Method for high-speed non-coordinated concurrent access of underwater acoustic Internet of Things

Abstract

The invention discloses a high-speed non-coordinated concurrent access method for an underwater sound internet of things, which is used for realizing a time-varying channel resisting Doppler and strong multipath, and improving the throughput of a time-varying underwater sound system while solving conflicts. The method comprises the following steps: 1, distributing two ZC sequences with different root indexes to different users, wherein the ZC sequences are respectively used as an auxiliary sequence and a modulation sequence; 2, a sliding correlator is adopted to carry out cross-correlation operation on received baseband signals, and a lead code is composed of three repeated ZC base sequences; when continuous equidistant signals exceeding a threshold value are detected, the potential peak and the corresponding root index are reserved, the earliest peak is determined as the initial position of a lead code, and then the data demodulation initial positions of different users are determined according to the length of the lead code; and 3, obtaining a template signal based on auxiliary sequence demodulation, obtaining a to-be-demodulated signal based on modulation sequence demodulation, introducing a Pearson correlation coefficient of adaptive weight to calculate a correlation coefficient of the template signal and the to-be-demodulated signal, and demodulating specified user data from superposed data symbols.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and relates to a method for processing received data symbols in a spread spectrum communication transmission scheme, to a system design for symbol processing in underwater acoustic communication facing multipath, Doppler and time-varying channels, and also to multi-user access in the Internet of Things, and in particular to a method for high-speed non-coordinated concurrent access of an underwater acoustic Internet of Things. Background Art

[0002] The Internet of Underwater Things (IoUT) is an emerging technology that extends the capabilities of the Internet of Things (IoT) to underwater environments, and its data collection and analysis promotes the exploration of the world's oceans. Unlike traditional wireless IoT, the underwater IoT usually uses sound waves for communication, because radio waves have high attenuation characteristics and their propagation distance underwater is extremely limited.

[0003] High-speed underwater acoustic communication is a challenging technology: on the one hand, due to the complex underwater environment (such as reflection and refraction on the seabed and water surface), the underwater channel has serious multipath effects; on the other hand, the Doppler effect and the dynamic environment caused by mobile devices or moving water make the UWA channel time-varying.

[0004] In addition, the design of efficient MAC protocols for underwater acoustic networks is also a challenge. The propagation delay underwater cannot be ignored because the propagation speed of sound waves in water is about 1500 meters per second, which is much lower than the radio propagation speed in the air, making the acoustic communication connection very unreliable. If underwater IoT devices want to access the shared acoustic medium in an orthogonal manner, a high coordination overhead is required. Uncoordinated random access protocols (such as ALOHA) are particularly suitable for underwater IoT data acquisition applications because of their simplicity. However, when users do not coordinate transmission, the performance of these protocols will be limited by severe conflicts, which will also affect the data transmission rate.

[0005] For high-speed underwater acoustic communication (UAC), existing technologies have proposed new modulation methods such as multi-carrier chirp spread spectrum (CSS) and multi-carrier orthogonal frequency division multiplexing (OFDM). The current underwater CSS and OFDM systems are designed for point-to-point communication. When there are multiple users concurrently, their demodulation methods cannot effectively handle collisions, resulting in limited networking performance.

[0006] The currently developed underwater CDMA (Code Division Multiple Access) system can support non-coordinated concurrent access and can reasonably handle user collisions. However, each symbol can only carry 1 bit, which affects the data rate. In addition, although the Rake receiver has relatively good performance in terms of multipath resistance, its anti-Doppler time-varying effect is limited; although the nCSK system can carry multiple bits per symbol and supports multi-user concurrent transmission, it also uses a Rake receiver for equalization, which is difficult to deal with Doppler and strong multipath time-varying channels. Summary of the invention

[0007] The purpose of the present invention is to provide a method for high-speed non-coordinated concurrent access of an underwater acoustic Internet of Things to solve the problems existing in the prior art. High-speed ZC modulation is adopted, and the orthogonality of different ZC sequences is utilized to enable a low-overhead non-coordinated concurrent access protocol. At the same time, a modulation and demodulation method based on an auxiliary sequence is adopted to realize a time-varying channel that counteracts Doppler and strong multipath, thereby resolving conflicts and improving the throughput of the time-varying underwater acoustic system.

[0008] In order to achieve the above object, the solution of the present invention is: A method for high-speed non-coordinated concurrent access of an underwater acoustic Internet of Things, comprising: Step 1: Symbol modulation Different root ZC sequences are used for data of different users; each user is assigned two ZC sequences with different root indices and the root index is not 1, one of which is used as an auxiliary sequence to generate a template signal, and the other is used as a modulation sequence to modulate the data; If different users have data to send, they can directly access the channel and send data signals asynchronously without coordination. The data signal includes a preamble and data symbols. The preamble consists of three repeated ZC base sequences. The data symbol is modulated by the two ZC sequences with different root indexes and the root index is not 1. The length of the data symbol is determined by the data length. Step 2: Frame synchronization A sliding correlator is used to perform cross-correlation operation on the received baseband signal; when continuous equidistant signals exceeding the threshold are detected, potential peaks and their corresponding root indexes are retained, and the earliest peak is determined as the starting position of the preamble code, and then the demodulation starting position of the data symbols of different users is determined according to the preamble code length; Step 3: Symbol demodulation Using the DFT-IDFT operation, the template signal is demodulated based on the auxiliary sequence assigned to each user, and the demodulated signal is demodulated based on the modulation sequence assigned to each user. The Pearson correlation coefficient with adaptive weights is introduced to calculate the correlation coefficient between the template signal and the signal to be demodulated, and the specified user data is demodulated from the superimposed data symbols.

[0009] Preferably, the specific steps of symbol modulation in step 1 include: 1.1. Define each ZC sequence as ,in represents the natural base, represents the imaginary unit, Represents the root index, Indicates the length of the symbol; adopts a coding mapping method based on a ZC sequence, assigns ZC sequences with different root indices to different users, and allocates two ZC sequences with different root indices to each user, one of which is used as an auxiliary sequence for generating a template signal, and the other is used as a modulation sequence for modulating data; 1.2. After cyclic shift, the ZC sequence is expressed as ,in, represents the number of cyclic shifts of the sequence, Represents a modulo operation; each symbol of a user is modulated based on a cyclic shift sequence of two root indices to obtain a The symbol of the bit, where .

[0010] Preferably, the specific steps of symbol demodulation in step 3 include: 3.1 Spectrum Demodulation According to step 2, the demodulation start position of the data symbols of different users in the received signal is obtained, and the received length is Symbol With possible The sequences of the two cases are correlated, and the sequence with the largest correlation value is used as the transmission sequence; specifically, in step 3.1, the demodulation is performed by DFT-IDFT, and the symbol The conjugate of the ZC basis sequence with different root indices is multiplied in the frequency domain and then converted to the time domain to obtain the sequence , and its calculation formula is: ; in Indicates The symbol sequence after spectrum demodulation is represents the inverse discrete Fourier transform, represents the inverse discrete Fourier transform, represents a ZC base sequence, with superscript represents the conjugate operation; then find the starting point with the largest peak value as the position of the transmitted data symbol, that is , get the sending sequence; 3.2. Matching demodulation based on auxiliary sequence Perform spectrum demodulation on each received data symbol and two root indices, and use the auxiliary sequence to find the template signal to obtain the first The template signal of , which is used to reflect the channel response of the data packet, including multipath, Doppler effect and time offset caused by frame synchronization error. Its calculation formula is: ; 3.3. Calculation of Pearson correlation coefficient of adaptive weights First, calculate the correlation between the template signal and the data symbol in the received spectrum demodulation sequence, and give the samples with amplitude greater than the preset threshold Set a higher weight; then the decoded data is: ; in , ; 3.4. According to Get the data packet bits corresponding to the user.

[0011] After adopting the above technical solution, the present invention has the following technical effects: (1) The symbol modulation method proposed in the present invention can essentially solve the problem of data symbol collision of different users during uncoordinated concurrent transmission of underwater IoT. By allocating ZC sequences with different root indexes to different users, it can better reduce interference between users and has better performance than traditional CSS modulation and OFDM modulation. (2) The present invention improves the data rate in multi-user concurrent access scenarios. Through the definition of the ZC sequence, each data symbol can carry n bits, which is n times the data rate improvement compared to the traditional CDMA concurrent system. (3) The present invention achieves high-performance demodulation and high system throughput under strongly time-varying channels. Specifically, it generates a template signal that reflects the channel characteristics of data symbols by defining an auxiliary sequence, which can track the rapidly changing underwater acoustic channel. It also introduces adaptive weights in the demodulation process to adjust the calculation of the Pearson correlation coefficient, thereby reducing the impact of noise samples under multiple users on decoding, improving the system's robustness to noise, and thus improving the system's throughput in concurrent scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the system framework and scenarios of the application of the present invention; Figure 2 is a flow chart of the present invention; Figure 3 It is a schematic diagram of the implementation process of the present invention; Figure 4 This is a schematic diagram of the channel response impact during the experimental test; Figure 5This is a schematic diagram of Doppler spectrum power density during experimental testing; Figure 6 This is the correlation spectrum of the received signals of two users during the experimental test; Figure 7 This is the correlation spectrum of the superimposed received signal during the experimental test; Figure 8 This is the signal-to-noise ratio-bit error rate curve during the experimental test; Fig. 9 It is a schematic diagram of the physical layer throughput of the present invention at different duty cycles during experimental testing. DETAILED DESCRIPTION

[0013] In order to further explain the technical solution of the present invention, the present invention is described in detail below through specific embodiments.

[0014] refer to Figure 1-3 As shown, Figure 1 The system framework and scenario diagram of the application of the present invention are given. Static and mobile underwater IoT devices transmit sensing data to a gateway close to the sea surface through the modulation technology of the present invention, and then the gateway calls the method proposed by the present invention to perform high-performance demodulation and decoding; Figure 2 The flowchart of the method of the present invention is given, and the steps of symbol modulation, frame synchronization, and symbol demodulation are performed in sequence. The specific implementation process is as follows Figure 3 .

[0015] Therefore, the present invention discloses a method for high-speed non-coordinated concurrent access of an underwater acoustic Internet of Things, comprising: Step 1: Symbol modulation Different root ZC (Zadoff-Chu) sequences are used for data of different users. Each user is assigned two ZC sequences with different root indices and the root index is not 1, one of which is used as an auxiliary sequence to generate a template signal (characterizing the channel response), and the other is used as a modulation sequence to modulate the data. If different users have data to send, they can directly access the channel and asynchronously send data signals without coordination. The data signal includes a preamble and data symbols. The preamble consists of three repeated ZC base sequences (with a root index of 1). The data symbol is modulated by the two ZC sequences with different root indexes and a root index other than 1. The length of the data symbol is determined by the data length. Step 2: Frame synchronization A sliding correlator is used to perform cross-correlation operation on the received baseband signal; the frame synchronization module of the receiver continuously performs cross-correlation and detects according to the correlation value. When a continuous equidistant signal exceeding the threshold is detected, the potential peak and its corresponding root index are retained, and the earliest peak is determined as the starting position of the preamble code. Then, the demodulation starting position of the data symbols of different users is determined according to the length of the preamble code; Step 3: Symbol demodulation Using the DFT-IDFT (discrete Fourier transform-inverse discrete Fourier transform) operation, the template signal is demodulated based on the auxiliary sequence assigned to each user, and the demodulated signal is demodulated based on the modulation sequence assigned to each user. The Pearson correlation coefficient with adaptive weights is introduced to calculate the correlation coefficient between the template signal and the signal to be demodulated, and the specified user data is demodulated from the superimposed data symbols.

[0016] Through the above scheme, the symbol modulation method proposed in the present invention can essentially solve the problem of data symbol collision of different users during uncoordinated concurrent transmission of underwater Internet of Things. By allocating ZC sequences with different root indexes to different users, interference between users can be better reduced, and the performance is better than traditional CSS modulation and OFDM modulation. The present invention realizes the improvement of data rate in multi-user concurrent access scenarios. Through the definition of ZC sequence, each data symbol can carry n bits, which has n times the data improvement compared with the traditional CDMA concurrent system. The present invention realizes high-performance demodulation and high system throughput under strong time-varying channels. Specifically, an auxiliary sequence is defined to generate a template signal reflecting the channel characteristics of the data symbol, which can track the rapidly changing underwater acoustic channel, and the calculation of the Pearson correlation coefficient is adjusted by introducing adaptive weights in the demodulation process, so that the influence of noise samples under multi-users on decoding is reduced, and the robustness of the system to noise is improved, thereby improving the throughput of the system in concurrent scenarios.

[0017] Specific embodiments of the present invention are shown below.

[0018] In the above step 1, the specific steps of symbol modulation include: 1.1. Define each ZC sequence as ,in represents the natural base, represents the imaginary unit, Represents the root index, Indicates the length of the symbol; adopts a coding mapping method based on a ZC sequence, assigns ZC sequences with different root indices to different users, and allocates two ZC sequences with different root indices to each user, one of which is used as an auxiliary sequence for generating a template signal, and the other is used as a modulation sequence for modulating data; 1.2. After cyclic shift, the ZC sequence is expressed as ,in, represents the number of cyclic shifts of the sequence, represents the modulo operator; each symbol of a user is modulated based on the cyclic shift sequence of two root indices to obtain The symbol of the bit, where .

[0019] In the above step 3, the specific steps of symbol demodulation include: 3.1 Spectrum Demodulation According to step 2, the demodulation start position of the data symbols of different users in the received signal is obtained, and the received length is Symbol With possible Correlate the sequences of the two situations and find the one with the largest correlation value as the sending sequence.

[0020] Furthermore, in the above step 3.1, in order to reduce the complexity, demodulation is performed by DFT-IDFT, firstly the symbol The conjugate of the ZC basis sequence with different root indices is multiplied in the frequency domain and then converted to the time domain to obtain the sequence , and its calculation formula is: ; in Indicates The symbol sequence after spectrum demodulation is represents the inverse discrete Fourier transform, represents the inverse discrete Fourier transform, represents a ZC base sequence, with superscript represents the conjugate operation; then find the starting point with the largest peak value as the position of the transmitted data symbol, that is , get the sending sequence.

[0021] Secondly, in the above step 3, the specific steps of symbol demodulation also include: 3.2. Matching demodulation based on auxiliary sequence Perform spectrum demodulation on each received data symbol and two root indices, and use the auxiliary sequence to find the template signal to obtain the first The template signal of , which is used to reflect the channel response of the data packet, including multipath, Doppler effect and time offset caused by frame synchronization error. Its calculation formula is: ; 3.3. Calculation of Pearson correlation coefficient of adaptive weights First, calculate the correlation between the template signal and the data symbol in the received spectrum demodulation sequence, and give the samples with amplitude greater than the preset threshold (manually set) Set higher weights (because these are probably data symbols, to suppress noise); then the decoded data is: ; in , ; 3.4. According to Get the data packet bits corresponding to the user.

[0022] The technical effect of the present invention is verified by experimental tests as follows.

[0023] The experiment uses a transducer, a hydrophone, and two NI acquisition boards for experimental evaluation. The transducer, power amplifier, and one of the NI acquisition boards are used as transmitters, and the hydrophone, STM32 MCU, and another NI acquisition board are used as receivers. NI LabVIEW is used in conjunction with the NI acquisition board to realize the sending and receiving of passband sampling. At the same time, the waveform and demodulation algorithm are implemented in MATLAB.

[0024] The experiment was conducted in a water pool with artificial motion to produce the Doppler effect. The channel response is shown in Figure 4 , 5 : Figure 4 It shows that there is a certain multipath effect, and the time delay of the energy peak position at each moment is offset; Figure 5 It shows that there is a serious Doppler diffusion phenomenon in this channel.

[0025] The frequency range of the hardware filter in the experiment is 20~30kHz, the center frequency of all signals is 25kHz, and the bandwidth is 6kHz. Each ZCMod contains a preamble and data symbols of three repeating root-indexed ZC sequences, and the data symbols have a zero-filled guard interval with a length of 1 / 6 of the symbol duration. The bit error rate at different signal-to-noise ratios is simulated by superimposing Gaussian white noise. In order to evaluate the network layer throughput, a random access system with 8 users and duty cycles (20%, 40%, 60%, 80%, 100%) is further considered, where the value defines the ratio of the message length to the frame length. The transmitter selects 8 positions to simulate real devices with different channels, and for each position, 240 packets are collected. Specifically, a random delay of each device is generated within the frame length, and its signal is added to the delay to generate a superimposed signal at the same transmission power (SNR = 10dB in the experiment) to simulate a multi-user concurrent scenario, and the superimposed signal is used for demodulation.

[0026] Figure 6 , 7 The actual measured cyclic correlation spectrum affected by multipath interference is shown, in which the transmitted signal is 1, but the position of its peak is severely interfered, which will lead to wrong judgment of the traditional peak demodulation method.

[0027] Figure 8 The signal-to-noise ratio-bit error rate curve during point-to-point transmission was evaluated. Compared with the CSS modulation method, the performance gain of the present invention is relatively large and the error floor is eliminated.

[0028] Fig. 9 The results show that the physical layer throughput of the present invention at different duty cycles in the experiment is significantly improved compared with the prior art.

[0029] The above embodiments and drawings do not limit the product form and style of the present invention. Any appropriate changes or modifications made by ordinary technicians in the relevant technical field should be deemed to be within the patent scope of the present invention.

Claims

1. A method for high-speed non-coordinated concurrent access of underwater acoustic Internet of Things, characterized in that include: Step 1: Symbol modulation For data of different users, use ZC sequences with different roots; Each user is assigned two ZC sequences with different root indices and root indices other than 1, one of which is used as an auxiliary sequence for generating a template signal, and the other is used as a modulation sequence for modulating data; If different users have data to send, they can directly access the channel and send data signals asynchronously without coordination. The data signal includes a preamble and data symbols. The preamble consists of three repeated ZC base sequences. The data symbol is modulated by the two ZC sequences with different root indexes and the root index is not 1. The length of the data symbol is determined by the data length. Step 2: Frame synchronization A sliding correlator is used to perform cross-correlation operation on the received baseband signal; when continuous equidistant signals exceeding the threshold are detected, potential peaks and their corresponding root indexes are retained, and the earliest peak is determined as the starting position of the preamble code, and then the demodulation starting position of the data symbols of different users is determined according to the preamble code length; Step 3: Symbol demodulation Using the DFT-IDFT operation, the template signal is demodulated based on the auxiliary sequence assigned to each user, and the demodulated signal is demodulated based on the modulation sequence assigned to each user. The Pearson correlation coefficient with adaptive weights is introduced to calculate the correlation coefficient between the template signal and the signal to be demodulated, and the specified user data is demodulated from the superimposed data symbols.

2. The method for high-speed non-coordinated concurrent access of the underwater acoustic Internet of Things as claimed in claim 1, characterized in that The specific steps of symbol modulation in step 1 include: 1.

1. Define each ZC sequence as ,in represents the natural base, represents the imaginary unit, Represents the root index, Indicates the length of the symbol; adopts a coding mapping method based on a ZC sequence, assigns ZC sequences with different root indices to different users, and allocates two ZC sequences with different root indices to each user, one of which is used as an auxiliary sequence for generating a template signal, and the other is used as a modulation sequence for modulating data; 1.

2. After cyclic shift, the ZC sequence is expressed as ,in, represents the number of cyclic shifts of the sequence, Represents a modulo operation; each symbol of a user is modulated based on a cyclic shift sequence of two root indices to obtain a The symbol of the bit, where .

3. The method for high-speed non-coordinated concurrent access of underwater acoustic Internet of Things according to claim 2, characterized in that The specific steps of symbol demodulation in step 3 include: 3.1 Spectrum Demodulation According to step 2, the demodulation start position of the data symbols of different users in the received signal is obtained, and the received length is Symbol With possible Correlate the sequences of the two situations and find the one with the largest correlation value as the sending sequence.

4. The method for high-speed non-coordinated concurrent access of the underwater acoustic Internet of Things as claimed in claim 3, characterized in that: In step 3.1, demodulation is performed by DFT-IDFT. First, the symbol The conjugate of the ZC basis sequence with different root indices is multiplied in the frequency domain and then converted to the time domain to obtain the sequence , and its calculation formula is: ； in Indicates The symbol sequence after spectrum demodulation is represents the inverse discrete Fourier transform, represents the inverse discrete Fourier transform, represents a ZC base sequence, with superscript represents the conjugate operation; then find the starting point with the largest peak value as the position of the transmitted data symbol, that is , get the sending sequence.

5. The method for high-speed non-coordinated concurrent access of the underwater acoustic Internet of Things as claimed in claim 4, characterized in that The specific steps of symbol demodulation in step 3 also include: 3.

2. Matching demodulation based on auxiliary sequence Perform spectrum demodulation on each received data symbol and two root indices, and use the auxiliary sequence to find the template signal to obtain the first The template signal of , which is used to reflect the channel response of the data packet, including multipath, Doppler effect and time offset caused by frame synchronization error. Its calculation formula is: ； 3.

3. Calculation of Pearson correlation coefficient of adaptive weights First, calculate the correlation between the template signal and the data symbol in the received spectrum demodulation sequence, and give the samples with amplitude greater than the preset threshold Set a higher weight; then the decoded data is: ； in , ; 3.

4. According to Get the data packet bits corresponding to the user.

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