Backscattering channel modeling and intelligent emission intensity dynamic matching method

Abstract

The invention provides a backscattering channel modeling and intelligent emission intensity dynamic matching method, which comprises the steps of constructing a physical model of an underwater backscattering channel based on far-field approximate hypothesis, establishing a signal propagation equation from an emission end to a receiving end in combination with a dynamic coupling relation of path loss, impedance mismatch and a backscattering cross section, and calculating an intelligent emission intensity of the underwater backscattering channel according to the signal propagation equation. The electro-acoustic conversion process of the transducer is represented through an equivalent circuit model; the SNR of the backscattering signal is measured in real time at a receiving end, the current SNR value is fed back to a transmitting end through an uplink, and according to the deviation value of the current SNR and a target SNR threshold value, a proportional-integral-differential control algorithm is adopted to calculate the transmitting power adjustment amount; and according to a preset fuzzy rule base and a membership function, the current transmitting power is dynamically adjusted based on the size and trend of the SNR deviation, and new transmitting power is generated by superposing the transmitting power adjustment amount to the current transmitting power. According to the invention, the purposes of maintaining a stable signal-to-noise ratio in a complex underwater environment and optimizing energy consumption can be achieved.

Description

Technical Field

[0001] This application relates to the field of underwater wireless communication technology, and particularly to a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity. Background Art

[0002] Traditional underwater communication technologies mainly rely on active transmission devices to achieve information transmission through acoustic waves or electromagnetic waves, but they have high energy consumption and large device complexity. In recent years, backscatter communication technology has gradually attracted attention due to its low-power consumption characteristics. This technology transmits data by modulating the reflection of environmental signals, significantly reducing the energy consumption of nodes. However, the complex acoustic characteristics of the underwater environment (such as multipath effects, absorption attenuation, and dynamic impedance mismatch) lead to severe fluctuations in the signal-to-noise ratio of backscatter signals. Existing systems mostly adopt a fixed transmission intensity strategy and are difficult to adapt to the dynamic changes of the channel. In addition, there is a lack of an accurate closed analytical model for underwater backscatter channels, and link budget analysis often relies on empirical formulas, resulting in inaccurate power allocation and further exacerbating the instability of communication quality. Although there are power optimization methods for radio frequency backscatter in the prior art, due to the significant differences in the physical characteristics between underwater acoustic channels and radio channels, directly transplanting such methods has limited effects.

[0003] Existing underwater backscatter communication systems have problems of unstable signal-to-noise ratio and low energy consumption efficiency in a dynamic environment, mainly due to the following technical defects: First, traditional link budget models do not fully consider the dynamic coupling relationships of path loss, impedance mismatch, and backscatter cross-section in underwater acoustic channels, resulting in a mismatch between transmitted power allocation and real-time channel conditions; Second, the fixed transmission intensity strategy cannot adaptively adjust according to the change of the signal-to-noise ratio at the receiving end, resulting in power waste or communication interruption. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related art to some extent.

[0005] To this end, the first object of this application is to propose a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity. By establishing an accurate analytical model of the underwater backscatter channel and designing an intelligent transmission intensity dynamic matching algorithm, the above problems are solved, and the goal of optimizing energy consumption while maintaining a stable signal-to-noise ratio in a complex underwater environment is achieved.

[0006] The second object of this application is to propose a device for modeling a backscatter channel and dynamically matching intelligent transmission intensity.

[0007] The third object of this application is to propose an electronic device.

[0008] The fourth object of this application is to propose a computer-readable storage medium.

[0009] The fifth object of this application is to propose a computer program product.

[0010] To achieve the above object, an embodiment of the first aspect of this application proposes a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity, including:

[0011] Based on the far-field approximation assumption, construct a physical model of the underwater backscatter channel, combine the dynamic coupling relationships of path loss, impedance mismatch, and backscattering cross-section, establish a signal propagation equation from the transmitter to the receiver, and characterize the electro-acoustic conversion process of the transducer through an equivalent circuit model;

[0012] At the receiver, the signal-to-noise ratio SNR of the backscattered signal is measured in real time, and the current SNR value is fed back to the transmitter through the uplink, where the feedback period is set according to the dynamic characteristics of the underwater channel;

[0013] Preset a target SNR threshold according to the communication quality requirement, and calculate the transmission power adjustment amount using a proportional-integral-derivative control algorithm according to the deviation between the current SNR and the target SNR threshold;

[0014] For the non-linear characteristics of the underwater channel, design a fuzzy logic optimization module, and dynamically adjust the current transmission power based on the preset fuzzy rule base and membership function according to the magnitude and trend of the SNR deviation. Generate a new transmission power by superimposing the transmission power adjustment amount on the current transmission power.

[0015] Optionally, the step of constructing a physical model of the underwater backscatter channel based on the far-field approximation assumption, combining the dynamic coupling relationships of path loss, impedance mismatch, and backscattering cross-section, establishing a signal propagation equation from the transmitter to the receiver, and characterizing the electro-acoustic conversion process of the transducer through an equivalent circuit model includes:

[0016] Adopt an equivalent circuit model to represent the transducer as a two-port network with electrical and acoustic ports. Apply a voltage V to the electrical port of the transducer and an incident acoustic pressure Fb to the acoustic port, and calculate the equivalent input impedance Z in and the equivalent acoustic-side impedance Z o ;

[0017] Describe the two-port element of the transducer through an impedance matrix, defined as:

[0018]

[0019] where V1 and I1 are the voltage and current on the electrical terminals of the transducer, and F2 and u2 are the force and velocity on the outer surface of the transducer;

[0020] Model the process of energy harvesting from an electrical emission source to a reflection node, including the following four sub-processes:

[0021] Sub-process 1: How the electrical power of the emission node is converted into acoustic power;

[0022] Sub-process 2: How the acoustic power propagates and reaches the backscatter node;

[0023] Sub-process 3: How the acoustic pressure incident on the backscatter node is converted into electrical power and energy is captured;

[0024] Sub-process 4: How the backscatter node uses the captured energy for signal reflection and transmission.

[0025] Optionally, the said Sub-process 1 includes:

[0026] According to the conversion relationship between the input electrical power P elec of the emission node and the output acoustic power P ac of the transducer, define the electromechanical coupling coefficient of the transducer, and the formula is:

[0027]

[0028] where η Tx is the electromechanical coupling coefficient of the transducer, representing the conversion efficiency of electrical energy to acoustic energy of the transducer;

[0029] Calculate the said input electrical power P elec , and the formula is:

[0030]

[0031] In the formula, R in is the input resistance, and Z 11 , Z 12 , Z 21 and Z 22 are the impedance matrix parameters of the transducer, and R r is the load resistance of the transducer;

[0032] Calculate the said output acoustic power P ac , and the formula is:

[0033]

[0034] F2 = Z 21 I1 + Z 22 u2

[0035] The derivation formula of the electromechanical coupling coefficient η Tx of the said transducer is further:

[0036]

[0037] Among them, η Tx is the electromechanical coupling coefficient of the transducer.

[0038] Optionally, the second sub-process includes:

[0039] Define the far-field sound level generated by the transducer as the sound source sound level, and its calculation formula is:

[0040] SL(Ω) = 170.8 + 10log(η Tx P elec ) + DI(Ω) [dB re 1μPa @ 1m]

[0041] Among them, SL is the sound source sound level, and DI is the directivity index of the transducer;

[0042] Considering the spreading loss and absorption loss, calculate the attenuation when the sound signal propagates at a single frequency f over a distance d. This attenuation is given by the path loss PL, and the formula is:

[0043] PL(d, f) = k10log(d) + α(f)d [dB]

[0044] Among them, k is the spreading factor, and α(f) is the absorption coefficient related to the frequency;

[0045] Calculate the received pressure level RL at the backscattering node. The formula is:

[0046] RL = SL - PL

[0047] Among them, RL is measured in dB re 1μPa, which represents the sound intensity at a distance of d meters.

[0048] Optionally, the third sub-process includes:

[0049] Calculate the electric power obtained in the sound pressure incident on the backscattering node. The formula is:

[0050]

[0051] Among them, Z e is the electrical load, Z th is the Thevenin equivalent resistance, V th is the Thevenin equivalent voltage, R e represents the real part of Z e ;

[0052] Calculate the downlink impedance mismatch loss IML D , and the formula is:

[0053]

[0054] By defining the gain G of the transducer as the logarithmic sum of its efficiency and directivity, the formula for capturing energy is simplified and expressed as:

[0055] G = 10 log(η) + DI

[0056] The formula for calculating the final energy capture of the backscatter node is:

[0057] P harv = 77.7 + 10 log(P elec ) + G Tx + G node - 20 log(f) - PL + IML D [dBm]

[0058] where P harv is the electrical power finally captured by the backscatter node.

[0059] Optionally, the sub-process four includes:

[0060] Define BL as the sound pressure level reflected at the hydrophone due to the backscattering of the backscatter node. The formula is:

[0061] BL = 170.8 + 10 log(P refl ) + DI node - PL

[0062] where P refl is the sound power reflected from the backscatter node;

[0063] Calculate the reflected power of the underwater transducer according to the scattering cross-section σ of the backscatter node. The formula is:

[0064] P refl = σI i

[0065] where I i is the incident sound intensity, given by:

[0066]

[0067] Then BL is expressed as:

[0068] BL = 159.8 + 10 log(η Tx P elec ) + DI Tx + 10 log(σ) + DI node - 2PL

[0069] Since the reflected power P refl is also a function of the sound power absorbed by the node P absorb and the maximum available power at the node, so Pabsorb Defined according to the sensor parameters as:

[0070]

[0071] The maximum available power is given by:

[0072]

[0073] The relationship between the two is:

[0074]

[0075] where Γ is the acoustic reflection coefficient;

[0076] The formula for the incident cross-sectional area and the reflection coefficient derived from the geometric relationship is:

[0077]

[0078] When the backscatter node communicates by switching between two states, its reflection coefficient also switches between two states, which forms the differential scattering cross-section, which is expressed as:

[0079]

[0080] Furthermore:

[0081]

[0082] According to circuit theory, it can be obtained that:

[0083]

[0084] The reflection coefficient is:

[0085]

[0086] Since the system is modulated between two impedance states, the differential reflection coefficient is simplified to:

[0087]

[0088] Therefore, BL can be updated:

[0089]

[0090] where the impedance mismatch loss is:

[0091]

[0092] According to the updated BL, calculate the signal-to-noise ratio at the hydrophone, and the formula is:

[0093] SNR = BL - (NL + 10log(BW))

[0094] Wherein, SNR is the signal-to-noise ratio at the hydrophone.

[0095] Optionally, preset a target SNR threshold according to the communication quality requirement, and calculate the transmit power adjustment amount by using a proportional-integral-derivative control algorithm according to the deviation amount between the current SNR and the target SNR threshold, including:

[0096] Preset a target SNR threshold SNR according to the communication quality requirement target , calculate the difference between the current SNR and the target SNR threshold SNR target , and the formula is:

[0097] ΔSNR = SNR target - SNR current

[0098] Wherein, ΔSNR is the deviation amount between the current SNR and the target SNR threshold;

[0099] Adopt a proportional-integral-derivative controller to calculate the transmit power adjustment amount P based on ΔSNR adjust , and the formula is:

[0100]

[0101] Wherein, K p , K i , K d are empirical tuning parameters.

[0102] To achieve the above object, an embodiment of the second aspect of the present application proposes a device for modeling and intelligent transmit intensity dynamic matching of a backscattering channel, including:

[0103] A backscattering channel modeling module, configured to construct a physical model of an underwater backscattering channel based on the far-field approximation hypothesis, establish a signal propagation equation from the transmitter to the receiver by combining the dynamic coupling relationships of path loss, impedance mismatch, and backscattering cross-section, and characterize the electro-acoustic conversion process of the transducer through an equivalent circuit model;

[0104] A measurement and feedback module, configured to measure the signal-to-noise ratio SNR of the backscattered signal in real time at the receiver and feedback the current SNR value to the transmitter through the uplink, wherein the feedback period is set according to the dynamic characteristics of the underwater channel;

[0105] An adjustment module, configured to preset a target SNR threshold according to the communication quality requirement, and calculate the transmit power adjustment amount by using a proportional-integral-derivative control algorithm according to the deviation amount between the current SNR and the target SNR threshold;

[0106] The superposition and generation module is used to design a fuzzy logic optimization module for the non-linear characteristics of the underwater channel. Based on the preset fuzzy rule base and membership function, it dynamically adjusts the current transmission power according to the magnitude and trend of the SNR deviation. By superimposing the transmission power adjustment amount on the current transmission power, a new transmission power is generated.

[0107] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0108] The memory stores computer-executable instructions;

[0109] The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the first aspects.

[0110] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first aspects.

[0111] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, and when the computer program is executed by a processor, it implements the method described in any one of the first aspects.

[0112] Additional aspects and advantages of the present application will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present application. Description of the Drawings

[0113] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0114] Figure 1 It is a schematic flowchart of a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity provided by an embodiment of the present application;

[0115] Figure 2 It is a schematic overall structure diagram of a typical underwater backscatter system provided by an embodiment of the present application;

[0116] Figure 3 It is a schematic structural diagram of an equivalent model of an underwater passive acoustic beacon transducer provided by an embodiment of the present application. Detailed Embodiments

[0117] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0118] In view of the problems existing in the prior art, the embodiments of the present application provide a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity. Figure 1 It is a schematic flowchart of a method for modeling a backscatter channel and dynamically matching intelligent transmission intensity provided by an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0119] Step 101, based on the far-field approximation assumption, construct a physical model of the underwater backscatter channel, combine the dynamic coupling relationships of path loss, impedance mismatch, and backscattering cross-section, establish a signal propagation equation from the transmitter to the receiver, and characterize the electro-acoustic conversion process of the transducer through an equivalent circuit model.

[0120] In order to overcome the deficiencies of the existing link budget model and capture the electro-acoustic transmission phenomenon, the present application models the entire path of signal propagation in underwater backscatter communication.

[0121] The overall structure of a typical underwater backscatter system is as Figure 2 shown. The system includes a transmitter (TX) that sends signals to the backscatter node on the downlink. The backscatter node transmits data by modulating the reflection of the downlink signal. The hydrophone receiver (Rx) senses the modulated reflections and uses them to decode the data sent by the backscatter node. The backscatter node can also obtain energy from the downlink signal to operate normally. Throughout the derivation process, it is assumed that the distance between the TX and the backscatter node is large enough to adopt the far-field approximation model.

[0122] In the embodiments of the present application, first, an equivalent circuit model is adopted to represent the transducer as a two-port network with electrical and acoustic ports, as Figure 3 shown.

[0123] In the embodiments of the present application, a voltage V is applied to the electrical port of the transducer, and an incident acoustic pressure Fb is applied to the acoustic port. The equivalent input impedance Z in and the equivalent acoustic-side impedance Z o of the transducer are calculated respectively. The two-port element of the transducer is described by an impedance matrix, which is defined as:

[0124]

[0125] Among them, V1 and I1 are the voltage and current on the electrical terminals of the transducer, and F2 and u2 are the force and velocity on the outer surface of the transducer. In this notation, the impedance matrix takes into account all the parameters of the transducer (including the acoustic radiation reactance Xr). It should be noted that unless otherwise specified, all physical variables are represented in root mean square.

[0126] Furthermore, this application models the process of energy harvesting from an electrical emission source to a reflection node, including the following four sub-processes:

[0127] (1) Sub-process one: How the electrical power of the emission node is converted into acoustic power.

[0128] This process describes how, in an underwater backscatter communication system, the emission node converts the input electrical power into acoustic power and transmits an acoustic signal to the underwater channel through a transducer. This process is affected by the electromechanical coupling efficiency of the transducer, and it is necessary to calculate the input electrical power, output acoustic power, and impedance matching of the transducer to optimize the electro-acoustic conversion efficiency.

[0129] (2) Sub-process two: How the acoustic power propagates and reaches the backscatter node.

[0130] This process describes how the acoustic wave emitted by the emission node propagates underwater and is affected by path loss (including spreading loss and absorption loss), and finally reaches the backscatter node. This process involves calculating path loss, propagation attenuation model, and incident sound pressure level to characterize the acoustic energy level received by the backscatter node.

[0131] (3) Sub-process three: How the acoustic pressure incident on the backscatter node is converted into electrical power and energy is captured.

[0132] This process describes how the transducer of the backscatter node converts the incident acoustic pressure into electrical energy and stores or utilizes this part of the energy on the load. This process involves calculating the Thevenin equivalent circuit model, impedance matching loss, and energy capture power to optimize the energy harvesting efficiency and ensure the continuous operation of the backscatter node.

[0133] (4) Sub-process four: How the backscatter node uses the captured energy for signal reflection and transmission.

[0134] This process describes how the backscatter node uses the captured energy to modulate its impedance state to reflect and modulate the upstream signal. It involves calculating the scattering power, scattering cross-section, and reflected sound pressure level of the backscatter node, and finally calculating the signal-to-noise ratio at the hydrophone. This process determines the quality of the backscatter communication link and affects the communication reliability of the system.

[0135] The following specifically describes the modeling process of each sub-process.

[0136] (1) Sub - process one.

[0137] It can be understood that the electromechanical coupling coefficient represents the conversion efficiency of electrical energy to acoustic energy of the transducer. In the embodiments of the present application, according to the input electrical power P of the transmitting node elec and the output acoustic power P of the transducer ac the electromechanical coupling coefficient of the transducer is defined according to the conversion relationship between them, and the formula is:

[0138]

[0139] where η Tx is the electromechanical coupling coefficient of the transducer, representing the conversion efficiency of electrical energy to acoustic energy of the transducer.

[0140] Furthermore, the present application calculates the input electrical power P elec , and the formula is:

[0141]

[0142] In the formula, R in is the input resistance, and Z 11 , Z 12 , Z 21 and Z 22 are the impedance matrix parameters of the transducer, and R r is the load resistance of the transducer.

[0143] And calculate the output acoustic power P ac , and the formula is:

[0144]

[0145] F2 = Z 21 I1 + Z 22 u2

[0146] Finally, the derivation formula of the electromechanical coupling coefficient η of the transducer Tx is further:

[0147]

[0148] where η Tx is the electromechanical coupling coefficient of the transducer.

[0149] (2) Sub - process two.

[0150] In the process of acoustic power to sound source level in the embodiments of the present application, the far - field sound level generated by the transducer is defined as the sound source sound level (SL). SL is based on the input electrical power (P elec ) in watts, the electromechanical efficiency (η Tx) and as a function of the directivity index (DI) of the transducer (in dB). SL is given by:

[0151] SL(Ω) = 170.8 + 10 log(η Tx P elec ) + DI(Ω) [dB re 1 μPa @ 1 m]

[0152] where SL is the sound source level and DI is the directivity index of the transducer.

[0153] Considering the spreading loss and absorption loss, the present application further calculates the attenuation that occurs when the acoustic signal propagates at a single frequency f over a distance d, which is given by the path loss PL with the formula:

[0154] PL(d, f) = k 10 log(d) + α(f) d [dB]

[0155] where k is the spreading factor (similar to the path loss exponent in a radio channel) and α(f) is the frequency-dependent absorption coefficient in decibels per meter. The first term in the above formula represents the spreading loss and the second term represents the absorption loss, and the absorption loss becomes more obvious at higher frequencies. The spreading factor k represents the propagation geometry of the traveling wave. Specifically, k = 2 represents spherical spreading, k = 1 represents cylindrical spreading, and k = 1.5 represents actual spreading.

[0156] Using the above path loss equation and the source level equation, the received pressure level RL at the backscattering node can be expressed as:

[0157] RL = SL - PL

[0158] It should be noted that RL is measured in dB re 1 μPa, which represents the sound intensity at a distance of d meters.

[0159] (3) Sub-process three.

[0160] The embodiment of the present application considers obtaining the obtained electric power from the acoustic pressure incident on the backscattering node. The Figure 2 shown equivalent model of the underwater passive acoustic beacon transducer is represented by a Thevenin equivalent circuit. In this circuit, Z e is the electrical load, Z th is the Thevenin equivalent resistance, V th is the Thevenin equivalent voltage, R e represents the real part of Z e and further simplifies to:

[0161]

[0162] where Z e is the electrical load, Z this the Thevenin equivalent resistance, V th is the Thevenin equivalent voltage, R e represents the real part of Z e of

[0163] Furthermore, the downlink impedance mismatch loss IML D is defined as:

[0164]

[0165] It depends on the impedance of the node and the connected electrical load. When the electrical load is conjugate-matched to the impedance of the node, it reaches a maximum value of 10log(1 / 4), in line with the maximum power transfer theorem. Simplifying the formula, by defining the gain G of the transducer as the logarithmic sum of its efficiency and directivity, the formula for capturing energy can be further simplified, expressed as:

[0166] G = 10log(η) + DI

[0167] Finally, the formula for calculating the final energy capture of the backscatter node is:

[0168] P0 harv = 77.7 + 10log(P elec ) + G Tx + G node - 20log(f) - PL + IML D [dBm]

[0169] where P harv is the electric power finally captured by the backscatter node.

[0170] (4) Sub-process four.

[0171] In the embodiment of the present application, first, BL is defined as the sound pressure level reflected at the hydrophone due to the backscattering of the backscatter node, and the formula is:

[0172] BL = 170.8 + 10log(P refl ) + DI node - PL

[0173] where P refl is the sound power reflected from the backscatter node.

[0174] The reflected power of the underwater transducer can be defined by the scattering cross-section represented by σ: The formula is:

[0175] P refl = σI i

[0176] where I i is the incident sound intensity, given by:

[0177]

[0178] Then BL is expressed as:

[0179] BL = 159.8 + 10 log(η Tx P elec ) + DI Tx + 10 log(σ) + DI node - 2PL

[0180] It should be noted that since the reflected power P refl is also a function of the acoustic power absorbed by node P absorb and the maximum available power at the node, P absorb is defined according to the sensor parameters as:

[0181]

[0182] The maximum available power is given by the following formula:

[0183]

[0184] The relationship between the two is:

[0185]

[0186] where Γ is the acoustic reflection coefficient;

[0187] According to the geometric relationship, the formula for the incident cross-sectional area and the reflection coefficient is derived as:

[0188]

[0189] It should be noted that when the backscatter node communicates by switching between two states, its reflection coefficient also switches between the two states, which forms the differential scattering cross-section, which is expressed as:

[0190]

[0191] Furthermore, we have:

[0192]

[0193] According to circuit theory, we can obtain:

[0194]

[0195] The reflection coefficient is:

[0196]

[0197] Since the system modulates between two impedance states, the differential reflection coefficient simplifies to:

[0198]

[0199] Therefore, BL can be updated:

[0200]

[0201] Among them, the impedance mismatch loss is:

[0202]

[0203] This loss can be calculated by separately measuring the impedance of the backscatter transducer and the switched electrical load. If the transducer is impedance-matched, the reflected power is only limited by the transducer efficiency, and BL can be further expressed as:

[0204]

[0205] This formula defines the forward scatter intensity according to the gain and impedance of the transducer. Combining with the underwater noise, BL is obtained from the signal power. The environmental noise in the ocean: the combination of shipping activities, thermal noise, water waves and turbulence, NL represents the power spectral density of the noise. This embodiment of the present application can use it to calculate the SNR, and the formula is:

[0206] SNR = BL - (NL + 10log(BW))

[0207] Among them, SNR is the signal-to-noise ratio at the hydrophone.

[0208] Step 102, measure the signal-to-noise ratio SNR of the backscatter signal in real time at the receiving end, and feedback the current SNR value to the transmitting end through the uplink, where the feedback period is set according to the dynamic characteristics of the underwater channel.

[0209] In the embodiment of the present application, step 102 involves measuring the signal-to-noise ratio (SNR) of the backscatter signal in real time at the receiving end, and feeding back the current SNR value to the transmitting end through the uplink. The feedback period is set according to the dynamic characteristics of the underwater channel to ensure the adaptability and stability of the system under different environmental conditions.

[0210] Based on the above SNR measurements, this application proposes an intelligent transmission intensity dynamic matching method based on feedback control to optimize the signal-to-noise ratio (SNR) at the receiving end. Specifically, in the embodiments of this application, the receiving end monitors the SNR of the backscattered signal in real time and feeds it back to the transmitter (TX). The feedback period is dynamically adjusted according to the changing characteristics of the underwater channel, and a typical value can be set to the millisecond level to balance system real-time performance and computational overhead. This feedback mechanism helps to adjust the power output of the transmitting end, enabling it to adapt to the transient changes in the underwater environment and ensuring communication stability and energy efficiency optimization.

[0211] Step 103: Preset a target SNR threshold according to the communication quality requirements, and calculate the transmission power adjustment amount using a proportional-integral-differential control algorithm based on the deviation between the current SNR and the target SNR threshold.

[0212] In the embodiments of this application, step 103 involves setting a target SNR threshold according to the communication quality requirements, and calculating the adjustment amount of the transmission power using a proportional-integral-differential (PID) control algorithm based on the deviation between the current SNR and the target SNR, so as to optimize the system performance.

[0213] Specifically, in the embodiments of this application, first preset a target SNR threshold SNR target , and calculate the difference between the current SNR and the target SNR threshold SNR target , and use this difference to drive the dynamic adjustment algorithm. The calculation formula is:

[0214] ΔSNR = SNR target - SNR current

[0215] where ΔSNR is the deviation between the current SNR and the target SNR threshold.

[0216] In addition, to achieve precise adjustment of the transmission power, the embodiments of this application use a proportional-integral-differential controller to calculate the transmission power adjustment amount P adjust based on ΔSNR, and the formula is:

[0217]

[0218] where K p , K i , K d are empirically tuned parameters.

[0219] It can be understood that the PID control strategy of this application can achieve precise adjustment of the transmission power by adjusting the proportional, integral, and differential parameters, ensure that the signal-to-noise ratio converges to the target value, and effectively cope with the dynamic changes of the underwater channel.

[0220] Step 104: In view of the non-linear characteristics of the underwater channel, a fuzzy logic optimization module is designed. Based on a preset fuzzy rule base and membership function, the current transmission power is dynamically adjusted according to the magnitude and trend of the SNR deviation. By adding the transmission power adjustment amount to the current transmission power, a new transmission power is generated.

[0221] In an embodiment of the present application, Step 104 involves designing a fuzzy logic optimization module for the non-linear characteristics of the underwater channel to dynamically adjust the current transmission power so that the system can adapt to the complex underwater propagation environment.

[0222] Specifically, in an embodiment of the present application, to make up for the deficiencies of the PID control method under strong non-linear underwater channel conditions, a fuzzy logic optimization module is introduced into the underwater backscatter system to adaptively adjust the transmission power based on the magnitude and change trend of the SNR deviation. This module relies on a preset fuzzy rule base and membership function to optimize the transmission power adjustment amount, enabling the system to more flexibly cope with sudden underwater channel fluctuations.

[0223] In an embodiment of the present application, the fuzzy rule base contains multiple empirical rules. For example, if ΔSNR is large, the transmission power is significantly increased; if ΔSNR is small, the transmission power is decreased.

[0224] Furthermore, in combination with the fuzzy membership function, the current SNR error is fuzzified, and the corresponding transmission power is generated according to the fuzzy inference mechanism. Subsequently, the adjustment amount P adjust is added to the current transmission power P elec to generate a new transmission power P new = P elec + P adjust .

[0225] In addition, to prevent the transmission power from exceeding the safe operating range of the transducer, the present application restricts the transmission power within the safe operating range of the transducer (P min ≤ P new ≤ P max ) to avoid hardware overload or energy waste.

[0226] In addition, to cope with the oscillations caused by feedback delay or channel mutations, the embodiment of the present application also introduces a hysteresis compensation mechanism into the underwater backscatter system. The short-term trend is predicted through historical power adjustment data to smooth the adjustment process. At the same time, the SNR measurement value is denoised by a Kalman filter to reduce the interference of environmental noise on the control accuracy.

[0227] In the embodiments of the present application, to further improve the adaptability of the underwater backscatter communication system in different environments, the dynamic matching mechanism of the present application deeply integrates the underwater backscatter channel analysis model and supports dual-mode switching to achieve a balance between energy efficiency optimization and communication stability. Specifically, in the embodiments of the present application, the underwater backscatter system can dynamically adjust the control strategy under different channel conditions: enable PID control in low-noise scenarios to reduce energy consumption; switch to fuzzy logic when there is high interference or rapid channel changes to improve the anti-interference ability of the overall backscatter communication system.

[0228] In addition, in the embodiments of the present application, to further optimize the transmit power adjustment strategy, a deep Q-network (DQN) agent can be trained based on the reinforcement learning framework to achieve model-free adaptive control. During the DQN optimization process, use SNR as the reward function to achieve model-free adaptive control. Divide ΔSNR into multiple intervals and correspond to fixed power adjustment steps, and set a fixed power adjustment step for each interval to reduce the computational complexity and improve the adjustment efficiency; through continuous training, the DQN agent can automatically select the optimal power adjustment strategy under different channel conditions to ensure that the SNR is stable within the target range while avoiding unnecessary energy waste.

[0229] Through the above optimization methods, the dynamic matching mechanism of the present application can flexibly adjust the transmit power under different underwater channel conditions, ensuring both communication stability and optimizing the system energy efficiency, thereby improving the overall performance of the underwater backscatter communication system.

[0230] To implement the above embodiments, the present application also proposes a device for modeling the backscatter channel and dynamically matching the intelligent transmit intensity. The device includes:

[0231] A backscatter channel modeling module, which is used to construct a physical model of the underwater backscatter channel based on the far-field approximation hypothesis, combine the dynamic coupling relationships of path loss, impedance mismatch, and backscatter cross-section, establish a signal propagation equation from the transmitter to the receiver, and characterize the electro-acoustic conversion process of the transducer through an equivalent circuit model;

[0232] A measurement and feedback module, which is used to measure the signal-to-noise ratio SNR of the backscatter signal in real time at the receiver and feedback the current SNR value to the transmitter through the uplink. The feedback period is set according to the dynamic characteristics of the underwater channel;

[0233] An adjustment module, which is used to preset a target SNR threshold according to the communication quality requirement, and calculate the transmit power adjustment amount using a proportional-integral-derivative control algorithm according to the deviation between the current SNR and the target SNR threshold;

[0234] The superposition and generation module is used to design a fuzzy logic optimization module for the non - linear characteristics of the underwater channel. Based on the preset fuzzy rule base and membership function, it dynamically adjusts the current transmission power according to the magnitude and trend of the SNR deviation. By superimposing the transmission power adjustment amount on the current transmission power, a new transmission power is generated.

[0235] Regarding the device in the above - mentioned embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0236] To implement the above - mentioned embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer - executable instructions; the processor executes the computer - executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0237] To implement the above - mentioned embodiments, the present application also proposes a computer - readable storage medium, in which computer - executable instructions are stored, and when the computer - executable instructions are executed by a processor, they are used to implement the method provided in the foregoing embodiments.

[0238] To implement the above - mentioned embodiments, the present application also proposes a computer program product, including a computer program, which when executed by a processor, implements the method provided in the foregoing embodiments.

[0239] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0240] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of these legal uses. In addition, such collection / sharing should be carried out after obtaining the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0241] The present application anticipates providing an implementation plan for users to selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0242] In the descriptions of the foregoing embodiments, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0243] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0244] Any process or method description in the flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0245] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0246] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0247] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0248] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0249] The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

[0250] It should be understood that various forms of the processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved. There is no limitation herein.

[0251] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for modeling and intelligent dynamic matching of transmission intensity of a backscatter channel, characterized in that: The following steps are involved: Based on the far-field approximation assumption, a physical model of the underwater backscatter channel is constructed. Combining the dynamic coupling relationship between path loss, impedance mismatch and backscatter cross section, the signal propagation equation from the transmitter to the receiver is established, and the electroacoustic conversion process of the transducer is characterized by an equivalent circuit model. The signal-to-noise ratio (SNR) of the backscattered signal is measured in real time at the receiving end, and the current SNR value is fed back to the transmitting end via the uplink, wherein the feedback period is set according to the dynamic characteristics of the underwater channel; The target SNR threshold is preset according to the communication quality requirements, and the transmit power adjustment amount is calculated using the proportional-integral-differential control algorithm according to the deviation between the current SNR and the target SNR threshold; In view of the nonlinear characteristics of the underwater channel, a fuzzy logic optimization module is designed. According to the preset fuzzy rule base and membership function, the current transmit power is dynamically adjusted based on the SNR deviation size and trend, and a new transmit power is generated by superimposing the transmit power adjustment amount on the current transmit power.

2. The method according to claim 1, characterized in that Based on the far-field approximation assumption, a physical model of the underwater backscatter channel is constructed, and the signal propagation equation from the transmitter to the receiver is established by combining the dynamic coupling relationship of path loss, impedance mismatch and backscatter cross section, and the electroacoustic conversion process of the transducer is characterized by an equivalent circuit model, including: Using the equivalent circuit model, the transducer is represented as a two-port network with an electrical port and an acoustic port. A voltage V is applied to the electrical port of the transducer, and an incident sound pressure Fb is applied to the acoustic port. The equivalent input impedance Z of the transducer is calculated respectively. in And the equivalent acoustic impedance Z o ; The two-port element of the transducer is described by the impedance matrix, defined as: Where V1 and I1 are the voltage and current at the transducer electrical terminals, F2 and u2 are the force and velocity on the outer surface of the transducer; Modeling the process of energy collection from the electric emission source to the reflection node includes the following four sub-processes: Sub-process 1: How to convert the electrical power of the transmitting node into acoustic power; Sub-process 2: How the acoustic power propagates and reaches the backscattering node; Sub-process three: How to convert the sound pressure incident on the backscattering node into electrical power and capture energy; Sub-process 4: How the backscatter node uses the captured energy for signal reflection and transmission.

3. The method according to claim 2, characterized in that The sub-process 1 comprises: According to the input power P of the transmitting node elec and the output sound power P of the transducer ac The conversion relationship between them defines the electromechanical coupling coefficient of the transducer, and the formula is: Among them, η Tx is the electromechanical coupling coefficient of the transducer, which indicates the efficiency of the transducer in converting electrical energy into acoustic energy; Calculate the input electrical power P elec , the formula is: In the formula, R in is the input resistance, Z 11 , Z 12 , Z 21 and Z 22 is the impedance matrix parameter of the transducer, R r is the load resistance of the transducer; Calculate the output sound power P ac , the formula is: <h2 style=";text-align:left;direction:ltr">F2 = Z<h2 style=";text-align:left;direction:ltr"> 21 <h2 style=";text-align:left;direction:ltr"> I1+Z<h2 style=";text-align:left;direction:ltr"> 22 <h2 style=";text-align:left;direction:ltr"> u2 The electromechanical coupling coefficient η of the transducer Tx The derivation formula is further: Among them, η Tx is the electromechanical coupling coefficient of the transducer.

4. The method according to claim 3, characterized in that The second sub-process comprises: The far-field sound level generated by the transducer is defined as the sound source level, and its calculation formula is: SL(Ω)=170.8+10log(η Tx P elec )+DI(Ω)[dBre1μPa@1m] Among them, SL is the sound source level, DI is the directivity index of the transducer; Taking into account the spreading loss and absorption loss, the attenuation of the acoustic signal when propagating at a single frequency f over a distance d is calculated. The attenuation is given by the path loss PL, which is given by: PL(d,f)=k10log(d)+α(f)d[dB] Where k is the expansion factor and α(f) is the frequency-dependent absorption coefficient; Calculate the receiving pressure level RL arriving at the backscatter node, the formula is: RL=SL-PL Here, RL is measured in dB re 1μPa, which represents the sound intensity at a distance of d meters.

5. The method according to claim 4, characterized in that The sub-process three includes: The electrical power obtained from the acoustic pressure incident on the backscattering node is calculated as: Among them, Z e is the electrical load, Z th is the Thevenin equivalent resistance, V th is the Thevenin equivalent voltage, R e Represents Z e The real part of Calculate the downstream impedance mismatch loss IML D , the formula is: The formula for captured energy can be simplified by defining the gain G of the transducer as the logarithm of its efficiency and directivity: G=10log(η)+DI The final energy capture of the backscatter node is calculated as: P harv =77.7+10log(P elec )+G Tx +G node -20log(f)-PL+IML D [dBm] Where P harv is the electrical power finally captured by the backscatter node.

6. The method according to claim 5, characterized in that The sub-process 4 includes: BL is defined as the sound pressure level reflected at the hydrophone due to backscattering from the backscattering node, as: BL6170.8+10log(P refl )+DI node -PL Among them, P refl is the acoustic power reflected from the backscatter node; According to the scattering cross section σ of the backscattering node, the reflected power of the underwater transducer is calculated as follows: P refl =σI i Among them, I i is the incident sound intensity and is given by: Then BL is expressed as: BL=159.8+10log(η Tx P elec )+DI Tx +10log(σ)+DI node -2PL Since the reflected power P refl Node P again absorb The absorbed acoustic power and the maximum available power at the node are functions of P absorb According to the sensor parameters defined as: The maximum available power is given by: The relationship between the two is: Where Γ is the acoustic reflection coefficient; The formula for the incident cross-sectional area and reflection coefficient is derived based on the geometric relationship: When a backscattering node communicates by switching between two states, its reflection coefficient also switches between two states, which forms the differential scattering cross section, which is expressed as: Further: According to circuit theory, we can get: The reflection coefficient is: Since the system modulates between two impedance states, the differential reflection coefficient simplifies to: Therefore, BL can be updated: Wherein, the impedance mismatch loss is: According to the updated BL, the signal-to-noise ratio at the hydrophone is calculated as follows: SNR = BL - (NL + 10log (BW)) Here, SNR is the signal-to-noise ratio at the hydrophone.

7. The method according to claim 6, characterized in that The target SNR threshold is preset according to the communication quality requirement, and the transmit power adjustment amount is calculated using a proportional-integral-differential control algorithm according to the deviation between the current SNR and the target SNR threshold, including: Preset target SNR threshold SNR according to communication quality requirements target , calculate the current SNR and the target SNR threshold SNR target The difference is: ΔSNR=SNR target -SNR current Among them, ΔSNR is the deviation between the current SNR and the target SNR threshold; A proportional-integral-derivative controller is used to calculate the transmit power adjustment value P based on ΔSNR. adjust , the formula is: Among them, K p , K i , K d are empirically tuned parameters.

8. A backscatter channel modeling and intelligent transmission intensity dynamic matching device, characterized in that: include: The backscatter channel modeling module is used to construct a physical model of the underwater backscatter channel based on the far-field approximation assumption, combine the dynamic coupling relationship of path loss, impedance mismatch and backscatter cross section, establish the signal propagation equation from the transmitter to the receiver, and characterize the electroacoustic conversion process of the transducer through an equivalent circuit model; The measurement and feedback module is used to measure the signal-to-noise ratio (SNR) of the backscattered signal in real time at the receiving end, and to feed back the current SNR value to the transmitting end through the uplink, wherein the feedback period is set according to the dynamic characteristics of the underwater channel; An adjustment module is used to preset a target SNR threshold according to communication quality requirements, and calculate a transmit power adjustment amount using a proportional-integral-differential control algorithm according to a deviation between a current SNR and the target SNR threshold; The superposition and generation module is used to design a fuzzy logic optimization module according to the nonlinear characteristics of the underwater channel. According to the preset fuzzy rule base and membership function, the current transmission power is dynamically adjusted based on the SNR deviation size and trend, and a new transmission power is generated by superimposing the transmission power adjustment amount on the current transmission power.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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