Data Link System Simulation and Performance Analysis Based on Simulink

The future battlefield will be a system-integrated operation under the network-centric warfare pattern, and the weapon data link used for guidance is an important part of it. Usually, the weapon data link is used to transmit target information. The amount of information is small, but the information transmission must be reliable. At the same time, the battlefield communication in the future is in a complex electromagnetic environment, and the weapon data link must have low interception and anti-interference performance. Based on this, the current weapon data link usually uses spread spectrum technology for information transmission.

1 Principle of spread spectrum system

Spread spectrum technology is a broadband signal transmission method that expands the spectrum of the baseband signal to a wider frequency band and then transmits it. It uses a pseudo-random sequence to expand the guidance information or missile return information to be sent to a suitable frequency band, that is, to expand the energy of the original information in the frequency band, thereby reducing the risk of the signal being discovered and increasing the difficulty of enemy interference (the range of interference required has become larger).

The receiving end uses the same pseudo-random sequence as the transmitting end to perform correlation processing on the received spread spectrum signal to restore the original information. Since the interference signal is not correlated with the pseudo-random sequence, it is expanded at the receiving end, which greatly reduces the power of the interference signal falling into the signal frequency band, thereby improving the output signal-to-noise ratio of the system and achieving the purpose of anti-interference. At the same time, the spectral density of the spread spectrum signal during transmission is very low, which can make the signal submerged in the noise and not easily intercepted and detected by the enemy, so it has a strong low interception characteristic.

The principle of the data link direct expansion system is shown in Figure 1. The output signal a(t) of the source at the transmitting end after encryption and encoding is an information stream with a code element duration of Ts, the pseudo-random code is c(t), and the width of each pseudo-random code element or chip is Tc. The signal a(t) is modulo-2 added with the pseudo-random code c(t) to generate a spread spectrum sequence with the same rate as the pseudo-random code rate, and then it is modulated and transmitted through the antenna. The signal sent after modulation can be expressed by formula (1):

At the receiving end, after amplification and mixing, the spread spectrum modulated signal of the intermediate frequency is despread using a pseudo-random sequence synchronized with the transmitting end. The spread spectrum signal received by the detector can be expressed by formula (2):

For interference signals and noise, since they are not related to the pseudo-random sequence, under the action of the correlation despreader, it is equivalent to a spread spectrum. After the interference signal and noise spectrum are expanded, their spectral density is reduced, which greatly reduces the interference power that can enter the signal passband, improves the input signal-to-interference ratio of the demodulator, and thus improves the anti-interference ability of the system.

Although spread spectrum technology has been widely used in the field of civil communications, it has not yet been maturely applied in missile weapon systems. In order to achieve low interception and anti-interference purposes, the guidance information is transmitted in bursts, and the information transmission time is short, so fast synchronization must be performed at the receiving end.

Due to the frequency difference between the transmitter and the receiver, the initial synchronization time of the receiving end is long. At the same time, the relative movement between the transmitter and the receiver causes the Doppler frequency shift and the primary and secondary change rates of the carrier and pseudo code at the receiving end, which leads to complex synchronization of the carrier and pseudo code. In order to meet the requirements of high detection probability, low missed alarm probability and fast capture time, as well as the miniaturization requirements of missile-borne equipment, the missile-borne receiving device must adopt an efficient synchronization strategy and perform corresponding algorithm optimization and synthesis.

2 System Simulation Based on Simulink

This paper uses Matlab/Simulink software platform to build a simulation system model of burst communication system; the graphical modeling capability and functional module library of Simulink environment are used to develop a synchronous demodulation model library. The high sampling rate brought by adding carrier frequency is effectively avoided by channel simulation at intermediate frequency. The correctness of the system design is verified by simulation, and the relationship between signal-to-noise ratio and capture probability when the intermediate frequency input signal strength changes is analyzed, which provides a basis for the allocation of system indicators.

2.1 Introduction to

Simulink As an important part of Matlab, Simulink is a graphical environment for interactive dynamic system modeling, simulation and analysis, and a basic development environment for model-based embedded system development. It can be used to model, simulate and analyze the system for traffic systems.

Simulink supports linear and nonlinear systems, continuous-time systems, discrete-time systems, continuous and discrete hybrid systems, and the system can be multi-process. It provides a friendly graphical interface (GUI), and the model is represented by a block diagram composed of modules. User modeling can be completed by simply clicking and dragging the mouse, making modeling very easy, more intuitive, convenient and flexible than traditional simulation software packages. [page]

2.2 Composition of the system model based on Simulink

Considering that encryption and decryption iterations basically do not involve bit error rate issues, when simulating the algorithm with the idea of ​​improving the system bit error rate, the encryption and decryption links are removed; considering that the impact of radio frequency is mainly the Doppler effect and the thermal noise of radio frequency devices, its impact is directly converted to the intermediate frequency, which can effectively avoid the high sampling rate problem caused by radio frequency.

The entire system simulation model consists of three parts: source channel module, fast synchronous demodulation processing module, and verification processing module. The system simulation block diagram is shown in Figure 2.

(1) Source channel module

The composition of the source channel module is shown in Figure 3. The main function is to provide analog input signals for the product's algorithm simulation, and to perform source data encoding, spread spectrum, modulation, Doppler frequency shift and Doppler acceleration simulation, and signal-to-noise ratio simulation. The encoding uses RS encoding, and the spread spectrum uses the method of selecting primitive polynomials and initial phases to select the appropriate m sequence as the spread spectrum code. The advantage is that it can be modified in real time according to actual needs without affecting the spread spectrum despreading algorithm simulation structure, which brings flexibility to future simulation development.

Because the bit error rate analysis is required, the information data is generated using the top-level M file and saved to the computer memory, and then the data is read from the memory and sampled to output the modulated data. AWGNChannel is used to simulate an additive white Gaussian noise channel. The signal-to-noise ratio of this channel can be set in three ways: Eb/N0, Es/N0, SNR. This simulation sets the signal-to-noise ratio of the channel according to the SNR method.

The intermediate frequency is modulated and output using a 70 MHz carrier.

(2) Fast synchronous demodulation processing module

It is the focus of this simulation system, completing the sampling, down-conversion, digital matching filtering and power processing, peak judgment and frequency search, peak capture judgment, PN code tracking and carrier synchronization, data demodulation, and decoding of the intermediate frequency signal. Among them, the sampling, down-conversion, preprocessing, digital matching filtering and power, and carrier synchronization modules are built using modules in Simulink, while the peak judgment and frequency search, peak capture judgment, and PN code tracking complex logic of the control part are implemented using Simulink-S function modules encapsulated in C language.

(3) Verification and processing module

Complete the bit error rate judgment of the demodulated data, record the key parameters and perform real-time post-processing. The results of each simulation in this model are saved as corresponding *.mat files under the control of the top-level M file. After the simulation is completed, this stored file is called to program post-data processing.

The simulation schematic diagram is shown in Figure 4.

3 Simulation results and performance analysis

In order to facilitate the modification of simulation parameters and batch simulation, the parameter settings of the simulation model and the simulation condition settings are all implemented by the top-level M file. The main purpose of this simulation is to test the performance under various signal-to-noise ratio conditions when the signal has different input power. The test results are as follows:

Figure 5 is a diagram showing the relationship between the threshold and peak value when the input signal power is -20 dBm and the signal-to-noise ratio is -16 dB. [page]

FIG6 is a diagram showing the relationship between the threshold and the peak value when the input signal power is -30 dBm and the signal-to-noise ratio is -16 dB.

Figure 7 is a graph showing the relationship between different signal-to-noise ratios and capture probabilities when the input signal strengths are 0 dBm, -10 dBm, -20 dBm, -30 dBm, and -40 dBm, respectively. As

can be seen from Figure 7, when the signal-to-noise ratio is greater than -14 dB, the input signal strength of 0 to -30 dBm can meet the system performance requirements; when the signal-to-noise ratio is greater than -15 dB, the capture probability can meet the system requirements when the input signal strength is -10 to -30 dBm; when the signal-to-noise ratio is greater than -16 dB, the capture probability can meet the system requirements when the input signal strength is -20 dBm and -30 dBm. Considering that although the truncation is simulated in the Simulink environment, there may be other effects in the actual hardware operation, so there will still be differences in actual use. According to the test results of the actual hardware, the performance of the actual intermediate frequency direct-spread receiver is 3 to 4 dB different from the simulation results, but this difference is acceptable. The analysis of the simulation results can provide a basis for the allocation of system indicators.

4. Brief Analysis of System Index Allocation

Usually, the receiver of a communication system has a certain distance from the sender. When the distance is determined, the dynamic range of the receiver must be determined. Usually, the dynamic range of the receiving system consists of two parts: the RF dynamic range and the IF dynamic range. When the signal-to-noise ratio converted to the IF demodulation is determined, the RF dynamic range is also determined.

Assuming that the sensitivity of a receiving system is -100 dBm and the dynamic range is 90 dB, that is, when the system input signal is from -10 to -100 dBm, the receiving system demodulation data bit error rate output is required to meet the requirements. If the indicator allocated by the system to the IF is 0 dBm for the input signal strength, the dynamic range of the RF must be 90 dB; if the indicator allocated by the system to the IF is 0 to -30 dBm, the dynamic range of the RF becomes 60 dB.

The dynamic range of the IF receiver mainly depends on the dynamic range of the A/D, the signal processing algorithm and the noise of the digital processing circuit itself. Since the dynamic range of the A/D is relatively large, the dynamic range of the actual IF receiver mainly depends on the signal processing algorithm itself, such as the truncation processing allowed by the entire system resources, whether there is internal AGC processing, etc.

Through simulation analysis, we can see that if the system allocates performance indicators and properly taps the dynamic capabilities of the IF receiver, on the one hand, the performance of the IF receiver can be improved accordingly, such as when the input signal strength is 0 dBm and the signal-to-noise ratio is greater than -14 dB, the demodulated data can meet the system requirements, and when the input signal is -30 dBm, the signal-to-noise ratio can still meet the system bit error rate requirements when it is -16 dB, which is equivalent to improving the receiving capability of the entire system; on the other hand, it also reduces the dynamic range of the RF and reduces the cost of the RF components accordingly. The design of the IF receiver algorithm usually does not involve hardware costs, but the improvement of RF indicators must be at the expense of hardware costs. Therefore, if the system allocates indicators reasonably, the cost performance of the entire receiving system can be improved.

5 Conclusion

Based on the introduction of data link technology, the working principle of spread spectrum communication system and the functions and characteristics of Matlab/Simulink, a simulation platform for a burst data link communication system was built using the Matlab/Simulink software platform; a component model library was developed using the graphical modeling capabilities and complete functional module library of the Simulink environment. Although the synchronization algorithm of this system needs to be further optimized, the simulation analysis of the system's rapid capture capability and the testing of the signal-to-noise ratio and capture probability performance under different input signal strengths provide a basis for the system's component indicator allocation.