 5Gbps subcarrier group under fixed bandwidth constraints and target bit rate constraints; S104: In a low-bit-rate and static front-haul network scenario, using the ILP control algorithm, based on the basic input power data of step S102, calculating the total power by summing the basic input power, and outputting the modulation level combination under the premise of meeting the bandwidth constraints and the bit rate constraints; S105: In a high-bit-rate complex front-haul network scenario, calling the ILP control algorithm, inputting the simulation power data of the same modulation level and multiple subcarrier groups in parallel transmission in step S101, the target bit rate and the fixed bandwidth of the scenario, and the ILP control algorithm outputs the combination with the optimal total power based on the revised power calculation logic and mutual exclusion constraints; or in a front-haul network scenario where the user load is time-varying, using the ILP control algorithm, combining FlexE time slot dynamic adjustment, and triggering the ILP control algorithm to update the modulation level combination of the 5Gbps subcarrier group in real time, so as to respond to bit rate changes and maintain optimal power efficiency.

2. The high-concurrency data simulation method of claim 1, wherein in step S103, the objective function is defined as the sum of the products of the power and the number of each modulation level subcarrier group; the fixed bandwidth constraint is defined as the sum of the products of the bandwidth and the number of each modulation level subcarrier group ≤ the fixed bandwidth; and the target bit rate constraint is defined as the sum of the products of the bit rate and the number of each modulation level subcarrier group = the target bit rate.

3. The high-concurrency data simulation method of claim 1, wherein in step S104, the integer linear programming control algorithm designed in step S103 is further optimized, specifically including: improving the total power calculation method: for multiple subcarrier groups of the same modulation level, the simulation power data of the modulation level and multiple subcarrier groups transmission in step S102 is called to calculate the total power; adding mutual exclusion constraints: ensuring that each modulation level corresponds to only one bit rate subcarrier group; and the optimized algorithm is referred to as an optimized ILP control algorithm.

4. The high-concurrency data simulation method of claim 1, wherein step S105 further comprises the following steps: ​ ​ ​ S1051: In the low bit rate and static front network scene, the corresponding relationship between the basic input power of the 5Gbps subcarrier group and the active proportion of the subcarrier in the subcarrier group is determined; S1052: According to the corresponding relationship determined in step S1051, the basic input power of each 5Gbps subcarrier group to be selected is corrected; S1053: Based on the basic input power of each 5Gbps subcarrier group corrected in step S1052, the modulation level combination is preliminarily screened out according to the objective function of the basic integer linear programming control algorithm designed in step S103; S1054: The power stability of the 5Gbps subcarrier group in each combination in the modulation level combination preliminarily screened out in step S1053 is verified in the low bit rate and static scene; S1055: From the modulation level combination verified by the power stability in step S1054, the combination with the smallest total power is selected as the final modulation level combination of the 5Gbps subcarrier group.

5. The high-concurrency data simulation method of claim 1, wherein step S105 further comprises the following steps: Obtaining environmental parameters of a high-bit-rate complex front network, including nonlinear loss coefficients of links, device aging degree, burst characteristics of service traffic; Reconfiguring the constraint conditions of the ILP control algorithm based on the multi-subcarrier group simulation power data obtained in step S102 and the environmental parameters; adjusting the bit rate of the mutual exclusion constraint, and embedding the nonlinear loss coefficient and the device aging degree as a correction factor into the objective function; Defining transmission reliability targets and service quality targets in the optimized ILP control algorithm; Training a neural network model based on historical traffic data, and adjusting the power allocation of the subcarrier group according to the prediction results; In the user load time-varying scene, combining the FlexE time slot adjustment mechanism and the power allocation to trigger the updated ILP control algorithm in real time.

6. The high-concurrency data simulation method of claim 5, wherein the objective function expression is:

7. The high-concurrency data simulation method of claim 1, wherein step S102 further comprises the following steps: Collecting the working environmental parameters of the electrical preamplifier in the optical link module, including environmental temperature gradient, optical fiber link loss distribution, and power supply voltage stability, to form an environmental characteristic data set containing time stamps; N is the number of modulation levels, is a single set of base input power for the modulation level i obtained in step S102; is a non-linear loss coefficient correction factor corresponding to the modulation level i; is a device aging degree correction factor corresponding to the modulation level i; is a reliability compensation term for the modulation level i;x i is the number of subcarrier groups for the modulation level i. Based on the collected environmental characteristic data set, a gain distortion correlation model of the electrical preamplifier is constructed; Under the condition of meeting the EVM threshold of the 3GPP standard, the gain of the electrical preamplifier is gradually increased by 0.05dB as the adjustment step; each time the gain is adjusted, the current environmental parameters, the amplifier output optical power, the EVM value of the transmitted signal, and the input power of the subcarrier group are recorded; when the EVM first reaches the threshold, the adjustment is stopped, and the input power at this time is recorded as the preliminary minimum power; For multiple adjustment records under the same modulation level, abnormal data points are removed; the remaining valid data is polynomially fitted to obtain the minimum input power curve of the modulation level under different environmental conditions; ​ ​ ​ The fitted minimum input power curve is associated with the inherent parameters of the corresponding modulation level to generate an environment adaptive minimum power database; each entry in the database contains modulation level identification, environment parameter range, corresponding minimum input power value and fitting confidence.

8. A high concurrency data simulation system, characterized by, The system is used to implement the high-concurrency data simulation method of any one of claims 1 to 7. The system comprises: The simulation model construction module is used to construct a DMT transmission joint simulation model, which comprises a transmitter, an optical link and a receiver; the transmitter is used to generate a DMT signal of a 5Gbps subcarrier group; the optical link is used to simulate a physical transmission process; and the receiver is used to recover the signal and verify whether the signal meets a preset EVM standard, so as to collect power data of the 5Gbps subcarrier group under different modulation levels; The power data collection module is used to adjust the gain of an electrical preamplifier in the optical link through the DMT transmission joint simulation model, record the minimum DMT signal input power when the preset EVM standard is met, and obtain basic input power data; The control algorithm design module is used to design an ILP control algorithm based on ILP, the ILP control algorithm takes the basic input power data as input, takes minimizing total power as an objective function, selects a modulation level combination of the 5Gbps subcarrier group under the constraints of fixed bandwidth and target bit rate, and outputs the modulation level combination. The scene application module is used to adopt the ILP control algorithm in a low-bit-rate and static front-haul network scene, calculate total power by summing the basic input power, and output the modulation level combination under the premise of meeting the bandwidth constraint and the bit rate constraint. The scene application module is used to call the ILP control algorithm in a high-bit-rate complex front-haul network scene, input simulation power data of modulation level multi-subcarrier group parallel transmission, target bit rate and fixed bandwidth of the scene, and output a combination with optimal total power based on the modified power calculation logic and mutual exclusion constraints. Or in a front-haul network scene where user load is time-varying, the ILP control algorithm is adopted in combination with FlexE time slot dynamic adjustment to trigger real-time update of the modulation level combination of the 5Gbps subcarrier group by the ILP control algorithm, so as to respond to and maintain optimal power efficiency when the bit rate changes.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the high-concurrency data simulation method of any one of claims 1 to 7 when executing the program.

10. A storage medium having stored thereon a computer program, characterized in that The computer program implements the steps of the high-concurrency data simulation method of any one of claims 1 to 7 when executed by the processor.

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