Radio frequency signal gain control method and device for low power dual-mode Bluetooth chip

Abstract

The present invention relates to the technical field of dual-mode Bluetooth chips, and discloses a method and device for controlling the gain of a radio frequency signal of a low-power dual-mode Bluetooth chip, wherein the method comprises: performing RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain an AC component of the radio frequency signal and a DC component of the radio frequency signal; performing bandwidth extension on the AC component of the radio frequency signal, and performing power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data; performing signal tracking ST time-frequency mapping, and extracting the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data; establishing a radio frequency parameter control network, performing AGC and ALC collaborative optimization calculations, generating a gain control sequence, and outputting radio frequency signal gain control parameters. The method effectively reduces signal distortion and interference during mode switching, and ensures the reliability and stability of gain control.

Description

Technical Field

[0001] The present invention relates to the technical field of dual-mode Bluetooth chips, and in particular to a method and device for controlling radio frequency signal gain of a low-power dual-mode Bluetooth chip. Background Art

[0002] Low-power dual-mode Bluetooth chips are widely used in mobile devices, which need to switch between standard Bluetooth mode and low-power Bluetooth mode. However, during the mode switching process, due to the difference in RF signal transmission mechanism, signal distortion and interference are prone to occur, affecting communication quality and power consumption performance.

[0003] Traditional RF signal gain control methods mainly use AGC and ALC algorithms for power regulation, but due to the lack of targeted optimization of specific signal transmission mechanisms, they face problems of unstable control accuracy and poor adaptability in dual-mode working conditions. In addition, although the RSSI detection circuit can monitor high-frequency dynamic AC components, it cannot accurately detect low-frequency static DC components due to bandwidth limitations, resulting in incomplete signal reconstruction. Existing signal analysis methods are difficult to effectively detect the time-varying characteristics of RF signals in dynamic working modes. Traditional FFT-based analysis methods have limited performance in nonlinear change scenarios, and although the signal tracking ST method does not require a fixed sampling rate, its application effect in nonlinear change conditions such as mode switching still needs to be improved. Summary of the invention

[0004] The present invention provides a method and device for controlling the radio frequency signal gain of a low-power dual-mode Bluetooth chip, which effectively reduces signal distortion and interference during mode switching and ensures the reliability and stability of gain control.

[0005] In a first aspect, the present invention provides a method for controlling the gain of a radio frequency signal of a low-power dual-mode Bluetooth chip, the method comprising:

[0006] Perform RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain the AC component and DC component of the radio frequency signal;

[0007] Performing bandwidth expansion on the AC component of the radio frequency signal through a bandwidth expansion BET model, and performing power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data;

[0008] Performing signal tracking ST time-frequency mapping on the reconstructed radio frequency signal data, and extracting the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data;

[0009] Constructing a dynamic impedance matching model based on the nonlinear characteristic data, calculating the transmission characteristic parameters of the radio frequency signal, and establishing a radio frequency parameter control network;

[0010] Performing AGC and ALC collaborative optimization calculations on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter, and a timing control parameter;

[0011] The radio frequency signal gain is adjusted according to the gain control sequence, a correction value of the gain adjustment coefficient is calculated by a closed-loop feedback method, and a radio frequency signal gain control parameter is output.

[0012] In a second aspect, the present invention provides a radio frequency signal gain control device for a low power dual-mode Bluetooth chip, the radio frequency signal gain control device for a low power dual-mode Bluetooth chip comprising:

[0013] The measurement module is used to perform RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain the AC component and the DC component of the radio frequency signal;

[0014] A reconstruction module, used to perform bandwidth extension on the AC component of the radio frequency signal through a bandwidth extension BET model, and perform power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data;

[0015] A mapping module, used to perform signal tracking ST time-frequency mapping on the reconstructed radio frequency signal data, and extract the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data;

[0016] A calculation module, used to construct a dynamic impedance matching model based on the nonlinear characteristic data, calculate the transmission characteristic parameters of the radio frequency signal, and establish a radio frequency parameter control network;

[0017] A generating module, used for performing AGC and ALC collaborative optimization calculation on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter and a timing control parameter;

[0018] The output module is used to adjust the RF signal gain according to the gain control sequence, calculate the correction value of the gain adjustment coefficient by a closed-loop feedback method, and output the RF signal gain control parameter.

[0019] The third aspect of the present invention provides a radio frequency signal gain control device for a low-power dual-mode Bluetooth chip, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the radio frequency signal gain control device for the low-power dual-mode Bluetooth chip executes the above-mentioned radio frequency signal gain control method for the low-power dual-mode Bluetooth chip.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for controlling the radio frequency signal gain of a low-power dual-mode Bluetooth chip.

[0021] In the technical solution provided by the present invention, the AC component and DC component of the RF signal are processed by the bandwidth extension BET model, which overcomes the problem of limited bandwidth of the traditional detection circuit and realizes complete RF signal reconstruction. The signal tracking ST time-frequency mapping and dynamic sampling mechanism are adopted to accurately extract the nonlinear change characteristics of the RF signal during the mode switching process, thereby improving the accuracy of signal analysis. Based on the dynamic impedance matching model and the RF parameter adaptive reconstruction algorithm, a RF parameter control network with a multi-layer neural network structure is constructed to enhance the adaptive ability of gain control. Through the coordinated optimization of the AGC and ALC algorithms, the precise adjustment of the gain control parameters is achieved, and the signal quality stability under the dual-mode working state is guaranteed. A closed-loop feedback control mechanism is introduced to correct the gain adjustment coefficient in real time, which effectively reduces the signal distortion and interference during the mode switching process. The present invention ensures the reliability and stability of the gain control through multi-level parameter verification and error correction, thereby improving the overall performance of the system.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0023] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of an embodiment of a method for controlling the gain of a radio frequency signal of a low-power dual-mode Bluetooth chip in an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of an embodiment of a radio frequency signal gain control device for a low-power dual-mode Bluetooth chip in an embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of an embodiment of a radio frequency signal gain control device of a low-power dual-mode Bluetooth chip in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or device ends.

[0029] To facilitate understanding of this embodiment, a method for controlling the radio frequency signal gain of a low-power dual-mode Bluetooth chip disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps:

[0030] 101. Perform RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain an AC component and a DC component of the radio frequency signal;

[0031] It is understandable that the execution subject of the present invention may be a RF signal gain control device of a low-power dual-mode Bluetooth chip, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject.

[0032] Specifically, the working states of the standard Bluetooth mode and the low-power Bluetooth mode of the low-power dual-mode Bluetooth chip are identified. The identification process depends on the operation of the internal state detection module of the chip. By analyzing the communication mode currently running in the chip, the corresponding working state data is extracted and generated. According to the working state data, the RF signal of the low-power dual-mode Bluetooth chip is collected through the RSSI detection circuit, and multiple key parameters of the RF signal are recorded in real time, including signal amplitude parameters, phase parameters and frequency parameters. The accuracy of the data is guaranteed by the high-sensitivity detection circuit and the optimized signal extraction algorithm. After the data acquisition is completed, the data separation processing is performed on the above signal parameters to decompose the original RF signal into AC components and other irrelevant components. The AC component is extracted by the signal separation algorithm. At the same time, the RF signal of the low-power dual-mode Bluetooth chip is monitored in real time based on the VSWR measurement circuit to obtain the distortion and interference strength of the RF signal. Based on the core principle of VSWR measurement, the transmission quality of the signal is evaluated by the proportional relationship between the reflected signal and the incident signal. According to the evaluation results of signal distortion and interference strength, the extracted AC component of the RF signal is compensated for data, and the deviation caused by distortion and interference is corrected, so that the final AC component can accurately reflect the original dynamic characteristics of the RF signal. The compensation algorithm needs to combine the chip's working status data and the results of real-time monitoring, comprehensively evaluate the error characteristics of the AC component, generate high-precision compensation data, and output the corrected AC component of the RF signal. Based on the RF power detection circuit, the RF signal of the low-power dual-mode Bluetooth chip is sampled to obtain the DC component of the RF signal. This process relies on the high-precision sampling capability of the RF power detection circuit, and generates a stable DC component output by carefully monitoring the power changes of the RF signal. The DC component reflects the average power characteristics of the RF signal, and together with the AC component, it constitutes a complete feature description of the RF signal.

[0033] 102. Perform bandwidth expansion on the AC component of the radio frequency signal through a bandwidth expansion BET model, and perform power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data;

[0034] Specifically, the frequency domain decomposition operation is performed on the AC component of the RF signal. Based on the spectrum characteristics of the RF signal, the AC component is decomposed in the frequency domain to extract the characteristic parameters of the high-frequency component, including the frequency offset, energy distribution and harmonic characteristics of the signal. The transfer function of the bandwidth extension BET model is constructed based on the characteristic parameters of the high-frequency component. The design of the transfer function needs to combine the bandwidth requirements, characteristic limitations and noise suppression goals of the RF signal to generate a bandwidth extension coefficient matrix. The coefficient matrix defines the amplitude and phase characteristics of the signal within the extended bandwidth range, thereby providing clear guidance for the bandwidth compensation operation. In the bandwidth extension stage, the bandwidth compensation calculation is performed on the AC component of the RF signal through the bandwidth extension coefficient matrix to obtain the extended AC component of the RF signal. The compensation calculation is intended to correct the distortion and weakening of the AC component caused by bandwidth limitation in the actual communication environment, so that the extended AC component of the RF signal can cover a wider spectrum range while maintaining the consistency of its amplitude and phase. At the same time, the DC component of the RF signal is measured by the RF power detection circuit to obtain the power reference value. The power reference value reflects the average power level of the DC component and serves as a reference standard for calibration. According to this reference value, the DC component is subjected to amplitude correction calculation. The power deviation caused by circuit nonlinearity, temperature drift or other environmental factors is eliminated through the correction process to generate a calibrated DC component of the RF signal. The expanded AC component and the calibrated DC component are input into the adaptive filter for signal fusion processing. The core of the adaptive filter is to dynamically adjust the fusion weight so that the AC component and the DC component can achieve optimal coordination in terms of signal characteristics and power level to obtain the initial reconstructed RF signal data. The initial reconstructed signal is subjected to VSWR comparison verification operation. The reconstruction error value of the signal is calculated by comparing the initial reconstructed signal with the VSWR measurement result. The size of the reconstruction error value directly reflects the comprehensive performance of the reconstructed signal in terms of distortion correction, spectrum compensation and power calibration. According to the calculated reconstruction error value, the initial reconstructed RF signal data is compensated and corrected. The correction process is achieved by dynamically adjusting the BET model parameters, the adaptive filter weights and the power calibration factor. The reconstructed RF signal data obtained after compensation correction has both the extended bandwidth characteristics and the accuracy of the power level.

[0035] 103. Perform signal tracking ST time-frequency mapping on the reconstructed RF signal data, and extract the amplitude change rate, phase offset and frequency drift of the RF signal through a dynamic sampling mechanism to generate nonlinear characteristic data;

[0036] Specifically, the reconstructed RF signal data is subjected to dual mapping operations in the time domain and the frequency domain. Based on the signal tracking technology, the corresponding ST time-frequency mapping matrix is ​​generated by analyzing the time domain dynamic behavior and the frequency domain energy distribution of the signal. A dynamic sampling control function is constructed according to the ST time-frequency mapping matrix. The dynamic sampling control function dynamically adjusts the signal sampling rate according to the time-frequency distribution characteristics to adapt to the complexity of the signal change. The sampling rate adjustment parameter is obtained by solving the parameters of the sampling control function. The sampling rate adjustment parameter is input into the sampling controller to dynamically adjust the sampling interval to generate an adaptive sampling sequence. The adaptive sampling sequence can adaptively adjust the sampling density according to the intensity of the signal change, increase the sampling rate in the rapidly changing signal area, and reduce the sampling density in the stable area, so as to achieve efficient sampling of the signal. Based on the generated adaptive sampling sequence, the reconstructed RF signal data is resampled. The resampling operation rearranges the original signal data so that the sampling point distribution is more consistent with the actual dynamic characteristics of the signal, and an equivalent sampling signal is obtained. The ST matrix decomposition operation is performed on the equivalent sampling signal. The ST matrix decomposition extracts the coupling relationship between the time domain and frequency domain characteristics of the signal by decomposing the time-frequency mapping matrix, and generates a signal change feature matrix. The feature matrix reflects the specific performance of the amplitude change rate, phase offset and frequency drift of the RF signal at different times. The amplitude change rate, phase offset and frequency drift are obtained by analytical calculation of the signal change feature matrix. These parameters together constitute a nonlinear change parameter set, which describes the dynamic change law of the RF signal. The nonlinear change parameter set is input into the feature extractor for data dimensionality reduction. The dimensionality reduction operation is based on feature correlation and importance analysis, and generates a feature vector containing core dynamic information by removing redundant information. The feature vector is normalized to convert each component of the feature vector into a standardized value with a uniform scale to form the final nonlinear feature data.

[0037] 104. Construct a dynamic impedance matching model based on nonlinear characteristic data, calculate the transmission characteristic parameters of the RF signal, and establish an RF parameter control network;

[0038] Specifically, the nonlinear characteristic data is subjected to characteristic decomposition operation, and the key parameters related to impedance are extracted from the complex nonlinear characteristic data. The impedance characteristic parameters include the sensitivity index to impedance adaptation and the frequency-related coupling characteristics during signal transmission. By analyzing the multi-dimensional information in the nonlinear characteristic data, the characteristic parameters highly related to dynamic impedance matching are extracted. The transfer function of the dynamic impedance matching model is constructed according to the extracted impedance characteristic parameters. The transfer function describes the nonlinear mapping relationship between the input signal and the output impedance. Its definition needs to be combined with the specific working frequency band, the physical characteristics of the chip, and the environmental noise level. By calculating the parameters of the transfer function, the impedance matching coefficient matrix is ​​generated. As the core data structure of the model, the matrix can dynamically adapt to the impedance requirements under different working conditions. The generated impedance matching coefficient matrix is ​​input into the RPAR (radio frequency parameter mapping) algorithm for parameter mapping processing. The RPAR algorithm generates a parameter mapping matrix that can describe the transmission characteristics of the RF signal by finely modeling the impedance matching coefficient matrix. The RF parameter mapping matrix reflects the transmission characteristics of the signal under different frequency bands and impedance conditions. By analyzing this matrix, the transmission characteristic parameters of the RF signal are calculated and the transmission parameter vector is generated. The dimension and elements of the transmission parameter vector depend on the working environment of the chip and the dynamic characteristics of the signal. Its contents include key parameters such as signal loss factor, reflection coefficient and power gain. The neural network topology is constructed based on the transmission parameter vector. The neural network models the transmission characteristics of the RF signal more accurately through nonlinear fitting ability. During the construction process, the number of layers, number of nodes and connection weight initialization of the network topology need to be optimized according to the characteristics of the transmission parameter vector. After the topology is built, the weight parameters of the neural network are trained. The weight parameter training process takes the transmission characteristic data as input, and repeatedly adjusts the network weight matrix through gradient descent or other optimization algorithms, so that the neural network can accurately describe the transmission characteristics of the RF signal in a dynamic environment. After the weight parameter training is completed, the forward propagation calculation is performed on the neural network topology based on the trained network weight matrix, and the corresponding control parameter output sequence is generated according to the input characteristics. The control parameters include gain adjustment coefficient, impedance adjustment factor and frequency compensation parameter. The output control parameter sequence is reversely corrected. By comparing the error between the calculation result and the actual signal transmission characteristics, the weight distribution of the neural network is optimized to ensure that the final output control parameters have higher accuracy and robustness. A radio frequency parameter control network including a dynamic impedance matching model and radio frequency signal transmission characteristic parameters is obtained.

[0039] 105. Perform AGC and ALC collaborative optimization calculation on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter, and a timing control parameter;

[0040] Specifically, the input layer of the RF parameter control network is loaded with nonlinear feature data and impedance matching coefficient matrix. In the initial calculation stage, a 3×3 convolution kernel is used to convolve the input data to extract AGC-related features. The result after the convolution operation is nonlinearly transformed by the ReLU activation function to generate an AGC feature map channel. The AGC feature map channel is subjected to a maximum pooling operation to retain key features and suppress redundant information to obtain an AGC gain coefficient matrix. The data in the matrix reflects the gain adjustment requirements of different signal channels. Based on the generated AGC gain coefficient matrix, an ALC power limitation model is constructed in the convolution layer to achieve dynamic control of the signal level. The ALC power limitation model performs a 5×5 convolution kernel deep operation on the AGC gain coefficient matrix, supplemented by batch normalization processing, to ensure the stability of the calculation and the consistency of the feature distribution. In this process, the operation results are nonlinearly mapped by the Sigmoid activation function to generate 32 ALC feature map channels. The core power limitation features of these channels are extracted by average pooling operation to form an ALC control parameter set, which accurately describes the change trend and limit boundary of the signal power at different time points. The AGC gain coefficient matrix and the ALC control parameter set are concatenated in the fully connected layer. The concatenated data is passed through a fully connected network with a weight of 128×256 for parameter fusion calculation. At the same time, batch normalization and ReLU activation function are used to optimize the calculation results to generate the initial gain control parameters. In order to improve the adaptability of the parameters, the initial gain control parameters are set to a feature screening operation with a deactivation rate of 0.5 in the first Dropout layer. This mechanism effectively removes redundant features. The filtered features are used to generate the gain adjustment step size through a calculation process with a weight matrix of 64×128, and the upper and lower threshold boundaries are calculated at the same time to form a complete feature set containing 64-dimensional dynamic adjustment parameters. The dynamic adjustment parameters are normalized in the batch normalization layer to ensure their consistency in value range and distribution. The timing-related features are extracted through a 32×64 timing constraint matrix. After nonlinear processing by the Tanh activation function, a 32-dimensional timing control parameter sequence is output. These parameters are used to characterize the time dependence and dynamic change characteristics of signal gain adjustment. Based on the timing control parameter sequence, the second Dropout layer is processed with a deactivation rate of 0.3 to enhance the generalization ability of the model. The processed results are input into the Softmax classifier, and a classification operation with a weight matrix of 16×32 is used to generate the probability distribution of the gain control category, reflecting the priority of different gain adjustment schemes, and forming an initial gain control sequence. The initial gain control sequence is input into the parameter verification unit, and the validity of the gain control sequence is verified by setting the verification threshold, and the verification result data containing the validity index is generated. According to the verification result data, the output layer performs the final parameter correction calculation.The correction calculation converts the verified control parameters into the actual working range through a linear mapping mechanism to generate the final gain control sequence, which includes gain adjustment coefficients, threshold parameters and timing control parameters.

[0041] 106. Adjust the RF signal gain according to the gain control sequence, calculate the correction value of the gain adjustment coefficient by a closed-loop feedback method, and output the RF signal gain control parameter.

[0042] Specifically, a preliminary gain adjustment operation is performed on the radio frequency signal of the low-power dual-mode Bluetooth chip according to the gain control sequence. By calling the gain control module inside the chip, the amplification ratio of the radio frequency signal is gradually adjusted according to the gain adjustment coefficient and the threshold parameter to obtain the initial adjustment result. The initial adjustment result is evaluated and optimized. The signal strength of the initial adjustment result is monitored in real time by the RSSI detection circuit, and the standing wave ratio characteristics of the signal are evaluated by the VSWR measurement circuit. The RSSI detection result provides the change trend of the radio frequency signal power strength, while the VSWR measurement reflects the transmission quality and matching status of the signal. Combining these two sets of data, the real-time signal quality parameters are calculated. Based on the real-time signal quality parameters, a comparative analysis is performed with the target gain control state, and the gain adjustment error value is calculated to reflect the deviation degree of the current gain adjustment, which is used as an important input data for feedback control to form feedback control data. The feedback control data is input into the closed-loop feedback controller to perform error compensation calculation. The closed-loop feedback controller dynamically calculates the correction value of the gain adjustment coefficient by adjusting the compensation weight and the gain adjustment rule. The generation of the correction value combines the feedback information of the real-time signal with the historical gain adjustment record to ensure that the gain adjustment process has fast responsiveness and long-term stability. The corrected gain adjustment coefficient is applied to the optimization calculation of the gain control sequence. By combining the corrected value of the gain adjustment coefficient with the original gain control sequence, the gain control sequence is corrected and calculated to generate a corrected gain control sequence. Using the corrected gain control sequence, the gain adjustment operation of the RF signal is performed again to generate an adjusted RF signal. In order to verify the actual effect of the gain adjustment, a signal quality verification operation is performed on the adjusted RF signal. The verification operation generates verification parameters including signal consistency, power stability and distortion by comprehensively analyzing the RSSI, VSWR and other dynamic characteristic parameters of the adjusted signal. These verification parameters reflect the final effect of the gain adjustment and serve as the basis for generating the RF signal gain control parameters. The RF signal gain control parameters are comprehensively calculated based on the verification parameters to generate a complete gain control parameter set, which includes the gain adjustment coefficient, threshold parameters and timing control parameters.

[0043] In the embodiment of the present invention, the AC component and DC component of the RF signal are processed by the bandwidth extension BET model, which overcomes the problem of limited bandwidth of the traditional detection circuit and realizes complete reconstruction of the RF signal. The signal tracking ST time-frequency mapping and dynamic sampling mechanism are adopted to accurately extract the nonlinear change characteristics of the RF signal during the mode switching process, thereby improving the accuracy of signal analysis. Based on the dynamic impedance matching model and the RF parameter adaptive reconstruction algorithm, a RF parameter control network with a multi-layer neural network structure is constructed to enhance the adaptive ability of gain control. Through the coordinated optimization of the AGC and ALC algorithms, the precise adjustment of the gain control parameters is achieved, and the stability of the signal quality under the dual-mode working state is guaranteed. A closed-loop feedback control mechanism is introduced to correct the gain adjustment coefficient in real time, which effectively reduces the signal distortion and interference during the mode switching process. The present invention ensures the reliability and stability of the gain control through multi-level parameter verification and error correction, thereby improving the overall performance of the system.

[0044] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0045] Identify the working states of the standard Bluetooth mode and the low-power Bluetooth mode of the low-power dual-mode Bluetooth chip to obtain working state data;

[0046] According to the working status data, the radio frequency signal of the low-power dual-mode Bluetooth chip is collected through the RSSI detection circuit to obtain the signal amplitude parameter, phase parameter and frequency parameter;

[0047] Performing data separation processing on the signal amplitude parameter, phase parameter and frequency parameter to obtain an AC component of the radio frequency signal;

[0048] Based on the VSWR measurement circuit, the RF signal of the low-power dual-mode Bluetooth chip is monitored in real time to obtain the signal distortion and interference strength;

[0049] Performing data compensation on the AC component of the radio frequency signal according to the signal distortion and interference strength to obtain the AC component of the radio frequency signal;

[0050] The radio frequency signal of the low-power dual-mode Bluetooth chip is sampled based on the radio frequency power detection circuit to obtain the DC component of the radio frequency signal.

[0051] Specifically, a working status recognition module is constructed, which determines whether the current chip is in standard Bluetooth mode or low-power Bluetooth mode by reading the chip internal register or external control signal. To identify, The value range is ,when When the chip is in standard Bluetooth mode, When the Bluetooth low energy mode is on, the result of state recognition is in the form of working state data. Represents a set of binary data ,in and Represents specific configurations in two modes. Based on working status data , collect the chip's RF signal through the RSSI detection circuit. The RSSI circuit detects the signal strength in real time and extracts its amplitude, phase and frequency parameters. Suppose the RF signal is ,in, Indicates the instantaneous amplitude of the signal; is the center frequency of the signal, equal to ,in is the frequency; is the instantaneous phase of the signal. The RSSI detection circuit analyzes The time domain characteristics of , and . Assume that the detection time window is , the amplitude, phase and frequency parameters are calculated by the following formula:

[0052] ;

[0053] ;

[0054] ;

[0055] Signal amplitude parameters , Phase parameters and frequency parameters Perform data separation processing to extract the AC component of the RF signal. AC component of the RF signal It refers to the dynamic signal portion after the DC offset is removed and is achieved by the following formula:

[0056] ;

[0057] This process effectively eliminates the DC component in the signal, making the subsequent analysis of dynamic characteristics more accurate. At the same time, the VSWR measurement circuit monitors the chip's RF signal in real time to evaluate the signal's distortion and interference strength. VSWR is defined as:

[0058] ;

[0059] in is the reflection coefficient, which represents the amplitude ratio of the reflected wave to the incident wave:

[0060] ;

[0061] In the above formula, is the load impedance, It is the characteristic impedance. VSWR is used to measure the impedance matching degree in signal transmission. When VSWR deviates from 1, it indicates that there is signal distortion and interference. Based on the real-time measured distortion and interference intensity parameters and , to compensate the AC component of the RF signal. The core of data compensation is based on the distortion and interference intensity For AC components Perform dynamic correction. Assume the compensation factor is , and its calculation formula is:

[0062] ;

[0063] in and is the adjustment coefficient, which indicates the influence weight of distortion and interference on the signal. The AC component after final compensation It is expressed as:

[0064] ;

[0065] The RF signal of the chip is sampled through the RF power detection circuit to obtain the DC component of the RF signal. Represents the average power level of the signal, and the calculation formula is:

[0066] ;

[0067] The sampling process relies on a high-precision power detection circuit to ensure that the calculation result of the DC component reflects the true power state of the signal.

[0068] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0069] Perform frequency domain decomposition operation on the AC component of the RF signal to obtain characteristic parameters of the high-frequency component, and construct a transfer function of the bandwidth extension BET model based on the characteristic parameters of the high-frequency component to obtain a bandwidth extension coefficient matrix;

[0070] Perform bandwidth compensation calculation on the AC component of the radio frequency signal according to the bandwidth expansion coefficient matrix to obtain the AC component of the radio frequency signal after expansion;

[0071] Performing a reference power measurement on the DC component of the radio frequency signal, and obtaining a power reference value through a radio frequency power detection circuit;

[0072] Performing amplitude correction calculation on the DC component of the radio frequency signal according to the power reference value to obtain a calibrated DC component of the radio frequency signal;

[0073] Inputting the expanded RF signal AC component and the calibrated RF signal DC component into an adaptive filter for signal fusion processing to obtain initial reconstructed RF signal data;

[0074] A VSWR comparison and verification operation is performed on the initial reconstructed radio frequency signal data to obtain a signal reconstruction error value, and the initial reconstructed radio frequency signal data is compensated and corrected according to the signal reconstruction error value to obtain reconstructed radio frequency signal data.

[0075] Specifically, the AC component of the RF signal is decomposed in the frequency domain, the signal is converted from the time domain to the frequency domain, and the characteristic parameters of the high-frequency component are extracted by analyzing the frequency components of the signal. Assume that the AC component of the RF signal is , whose frequency domain representation is calculated by Fourier transform as , the formula is as follows:

[0076] ;

[0077] in is the signal representation in the frequency domain, is a frequency variable, is an imaginary unit. Analyze the spectrum distribution and extract the characteristic parameters of high-frequency components, such as the center frequency ,bandwidth and the peak power spectral density These parameters define the energy distribution and bandwidth range of the high-frequency part of the signal. Based on the extracted high-frequency component characteristic parameters , construct the transfer function of the bandwidth extension BET model. Assume that the transfer function is , which is expressed in the following formula:

[0078] ;

[0079] in is the frequency response of the bandwidth extension model, is the center frequency, is the bandwidth. By discretizing , generate the bandwidth extension coefficient matrix , the elements of the matrix Represents the gain correction coefficients of different components within the frequency range. Using the bandwidth expansion coefficient matrix , for the original AC component Bandwidth compensation calculation is performed to expand its spectrum range and enhance high-frequency energy. The compensated AC component is expressed in the frequency domain as , the calculation formula is as follows:

[0080] ;

[0081] Then, by inverse Fourier transform Convert back to the time domain to get the expanded AC component :

[0082] ;

[0083] At the same time, the DC component of the RF signal is measured for reference power. Assume that the DC component is , its power reference value is calculated by the power detection circuit, the formula is:

[0084] ;

[0085] in is the power reference value of the DC signal, is the measurement time window. According to the measured reference power value, the DC component Perform amplitude correction. The corrected DC component The calculation is as follows:

[0086] ;

[0087] in is the target power value, which is used to calibrate the amplitude level of the DC component. After completing the independent processing of the AC and DC components, the expanded AC component and the calibrated DC component Input adaptive filter for signal fusion. The fusion process is done by filter weights. To achieve this, the formula is:

[0088] ;

[0089] in is the initial reconstructed RF signal data after fusion, and are the fusion weights of AC and DC components respectively. Perform VSWR comparison verification operation to evaluate the reconstruction quality of the signal. Assume that the target VSWR is VSW , the actual measured VSWR is , reconstruction error value Defined as:

[0090] ;

[0091] According to the error value , compensate and correct the initial reconstructed signal to obtain the corrected reconstructed RF signal data The compensation calculation formula is as follows:

[0092] ;

[0093] in is a compensation factor used to adjust the effect of the error on the reconstructed signal.

[0094] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0095] Perform dual mapping operations in time domain and frequency domain on the reconstructed RF signal data to obtain the ST time-frequency mapping matrix;

[0096] According to the ST time-frequency mapping matrix, a dynamic sampling control function is constructed to obtain a sampling rate adjustment parameter, and the sampling rate adjustment parameter is input into a sampling controller to dynamically adjust the sampling interval to obtain an adaptive sampling sequence;

[0097] Resampling the reconstructed radio frequency signal data based on an adaptive sampling sequence to obtain an equivalent sampling signal;

[0098] Perform ST matrix decomposition operation on the equivalent sampling signal to obtain the signal change characteristic matrix;

[0099] The amplitude change rate, phase shift and frequency drift parameters of the radio frequency signal are calculated according to the signal change characteristic matrix to obtain a nonlinear change parameter set;

[0100] The nonlinear change parameter set is input into the feature extractor for data dimension reduction processing to obtain a feature vector, and the feature vector is standardized to obtain nonlinear feature data.

[0101] Specifically, the short-time Fourier transform is used to perform a joint analysis of the time domain and the frequency domain on the reconstructed RF signal data. The expression of the short-time Fourier transform is:

[0102] ;

[0103] in is the complex form of the time-frequency mapping matrix, For time, is the frequency, is a sliding window function, which is used to limit the time domain range. The amplitude is calculated to obtain the time-frequency energy distribution matrix:

[0104] ;

[0105] in Describes the signal at different time points and frequency The energy distribution under the condition. This matrix is ​​the ST time-frequency mapping matrix. Based on the ST time-frequency mapping matrix , construct a dynamic sampling control function to adapt to the complexity of signal changes in the time domain and frequency domain. The form of the dynamic sampling control function is:

[0106] ;

[0107] in is the sampling rate adjustment parameter, is the proportionality coefficient, and are the lower and upper bounds of the frequency range of interest, respectively. Input to the sampling controller to achieve dynamic adjustment of the sampling interval. The sampling controller updates the sampling interval according to the following rules :

[0108] ;

[0109] Thus generating an adaptive sampling sequence , the interval between each sampling point according to Dynamic changes. Use the generated adaptive sampling sequence to reconstruct the RF signal data Resample to get equivalent sampling signal , reflecting the dynamic characteristics of the original signal and reducing data redundancy. Perform ST matrix decomposition on the equivalent sampling signal to Decompose into change feature matrix , whose elements Indicates that the signal is time period and The decomposition method uses singular value decomposition, and the specific formula is:

[0110] ;

[0111] in and is an orthogonal matrix, is a diagonal matrix whose diagonal elements Indicates the main component characteristic strength of the signal. According to the signal change characteristic matrix , calculate the signal's amplitude change rate, phase shift and frequency drift parameters, and obtain a nonlinear change parameter set. Defined as:

[0112] ;

[0113] in is the instantaneous amplitude, through The phase offset is calculated by normalizing the column vector of Calculated as:

[0114] ;

[0115] in is the instantaneous phase, through Calculate. Frequency drift Defined as

[0116] ;

[0117] in is the instantaneous frequency, which is calculated from the center of the spectrum. , , Combination to form a nonlinear change parameter set . Set the parameter Input feature extractor to reduce data dimension, extract main features through principal component analysis, and generate feature vector The dimensionality reduction formula is:

[0118] ;

[0119] in is the feature weight matrix, which is calculated by principal component analysis. Perform standardization, the formula is:

[0120] ;

[0121] in and They are The mean and standard deviation of the standardized feature data That is the final nonlinear characteristic data.

[0122] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0123] Performing characteristic decomposition operation on nonlinear characteristic data to obtain impedance characteristic parameters, and constructing a transfer function of a dynamic impedance matching model based on the impedance characteristic parameters to obtain an impedance matching coefficient matrix;

[0124] The impedance matching coefficient matrix is ​​input into the RPAR algorithm for parameter mapping processing to obtain a radio frequency parameter mapping matrix, and the transmission characteristic parameters of the radio frequency signal are calculated based on the radio frequency parameter mapping matrix to obtain a transmission parameter vector;

[0125] A neural network topology is constructed according to the transmission parameter vector, and weight parameters of the neural network topology are trained to obtain a network weight matrix;

[0126] A forward propagation calculation is performed on the neural network topology structure based on the network weight matrix to obtain a control parameter output sequence, and a reverse error correction is performed on the control parameter output sequence to obtain a radio frequency parameter control network.

[0127] Specifically, the core parameters related to impedance are extracted from the nonlinear characteristic data. These characteristic data are set as the matrix ,in is the number of time sampling points, is the number of frequency components. Through singular value decomposition (SVD), Broken down into three parts:

[0128] ;

[0129] in and is an orthogonal matrix, is a diagonal matrix whose diagonal elements is a singular value, reflecting the importance of different feature dimensions. singular values ​​and corresponding eigenvectors to form a low-dimensional feature matrix ,in Contains characteristic information that is highly relevant to impedance matching. Based on the extracted impedance characteristic parameters , construct the transfer function of the dynamic impedance matching model. Assume that the transfer function is in the form of:

[0130] ;

[0131] in is a frequency variable, It is the impedance characteristic frequency, which determines the dynamic response rate of the system. According to the impedance characteristic matrix The principal components in The value of can be used to adapt the transfer function to different working conditions. , generate the impedance matching coefficient matrix , each element of which Represents the gain adjustment coefficient under different frequency and time conditions. The impedance matching coefficient matrix Input RPAR (Radio Frequency Parameter Mapping) algorithm for processing to generate RF parameter mapping matrix The RPAR algorithm uses parameterized mapping to Mapped to the physical characteristic parameter space of RF signal. Assume ,in represents the time dimension of the parameter, Represents the frequency dimension of the parameter. The mapping process is expressed as:

[0132] ;

[0133] in and is the mapping weight matrix, which determines the nonlinear characteristics of the mapping. is a mapping function that improves the mapping accuracy by optimizing the weight matrix. Based on the RF parameter mapping matrix , calculate the transmission characteristic parameters of the RF signal. Assume that the transmission characteristic parameters include the reflection coefficient , loss factor and gain , and its calculation formula is:

[0134] ;

[0135] in and are the load impedance and characteristic impedance respectively, is a matrix In the obtained transmission parameter vector After that, a neural network topology is constructed to optimize the RF parameter control. The input layer of the neural network receives The characteristic parameters in the hidden layer adopt a multi-layer perceptron (MLP) structure, and the output layer generates a control parameter sequence. Assume that the number of hidden layer nodes is , the network weight matrix is , the bias vector is , the hidden layer output is:

[0136] ;

[0137] in is the activation function (such as ReLU). Through multiple layers of forward propagation, the calculation formula of the output layer is:

[0138] ;

[0139] in is a sequence of control parameters, and are the weight and bias of the output layer, respectively. is the activation function. The weights of the neural network are trained by the back propagation algorithm. The objective function Defined as a sequence of control parameters With target sequence The error between:

[0140] ;

[0141] Update the weight matrix using the gradient descent algorithm and bias

[0142] ;

[0143] in is the learning rate. After the network training is completed, the optimized weight matrix is ​​used for forward propagation calculation to generate the final control parameter output sequence. Then the sequence is reversely corrected and adjusted according to the actual RF environment to finally generate the RF parameter control network.

[0144] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0145] The nonlinear feature data and impedance matching coefficient matrix are loaded into the input layer of the RF parameter control network, and the AGC initial feature extraction calculation is performed through the 3×3 convolution kernel. The AGC feature mapping channel is obtained by processing with the ReLU activation function, and the AGC feature mapping channel is subjected to the maximum pooling operation to obtain the AGC gain coefficient matrix.

[0146] Based on the AGC gain coefficient matrix, an ALC power limitation model is constructed in the convolution layer. A 5×5 convolution kernel operation and batch normalization processing are performed on the AGC gain coefficient matrix. After the Sigmoid activation function, 32 ALC feature mapping channels are obtained. The power limitation features are extracted through average pooling operation to obtain the ALC control parameter set.

[0147] The AGC gain coefficient matrix and the ALC control parameter set are concatenated in the fully connected layer, and the parameter fusion calculation is performed through a fully connected network with a weight of 128×256. After batch normalization and ReLU activation function processing, the initial gain control parameters are obtained;

[0148] According to the initial gain control parameters, a deactivation rate of 0.5 is set in the first Dropout layer for feature screening. The screened features are calculated through a 64×128 weight matrix to obtain the gain adjustment step size. At the same time, the upper and lower threshold boundaries are calculated to obtain a 64-dimensional dynamic adjustment parameter.

[0149] The dynamic adjustment parameters are normalized in the batch normalization layer, the timing-related features are extracted through the 32×64 timing constraint matrix operation, and the 32-dimensional timing control parameter sequence is obtained through the Tanh activation function;

[0150] After setting the inactivation rate of 0.3 in the second Dropout layer based on the timing control parameter sequence, the processing result is input into the Softmax classifier, and the gain control category probability distribution is calculated through the 16×32 classification weight matrix to obtain the initial gain control sequence;

[0151] Inputting the initial gain control sequence into the parameter verification unit, performing boundary constraint verification through a set verification threshold, and generating verification result data including validity indicators;

[0152] Parameter correction calculation is performed in the output layer according to the verification result data, and the verified control parameters are converted to the actual working range through linear mapping to obtain a gain control sequence including gain adjustment coefficients, threshold parameters and timing control parameters.

[0153] Specifically, the nonlinear characteristic data and the impedance matching coefficient matrix are loaded into the input layer of the network. These input data contain the dynamic characteristics of the signal and the impedance matching characteristics, respectively, and are set as and The output of the input layer is the combination matrix , each element of which Represents the joint relationship between nonlinear characteristic data and impedance matching coefficient. In the convolution layer, a The convolution kernel For the input matrix Perform convolution operation to obtain preliminary feature extraction results:

[0154] ;

[0155] in is the convolution kernel weight, Is the bias term. Convolution result Represents the initial characteristics of the RF signal gain control. After the convolution operation, the ReLU activation function is used to Perform a nonlinear transformation:

[0156] ;

[0157] The role of the ReLU function is to introduce nonlinearity to make the features more expressive. Perform a max pooling operation to reduce feature dimensions and retain key information:

[0158] ;

[0159] Max pooling result Construct an AGC gain coefficient matrix to represent the gain adjustment factor of each area signal. , and construct the ALC power limitation model in the next convolutional layer. Use one The convolution kernel Perform convolution operation, the convolution formula is:

[0160] ;

[0161] And perform batch normalization on the convolution results, the formula is:

[0162] ;

[0163] in and The normalized results are processed by the Sigmoid activation function to generate 32 ALC feature mapping channels:

[0164] ;

[0165] The features of each channel are summarized through average pooling operation to obtain the ALC control parameter set . The AGC gain coefficient matrix and ALC control parameter sets After concatenation, input the fully connected network. Suppose the weight matrix of the fully connected network is , the output is:

[0166] ;

[0167] in is the ReLU activation function, is the bias term. After the full connection operation, the Dropout layer with a deactivation rate of 0.5 is used to filter the features to prevent overfitting. The filtered features are passed through a weight matrix Calculate the gain adjustment step size and threshold boundaries:

[0168] ;

[0169] The dynamic adjustment parameters obtained After batch normalization, the input timing constraint matrix Extracting time-related features:

[0170] ;

[0171] Finally, the timing control parameter sequence is generated The timing control parameter sequence is processed by the second Dropout layer with a deactivation rate of 0.3 and then input into the Softmax classifier. Compute the gain control category probability distribution:

[0172] ;

[0173] The initial gain control sequence is tested for validity by the parameter verification unit. If it exceeds the set threshold , then adjust to:

[0174] ;

[0175] Finally, the control parameters are corrected by linear mapping , generate the gain control sequence:

[0176] ;

[0177] in is the output layer weight matrix.

[0178] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0179] According to the gain control sequence, the radio frequency signal of the low-power dual-mode Bluetooth chip is gain-adjusted to obtain an initial adjustment result;

[0180] Perform RSSI detection and VSWR measurement on the initial adjustment result to obtain real-time signal quality parameters, and calculate the gain adjustment error value based on the real-time signal quality parameters to obtain feedback control data;

[0181] The feedback control data is input into the closed-loop feedback controller to perform error compensation calculation to obtain a correction value of the gain adjustment coefficient;

[0182] The gain control sequence is corrected and calculated according to the correction value of the gain adjustment coefficient to obtain a corrected gain control sequence;

[0183] Performing radio frequency signal gain adjustment based on the modified gain control sequence to obtain an adjusted radio frequency signal;

[0184] A signal quality verification operation is performed on the adjusted radio frequency signal to obtain a verification parameter, and a radio frequency signal gain control parameter is generated according to the verification parameter.

[0185] Specifically, the input gain control sequence is used to perform initial gain adjustment on the RF signal. Assume that the gain control sequence is , where each element Represents the time series of the gain adjustment coefficient. RF signal of the low-power dual-mode Bluetooth chip After gain adjustment, the initial adjustment result is output , and its relationship is expressed as:

[0186] ;

[0187] in is a continuous gain adjustment factor calculated by interpolation and used to compare with the signal in the time domain Match. After getting the initial adjustment result After that, the signal quality is tested to evaluate the current gain adjustment effect. The signal strength is measured by the RSSI (received signal strength indication) circuit. The RSSI calculation formula is:

[0188] ;

[0189] in is the length of the measurement time window, Indicates the signal power. At the same time, the reflection characteristics of the signal are obtained through the VSWR (voltage standing wave ratio) measurement circuit. The definition of VSWR is:

[0190] ;

[0191] in is the reflection coefficient, is the load impedance, is the characteristic impedance. Through the above two sets of parameters, we can get the real-time signal quality parameter set RSSI, VSWR . Compare these parameters to the target signal quality Compare and calculate the gain adjustment error value :

[0192] ;

[0193] in is the actual signal quality parameter, is the target parameter. The gain is adjusted to the error value Input closed-loop feedback controller to perform error compensation calculation and generate correction value of gain adjustment coefficient The feedback controller is based on the PID (proportional-integral-derivative) algorithm, and its output expression is:

[0194] ;

[0195] in They are proportional, integral and differential gains, respectively, to control the dynamic adjustment of the correction value. , for the original gain control sequence Perform correction calculations to generate a corrected gain control sequence :

[0196] ;

[0197] The modified gain control sequence is applied again to the RF signal conditioning to generate a conditioned RF signal :

[0198] ;

[0199] in is the modified gain function. After adjustment, the signal In the example above, the adjustment effect is checked by signal quality verification operation. The verification includes re-measurement of RSSI and VSWR and further signal consistency and stability checks. Assume that the verification parameter set is , which is calculated in the same way as the initial test. and target parameters , generate the final verification result:

[0200] ;

[0201] in Represents the verification effectiveness indicator. If it exceeds the allowable range, the parameters are further adjusted until the design requirements are met. The verification results are combined with the gain adjustment value through linear mapping to generate the RF signal gain control parameters. :

[0202] ;

[0203] in is the mapping coefficient, which is used to convert the control value into the physical parameter range.

[0204] The above describes the RF signal gain control method of the low-power dual-mode Bluetooth chip in the embodiment of the present invention. The following describes the RF signal gain control device of the low-power dual-mode Bluetooth chip in the embodiment of the present invention. Figure 2 , an embodiment of the radio frequency signal gain control device of the low power consumption dual-mode Bluetooth chip in the embodiment of the present invention includes:

[0205] The measuring module 201 is used to perform RSSI detection and VSWR measurement on the radio frequency signal of the low power dual-mode Bluetooth chip to obtain an AC component and a DC component of the radio frequency signal;

[0206] The reconstruction module 202 is used to perform bandwidth extension on the AC component of the radio frequency signal through a bandwidth extension BET model, and perform power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data;

[0207] A mapping module 203 is used to perform signal tracking ST time-frequency mapping on the reconstructed RF signal data, and extract the amplitude change rate, phase offset and frequency drift of the RF signal through a dynamic sampling mechanism to generate nonlinear characteristic data;

[0208] The calculation module 204 is used to construct a dynamic impedance matching model based on the nonlinear characteristic data, calculate the transmission characteristic parameters of the radio frequency signal, and establish a radio frequency parameter control network;

[0209] A generating module 205 is used to perform AGC and ALC collaborative optimization calculation on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter and a timing control parameter;

[0210] The output module 206 is used to adjust the RF signal gain according to the gain control sequence, calculate the correction value of the gain adjustment coefficient in a closed-loop feedback manner, and output the RF signal gain control parameter.

[0211] Through the synergy of the above-mentioned components, the AC and DC components of the RF signal are processed through the bandwidth expansion BET model, which overcomes the problem of limited bandwidth of the traditional detection circuit and realizes complete RF signal reconstruction. The signal tracking ST time-frequency mapping and dynamic sampling mechanism are adopted to accurately extract the nonlinear change characteristics of the RF signal during the mode switching process, and improve the accuracy of signal analysis. Based on the dynamic impedance matching model and the RF parameter adaptive reconstruction algorithm, a RF parameter control network with a multi-layer neural network structure is constructed to enhance the adaptive ability of gain control. Through the coordinated optimization of the AGC and ALC algorithms, the precise adjustment of the gain control parameters is achieved, and the stability of the signal quality under the dual-mode working state is guaranteed. The closed-loop feedback control mechanism is introduced to correct the gain adjustment coefficient in real time, which effectively reduces the signal distortion and interference during the mode switching process. The present invention ensures the reliability and stability of the gain control through multi-level parameter verification and error correction, and improves the overall performance of the system.

[0212] above Figure 2 The RF signal gain control device of the medium and low power dual-mode Bluetooth chip in the embodiment of the present invention is described in detail from the perspective of modular functional entity. The RF signal gain control device of the low power dual-mode Bluetooth chip in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0213] Figure 3It is a structural schematic diagram of a radio frequency signal gain control device for a low-power dual-mode Bluetooth chip provided by an embodiment of the present invention. The radio frequency signal gain control device 300 for the low-power dual-mode Bluetooth chip may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage medium 330 can be short-term storage or permanent storage. The program stored in the storage medium 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the radio frequency signal gain control device 300 for the low-power dual-mode Bluetooth chip. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, and execute a series of instruction operations in the storage medium 330 on the radio frequency signal gain control device 300 for the low-power dual-mode Bluetooth chip to implement the steps of the radio frequency signal gain control method for the low-power dual-mode Bluetooth chip.

[0214] The RF signal gain control device 300 of the low power dual-mode Bluetooth chip may also include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input and output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The structure of the RF signal gain control device of the low-power dual-mode Bluetooth chip shown does not constitute a limitation on the RF signal gain control device of the low-power dual-mode Bluetooth chip provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0215] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the radio frequency signal gain control method of the low-power dual-mode Bluetooth chip.

[0216] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0217] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0218] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the gain of a radio frequency signal of a low-power dual-mode Bluetooth chip, characterized in that: The method comprises: Perform RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain the AC component and DC component of the radio frequency signal; Performing bandwidth expansion on the AC component of the radio frequency signal through a bandwidth expansion BET model, and performing power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data; Performing signal tracking ST time-frequency mapping on the reconstructed radio frequency signal data, and extracting the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data; Constructing a dynamic impedance matching model based on the nonlinear characteristic data, calculating the transmission characteristic parameters of the radio frequency signal, and establishing a radio frequency parameter control network; Performing AGC and ALC collaborative optimization calculations on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter, and a timing control parameter; The radio frequency signal gain is adjusted according to the gain control sequence, a correction value of the gain adjustment coefficient is calculated by a closed-loop feedback method, and a radio frequency signal gain control parameter is output.

2. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 1, characterized in that: The RSSI detection and VSWR measurement of the radio frequency signal of the low-power dual-mode Bluetooth chip are performed to obtain the AC component and the DC component of the radio frequency signal, including: Identify the working states of the standard Bluetooth mode and the low-power Bluetooth mode of the low-power dual-mode Bluetooth chip to obtain working state data; According to the working status data, the radio frequency signal of the low-power dual-mode Bluetooth chip is collected through the RSSI detection circuit to obtain the signal amplitude parameter, phase parameter and frequency parameter; Performing data separation processing on the signal amplitude parameter, phase parameter and frequency parameter to obtain an AC component of the radio frequency signal; Based on the VSWR measurement circuit, the radio frequency signal of the low-power dual-mode Bluetooth chip is monitored in real time to obtain signal distortion and interference strength; Performing data compensation on the AC component of the radio frequency signal according to the signal distortion and interference strength to obtain the AC component of the radio frequency signal; The radio frequency signal of the low-power dual-mode Bluetooth chip is sampled based on the radio frequency power detection circuit to obtain a direct current component of the radio frequency signal.

3. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 2, characterized in that: The method of performing bandwidth extension on the AC component of the radio frequency signal through the bandwidth extension BET model and performing power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data includes: Performing frequency domain decomposition operation on the AC component of the radio frequency signal to obtain characteristic parameters of the high-frequency component, and constructing a transfer function of the bandwidth extension BET model based on the characteristic parameters of the high-frequency component to obtain a bandwidth extension coefficient matrix; Performing bandwidth compensation calculation on the AC component of the radio frequency signal according to the bandwidth expansion coefficient matrix to obtain an expanded AC component of the radio frequency signal; Performing a reference power measurement on the DC component of the radio frequency signal, and obtaining a power reference value through a radio frequency power detection circuit; Performing amplitude correction calculation on the DC component of the radio frequency signal according to the power reference value to obtain a calibrated DC component of the radio frequency signal; Inputting the expanded RF signal AC component and the calibrated RF signal DC component into an adaptive filter for signal fusion processing to obtain initial reconstructed RF signal data; A VSWR comparison and verification operation is performed on the initial reconstructed radio frequency signal data to obtain a signal reconstruction error value, and compensation correction is performed on the initial reconstructed radio frequency signal data according to the signal reconstruction error value to obtain reconstructed radio frequency signal data.

4. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 3, characterized in that: The step of performing signal tracking ST time-frequency mapping on the reconstructed radio frequency signal data and extracting the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data includes: Performing dual mapping operations in time domain and frequency domain on the reconstructed radio frequency signal data to obtain an ST time-frequency mapping matrix; Constructing a dynamic sampling control function according to the ST time-frequency mapping matrix to obtain a sampling rate adjustment parameter, and inputting the sampling rate adjustment parameter into a sampling controller to dynamically adjust the sampling interval to obtain an adaptive sampling sequence; Resampling the reconstructed radio frequency signal data based on the adaptive sampling sequence to obtain an equivalent sampling signal; Performing ST matrix decomposition operation on the equivalent sampling signal to obtain a signal change characteristic matrix; Calculating the amplitude change rate, phase shift and frequency drift parameters of the radio frequency signal according to the signal change characteristic matrix to obtain a nonlinear change parameter set; The nonlinear change parameter set is input into a feature extractor for data dimension reduction processing to obtain a feature vector, and the feature vector is standardized to obtain nonlinear feature data.

5. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 4, characterized in that: The step of constructing a dynamic impedance matching model based on the nonlinear characteristic data, calculating the transmission characteristic parameters of the radio frequency signal, and establishing a radio frequency parameter control network includes: Performing a characteristic decomposition operation on the nonlinear characteristic data to obtain impedance characteristic parameters, and constructing a transfer function of a dynamic impedance matching model according to the impedance characteristic parameters to obtain an impedance matching coefficient matrix; The impedance matching coefficient matrix is ​​input into the RPAR algorithm for parameterized mapping processing to obtain a radio frequency parameter mapping matrix, and the transmission characteristic parameters of the radio frequency signal are calculated based on the radio frequency parameter mapping matrix to obtain a transmission parameter vector; Constructing a neural network topology structure according to the transmission parameter vector, and performing weight parameter training on the neural network topology structure to obtain a network weight matrix; Based on the network weight matrix, forward propagation calculation is performed on the neural network topology to obtain a control parameter output sequence, and reverse error correction is performed on the control parameter output sequence to obtain a radio frequency parameter control network.

6. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 5, characterized in that: The performing of AGC and ALC collaborative optimization calculation on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter and a timing control parameter includes: The nonlinear feature data and the impedance matching coefficient matrix are loaded into the input layer of the radio frequency parameter control network, an AGC initial feature extraction calculation is performed through a 3×3 convolution kernel, an AGC feature mapping channel is obtained through ReLU activation function processing, and a maximum pooling operation is performed on the AGC feature mapping channel to obtain an AGC gain coefficient matrix; An ALC power limitation model is constructed in a convolutional layer based on the AGC gain coefficient matrix, a 5×5 convolution kernel operation and a batch normalization process are performed on the AGC gain coefficient matrix, 32 ALC feature mapping channels are obtained through a Sigmoid activation function, power limitation features are extracted through an average pooling operation, and an ALC control parameter set is obtained; The AGC gain coefficient matrix and the ALC control parameter set are concatenated in a fully connected layer, parameter fusion calculation is performed through a fully connected network with a weight of 128×256, and initial gain control parameters are obtained after batch normalization and ReLU activation function processing; According to the initial gain control parameter, a deactivation rate of 0.5 is set in the first Dropout layer to perform feature screening, and the screened features are calculated through a 64×128 weight matrix to obtain a gain adjustment step, and the upper and lower threshold boundaries are calculated at the same time to obtain a 64-dimensional dynamic adjustment parameter; The dynamic adjustment parameters are subjected to data normalization processing in a batch normalization layer, timing-related features are extracted through a 32×64 timing constraint matrix operation, and a 32-dimensional timing control parameter sequence is obtained through a Tanh activation function; After setting a deactivation rate of 0.3 in the second Dropout layer based on the timing control parameter sequence, the processing result is input into the Softmax classifier, and the gain control category probability distribution is calculated by a 16×32 classification weight matrix to obtain an initial gain control sequence; Inputting the initial gain control sequence into a parameter verification unit, performing boundary constraint verification through a set verification threshold, and generating verification result data including validity indicators; Parameter correction calculation is performed at the output layer according to the verification result data, and the verified control parameters are converted to the actual working range through linear mapping to obtain a gain control sequence including a gain adjustment coefficient, a threshold parameter and a timing control parameter.

7. The method for controlling the radio frequency signal gain of a low power consumption dual-mode Bluetooth chip according to claim 6, characterized in that: The step of adjusting the RF signal gain according to the gain control sequence, calculating the correction value of the gain adjustment coefficient in a closed-loop feedback manner, and outputting the RF signal gain control parameter includes: Performing gain adjustment on the radio frequency signal of the low-power dual-mode Bluetooth chip according to the gain control sequence to obtain an initial adjustment result; Performing RSSI detection and VSWR measurement on the initial adjustment result to obtain real-time signal quality parameters, and calculating the gain adjustment error value based on the real-time signal quality parameters to obtain feedback control data; Inputting the feedback control data into a closed-loop feedback controller to perform error compensation calculation to obtain a correction value of a gain adjustment coefficient; Performing correction calculation on the gain control sequence according to the correction value of the gain adjustment coefficient to obtain a corrected gain control sequence; Performing radio frequency signal gain adjustment based on the modified gain control sequence to obtain an adjusted radio frequency signal; A signal quality verification operation is performed on the adjusted radio frequency signal to obtain a verification parameter, and a radio frequency signal gain control parameter is generated according to the verification parameter.

8. A radio frequency signal gain control device for a low-power dual-mode Bluetooth chip, characterized in that: The device is used to execute the radio frequency signal gain control method of the low-power dual-mode Bluetooth chip according to any one of claims 1 to 7, and the device comprises: The measurement module is used to perform RSSI detection and VSWR measurement on the radio frequency signal of the low-power dual-mode Bluetooth chip to obtain the AC component and the DC component of the radio frequency signal; A reconstruction module, used to perform bandwidth extension on the AC component of the radio frequency signal through a bandwidth extension BET model, and perform power calibration on the DC component of the radio frequency signal to obtain reconstructed radio frequency signal data; A mapping module, used to perform signal tracking ST time-frequency mapping on the reconstructed radio frequency signal data, and extract the amplitude change rate, phase offset and frequency drift of the radio frequency signal through a dynamic sampling mechanism to generate nonlinear characteristic data; A calculation module, used to construct a dynamic impedance matching model based on the nonlinear characteristic data, calculate the transmission characteristic parameters of the radio frequency signal, and establish a radio frequency parameter control network; A generating module, used for performing AGC and ALC collaborative optimization calculation on the radio frequency parameter control network to generate a gain control sequence including a gain adjustment coefficient, a threshold parameter and a timing control parameter; The output module is used to adjust the RF signal gain according to the gain control sequence, calculate the correction value of the gain adjustment coefficient by a closed-loop feedback method, and output the RF signal gain control parameter.

9. A radio frequency signal gain control device for a low-power dual-mode Bluetooth chip, characterized in that: The radio frequency signal gain control device of the low-power dual-mode Bluetooth chip comprises: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instruction in the memory to enable the radio frequency signal gain control device of the low-power dual-mode Bluetooth chip to execute the radio frequency signal gain control method of the low-power dual-mode Bluetooth chip according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the radio frequency signal gain control method of the low-power dual-mode Bluetooth chip according to any one of claims 1 to 7 is implemented.

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