A method for constructing an adaptive communication transmission simulation platform
By constructing an adaptive communication transmission simulation platform, the adaptive problem of communication networks in modern warfare environments was solved, enabling dynamic adjustment and resource optimization in complex channel environments, and improving the adaptive capability and performance of communication systems.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGNAN ELECTROMECHANICAL DESIGN INST
- Filing Date
- 2026-03-30
- Publication Date
- 2026-07-14
AI Technical Summary
In modern warfare, the link status between communication network nodes is greatly affected by the field environment and the status of mission nodes. The network topology changes in real time and the link quality fluctuates randomly. Existing technologies are unable to achieve adaptation to the mission environment and rational utilization of resources.
An adaptive communication transmission simulation platform is constructed by determining the maximum communication capability index, establishing a signal-to-noise ratio estimation model and a baseband impulse response model, defining a control engine, and realizing intelligent perception and dynamic adjustment of the network environment. This includes the construction of signal-to-noise ratio estimation, baseband impulse response model, and control engine, thus building an adaptive communication transmission simulation platform.
It achieves adaptive capability to complex and variable channel environments, can adjust the communication network configuration in real time, improves the flexibility of communication rate and coding method, and outperforms the performance of traditional communication systems under bit error rate and channel conditions.
Smart Images
Figure CN122394738A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated communication systems, and more specifically, to a method for constructing an adaptive communication transmission simulation platform. Background Technology
[0002] In the real-world environment of modern warfare, the link status between nodes in a communication network is greatly affected by the field environment and the status of mission nodes. This is mainly manifested in the following ways: the random mobility of mission nodes causes real-time changes in the network topology; the complexity of the field terrain and electromagnetic spectrum environment causes random fluctuations in link quality and channel fading, thus affecting communication between nodes and information routing. In order to ensure the rational use of network resources and achieve adaptation to the mission environment and mission, the communication network must acquire real-time and accurate information on the field environment and the communication status of other nodes.
[0003] Therefore, a simulation scheme is needed to perceive the network environment and intelligently perceive the overall situation of the on-site environment. Summary of the Invention
[0004] To achieve the above objectives, this application provides a method for constructing an adaptive communication transmission simulation platform, comprising the following steps: Determine the maximum communication capacity specification, which is compatible with both large and small network devices; A signal-to-noise ratio (SNR) estimation model is established, which is used to calculate the SNR based on the error vector magnitude. A baseband impulse response model based on a multipath channel is established, which is used to estimate channel delay. Define and train a control engine, which is used to collect external sensing signals, process them and form control commands to control the parameters of each communication link; Based on the signal-to-noise ratio estimation model, baseband impulse response model, and control engine, an adaptive communication transmission simulation platform is constructed, and the sampling rate of the adaptive communication transmission simulation platform is set to adapt to the maximum communication capability index.
[0005] Determining the maximum communication capacity index includes: determining the maximum communication throughput of each device and calculating the air interface rate based on the highest communication throughput.
[0006] Furthermore, the signal-to-noise ratio (SNR) estimation model is implemented based on the error vector magnitude, and is expressed as: Where SNR is the signal-to-noise ratio and EVM is the error vector magnitude; in, , in, To measure the nth symbol in the data stream, For the corresponding The ideal constellation point, where N is the number of different constellation points in the constellation diagram.
[0007] Furthermore, under additive white Gaussian noise conditions, the calculation model for the EVM is as follows: ,in, , This represents the components of two orthogonal Gaussian white noise paths, I and Q. To normalize the power of the ideal constellation points, T is the number of symbols used to calculate the mean square EVM, and t is the symbol index of the mean square EVM, with a value range of [1, T].
[0008] The baseband impulse response model is expressed as follows: , in, For the number of multipaths, Let be the magnitude of the i-th path. Let be the relative delay of the i-th path. Let t be the relative extension of the i-th path, and t be the continuous time.
[0009] The output signal after passing through the baseband impulse response model is expressed as: , in, It is additive white Gaussian noise. For the training sequence over time delay τ, Let be the relative delay of the i-th path. This represents convolution.
[0010] The control engine takes the output of the sensing module as its input, and the output includes the optimal adjustment rate, coding efficiency, and modulation scheme. The output is then transmitted to the modulation and coding module and the source module to generate the minimum bit error rate. The sensing module consists of a signal-to-noise ratio estimation model and a baseband impulse response model.
[0011] Furthermore, the adaptive communication transmission simulation platform includes: calculating the signal-to-noise ratio (SNR) through a signal-to-noise ratio estimation model; setting a baseband impulse response model to calculate the channel delay; and outputting control commands through a cognitive engine based on the SNR and channel delay, feeding the signals back to the input and output to guide the entire communication system to make dynamic adjustments.
[0012] According to the present invention, the existing network environment can be autonomously perceived, and the configuration of the communication network can be adjusted in real time through understanding the network environment, so as to intelligently adapt to changes in the task environment. The adaptive communication transmission simulation platform constructed by the present invention has the ability to dynamically adjust the communication rate, encoding method and modulation method, and can realize adaptive communication transmission in the environment. Attached Figure Description
[0013] Figure 1 This is a schematic diagram illustrating the construction steps of an adaptive communication transmission simulation platform provided according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the time slot structure for signal transmission according to an embodiment of the present invention; Figure 3 This is a schematic diagram of automatic delay adjustment delay calculation for signal transmission according to an embodiment of the present invention; Figure 4 This is a schematic diagram of automatic delay adjustment for signal transmission provided in an embodiment of the present invention; Figure 5 This is a comparison chart of the simulation structure and simulation verification effect of the signal-to-noise ratio estimation algorithm provided in the embodiments of the present invention; Figure 6 This is a schematic diagram of the channel model provided according to an embodiment of the present invention; Figure 7 This is a schematic diagram of a Simulink-based simulation platform provided according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the channel delay results predicted by the baseband impulse response model according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the simulation results of the adaptive communication transmission simulation platform provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the burst bit error rate statistics of the adaptive communication transmission simulation platform provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the burst error rate statistics of a traditional communication system; Figure 12 This is a schematic diagram of signal-to-noise ratio fading in a Rayleigh channel within an adaptive communication transmission simulation platform provided according to an embodiment of the present invention; Figure 13 This is a schematic diagram of the control engine structure provided according to an embodiment of the present invention. Detailed Implementation
[0014] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.
[0015] The method for constructing the adaptive communication transmission simulation platform provided by this invention is as follows: Figure 1 As shown, it includes the following steps: Step S100: Determine the maximum communication capability index, which is compatible with both large and small network devices; In real-world applications, devices requiring communication can be categorized into large network devices, small network devices, and guidance command data links. When constructing a simulation environment, it is essential to ensure that the simulation system can meet the maximum communication capability requirements. 1) First, determine the maximum communication throughput of each device: Typically, different types of devices have different communication metrics, as shown in Table 1: Table 1 Communication Indicators of Each Device
[0016] Based on a line-of-sight communication range of 100km, the calculated communication throughput is shown in Table 2: Table 2 Comparison of communication throughput of each device
[0017] In this step, the communication metrics of all devices with communication needs are converted to a unified communication environment to determine the maximum communication throughput; as shown in Table 2, large network devices achieve the highest communication throughput.
[0018] 2) Calculate the air interface rate based on the highest communication throughput; As shown in Table 1, the maximum transmission distance of large network equipment is 35 kilometers. The transmission protection time is set to 120µs, and the power amplifier switch, MAC layer, and physical layer interface latency are all 40µs. The end-to-end latency requirement is less than 15ms. The frame structure is designed as follows: Figure 2 As shown, the main station time slot structure is as follows: Figure 3 As shown, the slave time slot structure is as follows: Figure 4 As shown.
[0019] During the calculation, RS encoding with a bit rate close to 7 / 8 (175, 151, 8 bits) is used. The amount of data in a single time slot is 151×8×12×8 / 7=16567 bits, and the total overhead bytes such as convergence bytes and protection bytes in the time slot are 1748 bits. Actual workload = data volume in a single time slot + overhead bytes, i.e., 16567 bits + 1748 bits = 18315 bits; The transmission time of a single time slot = data time slot time - protection time - power amplifier switching delay, i.e.: 498us - 120us - 5us = 373us; Therefore, the actual air interface rate = actual traffic volume / transmission time per time slot, that is: 18315bit / 373us = 49.10Mbps; According to the receiving threshold calculation formula: Receiver sensitivity = thermal noise level (kTB) + noise figure (NF) + demodulation threshold (S / NR); Among them, thermal noise level (kTB) refers to the thermal noise level under a specific bandwidth B; kTB = thermal noise power spectral density + bandwidth used, i.e. kT + 10lg10B; kT = Boltzmann constant × absolute temperature, i.e.: 10lg(1.381×10-23J / K×290K) = -174dBm; The demodulation threshold (S / NR) used in this invention is 12.1 dB, and the noise figure (NF) is 4.5 dB. Based on the receive threshold calculation formula, the receive threshold at the actual air interface rate is obtained; expressed as: Receive threshold P r0 = Thermal noise level (kTB) + Noise figure (NF) + Demodulation threshold (S / NR) That is: -174 + 10lg(49.10 × 10) 6 ) + 4.5 + 12.1 = -80.49 dBm; In contrast, the threshold for an air interface rate of 50 Mbps is: P r0 =-174+10lg(50×10 6 )+4.5+12.1=-80.41dBm.
[0020] Therefore, taking into account hardware versatility, scalability, and user requirements for thresholds, the air interface rate is designed to be 50Mbps.
[0021] Step S110: Establish a signal-to-noise ratio (SNR) estimation model, which is used to calculate the SNR based on the error vector magnitude; Signal-to-noise ratio (SNR) is a crucial indicator of channel quality. Multipath effects, channel fading, and increased noise all contribute to a decrease in SNR. Once the channel model, modulation scheme, and coding scheme are determined, there is a one-to-one correspondence between SNR and bit error rate (BER). EVM (Error Vector Magnitude) is an important indicator of the modulation accuracy of the transmitter's transmitted signal. On a modulation constellation diagram, the EVM value represents the degree of divergence at constellation points, indirectly indicating the performance comparison between signal and noise, thus allowing for SNR estimation.
[0022] In this step, a signal-to-noise ratio (SNR) estimation model is designed, which is implemented by calculating the SNR based on the error vector magnitude, and is expressed as: , Where SNR is the signal-to-noise ratio and EVM is the error vector magnitude; and: ,in, To measure the nth symbol in the data stream, For the corresponding The ideal constellation point, N is the number of different constellation points in the constellation diagram; for example, for QPSK modulation, N=4.
[0023] Under additive white Gaussian noise, the calculation model for EVM is as follows: ,in, , This represents the components of two orthogonal Gaussian white noise paths, I and Q. To normalize the power of the ideal constellation points, T is the number of symbols used to calculate the mean square EVM, and t is the symbol index of the mean square EVM, with a value range of [1,T].
[0024] When T is much larger than the number of different constellation points in the constellation diagram, the ratio of normalized noise power to normalized signal power can be replaced by a non-normalized value. In this case, the calculation model for EVM is: ,in, To normalize the signal power, This represents the normalized noise power.
[0025] The signal-to-noise ratio estimation algorithm was simulated using a Simulink module. The simulation structure is as follows: Figure 5 As shown in part (a); the results of the simulation verification are as follows Figure 5 As shown in part (b), the simulation results show that the estimation error is large in the low signal-to-noise ratio stage. When the signal-to-noise ratio is greater than 8dB, the estimated value is basically consistent with the actual value. In the actual application environment, the step point is basically concentrated above 8dB. Therefore, the signal-to-noise ratio estimation model provided in this step is consistent with the actual application environment and meets the simulation requirements.
[0026] Step S120: Establish a baseband impulse response model based on a multipath channel, wherein the baseband impulse response model is used to estimate channel delay; The baseband impulse response model predicts the channel impulse response and estimates the channel delay based on a known training sequence and the signal received at the receiver. When applied to a simulation system, when the transmitter sends valid data, the baseband impulse response model uses the pre-defined channel impulse response at the training sequence position and an interpolation algorithm to calculate the channel impulse response. Figure 6 A multipath channel model simulating a mountainous channel scenario is provided; the baseband impulse response model, on the other hand, transmits a known training sequence and processes it through a process such as... Figure 6 After the multipath channel is shown, the channel impulse response is estimated using the received signal and the known training sequence.
[0027] The received signal in a multipath channel consists of many attenuated, delayed, and phase-shifted transmitted signals, so the baseband impulse response model can be expressed as: , in, For the number of multipaths, Let be the magnitude of the i-th path. Let be the relative delay of the i-th path. Let t be the relative extension of the i-th path, and t be the continuous time.
[0028] The output signal after passing through the baseband impulse response model is expressed as: , in, It is additive white Gaussian noise. For the training sequence over time delay τ, Let be the relative delay of the i-th path. This represents convolution.
[0029] The frequency domain model is as follows: , ,in The imaginary unit; Its frequency domain channel estimation is expressed as: in, To receive signals Discrete frequency domain form, For training sequences The frequency domain discrete form, Channel impulse response The frequency domain form, yes The discrete points in the time domain.
[0030] The channel impulse response module was simulated and verified, and the simulation parameters are shown in Table 3.
[0031] Table 3 Simulation Parameters
[0032] The simulation results are as follows Figure 8 As shown in the figure, it can be seen that the channel impulse response estimation can accurately estimate the channel delay.
[0033] In this invention, the signal-to-noise ratio estimation model and the baseband impulse response model constitute a sensing module that can autonomously perceive the existing network environment.
[0034] Step S130: Define and train the control engine. The control engine is used to collect external sensing signals, process them, and form control commands to control the parameters of each communication link. In this step, the control engine structure is defined as having two hidden layers, each with 30 neurons. After training, the error of the control engine converges as follows: Figure 9 As shown.
[0035] Specifically, the control engine structure is as follows: Figure 13 As shown, its input vector is As shown in the picture =-1 is set as a threshold for the hidden layer neurons; hidden layer output vector In the picture =-1 is used to introduce a threshold for the output layer neurons; the output vector of the output layer. The expected output vector is The weight matrix between the input layer and the hidden layer is used. express, , where column vectors Let W be the weight vector corresponding to the j-th neuron in the hidden layer; the weight matrix between the hidden layer and the output layer is denoted by W. , where column vectors Let be the weight vector of the j-th neuron in the output layer; by adjusting the weights, the error can be continuously reduced.
[0036] In this invention, the input to the control engine is the output of the sensing module (such as estimated signal-to-noise ratio, multipath delay, multipath gain, and input layer threshold). The algorithm uses a neural network to establish a mapping relationship between the adjustment factor and the communication parameter scheme. The output results include the optimal adjustment rate, coding efficiency, and modulation method. The output results are transmitted to the modulation and coding module and the source module to finally generate the minimum bit error rate.
[0037] Step S140: Based on the signal-to-noise ratio estimation model, baseband impulse response model, and control engine, construct an adaptive communication transmission simulation platform and set the sampling rate of the adaptive communication transmission simulation platform to match the maximum communication capability index.
[0038] Simulink-based simulation platforms such as Figure 7 As shown, the signal-to-noise ratio (SNR) is calculated using a SNR estimation model in section P700; a channel estimation and equalization module is set up in section P710 to calculate the channel delay; based on the SNR and channel delay, the cognitive engine outputs control commands and provides feedback signals for input and output in section P720 to guide the entire communication system to make dynamic adjustments; therefore, even when facing a channel environment never encountered before, it can learn and upgrade based on the principles of reliable communication, thereby achieving true self-adaptation.
[0039] Traditional adaptive communication systems mainly select different communication strategies by setting different threshold values and comparing the sensed quantity with the threshold values. This is an adaptive method based on prior knowledge, but it cannot adapt to the real complex and ever-changing channel environment, or the adaptive system may fail due to the incompleteness of prior knowledge.
[0040] In application, the adaptive communication transmission simulation platform generated by this invention, combined with the 802.11b standard, adopts 64-QAM modulation and 3 / 4 code rate convolutional coding under the best channel conditions; and adopts BPSK modulation and 1 / 2 code rate convolutional coding under the worst channel conditions. Figure 10 , Figure 11 The bit error rate performance of the adaptive communication transmission simulation platform with cognitive engine and the traditional communication system are compared under Rayleigh channel conditions. It can be seen that the performance of the adaptive communication transmission simulation platform is better than that of the traditional communication system under the condition of burst deep fading in the channel.
[0041] Simulations were performed on an adaptive communication transmission simulation platform under different signal-to-noise ratios (SNR) to statistically analyze the overall bit error rate performance, assuming a bit error rate of 10%. -6 At that time, the system gain reached 8.5dB (e.g. Figure 12 As shown in the figure, its performance is superior to that of traditional communication systems.
[0042] The construction method provided by this invention, based on Simulink simulation software, can autonomously perceive the existing network environment and, through understanding the network environment, adjust the configuration of the communication network in real time to intelligently adapt to changes in the task environment. The adaptive communication transmission simulation platform constructed by this invention has the ability to dynamically adjust the communication rate, encoding method, and modulation method, and can work with the cognitive engine to complete an adaptive communication transmission system.
[0043] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.
Claims
1. A method for constructing an adaptive communication transmission simulation platform, characterized in that, Includes the following steps: Determine the maximum communication capability index, which is compatible with both large and small network devices; A signal-to-noise ratio (SNR) estimation model is established, which is used to calculate the SNR based on the error vector magnitude. A baseband impulse response model based on a multipath channel is established, which is used to estimate channel delay. Define and train a control engine, which is used to collect external sensing signals, process them and form control commands to control the parameters of each communication link; Based on the aforementioned signal-to-noise ratio estimation model, baseband impulse response model, and control engine, an adaptive communication transmission simulation platform is constructed, and the sampling rate of the adaptive communication transmission simulation platform is set to adapt to the aforementioned maximum communication capability index.
2. The method for constructing the adaptive communication transmission simulation platform according to claim 1, characterized in that, The determination of the maximum communication capability index includes: determining the maximum communication throughput of each device, and calculating the air interface rate based on the highest communication throughput.
3. The method for constructing the adaptive communication transmission simulation platform according to claim 1, characterized in that, The signal-to-noise ratio (SNR) estimation model is implemented based on the error vector magnitude to calculate the SNR, and is expressed as follows: Where SNR is the signal-to-noise ratio and EVM is the error vector magnitude; in, , in, To measure the nth symbol in the data stream, For the corresponding The ideal constellation point, where N is the number of different constellation points in the constellation diagram.
4. The method for constructing the adaptive communication transmission simulation platform according to claim 3, characterized in that, Under additive white Gaussian noise, the calculation model of the EVM is as follows: ,in, , This represents the components of two orthogonal Gaussian white noise paths, I and Q. To normalize the power of the ideal constellation points, T is the number of symbols used to calculate the mean square EVM, and t is the symbol index of the mean square EVM, with a value range of [1, T].
5. The method for constructing an adaptive communication transmission simulation platform according to claim 1, characterized in that, The baseband impulse response model is expressed as follows: , in, For the number of multipaths, Let be the magnitude of the i-th path. Let be the relative delay of the i-th path. Let t be the relative extension of the i-th path, and t be the continuous time.
6. The method for constructing the adaptive communication transmission simulation platform according to claim 5, characterized in that, The output signal after passing through the baseband impulse response model is expressed as follows: ， in, It is additive white Gaussian noise. For the training sequence over time delay τ, Let be the relative delay of the i-th path. This represents convolution.
7. The method for constructing an adaptive communication transmission simulation platform according to claim 1, characterized in that, The input to the control engine is the output of the sensing module, which includes the optimal adjustment rate, coding efficiency, and modulation scheme. The output is then transmitted to the modulation and coding module and the source module to generate the minimum bit error rate. The sensing module consists of a signal-to-noise ratio estimation model and a baseband impulse response model.
8. The method for constructing an adaptive communication transmission simulation platform according to claim 1, characterized in that, The adaptive communication transmission simulation platform includes: calculating the signal-to-noise ratio (SNR) using a signal-to-noise ratio estimation model; setting a baseband impulse response model to calculate the channel delay; and outputting control commands through a cognitive engine based on the SNR and channel delay, feeding the signals back to the input and output to guide the entire communication system to make dynamic adjustments.