Automatic evaluation and verification method and system for time sequence adaptive communication experiment

Abstract

The invention relates to the technical field of communication, in particular to a time sequence adaptive communication experiment automatic evaluation and verification method and system, and the method comprises the steps: S1, dynamically generating multi-scene channel parameters, including high-speed movement, dense multipath and burst interference scenes, and driving channel simulation through a predefined time-frequency domain parameter model; s2, monitoring key performance parameters of the communication system in real time, including bit error rate, synchronization error, delay jitter and spectral efficiency, and performing data acquisition at a sampling frequency not lower than 1kHz; s3, testing the adaptive algorithm through a dual-mode verification engine; s4, automatically calculating a synchronization retention rate, a spectrum efficiency improvement rate and a dynamic adjustment effectiveness index; and S5, generating a three-dimensional visualization report including a time domain performance curved surface, frequency domain feature distribution and an energy consumption analysis result. The invention discloses an automatic evaluation and verification method and system for a time sequence adaptive communication experiment. The test efficiency is remarkably improved, and the one-sidedness problem of traditional single-index evaluation is solved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and system for automatic evaluation and verification of a timing adaptive communication experiment. Background Art

[0002] The timing adaptive communication experiment aims to solve the problem of real-time performance optimization of communication systems in dynamic wireless environments. With the evolution of 5G / 6G communications to high-frequency bands and high-mobility scenarios (such as Internet of Vehicles, drone communications, and industrial Internet of Things), channel characteristics have shown rapid time-varying characteristics, which are typically manifested as Doppler frequency shifts exceeding 500Hz, delay spreads reaching microseconds, and frequent burst interference. Traditional fixed-parameter communication systems are difficult to maintain reliable connections in such dynamic environments, and timing adaptive technology is required to achieve real-time optimization of core parameters such as symbol synchronization accuracy (±0.1 symbol period) and dynamic reconstruction of frame structure (adjustment period <5ms). The core goal of this type of experiment is to verify the adaptability of the communication system in time-varying channels, ensuring that the bit error rate can still be achieved under extreme scenarios such as high-speed movement of 80km / h and multipath in dense urban areas. -5 , and the key performance indicator of end-to-end delay <5ms, providing technical support for real-time sensitive applications such as intelligent transportation and remote industrial control.

[0003] Current timing adaptive communication experimental methods have significant defects and are unable to meet the verification needs of complex dynamic environments. For example, due to the limitations of scenario simulation, traditional channel simulations mostly use static or periodic change models, which cannot accurately reproduce non-stationary characteristics such as instantaneous SINR drops (such as 30dB / 5ms) and rapid jumps in delay spread, resulting in insufficient verification of the effectiveness of the algorithm in real burst scenarios; the test dimension is single: existing evaluations mostly rely on single indicators such as bit error rate, lack of joint analysis of multiple parameters such as synchronization error, delay jitter, and spectrum efficiency, and the artificial sampling frequency (usually <100Hz) makes it difficult to capture microsecond timing fluctuations.

[0004] In order to solve the above problems, we make improvements and propose an automatic evaluation and verification method and system for timing adaptive communication experiments. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] The present invention provides a method for automatic evaluation and verification of a timing adaptive communication experiment, comprising the following steps:

[0007] S1. Dynamically generate channel parameters for multiple scenarios, including high-speed mobility, dense multipath, and sudden interference, and drive channel simulation through predefined time-frequency domain parameter models.

[0008] S2. Real-time monitoring of key performance parameters of the communication system, including bit error rate, synchronization error, delay jitter, and spectral efficiency, with data acquisition at a sampling frequency of no less than 1 kHz;

[0009] S3. Test the adaptive algorithm using a dual-mode verification engine, including:

[0010] a. White box testing: injecting a preset channel mutation sequence to verify the response timing of the decision logic;

[0011] b. Black box testing: Generate random channel perturbations based on the Monte Carlo method to evaluate the algorithm convergence speed;

[0012] S4. Automatically calculate synchronization retention rate, spectrum efficiency improvement rate, and dynamic adjustment effectiveness index;

[0013] S5. Generate a three-dimensional visualization report, including the time domain performance surface, frequency domain feature distribution, and energy consumption analysis results.

[0014] As a preferred technical solution of the present invention, the generation of the channel parameters includes:

[0015] Doppler frequency shift range 100Hz to 1kHz, step accuracy ±10Hz;

[0016] The delay spread parameter includes a linear distribution from 0.1μs to 3μs;

[0017] In the burst interference scenario, the SINR drops sharply by 20dB-30dB and lasts for 5ms-15ms.

[0018] As a preferred technical solution of the present invention, the white box test includes:

[0019] Preset symbol-synchronous loop mutation test sequence, including phase steps of ±0.2 symbol period;

[0020] The rising edge of the synchronization signal is captured by a high-speed oscilloscope, and the measurement time resolution is not less than 1ns.

[0021] As a preferred technical solution of the present invention, the calculation formula of the dynamic adjustment effectiveness index is:

[0022]

[0023] Among them, Q target is the target performance threshold, Q actual is the actual measured value.

[0024] As a preferred technical solution of the present invention, a comparative experimental group is also constructed, including three groups of controls: a traditional algorithm with fixed parameters, an adaptive algorithm without an AI module, and a complete system;

[0025] The system stability is evaluated by performing time-frequency joint analysis using Wigner-Ville distribution.

[0026] A method for automatically evaluating and verifying a system for a time-series adaptive communication experiment, comprising:

[0027] Software-defined radio (SDR) with at least an 8-antenna array and 100 MHz instantaneous bandwidth;

[0028] Channel simulation module, supporting Rayleigh time-varying channel modeling and real-time parameter injection;

[0029] Computing server, integrating GPU accelerator card and FPGA processing unit, for executing adaptive algorithms;

[0030] Real-time monitoring dashboard showing 3D performance surfaces and dynamic constellation diagrams.

[0031] As a preferred technical solution of the present invention, the channel simulation module includes:

[0032] Doppler frequency shift simulation unit, frequency adjustment accuracy ±5Hz;

[0033] Programmable delay line array, supporting delay extension of 0.1μs-5μs;

[0034] Burst interference generator, including 20dB-40dB adjustable attenuator.

[0035] As a preferred technical solution of the present invention, the real-time monitoring dashboard includes a symbol-level synchronization error heat map, a delay jitter probability density distribution map, and a Pareto front curve of spectrum efficiency and bit error rate.

[0036] The beneficial effects of the present invention are: a method and system for automatic evaluation and verification of timing adaptive communication experiments, which achieves 72 hours of continuous testing through dynamic scenario generation and automated evaluation process, which is 20 times more efficient than traditional manual testing. A single experiment can cover more than 1,000 parameter combinations, significantly improving testing efficiency; the innovative three-dimensional performance surface analysis (time domain / frequency domain / energy consumption) can simultaneously capture the system's performance in 12 orthogonal dimensions, solving the one-sidedness problem of traditional single-indicator evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0038] Figure 1 The present invention is a flowchart of a method and system for automatic evaluation and verification of a timing adaptive communication experiment. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0040] Example: Figure 1 As shown, a method for automatic evaluation and verification of a timing adaptive communication experiment includes the following steps:

[0041] S1. Dynamically generate channel parameters for multiple scenarios, including high-speed mobility, dense multipath, and burst interference. Channel simulation is driven by predefined time-frequency domain parameter models. Based on a scenario database (high-speed mobility, dense multipath, and burst interference), parameterized models are invoked to generate dynamic channel characteristics in both time and frequency dimensions. A Rayleigh time-varying channel model is used to superimpose burst interference pulses. The interference pulses have a rise time of ≤100ns and a programmable duration of 5ms-15ms.

[0042] S2. Real-time monitoring of key performance parameters of the communication system, including bit error rate, synchronization error, delay jitter, and spectral efficiency, with data acquisition at a sampling frequency of at least 1 kHz. Bit error rate (BER) is captured using a parallel data acquisition card at a 1 kHz sampling rate. Synchronization error is measured using a symbol-level sliding window (window length 10 symbol periods). Delay jitter monitoring uses high-precision timestamps (resolution 1 ns). Monitoring parameters are uploaded to a distributed database in real time. The data is stored in a time series tagged structure, consisting of a triplet of timestamp, channel status, and device status.

[0043] S3. Test the adaptive algorithm using a dual-mode verification engine, including:

[0044] a. White box testing: Injecting a preset channel mutation sequence to verify the response timing of the decision logic. This includes SNR step changes (±10dB / ms) and phase mutations (±0.2 symbol periods). Using a logic analyzer, capture the FPGA's internal state machine transitions to verify that the decision engine response time is ≤2ms.

[0045] b. Black-box testing: Random channel perturbations are generated using the Monte Carlo method to evaluate the algorithm's convergence speed. The parameter space covers Doppler shift (normal distribution σ = 150 Hz) and delay spread (uniform distribution 0.1-3 μs). Convergence is evaluated using the sliding window variance method. Convergence is considered achieved when the BER standard deviation within 10 consecutive windows is < 1e-6.

[0046] S4. Automatically calculate synchronization retention rate, spectrum efficiency improvement rate, and dynamic adjustment effectiveness index;

[0047] S5. Generate a 3D visualization report, including the time-domain performance surface, frequency-domain feature distribution, and energy consumption analysis results. Generate a time-frequency domain joint analysis surface plot, with time (0-100ms) on the horizontal axis and frequency (0-1kHz) on the vertical axis. A chromaticity map indicates the bit error rate level.

[0048] Comparative experimental group settings:

[0049] Baseline group: fixed symbol period length (4μs), constant modulation order (QPSK);

[0050] Experimental Group A: Adaptive algorithm based on traditional Kalman filter;

[0051] Experimental Group B: Hybrid optimization algorithm integrating deep reinforcement learning.

[0052] Preferably, the generation of channel parameters includes: Doppler frequency shift range of 100Hz to 1kHz, step accuracy of ±10Hz; delay extension parameters include a linear distribution of 0.1μs to 3μs; SINR drop amplitude in burst interference scenario is 20dB-30dB, duration is 5ms-15ms.

[0053] Preferably, the white box test includes: presetting a symbol synchronization loop mutation test sequence, including a phase step of ±0.2 symbol period; capturing the rising edge of the synchronization signal by a high-speed oscilloscope, and measuring a time resolution of not less than 1ns.

[0054] Preferably, the calculation formula for the dynamic adjustment effectiveness index is:

[0055]

[0056] Among them, Q target is the target performance threshold, Q actual is the actual measured value.

[0057] Preferably, a comparative experimental group is constructed, including three groups of controls: a traditional algorithm with fixed parameters, an adaptive algorithm without an AI module, and a complete system; and a time-frequency joint analysis is performed through Wigner-Ville distribution to evaluate the system stability.

[0058] An automatic evaluation and verification system for timing adaptive communication experiments includes: a software-defined radio (SDR) equipped with at least an 8-antenna array and 100 MHz instantaneous bandwidth; a channel simulation module that supports Rayleigh time-varying channel modeling and real-time parameter injection; a computing server integrating a GPU accelerator card and an FPGA processing unit for executing adaptive algorithms; and a real-time monitoring dashboard that displays a three-dimensional performance surface and a dynamic constellation diagram.

[0059] Preferably, the channel simulation module includes: a Doppler frequency shift simulation unit with a frequency adjustment accuracy of ±5Hz; a programmable delay line array supporting a delay extension of 0.1μs-5μs; and a burst interference generator including an adjustable attenuator of 20dB-40dB.

[0060] Preferably, the real-time monitoring dashboard includes a symbol-level synchronization error heat map, a delay jitter probability density distribution map, and a Pareto front curve of spectrum efficiency and bit error rate.

[0061] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for automatic evaluation and verification of a time-series adaptive communication experiment, characterized in that: The following steps are involved: S1. Dynamically generate channel parameters for multiple scenarios, including high-speed mobility, dense multipath, and sudden interference, and drive channel simulation through predefined time-frequency domain parameter models. S2. Real-time monitoring of key performance parameters of the communication system, including bit error rate, synchronization error, delay jitter, and spectral efficiency, with data acquisition at a sampling frequency of no less than 1 kHz; S3. Test the adaptive algorithm using a dual-mode verification engine, including: a. White box testing: injecting a preset channel mutation sequence to verify the response timing of the decision logic; b. Black box testing: Generate random channel perturbations based on the Monte Carlo method to evaluate the algorithm convergence speed; S4. Automatically calculate synchronization retention rate, spectrum efficiency improvement rate, and dynamic adjustment effectiveness index; S5. Generate a three-dimensional visualization report, including the time domain performance surface, frequency domain feature distribution, and energy consumption analysis results.

2. The method for automatic evaluation and verification of a timing adaptive communication experiment according to claim 1, characterized in that: The generation of the channel parameters includes: Doppler frequency shift range 100Hz to 1kHz, step accuracy ±10Hz; The delay spread parameter includes a linear distribution from 0.1μs to 3μs; In the burst interference scenario, the SINR drops sharply by 20dB-30dB and lasts for 5ms-15ms.

3. The method for automatic evaluation and verification of a timing adaptive communication experiment according to claim 1, characterized in that: The white box testing includes: Preset symbol-synchronous loop mutation test sequence, including phase steps of ±0.2 symbol period; The rising edge of the synchronization signal is captured by a high-speed oscilloscope, and the measurement time resolution is not less than 1ns.

4. The method for automatic evaluation and verification of a timing adaptive communication experiment according to claim 1, characterized in that: The calculation formula of the dynamic adjustment effectiveness index is: Among them, Q target is the target performance threshold, Q actual is the actual measured value.

5. The method for automatic evaluation and verification of a timing adaptive communication experiment according to claim 1, characterized in that: It also includes the construction of a comparative experimental group, including three groups of controls: a traditional algorithm with fixed parameters, an adaptive algorithm without an AI module, and a complete system; The system stability is evaluated by performing time-frequency joint analysis using Wigner-Ville distribution.

6. A timing adaptive communication experiment automatic evaluation and verification system according to claims 1-5, characterized in that: include: Software-defined radio (SDR) with at least an 8-antenna array and 100 MHz instantaneous bandwidth; Channel simulation module, supporting Rayleigh time-varying channel modeling and real-time parameter injection; Computing server, integrating GPU accelerator card and FPGA processing unit, for executing adaptive algorithms; Real-time monitoring dashboard showing 3D performance surfaces and dynamic constellation diagrams.

7. The automatic evaluation and verification system for timing adaptive communication experiments according to claim 6, characterized in that: The channel simulation module includes: Doppler frequency shift simulation unit, frequency adjustment accuracy ±5Hz; Programmable delay line array, supporting delay extension of 0.1μs-5μs; Burst interference generator, including 20dB-40dB adjustable attenuator.

8. The automatic evaluation and verification system for timing adaptive communication experiments according to claim 6, characterized in that: The real-time monitoring dashboard includes a symbol-level synchronization error heat map, a delay jitter probability density distribution map, and a Pareto front curve of spectrum efficiency and bit error rate.

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