Abstract
In both the military and the civilian world, multichannel electronic communication signals are widely used. The goal of signal processing in contemporary communication systems is to capture the signal. The traditional multichannel electronic communication signal automatic capture system has a limited range of performance, which slows down the rate of capture. This study used the sorting algorithm to build an automatic acquisition system for multichannel electronic communication signals in order to address this issue. The system processes the communication signal using the quick sorting method in the sorting optimization algorithm, achieving the goal of more precisely obtaining the carrier frequency and pseudo-code phase, and resolving the issue of slow system acquisition caused by the lengthy pseudo-code period. According to the experimental findings, the improved system’s acquisition rate is 6.89% higher than the conventional system’s, ensuring efficiency and reducing processing time in real-world communication applications.
1. Introduction
The traditional multichannel electronic communication signal acquisition system includes signal collector, controller, processor, integrator, and low-pass filter. Capture process: the signal collector captures the signal, processes it into a frame signal through the controller, transmits the frame signal to the processor for conversion, and obtains instruction decoding. After the code is obtained, the pulse data signal is obtained. At this point, the capture is completed. The acquisition speed of the traditional system is relatively slow. Legacy systems suffer from slower acquisition due to longer pseudocode cycles generated during acquisition. In order to meet the requirements of relevant users for the high-speed acquisition of multichannel electronic communication signals, it is necessary to increase the frequency usage of the spectrum. However, due to the different methods of multichannel communication signal message transmission, it is divided into simplex, half-duplex, and full-duplex; the signals in the same space become more and more concentrated. Therefore, it is necessary to improve the traditional capture algorithm in order to improve the capture rate. There are some researches on the automatic capture system of multichannel electronic communication signals by scholars: Zhang et al. [1] dealt with the existing electronic communication signal capture problem in a data-driven manner by mining the intrinsic characteristics of real data [1]; Li [2] focused on the research on the influence of atmospheric environment in coastal areas and the optimization of communication signal processing, and created a Lagrangian smog diffusion model based on pollution levels, meteorological conditions, and initial parameters of the subsurface near the surface [2]; Raza et al. [3] created a low-communication parallel distributed adaptive signal processing architecture for processing inefficient platforms [3]. To facilitate microfluidic circuit design, Bi and Deng [4] identified primary and secondary components, showing how to build tertiary basic modules and discussed the solution of four-level digital logic gate in designing, constructing, and testing microfluidic circuits with complex signal processing function [4]. Heath [5] separated electrical signals for multiple receivers in fiber optic communication systems into multiple groups. The time-domain multiplexed signal was obtained by multiplexing the electrical signals in multiple groups in the time domain, so that the time-domain multiplexed signal occupied each subband of the transmission bandwidth of the optical fiber communication system, thereby improving the transmission speed [5]; Mishra et al. [6] designed integrated waveform design and performance criteria designed for precise connections between network and radar functions to provide visual performance signals for mmwave systems [6]. Miglani and Malhotra [7] studied high-capacity links for enhancing digital signal processing performance [7].
In addition, many scholars have conducted in-depth research on sorting optimization algorithms: Martyniuk and Krukivskyi [8] analyzed a new method to sort a set of numbers in parallel. In the sorting process, a decrement operation that handled numeric array elements: traversing the array was performed, decrementing each element individually [8]. Wang et al. [9] proposed a new sorting algorithm. First, the position score of the returned result was standardized, and the similarity search word string between the retrieval result and the query result was combined into the algorithm to improve the sorting algorithm. The experimental results show that the improved sorting algorithm is better than the traditional sorting algorithm [9]. Chen and Chen [10] studied the application of deep learning and BP neural network sorting algorithm in financial news network communication [10]. Padwad [11] studied a sorting algorithm, which could be used to detect and track communication signals, which improved the speed of detection and tracking and has stronger applicability by optimizing sorting process and method [11]. Wagner et al. [12] proposed an iterative taxonomy that used event correlation to update the communication signal capture scheme [12]. Candelo-Becerra and Caicedo-Delgado [13] presented a method for calculating multiple voltage stabilization values and voltage stabilization curves. Sequencing algorithms to calculate the active and reactive power of loads and generators were used, and contingencies of network elements were performed [13]. Wang et al. [14] developed an improvement project based on the frequent sorting algorithm and studied an improved pulse width modulation scheme to balance the voltage power in order to achieve the purpose of improving the sorting algorithm [14]. Micco et al. [15] proposed a hybrid data sorting algorithm that executed serial and parallel instructions. The design of the system is done in a high-level language. Each time the system receives a vector of N elements, it provides an index set of L ordered elements to analyze the complexity of the algorithm in a generic way. Compared with traditional sorting algorithms, this hybrid data sorting algorithm has better performance [15].
With the rapid development and wide application of communication technology [16], how to automatically capture electronic communication signals in a limited channel and make the capture process fast, accurate and effective are a huge challenge in the field of communication. At present, scholars have made some achievements in the research of automatic acquisition system and sorting optimization algorithm of multichannel electronic communication signals, but they have not been well combined. Therefore, this study constructed a multichannel electronic communication signal automatic capture system based on a sorting optimization algorithm to solve the problem of the low capture rate of electronic communication signals.
2. Automatic Capture System of Multiple Electronic Communication Signals
2.1. Architecture of the Automatic Capture System for Multichannel Electronic Communication Signals
The multichannel electronic communication signal automatic capture system is composed of a first capture module and a secondary capture module. In the first capture module, the signal is firstly input, and after passing through the memory loop, the signal is subjected to a fast Fourier transform operation and is transmitted to the double memory buffer part at the same time. After the fast Fourier transform, the maximum value and interpolation are found, the signal decision is made, and the first acquisition operation ends. In the secondary capture module, a double memory cache operation is performed on the signal after the memory cycle and the signal after the signal judgment. Then, the operations of sorting algorithm optimization, correlation value calculation, and frequency calculation are carried out, and finally the acquisition of multichannel electronic communication signals is completed. The system architecture is shown in Figure 1.
In the actual communication system, the electronic communication signal has three characteristics: first, the length of each signal is relatively short, and the length of the cooperative transmission signal is relatively long. Therefore, traditional electronic communication algorithms cannot capture signals quickly, and new algorithms must be learned to adapt to short-term communication functions. Second, the first task in processing a signal is to call all the data to determine the start and end of the signal, due to the fact that the start and end of the electronic communication signal are characterized by instability that causes the signal to be masked. Third, the signal length and signal spacing are unstable. It is because of these properties that it is highly resistant to capture. These characteristics are challenges for signal capture technology. Communication systems require demodulators to achieve signal acquisition and signal coherence under acceptable minimum noise conditions. How to achieve signal acquisition with a high acquisition rate has become one of the technical difficulties in the communications industry.
2.2. Multichannel Electronic Communication Signal Capture Process
The so-called “signal acquisition” refers to the estimation of the signal strength of the carrier frequency value. The meaning of the carrier frequency is that in the process of signal transmission, the signal is not directly sent, but the corresponding signal is inserted into the fixed frequency wave. The process of insertion is called loading. In fact, changing the low signal frequency to a relatively higher wavelength, the low-frequency wave modified by the high frequency is called the “carrier frequency”, also known as the “fundamental frequency” [17]. Most of the existing carrier frequency estimation methods are based on cyclostationary wavelength estimation methods such as higher-order cumulants and cyclic spectra [18]. In addition, in the process of measuring the carrier frequency based on cyclostationarity, it is required to first find the best fit to a simple Gaussian, Cauchy, Poisson, or your curve in the data. Over a large range the curve is evaluated and subtracted from a copy of the data after recording its peak position to the curve to calculate the maximum autocorrelation (peak load time) of the signal, and then use the Wiener–Shenqin theorem to read out the command and automatically point to the link, to achieve the highest level of compliance. The spectral performance finally estimates the value of the carrier frequency according to the cyclic correlation spectral performance and the conclusion that the frequency of the machine frequency is not zero. When an unstable frequency occurs in a certain period of time in a continuous signal, it needs to be identified and modulated. The unstable frequency condition is shown in Figure 2.
Most multiplexed electronic communication signals have two pseudocodes, CL and CM. CL must be used for detection before recording, but the CL code cannot be obtained directly. Therefore, when the synchronization system is moved to the keep-synchronization phase, the purpose of reducing the acquisition time must be achieved by searching for electronic communication signals and limiting the frequency and length of the pseudocode offset region.
The multichannel electronic communication signal search framework is shown in Figure 3.
The search method is as follows: a channel is randomly selected to perform the relevant detection operation. If the detection result exceeds the threshold level, the search is successful. If the result is below the threshold, the search would fail. When the pseudocode section is delayed, the search is repeated. If the autocorrelation function in the single-chip microcomputer system is relatively clear and has almost no peak value, it means that the multichannel communication electronic signal has a good correlation; when the phase of the pseudocode is completely matched, the peak value generated during the connection process will be very obvious. If the pseudo-code phase is deviated, the difference exceeds the duration of one chip, that is, the connection correlation value is 0.
The automatic acquisition system of multichannel electronic communication signals further determines the communication interval by combining the principle of the Doppler frequency offset compensation algorithm, so that the reliability of the final acquisition result is higher [19]. The process is as follows:(1)Change the local C/A positioning code sequence to search the C/A code sequence, perform subcarrier modulation, and detect the range of Doppler frequency offset after standard sampling.(2)First, take the signals received by most of the electronic channels to form a median sample sequence of 2048 points and then apply the Doppler frequency domain formula to perform fast Fourier operations on the 2048 points.(3)Multiply the values and then perform the frequency-domain frequency offset operation.(4)Perform permutation and combination operations on the operation result sequence and reconstruct the correlation function.(5)The reconstructed maximum value of the correlation function is compared with the threshold value. If the maximum value of the reconstructed correlation function is smaller than the threshold value, the process from step 2 to step 5 needs to be repeated again to re-acquire the signal. If the maximum value of the reconstructed correlation function is greater than the threshold value, it indicates that the capture is successful and that the data parameters obtained in the capture phase are sent to the tracking module. So far, the capture of the pseudocode has been successfully implemented. In addition, in the calculation process, the coding method of the correlation density function and the pseudo-random sequence need to be ignored, and considering the problem of the capture speed, the fast capture principle of the cyclic spectrum needs to be applied. The extraction of signal features is achieved by suppressing the correlation of the carrier modulated signal in different regions of the frequency domain. Otherwise, the chip rate will be affected.
Finally, the design of the automatic signal capture algorithm is completed. The algorithm framework is shown in Figure 4.
2.3. Sorting Optimization Algorithm
Definition of a ranking optimization algorithm: one or more datasets are rearranged according to a specific model using specific algorithmic factors [20]. The optimized data follow general principles (that is, an approximate model is constructed to approximate the original function, and the sub-optimization is solved to obtain a relatively better solution) and has certain regularity. Therefore, the generated data is easy to filter and calculate, which greatly improves the computational efficiency. In order to ensure the smooth progress of the sorting, it is first required that the system should have a certain stability. Stability means that when two identical elements appear in the same sequence at the same time, after a certain sorting algorithm, the relative positions of the two elements before and after sorting are the same. The following are commonly used sorting optimization algorithms:(1)Bubble sort: each bubbling process starts with the first element of the first part of the sequence and then compares it with the rest of the elements in order. If the element is smaller than the adjacent element, the positions of the two are exchanged, and the larger element is used as the next reference element, so that the element with the larger value can be compared with its adjacent elements in the next step.(2)Insertion sort: the basic idea of insertion sort is to insert an unordered sequence into an ordered sequence. A record is inserted into an already sorted table, resulting in a new sorted table with the number of records incremented by 1. In its implementation process, a double-layer loop is used, the outer loop searches all elements except the first element, and the inner loop searches the order table in front of the current element to find the position to be inserted, and then moves. And so on, the final array is sorted from small to large. The time complexity of this algorithm is .(3)Hill sorting: Hill sorting improves the alignment based on the insertion sorting algorithm. The records are grouped by a certain increment of the index, and each group is sorted by the direct insertion sorting algorithm; as the increment gradually decreases, each group contains more and more keywords. When the increment decreases to 1, the entire file is just grouped into one group, and the sorting ends. The time complexity of the algorithm is greatly improved compared to the previous steps.(4)Quick sort: the basic principle is to divide the data set to be sorted into two parts that are independent of each other through a round of sorting. This division should ensure that the keywords of one of the data sets are higher than the keys of the other data set. If the characters are small, the two parts of the records can be further sorted to achieve the purpose of ordering the entire sequence.
3. Multichannel Electronic Communication Signal Capture
3.1. Orthogonal Transform Theory of Signals
The expression of the signal is set as , and the negative component signal is deduced from the frequency of the positive component of the expression, then the expression of its spectrum is
In the formula, the spectrum corresponding to the signal is . When the value of is greater than zero, in order to make the energy of and equal, then is equal to 2 times of . To convert into , the part of the signal needs to be filtered. At this time, is used, and its expression is
Formula (1) can be expressed as
To convert formula (3) into the time domain, that is, to transform the frequency domain into the time domain expression, the Fourier function needs to be introduced, and the inverse operation is performed to obtain the impulse function. Impulse functions can be used to linearly express continuous signals and can also be used to solve the zero-state response of linear time-invariant systems. The formula is as follows:
Define the Hilbert transform of ; the expression is
From formula (5), it can be known that the real part and the imaginary part are orthogonal, that is, and the result obtained after transformation has an orthogonal relationship. Usually is called the analytic expression of , then it can be known that
Express in polar coordinates:where represents the instantaneous amplitude of , and the expression is as follows:where represents the instantaneous phase of , and the expression is as follows:
Through the derivation and analysis of the formula, it can be seen that the instantaneous characteristic parameters of the signal can be obtained conveniently and quickly using the orthogonal transformation theory. The data are concentrated on a small number of coefficients as much as possible to minimize the correlation in the original data. The algorithm has low complexity and high real-time performance. In actual application activities, the performance of the system can be improved to a certain extent.
3.2. Fast Fourier Transform
The fast Fourier transform expression iswhere is the continuous spectrum of the signal . Then, is defined as
, that is, , is filled with zeros in , so that can obtain an integer solution, and then on this basis, it is divided according to its parity, namely,
Since is an integer, thenwhere can be expressed as
3.3. Instantaneous Parameter Extraction
Obtaining amplitude information, frequency information and phase information is an important part in the process of instantaneous parameter extraction. The sample function for a random signal can be written as
Using the formula, it can be obtained:
Decomposing formula (17), it can be obtained:
Instantaneous amplitude:
Instantaneous frequency:
4. Automatic Capture System for Multichannel Electronic Communication Signals
4.1. Signal Acquisition Success Rate
In this study, under the same simulation conditions, 10,000 Monte Carlo experiments are performed on the sample data to count the success rate of signal capture. The schematic diagram of the definition of signal acquisition estimation is shown in Figure 5.
4.2. Capture Model Building
As can be seen from the above, in the practical application of electronic communication, in order to improve the success rate of capturing electronic communication signals, the transmitted data needs to adopt a fixed frame length. Using this feature, a signal generation model can be built during the experiment, and the capture effect of the quicksort method as an improved function can be tested through the period signal generated by the model.
4.3. Experimental Test and Data Analysis
In order to ensure the comprehensiveness and reliability of the signal acquisition system experiment, this study would compare the experimental data of the pre-improved and improved acquisition systems under three conditions of different signal-to-noise ratios, different frequency offsets, and different smoothing window lengths.
4.3.1. Comparison of the Capture Rates of the Two Systems under Different Signal-to-Noise Ratios
Since there is an error between the estimated starting point position of the signal and the actual position when the signal is received, this study sets the simulation starting point 50 symbols before the actual starting point to verify whether the signal capture is successful or not, as is shown in Figure 6.
It can be seen from Figure 6 that the success rate of signal acquisition increases with the increase of the signal-to-noise ratio. When the signal-to-noise ratio of the improved capture system reaches 6 dB, the signal capture success rate reaches 100%. When the signal-to-noise ratio rises to 5 dB, the success rate of signal detection is 99%. Therefore, under the condition of a low signal-to-noise ratio, the performance of the improved capture system is much stronger than that of the pre-improved capture system.
4.3.2. Comparison of the Acquisition Rates of the Two Systems under Different Frequency Offset Conditions
In the simulation, the frequency offsets are set to 0 kHz, 10 kHz, and 30 kHz respectively. Capture rate comparisons were made under these conditions, as is shown in Figure 7.
As can be seen from Figure 7, the increase in frequency offset results in a corresponding increase in the success rate of signal acquisition. The signal acquisition success rate of both systems increases with the increase of frequency offset, and the acquisition rate of the improved acquisition system is slightly higher than that of the pre-improved acquisition system.
4.3.3. Comparison of the Capture Rates of the Two Systems under Different Smoothing Window Lengths
It is known that the length of the signal to be buffered increases as the window length of the smoothing window increases. In this study, the simulation conditions are the same, and the window length of the smoothing window is 16, 32, 64, 128, and 256 sampling points respectively. The capture success rate is shown in Figure 8.
From Figure 8, the following conclusions can be drawn: the improved system achieves a 100% capture success rate when the sampling point reaches 64, while the traditional system fails to achieve a 100% capture success rate within this window length interval. Therefore, the improved system can outperform the traditional system under the condition of different smooth window lengths.
5. Summary of the Experimental Results of the Automatic Capture System for Multichannel Electronic Communication Signals
The communications industry is closely related to human life and plays a catalytic role in the process of economic development. Fast and efficient capture of electronic communication signals is a major challenge in the field of communications. To this end, this study constructed an automated multichannel electronic signal capture system based on a sorting optimization algorithm to solve the problem of the low capture rate of electronic signals:(1)Comparison of the capture rates of the two systems under different signal-to-noise ratios Under the conditions of different signal-to-noise ratios, affected by the error between the position of the signal starting point and the actual position, the acquisition system before the improvement can never achieve a 100% acquisition rate, while the improved acquisition system can achieve a 100% acquisition rate. Under the condition of a low signal-to-noise ratio, the capture capability of the improved capture system is much higher than that of the pre-improved capture system.(2)Comparison of the acquisition rates of the two systems under different frequency offset conditions Under the conditions of different signal-to-noise ratios, the frequency offsets are set to 0 kHz, 10 kHz, and 30 kHz respectively in the simulation experiment. The overall capture rate of the capture system before the improvement is slightly lower than that of the improved system, and there is no significant performance gap between the two.(3)Comparison of the capture rates of the two systems under different smoothing window lengths Under the conditions of different smoothing window lengths, the smoothing window lengths in the simulation experiment are respectively 16, 32, 64, 128, and 256 sampling points. The capture system before the improvement can never achieve a 100% capture rate, but the improved capture system can achieve a 100% capture rate when the sampling point is 64. The improved capture system outperforms the pre-improved capture system under the condition that the smoothing window length is constantly changing.(4)Comprehensive comparison of capture success rate The three conditions of different signal-to-noise ratios, different frequency offsets, and different smoothing window lengths are respectively named as condition 1. The above-mentioned average calculation of the signal acquisition success rate of the two systems obtained under the above three conditions is carried out.
This results in Table 1.
The obtained signal acquisition success rate results are weighted to calculate the final comprehensive acquisition success rate, as is shown in Figure 9.
It can be seen from Figure 9 that the average acquisition success rate of the multichannel electronic communication signal automatic acquisition system based on the sorting optimization algorithm is 93% and the average acquisition success rate of the traditional system is 87%. The improved system has a better acquisition success rate than the traditional system. The capture success rate is increased by about 6.89%. The automatic acquisition system of multichannel electronic communication signals based on the sorting optimization algorithm can ensure the efficiency and save time in practical applications.
6. Conclusion
This study integrated the sorting optimization algorithm into the construction of the automatic signal acquisition system of multichannel electronic communication, obtained various information data, and used the quick sorting method to effectively process the signal data to make it more in line with the acquisition standard of the automatic signal acquisition system. The experimental results show that the acquisition success rate of the improved automatic acquisition system based on multichannel electronic communication signals is about 6.89% higher than that of the traditional system. The completion of the system construction provides ideas and solutions for multichannel electronic communication signal capture and helps the communication industry to better serve social and economic development.
Data Availability
The data used to support the findings of this study are available from the author upon request.
Conflicts of Interest
The author declares no conflicts of interest.