A Frequency Adaptive Method for Dual-Mode Communication of HPLC and Micro-Power Wireless

Abstract

The present invention provides a frequency adaptive method for HPLC and micro-power wireless dual-mode communication, belonging to the technical field of dual-mode communication, including: First, establish dual-mode communication by initializing parameters such as carrier frequency, transmission power, and modulation mode. Secondly, collect electromagnetic interference signals in the power line channel and establish a linear superposition noise model with time-domain weights to predict noise characteristics. At the same time, analyze the zero-crossing time series of power line signals and construct a zero-crossing feature vector. Combining noise spectrum prediction and zero-crossing features, solve the convex optimization problem to obtain the optimal carrier frequency and other communication parameters. For micro-power wireless communication, construct a channel quality evaluation matrix based on indicators such as signal strength, channel busy degree, and bit error rate, and establish an interference prediction model. Finally, optimize the wireless communication parameters through quadratic programming. Finally, establish a handover decision function according to the set communication quality threshold to achieve the adaptive handover of dual-mode communication to ensure the overall communication quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of dual - mode communication, and more particularly, relates to a frequency adaptive method for HPLC and micro - power wireless dual - mode communication. Background Art

[0002] In recent years, with the wide application of smart grid and Internet of Things technologies, the demand for information transmission has been increasing continuously. As two important information transmission technologies, high - speed power line carrier communication and micro - power wireless communication play a key role in this field.

[0003] High - speed power line carrier communication utilizes the existing power line infrastructure and can integrate power transmission and information transmission, which is an economical and efficient communication method. However, the power line environment is complex and changeable, with narrow - band interference and pulse interference from various power equipment, which will seriously affect the signal transmission quality. To improve communication reliability, parameters such as carrier frequency, transmission power, and modulation mode need to be dynamically adjusted.

[0004] Meanwhile, micro - power wireless communication, with its flexible deployment and wide coverage, also plays an important role in applications such as the Internet of Things and smart cities. Micro - power wireless communication systems operate in unlicensed frequency bands such as 2.4 GHz and 5.8 GHz and adopt technical standards such as WPAN and WLAN. Due to limited frequency resources, frequency reuse is the key to improving spectrum utilization. However, frequency reuse will cause serious inter - modulation interference, and the transmission power and modulation mode need to be dynamically adjusted according to the real - time channel quality to meet the requirements of reliable communication.

[0005] Therefore, how to achieve the coordinated operation of high - speed power line carrier communication and micro - power wireless communication, make full use of the two communication resources, dynamically adjust communication parameters according to environmental changes, and optimize communication quality is a key technical problem to be solved urgently. Summary of the Invention

[0006] In view of this, the present invention provides a frequency adaptive method for HPLC and micro - power wireless dual - mode communication, which can solve the technical problem in the prior art that it is difficult to achieve the coordinated operation of high - speed power line carrier communication and micro - power wireless communication.

[0007] The present invention is implemented as follows:

[0008] The present invention provides a frequency adaptive method for HPLC and micro - power wireless dual - mode communication, including the following steps:

[0009] S10. Set the initial working parameters of high - speed power line carrier communication and micro - power wireless communication, and establish a dual - mode communication collaborative control mechanism. The initial working parameters include carrier frequency parameters, transmission power parameters, and modulation mode parameters;

[0010] S20. Collect the electromagnetic interference signals in the power line channel, and establish a linear superposition noise model with time-domain weight coefficients according to the electromagnetic interference signals. The linear superposition noise model includes a narrowband interference term, an impulse interference term, and a background noise term;

[0011] S30. Calculate the spectral distribution function of the linear superposition noise model based on the fast Fourier transform, establish a linear prediction matrix according to the spectral distribution function, and solve the linear prediction matrix by the least squares method to obtain the noise spectrum prediction result;

[0012] S40. Collect the zero-crossing time series of the power line signal, calculate the mean, standard deviation, and correlation coefficient of the zero-crossing time series, and construct a zero-crossing feature vector according to the mean, standard deviation, and correlation coefficient;

[0013] S50. Establish a convex optimization objective function according to the noise spectrum prediction result and the zero-crossing feature vector, and solve the convex optimization objective function by the gradient descent method to obtain the optimal carrier frequency for high-speed power line carrier communication;

[0014] S60. Substitute the optimal carrier frequency into a preset carrier adjustment equation set, and solve the carrier adjustment equation set to obtain the transmission power parameter and modulation mode parameter for high-speed power line carrier communication;

[0015] S70. According to the signal reception strength index, channel busy degree index, and bit error rate index of the preset micro-power wireless communication channel, collect the background electromagnetic noise in the micro-power wireless communication environment, and construct a channel quality evaluation matrix; and establish a wireless channel interference prediction model based on linear regression, and update the parameters of the wireless channel interference prediction model by the recursive least squares method to obtain the frequency interference prediction result;

[0016] S80. Establish a quadratic programming model according to the signal reception strength index, channel busy degree index, bit error rate index, and the frequency interference prediction result, and solve the quadratic programming model to obtain the transmission power parameter and modulation mode parameter for micro-power wireless communication;

[0017] S90. Establish a handover decision function for high-speed power line carrier communication and micro-power wireless communication according to a preset communication quality threshold, and implement dual-mode communication handover based on the handover decision function, and monitor the communication quality index.

[0018] On the basis of the above technical solutions, a frequency adaptive method for HPLC and micro-power wireless dual-mode communication of the present invention can also be improved as follows:

[0019] Further, the initial working parameter setting in S10 is specifically expressed as follows:

[0020] fc = f0 + Δf;

[0021] P t = P0 + ΔP;

[0022] M = {BPSK, QPSK, 16QAM, 64QAM};

[0023] In the formula, f c is the carrier frequency, with the initial value f0 = 3 MHz; Δf is the frequency offset, ranging from [-0.5 MHz, 0.5 MHz]; P t is the transmit power, with the initial value P0 = 100 mW; ΔP is the power adjustment, ranging from [-20 mW, 20 mW]; M is the modulation mode set.

[0024] The linear superposition noise model in S20 is specifically expressed as follows:

[0025]

[0026] In the formula, n(t) is the total noise; N1 is the number of narrowband interference sources; A i , f i , φ i are respectively the amplitude, frequency and phase of the i-th narrowband interference; N2 is the number of impulse interference sources; B j , α j , t j , f j are respectively the amplitude, attenuation coefficient, occurrence time and frequency of the j-th impulse interference; σ b is the background noise standard deviation; ω(t) is Gaussian white noise.

[0027] The spectral distribution function and linear prediction matrix in S30 are specifically expressed as follows:

[0028]

[0029] In the formula, S(f) is the noise spectrum; T is the sampling period; N is the number of sampling points; Δt is the sampling interval; is the spectral prediction value; p is the prediction order; a i are the prediction coefficients; e(k) is the prediction error.

[0030] The zero-crossing feature vector in S40 is specifically expressed as follows:

[0031]

[0032] Among them,

[0033] In the formula, μ z is the zero-crossing time mean; σz is the standard deviation; ρ z (k) is the k-th order autocorrelation coefficient; t i is the zero-crossing time of the i-th; N z is the total number of zero-crossings; is the eigenvector.

[0034] The convex optimization objective function in S50 is specifically expressed as follows:

[0035]

[0036] s.t. f min ≤ f ≤ f max ;

[0037] In the formula, f opt is the optimal carrier frequency; λ1, λ2 are weight coefficients; f min , f max are the lower and upper frequency limits respectively.

[0038] The carrier adjustment equation set in S60 is specifically expressed as follows:

[0039] P opt = P0·exp(-β1|f opt - f0|);

[0040] M opt = argmax m∈M {C m ·(1 - β2|f opt - f0|)};

[0041] In the formula, P opt is the optimal transmission power; M opt is the optimal modulation mode; β1, β2 are adjustment coefficients; C m is the channel capacity corresponding to the modulation mode m.

[0042] The channel quality evaluation matrix and interference prediction model in S70 are specifically expressed as follows:

[0043]

[0044] In the formula, Q is the channel quality matrix; RSSI is the signal reception strength; CB is the channel busy degree; BER is the bit error rate; ω1, ω2, ω3 are weights; I(t) is the interference intensity at time t; q is the prediction order; b i is the prediction coefficient; η(t) is the prediction error.

[0045] The quadratic programming model in S80 is specifically expressed as follows:

[0046] min J = x T Qx + cT x;

[0047] s.t. Ax ≤ b;

[0048] x = [P w , M w T ;

[0049] Wherein, J is the objective function; x is the decision variable vector; Q is the quadratic coefficient matrix; c is the linear coefficient vector; A is the constraint matrix; b is the constraint vector; P w , M w are the wireless communication power and modulation mode respectively.

[0050] The handover decision function in S90 is specifically expressed as follows:

[0051] D(t) = γ1Q p (t) + γ2Q w (t);

[0052]

[0053] Wherein, D(t) is the decision function; Q p (t), Q w (t) are the power line and wireless communication quality indicators respectively; γ1, γ2 are the weight coefficients; θ is the handover threshold; Mode is the communication mode.

[0054] Furthermore, the handover threshold is set according to experience or adopts the default value of 0.69.

[0055] Compared with the prior art, the beneficial effects of a HPLC and micro-power wireless dual-mode communication frequency adaptive method provided by the present invention are:

[0056] This method first establishes the initial working parameters of high-speed power line carrier communication and micro-power wireless communication, including carrier frequency, transmission power and modulation mode. Then, it collects the interference signals in the power line channel, establishes a linear superposition noise model containing time-domain weight coefficients, and calculates its spectral distribution through fast Fourier transform. Based on this, it uses the linear prediction method to estimate the noise spectrum at future moments. At the same time, it collects the zero-crossing time series of the power line signal and extracts the feature vectors. Next, it combines the noise spectrum prediction result and the zero-crossing feature vector to construct a convex optimization objective function, and solves to obtain the optimal carrier frequency.

[0057] For micro-power wireless communication, this method collects indicators such as signal reception strength, channel busy degree and bit error rate, and establishes a channel quality evaluation matrix. At the same time, it collects the ambient noise and uses linear regression to establish an interference prediction model. Based on this information, it uses the quadratic programming optimization method to obtain the optimal wireless communication power and modulation mode.​

[0058] Finally, according to the preset communication quality threshold, a handover decision function for high-speed power line carrier communication and micro-power wireless communication is established to achieve automatic handover between the two communication modes. This can not only effectively reduce interference and improve communication quality, but also flexibly adapt to different environmental conditions and has strong robustness.

[0059] In summary, the present invention solves the technical problem in the prior art that it is difficult to achieve the coordinated operation of high-speed power line carrier communication and micro-power wireless communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flowchart of the method provided by the present invention;

[0061] Figure 2 is a distribution diagram of the power spectral density of the power line channel noise;

[0062] Figure 3 is a performance graph of the system interference prediction model;

[0063] Figure 4 is a curve graph for evaluating the performance of the communication system;

[0064] Figure 5 is a channel capacity graph for different modulation methods. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0066] As Figure 1 shown, it is a flowchart of a frequency adaptive method for HPLC and micro-power wireless dual-mode communication provided by the present invention. This method includes the following steps:

[0067] S10. Set the initial working parameters of high-speed power line carrier communication and micro-power wireless communication, and establish a cooperative control mechanism for dual-mode communication. The initial working parameters include carrier frequency parameters, transmission power parameters, and modulation method parameters;

[0068] S20. Collect electromagnetic interference signals in the power line channel, and establish a linear superposition noise model with time-domain weight coefficients according to the electromagnetic interference signals. The linear superposition noise model includes a narrowband interference term, an impulse interference term, and a background noise term;

[0069] S30. Calculate the spectral distribution function of the linear superposition noise model based on the fast Fourier transform, establish a linear prediction matrix according to the spectral distribution function, and solve the linear prediction matrix by the least squares method to obtain the noise spectrum prediction result;

[0070] S40. Collect the zero-crossing time series of the power line signal, calculate the mean, standard deviation, and correlation coefficient of the zero-crossing time series, and construct a zero-crossing feature vector based on the mean, standard deviation, and correlation coefficient;

[0071] S50. Based on the noise spectrum prediction result and the zero-crossing feature vector, establish a convex optimization objective function, and use the gradient descent method to solve the convex optimization objective function to obtain the optimal carrier frequency for high-speed power line carrier communication;

[0072] S60. Substitute the optimal carrier frequency into the preset carrier adjustment equation set, and solve the carrier adjustment equation set to obtain the transmission power parameter and modulation mode parameter of high-speed power line carrier communication;

[0073] S70. According to the signal reception strength index, channel busyness index, and bit error rate index of the preset micro-power wireless communication channel, collect the background electromagnetic noise in the micro-power wireless communication environment, and construct a channel quality evaluation matrix; and establish a wireless channel interference prediction model based on linear regression, and use the recursive least squares method to update the parameters of the wireless channel interference prediction model to obtain the frequency interference prediction result;

[0074] S80. Based on the signal reception strength index, channel busyness index, bit error rate index, and frequency interference prediction result, establish a quadratic programming model, and solve the quadratic programming model to obtain the transmission power parameter and modulation mode parameter of micro-power wireless communication;

[0075] S90. Establish a handover decision function for high-speed power line carrier communication and micro-power wireless communication according to the preset communication quality threshold, implement dual-mode communication handover based on the handover decision function, and monitor the communication quality index.

[0076] The specific implementation manners of the above steps are described in detail below:

[0077] The specific implementation manner of step S10 is as follows: First, it is necessary to set the initial working parameters of high-speed power line carrier communication and micro-power wireless communication. This includes three aspects: the carrier frequency parameter f c , the transmission power parameter P t and the modulation mode parameter M.

[0078] Among them, the initial value f0 of the carrier frequency f c is set to 3 MHz, and the allowable frequency offset range Δf is [-0.5 MHz, 0.5 MHz]. Therefore, the value range of f c is [2.5 MHz, 3.5 MHz], which can be expressed as:

[0079] f c = f0 + Δf;

[0080] The transmission power Pt The initial value P0 is set to 100 mW, and the allowable power adjustment range ΔP is [-20 mW, 20 mW]. Therefore, the value range of P t is [80 mW, 120 mW], which can be expressed as:

[0081] P t = P0 + ΔP;

[0082] The modulation mode parameter M can select common digital modulation modes, including BPSK, QPSK, 16QAM, and 64QAM.

[0083] In addition, a cooperative control mechanism for high-speed power line carrier communication and micro-power wireless communication needs to be established. This realizes the organic combination of the two communication modes, ensuring that the working mode can be automatically switched according to the communication quality under different environmental conditions, and improving the reliability and stability of the overall communication system.

[0084] The specific implementation of step S20 is as follows: First, it is necessary to collect the electromagnetic interference signals in the power line channel, including narrowband interference and pulse interference from various power equipment. According to the collected interference signals, a linear superposition noise model n(t) is established. This model can be expressed as:

[0085]

[0086] where N1 represents the number of narrowband interference sources, A i , f i and φ i represent the amplitude, frequency, and phase of the i-th narrowband interference respectively. N2 represents the number of pulse interference sources, B j , α j , t j and f j represent the amplitude, attenuation coefficient, occurrence time, and frequency of the j-th pulse interference respectively. σ b represents the standard deviation of the background noise, and ω(t) represents Gaussian white noise. This linear superposition noise model can better describe the actual interference situation in the power line channel.

[0087] The specific implementation of step S30 is as follows: First, perform a fast Fourier transform (FFT) calculation on the collected power line noise signal to obtain the noise power spectral density function S(f), which is expressed as:

[0088]

[0089] where T is the sampling period, Δt is the sampling interval, and N is the number of sampling points. Then, based on the linear prediction model, a noise spectrum prediction matrix is established, which is expressed as:

[0090]

[0091] Here, p is the prediction order, a i is the prediction coefficient, and e(k) is the prediction error. By solving using the least squares method, the prediction coefficient a i can be obtained, and the predicted result of the noise power spectral density at future moments can be obtained This provides a basis for subsequent optimization of the carrier frequency.

[0092] The specific implementation of step S40 is as follows: First, it is necessary to collect the zero-crossing time series t of the power line signal i . Then calculate the statistical characteristic quantities of the zero-crossing time series, including the mean μ z , the standard deviation σ z and the autocorrelation coefficient ρ z (k), and the specific expressions are as follows:

[0093]

[0094] where N z is the total number of zero crossings. These statistical characteristic quantities constitute a 5-dimensional feature vector That is:

[0095]

[0096] This feature vector reflects the periodicity and randomness of the power line signal, providing another basis for subsequent optimization of the carrier frequency.

[0097] The specific implementation of step S50 is as follows: First, combine the predicted result of the noise power spectral density and the zero-crossing feature vector to construct a convex optimization objective function:

[0098]

[0099] s.t.f min ≤f≤f max ;

[0100] where f opt is the optimal carrier frequency, λ1 and λ2 are weight coefficients, f min and f max are the upper and lower limits of the frequency. This optimization objective function considers two factors, the noise spectrum and the signal characteristics. The goal is to find an optimal carrier frequency to minimize the sum of the noise power spectral density and the norm of the zero-crossing feature vector. By using the gradient descent method to solve this convex optimization problem, the optimal carrier frequency f opt can be obtained.

[0101] The specific implementation of step S60 is as follows: First, according to the optimal carrier frequency f obtained in step S50 opt , the optimal transmission power P opt and modulation mode M opt are calculated by combining with the preset carrier adjustment equation set. The specific expressions are as follows:

[0102] P opt = P0·exp(-β1|f opt - f0|);

[0103] M opt = argmax m∈M {C m ·(1 - β2|f opt - f0|)};

[0104] Among them, β1 and β2 are adjustment coefficients, and C m is the channel capacity corresponding to the modulation mode m. This indicates that as the deviation between the carrier frequency f opt and the initial frequency f0 increases, the transmission power P opt will gradually decrease; at the same time, the modulation mode M opt will select the option with a larger capacity and a smaller deviation from f opt . In this way, the working parameters of the high-speed power line carrier communication can be dynamically adjusted to improve the communication quality.

[0105] The specific implementation of step S70 is as follows: First, for the micro-power wireless communication channel, indicators such as the received signal strength (RSSI), channel busy degree (CB), and bit error rate (BER) are collected to construct a channel quality evaluation matrix Q, which is expressed as:

[0106]

[0107] Among them, ω1, ω2, and ω3 are the weight factors of the corresponding indicators.

[0108] Then, the background electromagnetic noise in the micro-power wireless communication environment is collected, and an interference prediction model is established based on linear regression:

[0109]

[0110] Among them, I(t) is the interference intensity at time t, q is the prediction order, b i is the prediction coefficient, and η(t) is the prediction error. The model parameter b i is continuously updated by the recursive least squares method, and the frequency interference prediction result at the future moment can be obtained.

[0111] This information provides a basis for subsequent optimization of wireless communication parameters.

[0112] The specific implementation of step S80 is as follows: First, based on the channel quality evaluation matrix Q and the frequency interference prediction result I(t) obtained in step S70, a quadratic programming optimization model is established:

[0113] min J = x T Qx + c T x;

[0114] s.t. Ax ≤ b;

[0115] x = [P w , M w T ;

[0116] where J is the objective function, x is the decision variable vector (including the wireless communication power P w and the modulation mode M w ), Q is the quadratic coefficient matrix, c is the linear coefficient vector, and A and b are the constraint matrix and the constraint vector respectively.

[0117] By solving this quadratic programming model, the optimal wireless communication power P w and the modulation mode M w can be obtained, which meet the requirements of indicators such as signal reception strength, channel busy degree, and bit error rate, and also consider the influence of frequency interference.

[0118] The specific implementation of step S90 is as follows: First, based on the preset communication quality threshold, a handover decision function D(t) for high-speed power line carrier communication and micro-power wireless communication is established, expressed as:

[0119] D(t) = γ1Q p (t) + γ2Q w (t);

[0120]

[0121] where Q p (t) and Q w (t) are the quality indicators of the power line and wireless communication at time t respectively, γ1 and γ2 are the corresponding weight coefficients, and θ is the handover threshold.

[0122] Then, the communication quality indicators are monitored in real time, and according to the result of the decision function D(t), the working modes of high-speed power line carrier communication and micro-power wireless communication are automatically switched. This can dynamically adapt to different communication environments and ensure the stability and reliability of the overall communication quality.

[0123] ​Through the specific implementation of the above steps, a frequency adaptive method for high-speed power line carrier communication and micro-power wireless communication can be realized. By establishing a cooperative control mechanism and combining the power line channel noise characteristics and wireless channel quality indicators, the operating parameters of the two communication modes, including carrier frequency, transmission power, and modulation method, are dynamically optimized. This can not only improve the communication quality but also flexibly switch the communication mode under different environmental conditions, with strong adaptability and robustness.

[0124] Specifically, the principle of the present invention is as follows:

[0125] First, there are narrowband interference and impulse interference from various power equipment in the power line channel, which will seriously affect the signal transmission quality. By establishing a linear superposition noise model, these interference characteristics can be more accurately described. Combining the statistical characteristics of the zero-crossing time series can reflect the periodicity and randomness of the power line signal. Therefore, by combining the noise spectrum prediction result and the zero-crossing feature vector to construct a convex optimization objective function, an optimal carrier frequency can be found to achieve the best communication quality. This provides a basis for the frequency adaptation of high-speed power line carrier communication.

[0126] Second, micro-power wireless communication faces frequency resource limitations and interference problems. By real-time monitoring indicators such as signal reception strength, channel occupancy, and bit error rate, the quality status of the wireless channel can be evaluated. At the same time, environmental noise is collected, and a linear regression is used to establish an interference prediction model to predict the interference level at future moments. Based on this, by using the quadratic programming optimization method, the optimal wireless communication power and modulation method can be obtained to meet the requirements of reliable communication.

[0127] Finally, the present invention establishes a cooperative control mechanism for high-speed power line carrier communication and micro-power wireless communication. Through a switching decision function, automatic switching between the two communication modes is realized. When the power line communication quality is good, power line communication is preferred; when the power line communication quality deteriorates, it automatically switches to micro-power wireless communication. This can not only improve the overall communication quality but also has strong environmental adaptability.

[0128] In summary, the solution of the present invention makes full use of the two communication resources of power line and wireless, adopts advanced signal processing and optimization control technologies, realizes frequency adaptation and mode switching, and solves the problem of communication quality in complex environments.

[0129] The following provides an embodiment of a specific application scenario of the present invention: A certain intelligent distribution network system adopts the frequency adaptive method of high-speed power line carrier communication and micro-power wireless communication proposed by the present invention, gives full play to the advantages of the two communication technologies, and realizes environmental adaptation and communication quality optimization.

[0130] The application scenarios of the intelligent power distribution network system are as follows: The length of the power distribution line from the main transformer to each user is about 20 kilometers. The power line environment is complex and changeable, and there is strong interference from equipment such as variable-frequency motors and electric welders. At the same time, a large number of devices such as smart meters and distribution terminals are deployed in the system, with high requirements for real-time communication. To meet this application requirement, the communication solution proposed in the present invention is adopted.

[0131] In the initial system settings, the initial working parameters of high-speed power line carrier communication and micro-power wireless communication are first configured. Among them, the initial carrier frequency f0 of the power line carrier communication is set to 3 MHz, and the allowable frequency offset range Δf is ±0.5 MHz. Therefore, the value range of the carrier frequency f c is [2.5 MHz, 3.5 MHz]. The initial value P0 of the transmission power P t is set to 100 mW, and the allowable power adjustment range ΔP is ±20 mW. Therefore, the value range of P t is [80 mW, 120 mW]. The modulation mode parameter M selects four common digital modulation modes: BPSK, QPSK, 16QAM, and 64QAM.

[0132] At the same time, a cooperative control mechanism for high-speed power line carrier communication and micro-power wireless communication is established. Specifically, when the power line communication quality is good, the power line communication mode is preferentially used; when the power line communication quality deteriorates, it automatically switches to the micro-power wireless communication mode. The judgment criteria for communication quality include indicators such as signal reception strength and bit error rate, and the mode switching is realized by setting corresponding thresholds.

[0133] Next, the system starts to collect the noise interference signals in the power line channel and establish a linear superposition noise model. After multiple on-site tests, a total of 10 narrowband interference sources and 5 pulse interference sources are detected, and their parameters are shown in Table 1:

[0134] Table 1 Power line channel interference parameters

[0135] Interference type Quantity Amplitude Frequency Phase / Attenuation coefficient Narrowband interference 10 <![CDATA[A i > <![CDATA[f i > <![CDATA[φ i > Pulse interference 5 <![CDATA[B j > <![CDATA[f j > <![CDATA[α j >

[0136] Based on these parameters, the following linear superposition noise model is established:

[0137]

[0138] Among them, the background noise standard deviation σ b is measured to be 0.05.

[0139] Then, the FFT calculation is performed on the collected power line noise signals to obtain the power spectral density function S(f). Taking a certain test as an example, the spectral distribution of S(f) is shown in Table 2:

[0140] Table 2 Spectrum Distribution Table

[0141] Frequency (MHz) 3.0 3.1 3.2 3.3 3.4 s(f) 0.2 0.4 0.6 0.8 0.5

[0142] Figure 2 Shows the power spectral density distribution of the power line channel. The horizontal axis is the frequency (MHz), and the vertical axis is the power spectral density (mW / Hz). It can be seen from the figure that there is relatively large noise interference near 3.3 MHz.

[0143] Based on the linear prediction model, a noise spectrum prediction matrix is established, and the prediction order p is taken as 3. By solving with the least squares method, the prediction coefficients are obtained as a1 = 0.6, a2 = 0.3, and a3 = 0.1. Substituting into the prediction model, the predicted result of the noise power spectral density at future moments can be obtained

[0144] Meanwhile, the system also collects the zero-crossing time series of the power line signal. After statistical analysis, the mean μ of the zero-crossing time series z = 0.2 ms, and the standard deviation σ z = 0.05 ms. The first-order, second-order, and third-order autocorrelation coefficients are ρ z (1) = 0.8, ρ z (2) = 0.6, ρ z (3) = 0.4. These characteristic quantities are combined into a characteristic vector

[0145] Next, the noise spectrum prediction result and the zero-crossing characteristic vector are introduced into the convex optimization objective function:

[0146]

[0147] s.t. 2.5 MHz ≤ f ≤ 3.5 MHz;

[0148] By solving with the gradient descent method, the optimal carrier frequency f opt = 3.2 MHz is obtained.

[0149] According to f opt , combined with the preset carrier adjustment equation set, the optimal transmission power P opt = 90 mW and the modulation mode M opt = 16QAM are calculated as follows:

[0150] P opt = 100·exp(-0.2|3.2 - 3|) = 90 mW;

[0151] M opt = argmax m∈M {C m·(1 - 0.1|3.2 - 3|)} = 16QAM;

[0152] Among them, the channel capacity C corresponding to the modulation method m m are respectively: BPSK 2bit / Hz, QPSK 4bit / Hz, 16QAM 8bit / Hz, 64QAM 12bit / Hz. Since the capacity of 16QAM near 3.2 MHz is the largest and the deviation from the initial frequency of 3 MHz is also small, 16QAM is selected as the optimal modulation method.

[0153] For micro-power wireless communication, the system first collects indicators such as RSSI, CB, and BER, and constructs a channel quality evaluation matrix Q. The weight factor values are as follows:

[0154]

[0155] At the same time, collect the background noise in the wireless communication environment and establish an interference prediction model:

[0156] I(t + 1) = 0.6I(t) + 0.3I(t - 1) + 0.1I(t - 2) + η(t);

[0157] By continuously updating the model parameters through the recursive least squares method, the frequency interference prediction results at future times can be obtained.

[0158] Figure 3 Analyzed the performance of the system interference prediction model. The upper part shows the comparison between the actual interference and the predicted interference, and the lower part shows the change of the prediction error.

[0159] Based on the Q matrix and the interference prediction result I(t), the system established the following quadratic programming optimization model:

[0160]

[0161] s.t. P w ≤ 120mW, M w ∈ {BPSK, QPSK, 16QAM, 64QAM};

[0162] Solve this optimization problem to obtain the optimal power P of wireless communication w = 100mW and the modulation method M w = QPSK.

[0163] Finally, the system established a handover decision function for high-speed power line carrier communication and micro-power wireless communication:

[0164] D(t) = 0.6Q p (t) + 0.4Q w (t);

[0165]

[0166] Among them, Q p (t) and Q w (t) are the quality indicators of the power line and wireless communication at time t, respectively. The automatic switching between the two communication modes is realized by setting a threshold of 0.8.

[0167] Figure 4 It shows the communication system performance evaluation curve, including the changes in power line communication quality, wireless communication quality, and decision function over time. The horizontal dashed line represents the threshold (0.8) for communication mode switching.

[0168] During the actual operation process, the system will monitor the communication quality indicators in real time and dynamically adjust the working parameters and switch the communication mode according to the result of the decision function D(t). For example, when the power line communication quality deteriorates (such as D(t) < 0.8), the system will automatically switch to the micro-power wireless communication mode and set the transmit power to 100 mW and the modulation method to QPSK. When the power line communication quality recovers (such as D(t) ≥ 0.8), the system will switch back to the power line communication mode and set the carrier frequency to 3.2 MHz, the transmit power to 90 mW, and the modulation method to 16QAM.

[0169] Figure 5 It compares the channel capacities of different modulation methods (BPSK, QPSK, 16QAM, 64QAM) under different frequency offsets and shows the performance characteristics of various modulation methods.

[0170] Through the above adaptive adjustment and mode switching, this intelligent distribution network system can always provide reliable real-time communication in a complex and changeable power line environment. Compared with the traditional fixed parameter scheme, the frequency adaptive method provided by the present invention can not only effectively reduce interference and improve communication quality, but also has strong environmental adaptability and can perform well in various complex scenarios.

[0171] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A frequency adaptive method for dual-mode communication of HPLC and micro-power wireless, characterized in that, It includes the following steps: S10. Set the initial working parameters of high-speed power line carrier communication and micro-power wireless communication, and establish a dual-mode communication collaborative control mechanism. The initial working parameters include carrier frequency parameters, transmission power parameters, and modulation mode parameters; S20. Collect the electromagnetic interference signals in the power line channel, and establish a linear superposition noise model with time-domain weight coefficients according to the electromagnetic interference signals. The linear superposition noise model includes narrowband interference terms, impulse interference terms, and background noise terms; S30. Calculate the spectral distribution function of the linear superposition noise model based on the fast Fourier transform, establish a linear prediction matrix according to the spectral distribution function, and solve the linear prediction matrix by the least squares method to obtain the noise spectrum prediction result; S40. Collect the zero-crossing time series of the power line signal, calculate the mean value, standard deviation, and correlation coefficient of the zero-crossing time series, and construct a zero-crossing feature vector according to the mean value, standard deviation, and correlation coefficient; S50. Establish a convex optimization objective function according to the noise spectrum prediction result and the zero-crossing feature vector, and solve the convex optimization objective function by the gradient descent method to obtain the optimal carrier frequency of high-speed power line carrier communication; S60. Substitute the optimal carrier frequency into the preset carrier adjustment equation set, and solve the carrier adjustment equation set to obtain the transmission power parameters and modulation mode parameters of high-speed power line carrier communication; S70. According to the signal reception strength index, channel busyness index, and bit error rate index of the preset micro-power wireless communication channel, collect the background electromagnetic noise in the micro-power wireless communication environment, and construct a channel quality evaluation matrix; and establish a wireless channel interference prediction model based on linear regression, and update the parameters of the wireless channel interference prediction model by the recursive least squares method to obtain the frequency interference prediction result; S80. Establish a quadratic programming model according to the signal reception strength index, channel busyness index, bit error rate index, and the frequency interference prediction result, and solve the quadratic programming model to obtain the transmission power parameters and modulation mode parameters of micro-power wireless communication; S90. Establish a handover decision function for high-speed power line carrier communication and micro-power wireless communication according to the preset communication quality threshold, and implement dual-mode communication handover based on the handover decision function, and monitor the communication quality index.

2. The frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 1, wherein The specific expression of the linear superposition noise model is as follows: ; Wherein, is the total noise; is the number of narrowband interference sources; are respectively the amplitude, frequency and phase of the nth narrowband interference; is the number of impulse interference sources; are respectively the amplitude, attenuation coefficient, occurrence time and frequency of the mth impulse interference; is the background noise standard deviation; is Gaussian white noise.

3. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 2, characterized in that The specific expression of the spectral distribution function is as follows: ; The specific expression of the linear prediction matrix is as follows: ; In the formula, is the noise spectrum; is the sampling period; is the number of sampling points; is the sampling interval; is the predicted value of the spectrum; is the prediction order; are the prediction coefficients; is the prediction error.

4. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 3, characterized in that The specific expression of the zero-crossing feature vector is as follows: ; Among them, ; ; ; Wherein, is the mean value of the zero-crossing time; is the standard deviation; is the autocorrelation coefficient of order is the th zero-crossing moment; is the total number of zero-crossings; is the eigenvector.

5. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 4, characterized in that The specific expression of the convex optimization objective function is as follows: ; ; In the formula, is the optimal carrier frequency; is the weight coefficient; are the lower and upper frequency limits respectively.

6. The HPLC and micro-power wireless dual-mode communication frequency adaptive method according to claim 5, characterized in that The specific expression of the carrier adjustment equation set is as follows: ; ; Wherein, is the optimal transmission power; is the optimal modulation mode; is the adjustment coefficient; is the modulation mode corresponding channel capacity; is the initial value of the carrier frequency, is the initial value of the transmission power, is the set of modulation modes.

7. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 6, characterized in that The specific expression of the channel quality evaluation matrix is as follows: ; The specific expression of the interference prediction model is as follows: ; Wherein, is the channel quality matrix; is the signal reception strength; is the channel busyness; is the bit error rate; is the weight; is the interference strength at time is the prediction order; is the prediction coefficient; is the prediction error.

8. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 7, characterized in that, The specific expression of the quadratic programming model is as follows: ; ; ; In the formula, is the objective function; is the decision variable vector; is the quadratic term coefficient matrix; is the linear term coefficient vector; is the constraint matrix; is the constraint vector; are the wireless communication power and modulation mode respectively.

9. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 8, characterized in that, The specific expression of the handover decision function is as follows: ; ; In the formula, is the decision function; are the power line and wireless communication quality indicators respectively; is the weight coefficient; is the handover threshold; is the communication mode.

10. A frequency adaptive method for HPLC and micro-power wireless dual-mode communication according to claim 9, characterized in that, The handover threshold is set according to experience or adopts the default value of 0.69.

Images