LoRa modulation method and system for underwater Internet of Things, and medium

Abstract

The invention provides a LoRa modulation method and system for an underwater Internet of Things, and a medium. The method comprises the following steps: S1, constructing a LoRa underwater acoustic communication signal; s2, dividing the data bit into a plurality of chips, performing spread spectrum transmission, performing cyclic shift to realize LoRa modulation, generating a modulation signal, and transmitting the modulation signal through an underwater acoustic channel; s3, performing frequency domain cross-correlation processing on the modulation signal to obtain a cyclic shift value, and realizing LoRa demodulation; and S4, generating a plurality of groups of spread spectrum factor and shift step length combinations, screening out an optimal modulation parameter by evaluating the transmission performance in the underwater acoustic channel, and setting the optimal modulation parameter as a modulation parameter of a subsequent communication process. The LoRa underwater acoustic communication method designed by the invention is constructed based on a cyclic shift frequency modulation spread spectrum system, has high anti-noise performance and good concealment under the condition of not depending on any channel prior information, can realize high-reliability, long-distance and low-power underwater acoustic information transmission, and meets the communication requirements of the underwater Internet of Things.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic communication technology, and more specifically, to a LoRa modulation method, system, and medium for underwater Internet of Things (IoT). Background Technology

[0002] Underwater IoT, as an extension of IoT to the underwater environment, has developed rapidly in recent years, becoming a new way for people to explore the underwater world and a global research focus. However, realizing underwater IoT faces many challenges, among which underwater wireless communication technology is particularly crucial.

[0003] Electromagnetic wave signals attenuate significantly underwater, making long-distance transmission impossible for underwater optical and magnetic induction communication. While Underwater Acoustic Communication (UAC) is currently the primary method, it suffers from numerous drawbacks. Its transmission speed is slow, multipath and time-varying effects are significant, bandwidth is narrow, and acoustic signal communication is susceptible to interference from various factors such as transmission power, battery status, multipath and Doppler effects, marine environmental noise (e.g., turbulence, ships, wind-driven waves, and thermal noise), and underwater obstacles. This leads to spatial and temporal variations in the underwater acoustic channel, making reliable signal detection at the receiving end difficult. The network topology and connectivity of underwater IoT are directly or indirectly affected by signal irregularities, making robust underwater acoustic communication extremely challenging to implement. Furthermore, underwater nodes are more expensive than ordinary IoT nodes, underwater networks are sparse, and establishing and maintaining communication between nodes is more complex. Compared to the rapidly developing and highly reliable and efficient airborne electromagnetic wave wireless communication, the disadvantages of underwater acoustic communication severely hinder the progress of underwater IoT.

[0004] LoRa, a novel spread spectrum long-range communication technology with significant research value in the Internet of Things (IoT) field, is based on linear frequency modulation (LFM) spread spectrum (CSS) modulation. Its demodulation signal-to-noise ratio (SNR) has an extremely low tolerance, greatly improving receiver sensitivity. It is applied in the widely used low-power local area network LoRaWAN, encompassing various network communication links such as repeaters, communication satellites, and terminal node chips. It is widely used in agriculture, commerce, industry, environmental protection, defense, logistics, smart cities, and many other industries. In 2013, Semtech rapidly deployed its IoT solutions, adding various network services such as location services and roaming, significantly improving service security, reliability, and quality. LoRa signals have strong anti-multipath and anti-interference capabilities, excellent long-distance communication performance, and LoRa chips can operate with low current and achieve low power consumption through duty cycle modes.

[0005] A search of patent documents revealed an invention patent with publication number CN113595585B, which discloses an M-element cyclic shift Chirp spread spectrum mobile underwater acoustic communication method, device, and storage medium. The method combines M-element spread spectrum modulation with cyclic shift spread spectrum modulation. This patent focuses on mobile underwater acoustic communication and is insufficient for other complex scenarios, resulting in slightly weaker application scalability.

[0006] In summary, research on LoRa technology in underwater information transmission is currently lacking. Given the urgent need for long-range, low-power, and high-reliability communication in underwater IoT, and the shortcomings of existing underwater acoustic communication technologies, this paper proposes an underwater LoRa acoustic communication method, drawing on the principles of LoRa technology, to meet the pressing communication requirements of underwater IoT. This has become a crucial task that urgently needs to be addressed. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the purpose of this invention is to provide a LoRa modulation method, system and medium for underwater Internet of Things.

[0008] A LoRa modulation method for underwater Internet of Things (IoT) according to the present invention includes the following steps:

[0009] Step S1: Construct a LoRa underwater acoustic communication signal based on the frequency modulation and spread spectrum method of underwater acoustic signals;

[0010] Step S2: Based on the LoRa underwater acoustic communication signal, the data bits are divided into multiple chips for spread spectrum transmission. Then, the spread spectrum signal is cyclically shifted to generate a modulated signal, which is then transmitted through the underwater acoustic channel.

[0011] Step S3: Receive the modulated signal, perform frequency domain cross-correlation processing on the modulated signal to obtain the peak position, i.e., the cyclic shift value, and realize LoRa demodulation;

[0012] Step S4: Based on the transmission characteristic data of each underwater acoustic channel, construct multiple sets of spreading factors and shift step size combinations within a preset range. By evaluating the transmission performance in the underwater acoustic channel, select the optimal modulation parameter combination and set it as the modulation parameter for subsequent communication processes.

[0013] Preferably, step S1 includes the following sub-steps:

[0014] Step S1.1: Select a LoRa modulation signal according to the requirements of underwater acoustic communication;

[0015] Step S1.2: Select the spreading factor.

[0016] Preferably, in step S1.1, the LoRa modulation signal is a Chirp signal or an HFM signal.

[0017] Preferably, in step S1.2, the spreading factor is between 5 and 8.

[0018] Preferably, step S2 includes: dividing the data bits into N using a spreading factor. SF Each chip is spread spectrum transmitted, and then the LoRa modulation signal is cyclically shifted to generate the modulation signal.

[0019] Preferably, step S2 includes the following sub-steps:

[0020] Step S2.1: Convert the data bits of the LoRa underwater acoustic communication signal from binary to decimal to obtain the converted LoRa baseband signal;

[0021] Step S2.2: Cyclicly shift the converted LoRa baseband signal to generate a modulation signal;

[0022] Step S2.3: The modulated signal is transmitted through the underwater acoustic channel to complete the LoRa underwater acoustic communication process and realize the transmission of information from the transmitting end to the receiving end.

[0023] Preferably, step S3 includes the following sub-steps:

[0024] Step S3.1: Convert the modulated signal into frequency domain form and multiply it by the frequency domain conjugate of the basic waveform to obtain the frequency domain multiplication result;

[0025] Step S3.2: Obtain the peak position of the frequency domain multiplication result, i.e., the cyclic shift value, and convert the cyclic shift value into binary to obtain the transmission bits.

[0026] Preferably, step S4 includes the following sub-steps:

[0027] Step S4.1: Based on the transmission characteristic data of each underwater acoustic channel, construct multiple sets of spreading factors and shift step size combinations within a preset range; enable the signals corresponding to the multiple sets of spreading factors and shift step size combinations to pass through underwater acoustic channels of different complexities, and obtain the transmission characteristic data of the signals in each underwater acoustic channel.

[0028] Step S4.2: Analyze the transmission characteristic data, comprehensively consider channel attenuation, multipath effect and noise interference, select the optimal modulation parameters, and set them as the modulation parameters for subsequent communication processes.

[0029] The present invention also provides a LoRa modulation system for underwater Internet of Things, comprising:

[0030] Module M1 constructs LoRa underwater acoustic communication signals based on the frequency modulation spread spectrum method for underwater acoustic signals;

[0031] Module M2, based on LoRa underwater acoustic communication signals, divides data bits into multiple chips for spread spectrum transmission, then performs cyclic shifting on the spread spectrum signal to generate a modulated signal, which is then transmitted through the underwater acoustic channel.

[0032] Module M3 receives the modulated signal, performs frequency domain cross-correlation processing on the modulated signal to obtain the peak position, i.e., the cyclic shift value, and realizes LoRa demodulation.

[0033] Module M4 constructs multiple sets of spreading factors and shift step size combinations within a preset range based on the transmission characteristic data of each underwater acoustic channel. By evaluating the transmission performance in the underwater acoustic channel, it selects the optimal modulation parameter combination and sets it as the modulation parameter for subsequent communication processes.

[0034] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the LoRa modulation method for underwater Internet of Things described above.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The LoRa underwater acoustic communication method designed in this invention is based on a cyclic shift frequency modulation spread spectrum system. Without relying on any prior channel information, it has high noise resistance and good concealment, and can realize highly reliable, long-distance, and low-power underwater acoustic information transmission, meeting the communication needs of underwater Internet of Things.

[0037] 2. The underwater acoustic communication system adopts a spread spectrum structure, which can achieve a balance between communication data rate and reliability by flexibly adjusting the spread spectrum factor and shift step size, significantly enhancing the flexibility of the communication system and adapting to underwater acoustic channels of varying complexity. Attached Figure Description

[0038] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0039] Figure 1 This is a comparison diagram of the LoRa signals after sampling in an embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of LoRa modulation in an embodiment of the present invention;

[0041] Figure 3 This is a flowchart of the LoRa demodulation process in an embodiment of the present invention;

[0042] Figure 4 This refers to the random multipath channel used in the simulation of this invention embodiment;

[0043] Figure 5This is a correlation peak diagram of the LoRa signal demodulated via the AWGN channel in an embodiment of the present invention;

[0044] Figure 6 This is a correlation peak diagram of the LoRa signal demodulated via a multipath channel in an embodiment of the present invention;

[0045] Figure 7 This is a LoRa bit error rate performance graph in an embodiment of the present invention;

[0046] Figure 8 This is a graph showing the HFM-LoRa bit error rate performance in an embodiment of the present invention;

[0047] Figure 9 This is a simulation diagram of the AWGN channel performance under different time slot intervals and spreading factors in an embodiment of the present invention;

[0048] Figure 10 This is a simulation diagram of the performance of a multipath channel with different time slot intervals and spreading factors in an embodiment of the present invention. Detailed Implementation

[0049] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0050] This invention draws on the LoRa modulation concept of the Internet of Things (IoT) and addresses the problems of low reliability and poor robustness of current underwater acoustic communication systems under strong multipath environments. It innovatively constructs an underwater LoRa modulation method for underwater IoT by using frequency modulation spread spectrum cyclic shift, effectively meeting the urgent needs of underwater IoT for long-distance, low-power, and high-reliability communication.

[0051] Example 1:

[0052] This embodiment provides a LoRa modulation method for underwater Internet of Things (IoT), including the following steps:

[0053] Step S1: Construct LoRa underwater acoustic communication signals based on the frequency modulation and spread spectrum method of underwater acoustic signals.

[0054] Specifically, step S1 includes the following sub-steps:

[0055] Step S1.1: Select the LoRa modulation signal according to the requirements of underwater acoustic communication.

[0056] In this embodiment, the LoRa modulation signal is a Chirp signal or an HFM signal.

[0057] The following are the characteristics of Chirp and HFM signals. Based on these characteristics and the requirements of underwater acoustic communication, LoRa modulation signal is selected:

[0058] The traditional LoRa method is a linear frequency modulation (LFM) spread spectrum method based on chirp signals. Chirp LFM signals are signals whose instantaneous frequency changes linearly with time. Assuming the instantaneous angular frequency change of the signal is linearly related to time, within the channel code duration T, its mathematical expression can be written as: In the formula, ω0=2πf0, f0 is the center frequency, and F is the range of instantaneous frequency variation, i.e., a frequency shift of twice. Since the instantaneous phase and instantaneous angular frequency of a signal have a calculus-integral relationship, i.e. The time-domain representation of the Chirp signal can be written as: s(t) = exp(j2πf0t + jπμt) 2 ), 0≤t≤T, To simplify the calculation, according to the principle of communication carrier, a Chirp signal with a center frequency of 0 can be modulated by a single-frequency carrier f0 to obtain a Chirp signal with a center frequency of f0. Therefore, the time-domain expression of the Chirp signal can be simplified to: s(t)=exp(jπμt) 2 Chirp signals have the property of converting between time and frequency, 0 ≤ t ≤ T. As can be seen, the time delay τ is equivalent to a frequency shift of the signal s(t) by Δf = μτ. The initial phase. When That is, within one symbol duration T, the signal frequency sweeps across the entire bandwidth B. Correspondingly, at intervals... Sampling was performed on the following: in: This represents the number of sampling points. For the sampled Chirp sequence: The equation shows that the time delay *m* is equivalent to a frequency shift. Given an initial phase and time-frequency conversion characteristics, when m = N, we have: From the formula, when N is even, s(n) = s(n+N); when N is odd, s(t) = -s(n+N), indicating that the Chirp signal has periodicity. As can be seen from the formula, different values ​​of 's' are orthogonal. Chirp signals possess orthogonality. The Chirp signal is represented by h(t) = s(-t). * Matched filters can achieve narrow pulse characteristics, which are as follows: The autocorrelation plot of the Chirp signal after sampling is shown below. Figure 1 As shown.

[0059] HFM is also a frequency-modulated signal and can be used as the base signal for HFM-LoRa. It differs from Chirp signals in that their frequency modulation functions are different. For an HFM signal with a start frequency of f0, a cutoff frequency of f1, and a duration of T, its time-domain expression can be written as: Where A(t) represents the amplitude, If the frequency is regulated, then the instantaneous frequency of the signal is: Similar to the Chirp signal, the basic waveform of the HFM signal is f H (t) and its shift waveform g H (t) also exhibits good cyclic correlation characteristics.

[0060] Step S1.2: Select the spreading factor (SF).

[0061] This embodiment is for the current underwater acoustic communication system, with a spreading factor of 5 to 8. The larger the SF, the better the performance, but the lower the data rate.

[0062] Step S2: Based on the LoRa underwater acoustic communication signal, the data bits are divided into multiple chips for spread spectrum transmission. Then, the spread spectrum signal is cyclically shifted to generate a modulated signal, which is then transmitted through the underwater acoustic channel.

[0063] Specifically, the data bits are divided into N using a spreading factor. SF Each chip is spread spectrum transmitted, and then the LoRa modulation signal is cyclically shifted to generate the modulation signal.

[0064] More specifically, step S2 includes the following sub-steps:

[0065] Step S2.1: Convert the data bits of the LoRa underwater acoustic communication signal from binary to decimal to obtain the converted LoRa baseband signal.

[0066] Step S2.2: Cyclicly shift the converted LoRa baseband signal to generate a modulated signal.

[0067] Step S2.3: The modulated signal is transmitted through the underwater acoustic channel to complete the LoRa underwater acoustic communication process and realize the transmission of information from the transmitting end to the receiving end.

[0068] Figure 2 This is a schematic diagram of LoRa modulation in an embodiment of the present invention.

[0069] like Figure 2 As shown, LoRa is a modulation method based on linear frequency modulation spread spectrum. Compared to traditional direct sequence spread spectrum, LoRa modulation divides a data bit into N parts using a spreading factor (SF).SF One chip is used for spread spectrum transmission, and one LoRa modulated signal transmits SF data bits C = {c0, c1, ..., c2}. SF-1 Convert these SF data bits from binary to decimal K:

[0070] When using the Chirp signal as the modulation basis, the Chirp signal is split into 2. SF A spread spectrum transmission uses chips that transmit data by cyclically shifting the chips, and the chip rate R... c and bit rate R b Defined as: In the formula: B is the modulation bandwidth.

[0071] The instantaneous frequency of the Chirp signal is:

[0072]

[0073] Each chip in the Chirp signal also represents a frequency shift Δf, where Δf = B / 2 SF Furthermore, the LoRa signal's frequency range is the entire bandwidth, and the up-modulated LoRa signal frequency linearly increases from f0 to f... max Back jump becomes f min Then from f min The linear modulation frequency (LMC) of these two signal segments is the same when the signal linearly increases to f0. The transmission time of a LoRa signal is T = 2. SF / B, the frequency transition time T0 = TK / B is calculated.

[0074] Therefore, according to the formula, the instantaneous frequency of the LoRa modulated signal is:

[0075] A vector consisting of SF data bits is That is, the information bits to be transmitted are modulated into a symbol for transmission. The LoRa modulated signal waveform can be obtained by combining equations:

[0076] Figure 3 A schematic diagram of LoRa modulation by cyclic shifting the Chirp signal.

[0077] Step S3: Receive the modulated signal, perform frequency domain cross-correlation processing on the modulated signal to obtain the peak position, i.e., the cyclic shift value, and realize LoRa demodulation.

[0078] Specifically, step S3 includes the following sub-steps:

[0079] Step S3.1: Convert the modulated signal into frequency domain form and multiply it by the frequency domain conjugate of the basic waveform to obtain the frequency domain multiplication result;

[0080] Step S3.2: Obtain the peak position of the frequency domain multiplication result, i.e., the cyclic shift value, and convert the cyclic shift value into binary to obtain the transmission bits.

[0081] In this embodiment, under the AWGN channel, the LoRa received signal is represented as: r(t) = s(t) + n(t), where s(t) is the LoRa transmitted signal after cyclic shifting via the Chirp signal, and n(t) is additive white Gaussian noise. The transmitted information is embedded in the cyclic shift length K of the basic waveform. The above equation can be written in frequency domain form as follows:

[0082] R(w) = S(w) + N(w). c(t) is the basic waveform of the Chirp signal, with frequency domain form C(w). The frequency domain form of the received signal R(w) is then compared with... The conjugate multiplication of the basic waveforms in the frequency domain yields: R(w)C * (w)=S(w)C * (w)+N(w)C * (w),

[0083] Converting to the time domain, it becomes:

[0084] The LoRa transmitted signal s(t) is coherent with the Chirp fundamental waveform c(t), where S(w) = C. * (w) represents the frequency domain cross-correlation result of s(t) and c(t), N(w)C * (w) represents the frequency domain cross-correlation result of s(t) and n(t), which is then expressed in the time domain after sampling as follows: Therefore, the peak position of the cross-correlation function is the transmitted information. Converting the transmitted signal into binary will yield SF transmitted bits.

[0085] Therefore: The signal spectrum, after being subjected to a discrete Fourier transform, reaches a peak value N at a cyclic shift value K, and the spectral values ​​at other points are all 0. Using this characteristic, the transmitted information can be obtained by demodulating the signal through the cyclic shift value K.

[0086] Figure 4 This refers to the random multipath channel used in the simulation of this invention embodiment.

[0087] The LoRa demodulation process is as follows: Figure 4 As shown, the LoRa modulation method has strong noise immunity because the noise s(t) and the initial signal c(t) are weakly correlated.

[0088] Step S4: To optimize communication performance, based on the transmission characteristic data of each underwater acoustic channel, multiple combinations of spreading factors and shift step sizes are constructed within a preset range. Their transmission performance is evaluated, with indicators including bit error rate, transmission rate, anti-interference capability, and the degree of multipath effect. Through comprehensive analysis, the optimal modulation parameter combination for the current channel is selected for subsequent communication, ensuring efficient and stable system operation.

[0089] In this embodiment, the signal is passed through different complex underwater acoustic channels to obtain the transmission characteristic data of the signal in each underwater acoustic channel. For multiple combinations of spreading factors and shift step sizes, the transmission effect of the signal in the underwater acoustic channel is evaluated. Multiple combinations of spreading factors and shift step sizes that meet the requirements of low computational complexity and can ensure stable and efficient transmission of the signal in the underwater acoustic channel are selected and set as the spreading factors and shift step sizes used in the subsequent communication process to improve the reliability of communication.

[0090] Specifically, step S4 includes the following sub-steps:

[0091] Step S4.1: Based on the transmission characteristic data of each underwater acoustic channel, construct multiple sets of spreading factors and shift step size combinations within a preset range; enable the signals corresponding to the multiple sets of spreading factors and shift step size combinations to pass through underwater acoustic channels of different complexities, and obtain the transmission characteristic data of the signals in each underwater acoustic channel.

[0092] In this embodiment, the LoRa underwater acoustic communication system is simulated based on the modulation signal and the cyclic shift value. The relevant simulation parameters are shown in the table. Each group transmits 50 symbols, the sampling frequency is set to 96kHz, the frequency range is between 13kHz and 19kHz, the center frequency is 16kHz, and convolutional coding with a coding efficiency of 1 / 2 is used, with the generator polynomial being [5, 7].

[0093] Simulation parameter table

[0094]

[0095] The simulation is divided into two types of channels: 1) AWGN channel 2) underwater acoustic multipath channel, such as Figure 4 As shown.

[0096] Figure 5 Correlation peak diagram of LoRa signal demodulated via AWGN channel; Figure 6 This is a correlation peak diagram of the LoRa signal after demodulation via a multipath channel.

[0097] The simulation results are as follows: the correlation peak pairs of the LoRa signal when demodulated through different channels are shown in the figure. Figure 5 , 6 As shown.

[0098] Figure 7 The LoRa bit error rate performance graph; Figure 8This is a graph showing the bit error rate performance of HFM-LoRa.

[0099] Bit error rate performance of underwater acoustic LoRa and HFM-LoRa communication systems, such as Figure 7 , 8 .

[0100] Figure 9 Simulation diagram of AWGN channel performance under different time slot intervals and spreading factors; Figure 10 Simulation diagram of multipath channel performance with different time slot intervals and spreading factors.

[0101] Performance of bit error rate under different time slot intervals and spreading factors in AWGN and underwater acoustic channels, such as Figure 9 , 10 As shown.

[0102] Step S4.2: Analyze the transmission characteristic data, comprehensively consider factors such as channel attenuation, multipath effect and noise interference, select the optimal modulation parameters, and set them as the modulation parameters for subsequent communication processes.

[0103] Specifically, in-depth analysis and research of underwater acoustic channel transmission characteristic data, precise consideration of factors such as channel attenuation, multipath effects, and noise interference, and careful selection of modulation parameters that meet the communication requirements of underwater acoustic channels by dynamically adjusting different spreading factors and shift step sizes. Specifically, when adjusting the spreading factor, it is flexibly varied within a certain range based on indicators such as channel bandwidth utilization, signal transmission reliability, and anti-interference capability; for the adjustment of the shift step size, it is meticulously optimized in conjunction with requirements such as signal transmission delay and synchronization accuracy, ensuring that the selected modulation method can guarantee both low computational complexity and robust underwater acoustic communication transmission.

[0104] Example 2:

[0105] The present invention also provides a LoRa modulation system for underwater Internet of Things (IoT). The LoRa modulation system for underwater IoT can be implemented by executing the procedural steps of the LoRa modulation method for underwater IoT. That is, those skilled in the art can understand the LoRa modulation method for underwater IoT as a preferred embodiment of the LoRa modulation system for underwater IoT.

[0106] Specifically, the LoRa modulation system for underwater IoT includes:

[0107] Module M1 constructs LoRa underwater acoustic communication signals based on the frequency modulation spread spectrum method for underwater acoustic signals;

[0108] Module M2, based on LoRa underwater acoustic communication signals, divides data bits into multiple chips for spread spectrum transmission, then performs cyclic shifting on the spread spectrum signal to generate a modulated signal, which is then transmitted through the underwater acoustic channel.

[0109] Module M3 receives the modulated signal, performs frequency domain cross-correlation processing on the modulated signal to obtain the peak position, i.e., the cyclic shift value, and realizes LoRa demodulation.

[0110] Module M4 constructs multiple sets of spreading factors and shift step size combinations within a preset range based on the transmission characteristic data of each underwater acoustic channel. By evaluating the transmission performance in the underwater acoustic channel, it selects the optimal modulation parameter combination and sets it as the modulation parameter for subsequent communication processes.

[0111] Specifically, module M1 includes the following sub-modules:

[0112] Module M1.1 selects the LoRa modulation signal according to the requirements of underwater acoustic communication.

[0113] In this embodiment, the LoRa modulation signal is a Chirp signal or an HFM signal.

[0114] The following are the characteristics of Chirp and HFM signals. Based on these characteristics and the requirements of underwater acoustic communication, LoRa modulation signal is selected:

[0115] The traditional LoRa method is a linear frequency modulation (LFM) spread spectrum method based on chirp signals. Chirp LFM signals are signals whose instantaneous frequency changes linearly with time. Assuming the instantaneous angular frequency change of the signal is linearly related to time, within the channel code duration T, its mathematical expression can be written as: In the formula, ω0=2πf0, f0 is the center frequency, and F is the range of instantaneous frequency variation, i.e., a frequency shift of twice. Since the instantaneous phase and instantaneous angular frequency of a signal have a calculus-integral relationship, i.e. The time-domain representation of the Chirp signal can be written as: s(t) = exp(j2πf0t + jπμt) 2 ), 0≤t≤T. To simplify the calculation, according to the principle of communication carrier, a Chirp signal with a center frequency of 0 can be modulated by a single-frequency carrier f0 to obtain a Chirp signal with a center frequency of f0. Therefore, the time-domain expression of the Chirp signal can be simplified to: s(t)=exp(jπμt) 2 Chirp signals have the property of converting between time and frequency, 0 ≤ t ≤ T. As can be seen, the time delay τ is equivalent to a frequency shift of the signal s(t) by Δf = μτ. The initial phase. When That is, within one symbol duration T, the signal frequency sweeps across the entire bandwidth B. Correspondingly, at intervals... Sampling was performed on the following: in: This represents the number of sampling points. For the sampled Chirp sequence: The equation shows that the time delay *m* is equivalent to a frequency shift. Given an initial phase and time-frequency conversion characteristics, when m = N, we have: From the formula, when N is even, s(n) = s(n+N); when N is odd, s(n) = -s(n+N), indicating that the Chirp signal has periodicity. As can be seen from the formula, different values ​​of 's' are orthogonal. Chirp signals possess orthogonality. The Chirp signal is represented by h(t) = s(-t). * Matched filters can achieve narrow pulse characteristics, which are as follows: The autocorrelation plot of the Chirp signal after sampling is shown below. Figure 1 As shown.

[0116] HFM is also a frequency-modulated signal and can be used as the base signal for HFM-LoRa. It differs from Chirp signals in that their frequency modulation functions are different. For an HFM signal with a start frequency of f0, a cutoff frequency of f1, and a duration of T, its time-domain expression can be written as: Where A(t) represents the amplitude, If the frequency is regulated, then the instantaneous frequency of the signal is: Similar to the Chirp signal, the basic waveform of the HFM signal is f H (t) and its shift waveform g H (t) also exhibits good cyclic correlation characteristics.

[0117] Module M1.2, select the spreading factor (SF).

[0118] This embodiment is for the current underwater acoustic communication system, with a spreading factor of 5 to 8. The larger the SF, the better the performance, but the lower the data rate.

[0119] More specifically, module M2 includes the following sub-modules:

[0120] Module M2.1 converts the data bits of the LoRa underwater acoustic communication signal from binary to decimal, obtaining the converted LoRa baseband signal.

[0121] Module M2.2 performs cyclic shifting on the converted LoRa baseband signal to generate a modulated signal.

[0122] Module M2.3 transmits the modulated signal through the underwater acoustic channel to complete the LoRa underwater acoustic communication process, realizing the transmission of information from the transmitting end to the receiving end.

[0123] Figure 2This is a schematic diagram of LoRa modulation in an embodiment of the present invention.

[0124] like Figure 2 As shown, LoRa is a modulation method based on linear frequency modulation spread spectrum. Compared to traditional direct sequence spread spectrum, LoRa modulation divides a data bit into N parts using a spreading factor (SF). SF One chip is used for spread spectrum transmission, and one LoRa modulated signal transmits SF data bits C = {c0, c1, ..., c2}. SF-1 Convert these SF data bits from binary to decimal K:

[0125] When using the Chirp signal as the modulation basis, the Chirp signal is split into 2. SF A spread spectrum transmission uses chips that transmit data by cyclically shifting the chips, and the chip rate R... c and bit rate R b Defined as: In the formula: B is the modulation bandwidth.

[0126] The instantaneous frequency of the Chirp signal is:

[0127]

[0128] Each chip in the Chirp signal also represents a frequency shift Δf, where Δf = B / 2 SF Furthermore, the LoRa signal's frequency range is the entire bandwidth, and the up-modulated LoRa signal frequency linearly increases from f0 to f... max Back jump becomes f min Then from f min The linear modulation frequency (LMC) of these two signal segments is the same when the signal linearly increases to f0. The transmission time of a LoRa signal is T = 2. SF / B, the frequency transition time T0 = TK / B is calculated.

[0129] Therefore, according to the formula, the instantaneous frequency of the LoRa modulated signal is: A vector composed of data bits is That is, the information bits to be transmitted are modulated into a symbol for transmission. The LoRa modulated signal waveform can be obtained by combining equations:

[0130] Figure 3 A schematic diagram of LoRa modulation by cyclic shifting the Chirp signal.

[0131] Specifically, module M3 includes the following sub-modules:

[0132] Module M3.1 converts the modulated signal into frequency domain form and multiplies it by the frequency domain conjugate of the basic waveform to obtain the frequency domain multiplication result;

[0133] Module M3.2 obtains the peak position of the frequency domain multiplication result, i.e., the cyclic shift value, converts the cyclic shift value into binary, and obtains the transmission bits.

[0134] In this embodiment, under the AWGN channel, the LoRa received signal is represented as: r(t) = s(t) + n(t), where s(t) is the LoRa transmitted signal after cyclic shifting via the Chirp signal, and n(t) is additive white Gaussian noise. The transmitted information is embedded in the cyclic shift length K of the basic waveform. The above equation can be written in frequency domain form as follows:

[0135] R(w) = S(w) + N(w). c(t) is the basic waveform of the Chirp signal, with frequency domain form C(w). The frequency domain form of the received signal R(w) is then compared with... The conjugate multiplication of the basic waveforms in the frequency domain yields: R(w)C * (w)=S(w)C * (w)+N(w)C * (w),

[0136] Converting to the time domain, it becomes:

[0137] The LoRa transmitted signal s(t) is coherent with the Chirp fundamental waveform c(t), where S(w) = C. * (w) represents the frequency domain cross-correlation result of s(t) and c(t), N(w)C * (w) represents the frequency domain cross-correlation result of s(t) and n(t), which is then expressed in the time domain after sampling as follows: Therefore, the peak position of the cross-correlation function is the transmitted information. Converting the transmitted signal into binary will yield SF transmitted bits.

[0138] Therefore: The signal spectrum, after being subjected to a discrete Fourier transform, reaches a peak value N at a cyclic shift value K, and the spectral values ​​at other points are all 0. Using this characteristic, the transmitted information can be obtained by demodulating the signal through the cyclic shift value K.

[0139] Figure 4 This refers to the random multipath channel used in the simulation of this invention embodiment.

[0140] The LoRa demodulation process is as follows: Figure 4 As shown, the LoRa modulation method has strong noise immunity because the noise s(t) and the initial signal c(t) are weakly correlated.

[0141] Specifically, module M4 includes the following sub-modules:

[0142] Module M4.1 constructs multiple sets of spreading factors and shift step size combinations within a preset range based on the transmission characteristic data of each underwater acoustic channel; it enables the signals corresponding to the multiple sets of spreading factors and shift step size combinations to pass through underwater acoustic channels of different complexities, thereby obtaining the transmission characteristic data of the signals in each underwater acoustic channel.

[0143] In this embodiment, the LoRa underwater acoustic communication system is simulated based on the modulation signal and the cyclic shift value. The relevant simulation parameters are shown in the table. Each group transmits 50 symbols, the sampling frequency is set to 96kHz, the frequency range is between 13kHz and 19kHz, the center frequency is 16kHz, and convolutional coding with a coding efficiency of 1 / 2 is used, with the generator polynomial being [5, 7].

[0144] Simulation parameter table

[0145]

[0146] The simulation is divided into two types of channels: 1) AWGN channel 2) underwater acoustic multipath channel, such as Figure 4 As shown.

[0147] Figure 5 Correlation peak diagram of LoRa signal demodulated via AWGN channel; Figure 6 This is a correlation peak diagram of the LoRa signal after demodulation via a multipath channel.

[0148] The simulation results are as follows: the correlation peak pairs of the LoRa signal when demodulated through different channels are shown in the figure. Figure 5 , 6 As shown.

[0149] Figure 7 The LoRa bit error rate performance graph; Figure 8 This is a graph showing the bit error rate performance of HFM-LoRa.

[0150] Bit error rate performance of underwater acoustic LoRa and HFM-LoRa communication systems, such as Figure 7 , 8 .

[0151] Figure 9 Simulation diagram of AWGN channel performance under different time slot intervals and spreading factors; Figure 10 Simulation diagram of multipath channel performance with different time slot intervals and spreading factors.

[0152] Performance of bit error rate under different time slot intervals and spreading factors in AWGN and underwater acoustic channels, such as Figure 9 , 10 As shown.

[0153] Module M4.2 analyzes transmission characteristic data, comprehensively considers factors such as channel attenuation, multipath effect and noise interference, selects the optimal modulation parameters, and sets them as the modulation parameters for subsequent communication processes.

[0154] Specifically, in-depth analysis and research of underwater acoustic channel transmission characteristic data, precise consideration of factors such as channel attenuation, multipath effects, and noise interference, and careful selection of modulation parameters that meet the communication requirements of underwater acoustic channels by dynamically adjusting different spreading factors and shift step sizes. Specifically, when adjusting the spreading factor, it is flexibly varied within a certain range based on indicators such as channel bandwidth utilization, signal transmission reliability, and anti-interference capability; for the adjustment of the shift step size, it is meticulously optimized in conjunction with requirements such as signal transmission delay and synchronization accuracy, ensuring that the selected modulation method can guarantee both low computational complexity and robust underwater acoustic communication transmission.

[0155] Example 3:

[0156] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a LoRa modulation method for underwater Internet of Things as described in Embodiment 1 above.

[0157] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0158] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A LoRa modulation method for underwater Internet of Things, characterized in that, Includes the following steps: Step S1: Construct a LoRa underwater acoustic communication signal based on the frequency modulation and spread spectrum method of underwater acoustic signals; Step S2: Based on the LoRa underwater acoustic communication signal, the data bits are divided into multiple chips for spread spectrum transmission. Then, the spread spectrum signal is cyclically shifted to generate a modulated signal, which is then transmitted through the underwater acoustic channel. Step S3: Receive the modulation signal, perform frequency domain cross-correlation processing on the modulation signal to obtain the peak position, i.e., the cyclic shift value, and realize LoRa demodulation; Step S4: Based on the transmission characteristic data of each underwater acoustic channel, construct multiple sets of spreading factors and shift step size combinations within a preset range. By evaluating the transmission performance in the underwater acoustic channel, select the optimal modulation parameter combination and set it as the modulation parameter for subsequent communication processes.

2. The LoRa modulation method for underwater IoT according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S1.1: Select a LoRa modulation signal according to the requirements of underwater acoustic communication; Step S1.2: Select the spreading factor.

3. The LoRa modulation method for underwater IoT according to claim 2, characterized in that, In step S1.1, the LoRa modulation signal is a Chirp signal or an HFM signal.

4. The LoRa modulation method for underwater IoT according to claim 2, characterized in that, In step S1.2, the spreading factor is 5 to 8.

5. The LoRa modulation method for underwater IoT according to claim 1, characterized in that, Step S2 includes: dividing the data bits into N using the spreading factor. SF Each chip is spread spectrum transmitted, and then the LoRa modulation signal is cyclically shifted to generate a modulation signal.

6. The LoRa modulation method for underwater IoT according to claim 5, characterized in that, Step S2 includes the following sub-steps: Step S2.1: Convert the data bits of the LoRa underwater acoustic communication signal from binary to decimal to obtain the converted LoRa baseband signal; Step S2.2: Cyclicly shift the converted LoRa baseband signal to generate a modulation signal; Step S2.3: The modulated signal is transmitted through the underwater acoustic channel to complete the LoRa underwater acoustic communication process and realize the transmission of information from the transmitting end to the receiving end.

7. The LoRa modulation method for underwater IoT according to claim 1, characterized in that, Step S3 includes the following sub-steps: Step S3.1: Convert the modulated signal into frequency domain form and multiply it by the frequency domain conjugate of the basic waveform to obtain the frequency domain multiplication result; Step S3.2: Obtain the peak position of the frequency domain multiplication result, i.e., the cyclic shift value, and convert the cyclic shift value into binary to obtain the transmission bits.

8. The LoRa modulation method for underwater IoT according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S4.1: Based on the transmission characteristic data of each underwater acoustic channel, construct multiple sets of spreading factors and shift step size combinations within a preset range; enable the signals corresponding to the multiple sets of spreading factors and shift step size combinations to pass through underwater acoustic channels of different complexities, and obtain the transmission characteristic data of the signals in each underwater acoustic channel. Step S4.2: Analyze the transmission characteristic data, comprehensively consider channel attenuation, multipath effect and noise interference, select the optimal modulation parameters, and set them as the modulation parameters for subsequent communication processes.

9. A LoRa modulation system for underwater Internet of Things, characterized in that, include: Module M1 constructs LoRa underwater acoustic communication signals based on the frequency modulation spread spectrum method for underwater acoustic signals; Module M2, based on the LoRa underwater acoustic communication signal, divides the data bits into multiple chips, performs spread spectrum transmission, then performs cyclic shift on the spread spectrum signal to generate a modulated signal, and transmits it through the underwater acoustic channel; Module M3 receives the modulation signal, performs frequency domain cross-correlation processing on the modulation signal to obtain the peak position, i.e., the cyclic shift value, and realizes LoRa demodulation. Module M4 constructs multiple sets of spreading factors and shift step size combinations within a preset range based on the transmission characteristic data of each underwater acoustic channel. By evaluating the transmission performance in the underwater acoustic channel, it selects the optimal modulation parameter combination and sets it as the modulation parameter for subsequent communication processes.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the LoRa modulation method for underwater Internet of Things as described in any one of claims 1 to 8.

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