Network performance test method and system based on 5G communication terminal

Abstract

The invention belongs to the field of network testing, and provides a network performance testing method and system based on a 5G communication terminal, and the method comprises the steps: configuring N + 1 5G communication terminals and N hardware relay devices; selecting one of the N + 1 5G communication terminals as a current test terminal, and enabling the other N 5G communication terminals to be in one-to-one correspondence with the N hardware relay devices respectively; performing signal amplification and multipath distribution on signals of the corresponding 5G communication terminals by using the N hardware relay devices; controlling the N + 1 5G communication terminals to execute a data transmission test; obtaining test data of the current test terminal, and adding the test data into a first test data group; acquiring test data of the rest N 5G communication terminals, and adding the test data into a second test data group; and analyzing and determining the network performance based on the data of the first test data set and the second test data set.

Description

Technical Field

[0001] The present invention belongs to the field of network testing, and more particularly relates to a network performance testing method and system based on a 5G communication terminal. Background Art

[0002] With the rapid development of the fifth-generation mobile communication technology (5G), 5G networks, with their characteristics of high speed, low latency, and large-scale connection, are widely used in industrial Internet of Things, smart cities, remote medical treatment, autonomous driving, and other fields. However, the complexity of 5G networks also brings challenges to performance testing. The accurate evaluation of network performance is not only related to the optimized design of 5G networks but also directly affects user experience and the stability of business applications.

[0003] In existing 5G network testing methods, the test results of a single terminal usually cannot comprehensively reflect the network performance in a multi-user scenario. For example, in actual 5G application scenarios, there are usually multiple user terminals accessing simultaneously, and performance interference will occur between terminals due to resource competition. At the same time, the dynamic characteristics of the network (such as resource scheduling, slicing technology) cause the network performance to change with time and load conditions. Therefore, traditional single-terminal static testing methods cannot effectively capture these complex performance characteristics.

[0004] To address the above problems, joint testing of multiple terminals has become an important method for evaluating 5G network performance. By configuring multiple terminals and hardware relay devices to simulate large-scale access and diverse load conditions, the actual operation performance of the network can be more realistically reflected. However, the existing testing methods still have the following deficiencies in dealing with multi-terminal background traffic and dynamic performance analysis: Traditional testing methods mainly focus on static performance data at a single moment and lack the ability to dynamically analyze the change of network performance over time. This limitation results in the inability to capture the performance transfer law of the network under different load conditions.

[0005] 5G network performance involves multiple key indicators (such as throughput, latency, packet loss rate, signal quality, etc.), and there are complex dependencies between different indicators. Existing methods are difficult to extract meaningful information from multi-dimensional data.

[0006] In a multi-user access scenario, background load (such as traffic generated by auxiliary terminals) has a significant impact on network performance, but traditional methods usually fail to effectively integrate background load data for joint analysis. Summary of the Invention

[0007] To solve the problems in the prior art, the present invention provides a network performance testing method based on a 5G communication terminal, including the following steps: Step 1, configure N + 1 5G communication terminals and N hardware relay devices; Step 2: Select one of the N + 1 5G communication terminals as the current test terminal, and respectively correspond the remaining N 5G communication terminals to the N hardware relay devices one by one; Step 3: Use the N hardware relay devices to amplify and multiplex the signals of the corresponding 5G communication terminals; Step 4: Control the N + 1 5G communication terminals to perform data transmission tests; Step 5: Obtain the test data of the current test terminal and add it to the first test data group; Step 6: Obtain the test data of the remaining N 5G communication terminals and add it to the second test data group; Step 7: Repeat Steps 2 to 6, and successively use each of the N + 1 5G communication terminals as the current test terminal, obtain test data, add the test data of the current test terminal to the first test data group, and add the test data of the remaining N 5G communication terminals to the second test data group; Step 8: Analyze and determine the network performance based on the data in the first test data group and the second test data group.

[0008] Further, the 5G communication terminal is one or more of a smart phone, a fixed wireless access device, and an industrial-grade 5G module.

[0009] Further, the hardware relay device refers to a hardware device that can expand, amplify, and distribute wireless signals.

[0010] Further, the current test terminal acts as an active test device to simulate user behavior; the remaining N 5G communication terminals are used to generate background traffic to simulate a multi-user scenario.

[0011] Further, the multiplexing includes: for each multiplexed signal, the relay device adjusts its directivity to ensure that the coverage areas of the multiplexed signals do not overlap, and sets different powers for each multiplexed signal to simulate user access environments with different intensities.

[0012] Further, the control of the N + 1 5G communication terminals to perform data transmission tests includes: The current test terminal sends large data packets to a target server or network to test the uplink throughput and stability of the network; The current test terminal receives large data packets from a target server or network to test the downlink throughput and stability of the network; The current test terminal measures the end-to-end delay using small data packets and statistically analyzes the variation in the time intervals at which the data packets arrive; The current test terminal sends a preset number of data packets and statistically analyzes the proportion of lost data packets to evaluate the reliability of the network; Each auxiliary terminal sends and receives data according to a preset traffic model to simulate the background user traffic in a real scenario; Different auxiliary terminals are set with different service types and data rates; The auxiliary terminals simulate the traffic changes in different time periods.

[0013] Further, the test data includes: throughput, latency, jitter, packet loss rate, and signal quality.

[0014] Further, after each round, the data of the current test terminal is classified into the first test data group, and the data of the auxiliary terminals is classified into the second test data group; The data is marked according to the terminal number and the test round. After all terminals have served as the current test terminal once, the tests for all rounds are completed.

[0015] On the other hand, the present invention also provides a network performance test system based on a 5G communication terminal, which performs network performance tests using the aforementioned method.

[0016] A network performance test method based on a 5G communication terminal proposed by the present invention, by combining the first test data group and the second test data group, comprehensively analyzes the dynamic change law of network performance under different load conditions. Compared with the prior art, it has the following beneficial effects: This method combines the second test data group to introduce background load conditions, dynamically adjusts the hidden state transition probability and the observation probability distribution, so as to be able to reflect the real impact of background load on network performance in the scenario of multi-terminal concurrent access. This improvement makes the evaluation results more representative and can provide data support for resource allocation optimization in multi-user scenarios.

[0017] By combining multiple 5G terminals and hardware relay devices, this method can simulate large-scale user access and complex network environments under limited physical terminal conditions, is applicable to performance tests of various 5G application scenarios (such as industrial Internet of Things, autonomous driving, etc.), and the test results can truly reflect the network performance in the actual operating environment.

[0018] By introducing the Hidden Markov Model (HMM), this method can reveal the hidden states and transition laws of network performance changing over time, so as to capture the dynamic changes of 5G network performance under different load conditions. This dynamic analysis ability can help identify performance bottlenecks and abnormal transitions, and provide guidance for network optimization. This method uses an improved HMM to jointly model multi-dimensional performance indicators such as throughput, latency, and packet loss rate, and can more accurately reflect the comprehensive characteristics of network performance by capturing the potential correlations between various indicators.

[0019] Based on the state transition law of the Hidden Markov Model, this method can not only analyze the current network performance, but also predict the future performance state, helping to discover potential risks in advance and guiding the optimization of resource scheduling and configuration strategies for 5G networks.

[0020] In summary, the present invention realizes a comprehensive evaluation of 5G network performance by introducing an improved Hidden Markov Model and a multi-terminal joint testing method, and can provide a scientific basis for the optimization design, stable operation and user experience improvement of 5G networks, having significant technical value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Next, a preferred description of the invention will be made in combination with the drawings and the specific embodiments.

[0024] This embodiment solves the above problems through the following steps: In one embodiment, referring to Figure 1 , the present invention provides a network performance testing method based on 5G communication terminals, which involves comprehensively testing and analyzing various performance indicators of 5G networks by deploying and using multiple 5G communication terminal devices and auxiliary hardware relay devices, so as to evaluate the throughput, latency, jitter, packet loss rate, signal coverage range and handover performance of the network, thereby realizing an accurate measurement of the operating state of 5G networks and providing data support for network optimization and user experience improvement. Specifically, the method includes the following steps: Step 1, configure N + 1 5G communication terminals and N hardware relay devices.

[0025] The purpose of this step is to provide the necessary equipment configuration and basic conditions for the subsequent implementation of network performance testing.

[0026] A 5G communication terminal refers to a user terminal device that supports the fifth-generation mobile communication technology (5G), including but not limited to smartphones, fixed wireless access devices (such as CPE devices), industrial-grade 5G modules, etc. These terminal devices can connect to the 5G network, support 5G features such as high rate, low latency, and large-scale connection, and are the core devices for network performance testing. In this step, configure N + 1 5G communication terminals, where N is the number of terminals participating in signal amplification and distribution testing, which can be determined according to the test scenario or the existing number of terminals. For example, N can take values such as 10, 15, etc. The additional one terminal is used for the collection of actual test data.

[0027] A hardware relay device refers to a hardware device that can expand, amplify, and distribute wireless signals, including radio frequency repeaters, signal amplifiers, or protocol splitting devices. This device is used to receive the signals transmitted by 5G terminals and amplify them before distributing them to multiple virtual signal channels to simulate a larger-scale terminal access scenario. In this step, configure N hardware relay devices corresponding to N 5G communication terminals to ensure the quality and distribution of signal transmission.

[0028] In specific implementation, when selecting N + 1 5G communication terminals, it is necessary to ensure that the hardware performance and software configuration of the terminal devices meet the test requirements, including supporting the target test frequency band, having high data throughput capabilities, and being compatible with the protocol stack of the target test network (such as the 5GNR protocol). At the same time, the N configured hardware relay devices should have high-gain amplification characteristics and low-noise interference capabilities to ensure that the quality during signal amplification is not significantly lost. In addition, basic network connection configurations should be performed on all devices to ensure that the terminal devices can normally access the test network and the signal transmission between the hardware relay devices and the terminals is stable, so as to form a complete test environment.

[0029] In addition, the layout positions of the terminals and relays affect the test results, but the layout positions are not the focus of this invention. Those skilled in the art can determine the layout positions according to the test scenario. For example, indoor dense scenarios, outdoor wide-area scenarios, high-speed mobile scenarios, etc. are all commonly used test scenarios.

[0030] Step 2: Select one of the N + 1 5G communication terminals as the current test terminal, and respectively correspond the remaining N 5G communication terminals to the N hardware relay devices one by one.

[0031] The core of this step is to clarify the division of labor between the test terminal and the auxiliary terminals and establish a signal expansion and distribution path through the hardware relay devices to provide basic support for the subsequent collection of test data.

[0032] Select one of the configured N + 1 5G communication terminals as the current test terminal for the collection and analysis of actual test data.

[0033] The responsibilities of the current test terminal are as follows: Establish a direct connection with the target network.

[0034] Receive test data from the network (such as throughput, latency, and other metrics).

[0035] During the entire test process, as an active test device, simulate user behavior.

[0036] Designate the remaining N 5G communication terminals as auxiliary terminals. The main responsibilities of these terminals are: Establish a connection with the corresponding hardware relay device to complete the tasks of signal reception and transmission.

[0037] Cooperate with the relay device to complete signal amplification and distribution, providing support for simulating a larger-scale user behavior.

[0038] When the current test terminal is conducting a test, these auxiliary terminals are mainly responsible for generating background traffic to simulate a multi-user scenario.

[0039] For the N auxiliary terminals, map and bind them to the N hardware relay devices one by one to establish a corresponding relationship: Each auxiliary terminal is only connected to one relay device to ensure the independence of the signal path.

[0040] After receiving the signal from the auxiliary terminal, the hardware relay device amplifies and distributes it so that the signal can cover the target area or extend to other test areas.

[0041] Step 3: Use the N hardware relay devices to amplify and multiplex the signals of the corresponding 5G communication terminals.

[0042] Before signal amplification and distribution, the hardware relay device needs to be initialized and configured to ensure that it can correctly process the signals from the corresponding 5G communication terminals.

[0043] Ensure that each hardware relay device corresponds to one 5G communication terminal one by one, configure the device identifier (such as device ID or MAC address), and establish a connection relationship.

[0044] According to the actual working frequency band of the test target network (such as Sub-6GHz or millimeter wave band), configure the working frequency range of the relay device to match the signal characteristics of the 5G communication terminals.

[0045] Configure the signal gain parameter (such as amplification factor) of the relay device to ensure that the signal quality will not deteriorate due to insufficient amplification during signal transmission, nor will noise be introduced due to excessive amplification.

[0046] Signal amplification is a crucial step for hardware relay devices to enhance the received 5G signals, which is used to improve the signal strength and coverage.

[0047] The hardware relay device captures the radio frequency signals from the corresponding 5G communication terminals through the built-in receiving module.

[0048] Ensure that the quality of the received signal reaches a certain level (for example, RSRP is not lower than -110dBm).

[0049] The relay device amplifies the received signals, increases the signal power, and makes it adapt to the transmission requirements over longer distances.

[0050] During the amplification process, a low-noise amplifier (LNA) is used to reduce noise interference and ensure the integrity of the original data in the amplified signal.

[0051] The quality of the amplified signal is verified through signal testing tools to ensure that the signal strength and signal quality (such as signal-to-noise ratio SNR) meet the target requirements.

[0052] After signal amplification is completed, the relay device distributes the enhanced signals multiplexly to support the signal coverage requirements of multiple virtual terminals or different directions.

[0053] The relay device divides the enhanced signals into multiple paths through the built-in signal distribution module, and each path of the signal can be used independently or sent to a specific direction.

[0054] The number of distributed paths is set according to the test requirements. For example, it can be divided into 2 paths, 4 paths or more.

[0055] For each path of the distributed signal, the relay device can adjust its directivity (such as controlling the signal coverage range through a directional antenna) to adapt to the specific requirements of the test scenario.

[0056] Ensure that the coverage areas of the distributed signals do not overlap to reduce possible signal interference.

[0057] Set different powers for each path of the distributed signal to simulate user access environments with different intensities, such as strong signal areas and weak signal areas.

[0058] After signal amplification and multiplex distribution are completed, it is necessary to verify the signals to ensure that no errors or interference are introduced during the distribution process.

[0059] Use signal testing tools to measure the strength of each path of the distributed signal to ensure that its power level meets the set value.

[0060] Avoid problems such as excessive signal strength attenuation or uneven power distribution during the distribution process.

[0061] Measure the quality of each signal path (such as SNR, RSRP, RSRQ) to verify whether it meets the expected signal coverage requirements.

[0062] This step realizes signal amplification and multi-channel distribution through a hardware relay device, effectively expanding the signal coverage range of 5G communication terminals and creating a real and complex network environment for multi-terminal performance testing. Through reasonable signal amplification and distribution configurations, large-scale user behaviors in actual network scenarios can be effectively simulated, ensuring the representativeness and accuracy of test results.

[0063] Step 4: Control the N + 1 5G communication terminals to perform data transmission tests.

[0064] Data transmission of the current test terminal The current test terminal selected from the N + 1 5G communication terminals will undertake the main data transmission tasks: Uplink test: The current test terminal sends large data packets to the target server or network to test the uplink throughput and stability of the network.

[0065] Downlink test: The current test terminal receives large data packets from the target server or network to test the downlink throughput and stability of the network.

[0066] Latency and jitter test: Use small data packets (such as ICMP Ping) to measure the end-to-end latency and count the variation in the time intervals between the arrival of data packets (jitter).

[0067] Packet loss rate test: Send a certain number of data packets and count the proportion of lost data packets to evaluate the reliability of the network.

[0068] Cooperation tasks of auxiliary terminals The remaining N auxiliary terminals communicate with the target network through relay devices to simulate a multi-terminal concurrent environment: Background traffic generation: Each auxiliary terminal sends and receives data according to a preset traffic model (such as continuous video stream, large file download, periodic communication of IoT devices) to simulate background user traffic in a real scenario.

[0069] Terminal diversity: Different auxiliary terminals are set with different service types and data rates. For example, some terminals run high-bandwidth services (such as high-definition video streams), while others run low-bandwidth services (such as sensor data upload).

[0070] Dynamic load variation: Simulate traffic variations at different time periods, such as sudden high loads within a short period, to test the dynamic adjustment ability of the network.

[0071] Step 5: Obtain the test data of the current test terminal and add it to the first test data group.

[0072] Test data refers to the performance metrics collected through the interaction between the current test terminal and the target network during the data transmission test. It mainly includes: Throughput: The maximum data transmission rate of the network in the uplink or downlink, usually measured in Mbps or Gbps.

[0073] Latency: The time required from the terminal sending a data packet to receiving the response packet, measured in milliseconds (ms).

[0074] Jitter: The fluctuation of the data packet transmission time, reflecting the stability of the network.

[0075] Packet Loss Rate: The proportion of data packets lost during transmission, usually expressed as a percentage.

[0076] Signal Quality (RSRP, SINR, etc.): The strength and interference situation of the network signal.

[0077] The first test data group refers to the set used to store the test data generated by the current test terminal. This data group contains the network performance data recorded by each terminal as the current test terminal in all rounds. Summarize the independent performance metrics of the current test terminal to facilitate subsequent analysis of the performance differences between different terminals.

[0078] Step 6, obtain the test data of the remaining N 5G communication terminals and add them to the second test data group.

[0079] The remaining N 5G communication terminals refer to the N terminal devices that are not selected as the "current test terminal" in the current test round. They establish connections with the target network through hardware relay devices and serve as background traffic terminals or auxiliary terminals for simulating a multi-user environment. After signal amplification and distribution through the relay device, they are connected to the target network. Simulate different types of user behaviors, such as high-bandwidth requirements (video streaming), low-latency requirements (IoT), or sudden load changes. Generate background traffic to increase the load pressure on the target network. Record the performance data they experience during signal amplification and distribution.

[0080] The second test data group is used to store the set of test data generated by all auxiliary terminals in the current test round. The content of the data group: contains the test data of each auxiliary terminal, such as performance metrics like signal quality, throughput, latency, jitter, packet loss rate, etc.

[0081] For subsequent analysis of the effects of signal amplification and distribution. Evaluate the performance differences of auxiliary terminals in a multi-user environment.

[0082] Step 7: Repeat Steps 2 to 6. Take each of the N + 1 5G communication terminals as the current test terminal in turn, obtain test data, add the test data of the current test terminal to the first test data group, and add the test data of the remaining N 5G communication terminals to the second test data group.

[0083] In this step, by taking each of the N + 1 5G communication terminals as the current test terminal in turn and repeating Steps 2 to 6, the performance data of all terminals in different test rounds are completely collected and stored in the first test data group and the second test data group respectively. This step ensures that each terminal can participate in the main test task, and at the same time simulates the impact of other terminals as background loads on the target network, providing comprehensive data support for network performance analysis in a multi-terminal environment.

[0084] The complete test process is as follows: Test round initialization Define N + 1 test rounds. In each round, select one 5G communication terminal as the current test terminal, and the remaining N terminals as auxiliary terminals.

[0085] When switching test rounds, the roles of all terminals are exchanged in turn to ensure that each terminal can participate in both independent testing and background traffic generation.

[0086] Repeat Steps 2 to 6 Select the current test terminal (Step 2): In the i-th round of testing, select the i-th terminal (Ti) as the current test terminal, and the remaining terminals (T1 to Ti - 1 and Ti + 1 to TN + 1) as auxiliary terminals, and connect to the relay device to generate background traffic.

[0087] Signal amplification and distribution of the relay device (Step 3): The relay device amplifies and distributes the signals of all auxiliary terminals to simulate signal expansion in a multi-terminal environment.

[0088] Execute data transmission testing (Step 4): The current test terminal communicates directly with the target network to complete the testing of key indicators such as throughput, latency, jitter, and packet loss rate.

[0089] The auxiliary terminals generate background traffic to simulate an actual multi-user scenario.

[0090] Obtain the test data of the current test terminal (Step 5): Collect all test data from the current test terminal and store it in the first test data group.

[0091] Obtain the test data of the auxiliary terminals (Step 6): Collect test data from the auxiliary terminal and store it in the second test data group.

[0092] After each round, classify the data of the current test terminal into the first test data group and the data of the auxiliary terminal into the second test data group.

[0093] Mark the data according to the terminal number and test round to ensure the accuracy and consistency of subsequent data analysis.

[0094] After all terminals have served as the current test terminal once, complete all rounds of testing.

[0095] Exemplarily, assume there are 3 5G communication terminals (T1, T2, T3) and 2 hardware relay devices. The specific round operations of the test are as follows: Round 1: T1 serves as the current test terminal Current test terminal: T1 communicates directly with the target network, and records performance data such as throughput (250 Mbps), latency (15 ms), packet loss rate (0.5%).

[0096] The data is stored in the first test data group.

[0097] Auxiliary terminal: T2 and T3 connect to the network through relay devices, generate background traffic, and record throughput (120 Mbps and 150 Mbps) and other metrics respectively.

[0098] The data is stored in the second test data group.

[0099] Round 2: T2 serves as the current test terminal Current test terminal: T2 conducts direct testing and records performance data such as throughput (200 Mbps), latency (20 ms), packet loss rate (1.2%).

[0100] The data is stored in the first test data group.

[0101] Auxiliary terminal: T1 and T3 serve as auxiliary terminals, generate background traffic, and record throughput (140 Mbps and 170 Mbps) and other metrics respectively.

[0102] The data is stored in the second test data group.

[0103] Round 3: T3 serves as the current test terminal Current test terminal: T3 conducts direct testing and records performance data such as throughput (230 Mbps), latency (18 ms), packet loss rate (0.8%).

[0104] The data is stored into the first test data group.

[0105] Auxiliary terminals: T1 and T2 are used as auxiliary terminals, and respectively record the throughput (160 Mbps and 140 Mbps) and other metrics.

[0106] The data is stored into the second test data group.

[0107] Step 8: Analyze and determine the network performance based on the data of the first test data group and the second test data group.

[0108] In this step, by comprehensively analyzing the performance data of the first test data group and the second test data group, the hidden Markov model (HMM) is used to mine the hidden states in the time series data, revealing the state transition law of the network performance under different load conditions, and providing a basis for evaluating the network adaptability and optimization strategies.

[0109] The traditional hidden Markov model (HMM) is mainly applicable to single-variable time series modeling, such as the analysis of the change of a single performance metric (such as throughput) over time. However, in this method, the network performance evaluation depends on multiple key metrics (such as throughput, delay, packet loss rate, etc.) and background load conditions (performance data from auxiliary terminals). There are usually complex non-linear relationships and dependencies among these metrics, and it is difficult for the single-variable HMM to capture these interaction information.

[0110] To adapt to multi-variable time series data, the core of the improvement of the hidden Markov model in the present invention lies in: Jointly model multiple observed variables (such as throughput, delay, packet loss rate) to capture the correlation among the metrics.

[0111] Dynamically fuse the background load information to make the relationship between the hidden states and the observed variables closer to the real network scenario.

[0112] Specifically, it includes the following steps: Step 8.1: Prepare and preprocess the data Extract the time series data (such as performance metrics like throughput, delay, packet loss rate) of the current test terminal from the first test data group.

[0113] Extract the performance data (such as background load traffic, signal strength, etc.) of the auxiliary terminals from the second test data group as a reference for the background load conditions.

[0114] Normalize the metrics such as throughput, delay, and packet loss rate to make their ranges consistent for easy modeling.

[0115] If the acquisition times of the first test data set and the second test data set are not synchronized, interpolation or alignment operations are used to ensure that the timestamps are consistent.

[0116] Step 8.2: Define the Hidden Markov Model structure Divide the network performance into three hidden states: : High-performance state (high throughput, low latency, low packet loss rate).

[0117] : Medium-performance state (moderate throughput, moderate latency and packet loss rate).

[0118] : Low-performance state (low throughput, high latency, high packet loss rate).

[0119] Define the observation variables: The observation vector includes throughput ( ), latency ( ), and packet loss rate ( ), forming a multi-dimensional time series: Introduce the background load condition: Use the background load data (throughput or traffic intensity of the auxiliary terminal) as a dynamic influencing factor: , that is, the background load at time t.

[0120] Step 8.3: Initialize the model parameters Initial state distribution: Set the initial hidden state probability. The initial state probability can be estimated according to the observation data distribution at the first time point, such as: State transition probability matrix: The state transition probability matrix A describes the transition law between hidden states. The possible state transitions can be statistically analyzed according to the performance change patterns of every two consecutive time points in the observation sequence. Initialize the transition probability matrix of the hidden states as The sum of each row is 1, indicating the total probability of transitioning from a certain state to other states.

[0121] Observation probability distribution: Assume that the observation variables follow a normal distribution, and initialize the mean and covariance matrices: Among them, represents the state at time t, j represents the state value, represents the mean vector of each hidden state; represents the covariance matrix of each hidden state.

[0122] Step 8.4: Train the model Model input: Input multi-dimensional observation data (throughput, latency, packet loss rate) and background load data.

[0123] Training algorithm: Use the Baum-Welch algorithm to train the model parameters and update the state transition probability matrix , considering the dynamic impact of background load: where represents the probability of transitioning from state i to state j, , is the transition probability function under the influence of background load conditions, represents the state transition probability matrix at time t, and update the observation probability distribution parameters ( , ).

[0124] Convergence condition: Iteratively train until the log-likelihood value converges (e.g., the change is less than the set threshold).

[0125] Step 8.5: State decoding Input data: Use the observation sequence (test data) and the trained model parameters.

[0126] Decoding algorithm: Apply the Viterbi algorithm to infer the hidden state sequence corresponding to each time point.

[0127] Result output: Generate the hidden state sequence to reveal the dynamic changes of network performance over time.

[0128] Step 8.6: Analyze the hidden state and state transition rules State distribution analysis: Statistically analyze the proportion of each hidden state to evaluate the distribution of the network in high-performance, medium-performance, and low-performance states.

[0129] Example result: High-performance state : 60% of the time.

[0130] Medium-performance state : 30% of the time.

[0131] Low-performance state : 10% of the time.

[0132] State transition analysis: Use the trained transition probability matrix to analyze the transition rules between states: High performance ( ) → Medium performance ( ): Probability 0.15.

[0133] Medium performance ( ) → Low performance ( ): Probability 0.2.

[0134] Influence of background load on state transition: During the background load peak, → the transition probability significantly increases, indicating that the network is prone to enter the low - performance state under high load.

[0135] Through the above steps, based on the improved Hidden Markov Model (HMM), a comprehensive time - series analysis of the test data can be carried out, revealing the hidden states of the network performance and its dynamic transition rules. At the same time, embedding the background load condition makes the model more realistic, providing profound insights for the resource optimization and performance improvement of 5G networks.

[0136] On the other hand, the present invention also provides a network performance testing system based on a 5G communication terminal, which uses the aforementioned method for network performance testing.

[0137] For the part of the module structure not specifically defined in the present invention, it shall be subject to the content recorded in the prior art. The prior art mentioned in the foregoing background art part and the specific embodiment part of the present invention can be used as a part of the present invention to understand the meaning of some technical features or parameters.

Claims

1. A network performance testing method based on a 5G communication terminal, characterized in that, The method includes the following steps: Step 1, configure N + 1 5G communication terminals and N hardware relay devices; Step 2, select one of the N + 1 5G communication terminals as the current test terminal, and correspond the remaining N 5G communication terminals to the N hardware relay devices one by one; Step 3, use the N hardware relay devices to amplify and multiplex the signals of the corresponding 5G communication terminals; Step 4, control the N + 1 5G communication terminals to perform data transmission tests; Step 5, obtain the test data of the current test terminal and add it to the first test data group; Step 6, obtain the test data of the remaining N 5G communication terminals and add it to the second test data group; Step 7, repeat steps 2 to 6, successively use each of the N + 1 5G communication terminals as the current test terminal, obtain test data, add the test data of the current test terminal to the first test data group, and add the test data of the remaining N 5G communication terminals to the second test data group; Step 8, analyze and determine the network performance based on the data in the first test data group and the second test data group.

2. The network performance testing method based on a 5G communication terminal according to claim 1, wherein, The 5G communication terminal is one or more of a smart phone, a fixed wireless access device, and an industrial-grade 5G module.

3. A network performance testing method based on a 5G communication terminal according to claim 1, wherein, The hardware relay device refers to a hardware device that can expand, amplify, and distribute wireless signals.

4. A network performance testing method based on a 5G communication terminal according to claim 1, characterized in that, The current test terminal acts as an active test device to simulate user behavior; the remaining N 5G communication terminals are used to generate background traffic to simulate a multi-user scenario.

5. A network performance testing method based on a 5G communication terminal according to claim 1, characterized in that, The multiplexing includes: for each allocated signal, the relay device adjusts its directivity to ensure that the coverage areas of the allocated signals do not overlap, and sets different powers for each allocated signal to simulate different-intensity user access environments.

6. A network performance testing method based on a 5G communication terminal according to claim 1, characterized in that, The control of the N + 1 5G communication terminals to perform data transmission tests includes: The current test terminal sends a large data packet to a target server or network to test the uplink throughput and stability of the network; The current test terminal receives a large data packet from a target server or network to test the downlink throughput and stability of the network; The current test terminal uses small data packets to measure the end-to-end delay and statistically analyze the change in the time interval when the data packets arrive; The current test terminal sends a preset number of data packets and statistically analyzes the proportion of lost data packets to evaluate the reliability of the network; Each auxiliary terminal sends and receives data according to a preset traffic model to simulate the background user traffic in a real scenario; Different auxiliary terminals are set with different service types and data rates; The auxiliary terminals simulate the traffic changes at different time periods.

7. A network performance testing method based on a 5G communication terminal according to claim 1, characterized in that The test data includes: throughput, delay, jitter, packet loss rate, and signal quality.

8. A network performance testing method based on a 5G communication terminal according to claim 1, characterized in that, Step 7 includes: After each round, classify the data of the current test terminal into the first test data group and the data of the auxiliary terminals into the second test data group; Mark the data according to the terminal number and the test round. After all terminals have served as the current test terminal once, complete all rounds of tests.

9. A network performance testing system based on a 5G communication terminal, characterized in that, The system uses the method described in any one of claims 1-8 for network performance testing.

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