Anti-interference method and system for ad hoc network radio station based on signal processing

Abstract

The present invention belongs to the field of communication technology, and in particular, relates to a self-organizing network radio station anti-interference method and system based on signal processing, the method comprising: recording the received signal strength, spectrum information and interference signal characteristics within a period, converting them into frequency domain signals through Fourier transform; constructing interference signal characteristic parameters, and updating interference models; calculating the optimal transmission path and frequency based on an adaptive filtering algorithm, and generating expected transmission signals; constructing transmission preprocessing equations, and obtaining expected signals for the next period; estimating transmission signal deviations, constructing deadbeat filtering equations, and calculating modulated signals with feedforward compensation; generating transmission control signals after processing, and transmitting them to radio stations. The method realizes accurate identification and effective suppression of multi-source interference.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a signal processing-based anti-interference method and system for a self-organizing network radio station. Background Art

[0002] In modern communication systems, the application of self-organizing networks (MANETs, ​​Mobile Ad-Hoc Networks) has become increasingly widespread, especially in environments without fixed infrastructure support. These networks consist of multiple mobile or fixed nodes, which can freely form a temporary network. MANET radio stations are widely used in military, disaster relief, outdoor activities and other scenarios due to their flexible deployment and powerful communication capabilities. However, the communication quality of these networks is easily affected by various interferences in complex electromagnetic environments, such as electromagnetic interference, signal reflections and interference sources.

[0003] Traditional anti-interference technologies usually rely on fixed signal processing algorithms, such as frequency hopping and spread spectrum technology, which perform well when the interference is stable and predictable. However, in a dynamically changing environment, these traditional methods often have difficulty coping with the rapidly changing interference characteristics, resulting in communication interruption or signal quality degradation. In addition, most existing solutions require expensive hardware support or additional spectrum resources, increasing the complexity and cost of the system. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] The present invention mainly aims at the above problems and proposes a signal processing-based anti-interference method and system for ad hoc network radio stations, which aims to solve the technical problems of achieving real-time interference management and signal optimization for ad hoc network radio stations in a communication environment with complex and changeable interference.

[0006] (II) Technical solution

[0007] In order to solve the above technical problems, the first aspect of the present invention provides a signal processing-based anti-interference method for a self-organizing network radio station, the method comprising the following steps:

[0008] S1: Record the received signal strength, spectrum information and interference signal characteristics in each operation cycle, and convert the time domain signal into a frequency domain signal through Fourier transform according to the received signal and the interference signal;

[0009] S2: construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model;

[0010] S3: According to the set communication requirements and the current channel conditions, based on the adaptive filtering algorithm, the optimal transmission path and frequency are calculated to generate the expected transmission signal; according to the expected transmission signal and the currently received signal, a transmission preprocessing equation is constructed, and according to the transmission preprocessing equation, the expected signal of the next cycle is obtained;

[0011] S4: constructing a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculating and obtaining the transmission signal deviation estimation of the current cycle;

[0012] S5: constructing a deadbeat filter equation according to the expected signal, the actual received signal and the interference model, and using the transmission signal deviation estimate as a feedforward compensation of the deadbeat filter equation to calculate a modulation signal for the next cycle;

[0013] S6: After the modulation signal of the next cycle is subjected to inverse Fourier transformation, it is processed based on the current channel conditions to generate a transmission control signal of the next cycle, which is finally transmitted to the radio station.

[0014] Furthermore, the frequency domain conversion method of the signal in step S1 includes: according to the estimated interference signal of the previous cycle; obtaining the frequency domain representation of the signal through fast Fourier transform operation; obtaining the current state of the channel through the set adaptive filtering equation, and obtaining the frequency domain characteristics of the signal according to the channel state and the observation period; wherein the fast Fourier transform operation equation is:

[0015] Y(f)=FFT(x(t));

[0016] Among them, x(t) is the time domain signal, that is, the original received signal; Y(f) is the frequency domain representation of the signal, and the frequency (f), amplitude and phase information are obtained.

[0017] Furthermore, the adaptive filtering equation is:

[0018]

[0019] Among them, P SX (f) is the cross-power spectral density of the signal and the interference, indicating the correlation between the signal and the interference in the frequency domain; P XX (f) is the power spectral density of the signal, which describes the strength of the signal at different frequencies; P NN (f) is the power spectral density of the noise, which describes the intensity of interference or noise at different frequencies; H(f) is the frequency domain transfer function of the filter, which is used to filter out noise and interference from the received signal.

[0020] Furthermore, the interference signal characteristic parameter extraction equation in step S2 is:

[0021] Features={Amplitude(Y(f)),Phase(Y(f)),Frequency(f)Y(f)≠0}

[0022] Among them, Amplitude(Y(f)) is the amplitude of the signal at frequency f; Phase(Y(f)) is the phase of the signal at frequency f; Frequency(f) is the frequency point under investigation;

[0023] The steps of updating the interference model according to the interference features extracted from the frequency domain signal include:

[0024] Let M(f,t) represent the interference model at frequency f and time t, which is updated by the following iterative formula:

[0025] M(f,t)=λM(f,t-1)+(1-λ)Features(f,t)

[0026] Among them, λ is the forgetting factor, which controls the model's dependence on historical data; Features(f,t) includes the interference features of the current period.

[0027] Furthermore, the transmission preprocessing equation in step S3 is:

[0028] S next =S desired -H(f)·X(f)

[0029] Among them, S desired is the frequency domain representation of the desired transmission signal; X(f) is the frequency domain representation of the currently received signal; S next The frequency domain representation of the next periodic signal to be generated;

[0030] In step S3, an adaptive algorithm is used to update the filter transfer function H(f):

[0031] H new (f) = H old (f)+μ·(D(f)-H old (f)·X(f))·X * (f)

[0032] Where D(f) is the required interference compensation; X(f) is the current input signal; μ is the adaptive step size parameter; X * (f) is the complex conjugate of X(f).

[0033] Furthermore, the transmission signal deviation estimation equation in step S4 is:

[0034] ΔS=Sprevious -H(f)·X(f) previous

[0035] Among them, S previous is the frequency domain representation of the signal strength of the previous cycle; X(f) previous is the frequency domain representation of the transmission signal of the previous cycle; ΔS is the transmission signal deviation estimate of the current cycle.

[0036] Furthermore, the deadbeat filtering equation in step S5 is:

[0037] S next =S desired -(ΔS+H(f)·N(f))

[0038] Where N(f) is the frequency domain representation of the noise signal estimated according to the interference model.

[0039] Furthermore, in step S5, the modulated signal of the current period is subjected to inverse Fourier transformation and then processed based on the current channel conditions, including using the following conversion formula:

[0040] x(t)=IFFT(S next (f))

[0041] Among them, S next (f) is the frequency domain representation of the modulated signal; x(t) is the time domain signal generated for the next cycle.

[0042] In order to solve the above technical problems, the second aspect of the present invention provides an anti-interference system for a self-organizing network radio station based on signal processing, the system comprising the following modules:

[0043] A signal recording module is used to record the received signal strength, spectrum information and interference signal characteristics in each operation cycle;

[0044] A Fourier transform module, used to convert the received signal and the interference signal from the time domain to the frequency domain through Fourier transform;

[0045] An interference analysis module is used to construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model;

[0046] The adaptive filtering module is used to calculate the optimal transmission path and frequency based on the adaptive filtering algorithm according to the set communication requirements and the current channel conditions, and generate the expected transmission signal; construct a transmission preprocessing equation according to the expected transmission signal and the currently received signal, and obtain the expected signal of the next cycle;

[0047] The deviation estimation module is used to construct a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculate and obtain the transmission signal deviation estimation of the current cycle;

[0048] A deadbeat filtering module is used to construct a deadbeat filtering equation according to the expected signal, the actual received signal and the interference model, and use the transmission signal deviation estimation as feedforward compensation to calculate the modulation signal of the next cycle;

[0049] The inverse Fourier transform module is used to process the modulation signal of the next cycle based on the current channel status after inverse Fourier transform, generate the transmission control signal of the next cycle, and finally transmit it to the radio station.

[0050] (III) Beneficial effects

[0051] Compared with the prior art, the present invention provides a signal processing-based self-organizing network radio station anti-interference method and system, which realizes accurate identification and effective suppression of multi-source interference in complex electromagnetic environments by introducing real-time signal feature extraction and dynamic interference model update, combined with an adaptive filtering algorithm. Specifically, the present invention performs frequency domain analysis on the received signal in each operation cycle, extracts interference features and updates the interference model, and then uses an adaptive filtering algorithm to dynamically adjust the filtering parameters according to the real-time environment, optimizes the transmission signal processing, thereby improving the robustness and reliability of the communication system, and does not require additional hardware support and spectrum resources, reducing system complexity and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is an overall flow chart of a signal processing-based anti-interference method for a self-organizing network radio station of the present invention;

[0053] Figure 2 The present invention is an overall framework diagram of a signal processing-based ad hoc network radio station anti-interference system.

[0054] Figure 3 A schematic diagram of Fourier transform of the present invention.

[0055] Figure 4 This is a signal spectrum diagram and power spectrum density diagram of the present invention.

[0056] Figure 5 The present invention provides a parameter adjustment diagram for updating an interference model and an adaptive filtering equation. DETAILED DESCRIPTION

[0057] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0058] like Figure 1 As shown, the present invention provides an anti-interference method for a self-organizing network radio station based on signal processing, and the specific implementation steps are as follows:

[0059] S1: Record the received signal strength, spectrum information and interference signal characteristics in each operation cycle, and convert the time domain signal into a frequency domain signal through Fourier transform according to the received signal and the interference signal;

[0060] In step S1, the intensity and spectrum information of the received signal are recorded, which helps to identify the various frequency components and corresponding intensity levels in the signal, as well as record the characteristics of the interference signal, such as the intensity and frequency distribution of the interference. Then, the Fourier transform, a mathematical tool, is used to convert the collected time domain signal (i.e., the form in which the signal changes over time) into a frequency domain signal, so that the distribution of the signal in the frequency dimension becomes clearly visible, thereby facilitating the analysis of the signal structure and the identification of the interference source.

[0061] S2: construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model;

[0062] Specifically, the system extracts key interference features from the frequency domain signal, including the frequency, amplitude, and phase information of the interference signal. These parameters provide detailed quantitative data for describing the characteristics of the interference signal, and can reveal the intensity and change pattern of the interference signal at different frequencies. Subsequently, these extracted characteristic parameters are used to update the existing interference model. Through this update, the interference model can reflect the current state and change trend of the interference in the environment in real time, so that the model can more accurately describe and predict the behavior of the interference. Such model updates are dynamic and rely on continuously input real-time data, allowing the system to adapt to changes in interference in the environment, so as to more effectively carry out subsequent signal processing and interference suppression activities.

[0063] S3: According to the set communication requirements and the current channel conditions, based on the adaptive filtering algorithm, the optimal transmission path and frequency are calculated to generate the expected transmission signal; according to the expected transmission signal and the currently received signal, a transmission preprocessing equation is constructed, and according to the transmission preprocessing equation, the expected signal of the next cycle is obtained;

[0064] First, the characteristics of the current channel are evaluated, such as the noise level, interference and channel capacity of the channel. Based on this information, the adaptive filtering algorithm dynamically adjusts the parameters of the transmission signal to ensure the optimal performance of signal transmission. Then, the desired transmission signal is generated using the calculated optimal transmission path and frequency. In addition, the system compares the currently received signal with this expected transmission signal to construct a transmission preprocessing equation. This equation is used to adjust and optimize the signal to be sent to match the channel conditions and reduce errors. Through this preprocessing equation, the system predicts and generates the expected signal for the next cycle, aiming to adjust and optimize the signal processing strategy in advance to adapt to possible channel changes and maintain the stability of communication quality.

[0065] S4: constructing a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculating and obtaining the transmission signal deviation estimation of the current cycle;

[0066] Specifically, the strength and spectrum information of the signal received in the previous cycle are first collected and analyzed, and the transmission signal of the previous cycle is also reviewed. Based on this data, a transmission signal deviation estimation equation is constructed, which can identify the various changes and losses that the signal may suffer from the process from sending to receiving. Through this equation, the system calculates and predicts the deviation of the signal transmission process in the current cycle, including changes caused by channel noise, interference or signal attenuation. This deviation estimation enables the system to make necessary adjustments and optimizations to the signal to be transmitted, thereby ensuring stable transmission and reception of signals in complex environments, reducing errors, and improving communication quality.

[0067] S5: constructing a deadbeat filter equation according to the expected signal, the actual received signal and the interference model, and using the transmission signal deviation estimate as a feedforward compensation of the deadbeat filter equation to calculate a modulation signal for the next cycle;

[0068] In this step, the expected signal (i.e., the signal expected to be sent in the ideal), the actual received signal (i.e., the signal actually received in the channel, which may be affected by various interferences and noises), and the interference model (a mathematical model that describes and predicts interference characteristics) are first used in combination. Based on this information, a deadbeat filtering equation is constructed, the purpose of which is to reduce or eliminate the deviation between the expected signal and the actual received signal. In order to further improve the control effect, the transmission signal deviation estimate (i.e., the deviation data obtained from step S4) is input into the filtering equation as a feedforward compensation. This compensation mechanism enables the system to pre-adjust the signal to be sent to adapt to the predicted channel conditions and interference status, thereby ensuring that the modulation signal of the next cycle is more stable and accurate, effectively improving the communication system's adaptability to environmental changes and overall communication efficiency.

[0069] S6: After the modulation signal of the next cycle is subjected to inverse Fourier transformation, it is processed based on the current channel conditions to generate a transmission control signal of the next cycle, which is finally transmitted to the radio station.

[0070] In step S6, the system converts the optimized and adjusted next cycle modulated signal from the frequency domain back to the time domain for actual transmission. This conversion is achieved through an inverse Fourier transform (IFFT), which restores the signal from its frequency domain representation to the time domain representation. After the conversion is completed, the system will further process and optimize this time domain signal according to the current channel conditions to ensure that the signal can adapt to possible channel changes and interference during transmission. This includes adjusting parameters such as the amplitude, phase and time alignment of the signal to optimize the transmission performance of the signal. After completing these adjustments, the system generates the final transmission control signal and sends it to the ad hoc network radio for broadcast or point-to-point communication.

[0071] The above steps describe in detail the implementation method of the present invention. Through this method, interference in a complex electromagnetic environment can be effectively resisted, ensuring the communication effect of the ad hoc network radio station under various conditions.

[0072] like Figure 3-Figure 5 As shown, in this embodiment, the frequency domain conversion method of the signal in step S1 includes: according to the estimated interference signal of the previous period; obtaining the frequency domain representation of the signal by fast Fourier transform operation; obtaining the current state of the channel by setting the adaptive filtering equation, and obtaining the frequency domain characteristics of the signal according to the channel state and the observation period; wherein the fast Fourier transform operation equation is:

[0073] Y(f)=FFT(x(t))

[0074] Among them, x(t) is the time domain signal, that is, the original received signal; Y(f) is the frequency domain representation of the signal, and the frequency (f), amplitude and phase information are obtained.

[0075] The adaptive filtering equation is:

[0076]

[0077] Among them, P SX (f) is the cross-power spectral density of the signal and the interference, indicating the correlation between the signal and the interference in the frequency domain; P XX (f) is the power spectral density of the signal, which describes the strength of the signal at different frequencies; P NN (f) is the power spectral density of the noise, which describes the intensity of interference or noise at different frequencies; H(f) is the frequency domain transfer function of the filter, which is used to filter out noise and interference from the received signal.

[0078] The interference signal characteristic parameter extraction equation in step S2 is:

[0079] Features={Amplitude(Y(f)),Phase(Y(f)),Frequency(f)Y(f)≠0}

[0080] Among them, Amplitude(Y(f)) is the amplitude of the signal at frequency f; Phase(Y(f)) is the phase of the signal at frequency f; Frequency(f) is the frequency point under investigation;

[0081] The steps of updating the interference model according to the interference features extracted from the frequency domain signal include:

[0082] Let M(f,t) represent the interference model at frequency f and time t, which is updated by the following iterative formula:

[0083] M(f,t)=λM(f,t-1)+(1-λ)Features(f,t)

[0084] Among them, λ is the forgetting factor, which controls the model's dependence on historical data; Features(f,t) includes the interference features of the current period.

[0085] The transmission preprocessing equation in step S3 is:

[0086] S next =S desired -H(f)·X(f)

[0087] Among them, S desired is the frequency domain representation of the desired transmission signal; X(f) is the frequency domain representation of the currently received signal; S next The frequency domain representation of the next periodic signal to be generated;

[0088] In step S3, an adaptive algorithm is used to update the filter transfer function H(f):

[0089] H new (f) = H old (f)+μ·(D(f)-H old (f)·X(f))·X * (f)

[0090] Where D(f) is the required interference compensation; X(f) is the current input signal; μ is the adaptive step size parameter; X * (f) is the complex conjugate of X(f).

[0091] The transmission signal deviation estimation equation in step S4 is:

[0092] ΔS=S previous -H(f)·X(f) previous

[0093] Among them, S previous is the frequency domain representation of the signal strength of the previous cycle; X(f) previous is the frequency domain representation of the transmission signal of the previous cycle; ΔS is the transmission signal deviation estimate of the current cycle.

[0094] The deadbeat filtering equation in step S5 is:

[0095] S next =S desired -(ΔS+H(f)·N(f))

[0096] Where N(f) is the frequency domain representation of the noise signal estimated according to the interference model.

[0097] In step S5, the modulated signal of the current period is subjected to inverse Fourier transformation and then processed based on the current channel conditions, including using the following conversion formula:

[0098] x(t)=IFFT(S next (f))

[0099] Among them, S next (f) is the frequency domain representation of the modulated signal; x(t) is the time domain signal generated for the next cycle.

[0100] Example 1: There is a communication system that receives the following time domain signal (x(t)) in a certain period, which includes the target signal and the interference signal. This signal will be processed through the above steps to optimize the transmission effect:

[0101] Step S1: Signal conversion

[0102] Assume there is a time domain signal x(t) = 2cos(2π10t) + 0.5cos(2π50t). Convert this time domain signal into a frequency domain signal through FFT, and we get:

[0103] When f = 10, the value of function Y(f) is 2;

[0104] When f = 50, the value of function Y(f) is 0.5;

[0105] In all other cases, the value of function Y(f) is 0.

[0106] Step S2: Construct characteristic parameters of interference signal and update interference model

[0107] Assume that the component with a frequency of 50 Hz is detected as an interference signal, and extract its characteristic parameters:

[0108] Frequency = 50HZ, that is, the signal repeats 50 times per second;

[0109] Amplitude = 0.5;

[0110] Phase = 0 degrees.

[0111] These parameters are used to update the interference model using a dynamic update strategy. For example:

[0112] M(f,t)=0.9·M(f,t-1)+0.1·Features(f,t)

[0113] If the previous model M(f, t-1) has a value of 0.45 at (f=50) Hz, then after the update: the newly extracted feature value Features(f, t) has a value of 0.5 at (f=50) Hz.

[0114] Calculate the new model value according to the formula:

[0115] M(50,t)=0.9·0.45+0.1·0.5=0.405+0.05=0.455.

[0116] Step S3: Calculate the optimal transmission path and frequency to generate the desired transmission signal

[0117] Using the adaptive filter, the optimal transmission path and frequency are calculated based on the current interference and channel status. Assume that the optimal frequency is still 10Hz because 50Hz is interfered.

[0118] Expected transmission signal: When f=10HZ, the expected signal strength is 2; for all other frequencies, the expected signal strength is 0.

[0119] Assume that the currently received signal is represented in the frequency domain as X(f)=Y(f), that is:

[0120] When f = 10 Hz, the amplitude of the received signal is 2. This means that at the 10 Hz frequency, the received signal strength is very high, indicating that the signal transmission effect of this frequency is good;

[0121] When f = 50 Hz, the amplitude of the received signal is 0.5, which indicates that there is also a signal at 50 Hz, but the amplitude is lower, which may be due to interference at this frequency or the signal itself is weak.

[0122] For all other frequencies, the amplitude of the received signal is 0. This means that no valid signals are detected at frequencies other than 10Hz and 50Hz.

[0123] According to the currently received signal X(f) and the expected transmission signal S desired (f) to adjust the signal S for the next transmission next Here, the filter H(f) is used, and its initial state is assumed to be unity gain (ie, H(f) = 1), which means that the filter does not change the amplitude of the signal at all frequencies.

[0124] The specific calculation is as follows:

[0125] At f = 10 Hz, the desired transmission signal S desired (f) = 2. The current received signal X(f) = 2. The calculation result after using the filter is 2-1*2 = 0, so at this frequency, the next transmission signal S next (f) will be 0, indicating that there is no need to transmit additional signals on this frequency because the received signal is already strong enough.

[0126] At f = 50 Hz, the desired transmission signal S desired (f) = 0 (it is hoped that no signal will be transmitted at this frequency, possibly due to interference). The current received signal X(f) = 0.5. The result is 0-1*0.5 = -0.5, indicating that at this frequency, the next transmitted signal should be negative, which means that some form of anti-phase signal needs to be sent to cancel the received interference signal.

[0127] At other frequencies, since no signal X(f) = 0 is received and no transmission S is expected desired (f) = 0, so S next (f) is also 0.

[0128] Step S4: Construct the transmission signal deviation estimation equation

[0129] Received signal strength S in the previous period previous (f):

[0130] The intensity at f = 10 Hz is 1.8;

[0131] The intensity at f = 50 Hz is 0.6;

[0132] At other frequencies it is 0.

[0133] The signal X(f) of the previous cycle previous :

[0134] The intensity at f = 10 Hz is 2.2;

[0135] The intensity at f = 50 Hz is 0.55;

[0136] At other frequencies it is 0.

[0137] Using the filter H(f) value of 1, the deviation of the above data is calculated:

[0138] ΔS=S previous -H(f)·X(f) previous

[0139] The specific calculation is as follows:

[0140] At f = 10 Hz:

[0141] ΔS(10)=1.8-1×2.2=-0.4

[0142] This means that at this frequency, the received signal strength in the previous cycle was 0.4 units lower than the signal in the previous cycle, indicating that the signal strength at this frequency may need to be increased or other adjustments may need to be made to achieve the desired transmission effect.

[0143] At f = 50 Hz:

[0144] ΔS(50)=0.6-1×0.55=0.05. The deviation here is a positive value of 0.05, which means that the signal strength of the previous cycle is slightly higher than that of the previous cycle, which means that the signal processing effect at this frequency is better or the signal environment is improved.

[0145] At other frequencies:

[0146] ΔS(other)=0-1×0=0, no deviation because there is no signal at other frequencies.

[0147] Step S5: Construct the deadbeat filter equation and calculate the modulation signal of the next cycle

[0148] S next =S desired -(ΔS+H(f)·N(f)) uses the deviation as the feedforward compensation input, which is calculated as follows:

[0149] At f = 10 Hz:

[0150] Expected transmission signal S desired (10) = 2

[0151] Deviation ΔS(10)=-0.4

[0152] Noise N(10) = 0

[0153] Calculate S next (10) = 2 - (-0.4 + 1.0) = 2 + 0.4 = 2.4

[0154] This means that in the next cycle, the transmission power of this frequency needs to be increased to compensate for the deficiency observed in the previous cycle.

[0155] At f = 50 Hz:

[0156] Expected transmission signal S desired (50) = 0 (do not transmit on this frequency)

[0157] Deviation ΔS(50)=0.05

[0158] Noise N(50) = 0

[0159] Calculate S next (50) = 0 - (0.05 + 1.0) = -0.05

[0160] This means that in the next cycle, ideally there should be a small portion of the reverse signal sent to further suppress the undesired signal at that frequency.

[0161] At other frequencies:

[0162] There is neither expected signal nor bias, so S next (Other)=0.

[0163] Step S6: Inverse Fourier transform and optimize channel conditions to generate transmission control signals

[0164] For signal S next (f) Performing an inverse Fourier transform (IFFT) converts the signal from the frequency domain back to the time domain so that actual signal transmission can be performed. The time domain signal x obtained after the inverse transform is next (t) is represented as a combination of two cosine waves of different frequencies.

[0165] Assume that the converted signal is:

[0166] x next (t)=2.4cos(2π10t)-0.05cos(2π50t)

[0167] The optimized signal is further adjusted based on the current channel conditions, and then the final transmission control signal is generated and sent to the radio station.

[0168] Through this example, we can see that adaptive filtering technology and dynamic updating of interference models can effectively help radio stations maintain clarity and stability of communication in complex and changing environments. This approach can not only adapt to changing interference conditions, but also automatically adjust processing strategies based on real-time data, thereby improving the robustness and efficiency of the entire communication system.

[0169] like Figure 2 As shown, the present invention also provides an anti-interference system for a self-organizing network radio station based on signal processing, comprising the following modules:

[0170] A signal recording module is used to record the received signal strength, spectrum information and interference signal characteristics in each operation cycle;

[0171] A Fourier transform module, used to convert the received signal and the interference signal from the time domain to the frequency domain through Fourier transform;

[0172] An interference analysis module is used to construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model;

[0173] The adaptive filtering module is used to calculate the optimal transmission path and frequency based on the adaptive filtering algorithm according to the set communication requirements and the current channel conditions, and generate the expected transmission signal; construct a transmission preprocessing equation according to the expected transmission signal and the currently received signal, and obtain the expected signal of the next cycle;

[0174] The deviation estimation module is used to construct a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculate and obtain the transmission signal deviation estimation of the current cycle;

[0175] A deadbeat filtering module is used to construct a deadbeat filtering equation according to the expected signal, the actual received signal and the interference model, and use the transmission signal deviation estimate as a feedforward compensation to calculate the modulation signal of the next cycle;

[0176] The inverse Fourier transform module is used to process the modulation signal of the next cycle based on the current channel status after inverse Fourier transform, generate the transmission control signal of the next cycle, and finally transmit it to the radio station.

[0177] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A signal processing-based anti-interference method for a self-organizing network radio station, characterized in that: The steps include: S1: Record the received signal strength, spectrum information and interference signal characteristics in each operation cycle, and convert the time domain signal into a frequency domain signal through Fourier transform according to the received signal and the interference signal; S2: construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model; S3: According to the set communication requirements and the current channel conditions, the optimal transmission path and frequency are calculated based on the adaptive filtering algorithm to generate the expected transmission signal; Constructing a transmission preprocessing equation according to the expected transmission signal and the currently received signal, and obtaining an expected signal of the next cycle according to the transmission preprocessing equation; S4: constructing a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculating and obtaining the transmission signal deviation estimation of the current cycle; S5: constructing a deadbeat filter equation according to the expected signal, the actual received signal and the interference model, and using the transmission signal deviation estimation as the feedforward compensation of the deadbeat filter equation to calculate the modulation signal of the next cycle; S6: After the modulation signal of the next cycle is subjected to inverse Fourier transformation, it is processed based on the current channel conditions to generate a transmission control signal of the next cycle, which is finally transmitted to the radio station.

2. The anti-interference method for a self-organizing network radio station based on signal processing as claimed in claim 1, characterized in that: The frequency domain conversion method of the signal in step S1 includes: according to the estimated interference signal of the previous cycle; obtaining the frequency domain representation of the signal through fast Fourier transform operation; obtaining the current state of the channel through the set adaptive filtering equation, and obtaining the frequency domain characteristics of the signal according to the channel state and the observation period; wherein the fast Fourier transform operation equation is: Y(f)=FFT(x(t)); Among them, x(t) is the time domain signal; Y(f) is the frequency domain representation of the signal.

3. The anti-interference method for a self-organizing network radio station based on signal processing as claimed in claim 2, characterized in that: The adaptive filtering equation is: Among them, P SX (f) is the cross power spectral density of the signal and the interference, indicating the correlation between the signal and the interference in the frequency domain; P XX (f) is the power spectral density of the signal, which describes the strength of the signal at different frequencies; P NN (f) is the power spectral density of the noise, which describes the intensity of interference or noise at different frequencies; H(f) is the frequency domain transfer function of the filter, which is used to filter out noise and interference from the received signal.

4. The anti-interference method for a self-organizing network radio station based on signal processing as claimed in claim 3, characterized in that: The interference signal characteristic parameter extraction equation in step S2 is: Features={Amplitude(Y(f)),Phase(Y(f)),Frequency(f)Y(f)≠0} in, Amplitude(Y(f)) is the amplitude of the signal at frequency f; Phase(Y(f)) is the phase of the signal at frequency f; Frequency(f) is the frequency point under investigation; The steps of updating the interference model according to the interference features extracted from the frequency domain signal include: Let M(f,t) represent the interference model at frequency f and time t, which is updated by the following iterative formula: M(f,t)=λM(f,t-1)+(1-λ)Features(f,t) Among them, λ is the forgetting factor, which controls the model's dependence on historical data; Features(f,t) includes the interference features of the current period.

5. The anti-interference method for a self-organizing network radio station based on signal processing as claimed in claim 4, characterized in that: The transmission preprocessing equation in step S3 is: S next =S desired -H(f)·X(f) Among them, S desired is the frequency domain representation of the desired transmission signal; X(f) is the frequency domain representation of the currently received signal; S next The frequency domain representation of the next periodic signal to be generated; In step S3, an adaptive algorithm is used to update the filter transfer function H(f): H new (f)=H old (f)+μ·(D(f)-H old (f)·X(f))·X * (f) Where D(f) is the required interference compensation; X(f) is the current input signal; μ is the adaptive step size parameter; X * (f) is the complex conjugate of X(f).

6. The anti-interference method for a self-organizing network radio station based on signal processing as claimed in claim 5, characterized in that: The transmission signal deviation estimation equation in step S4 is: ΔS=S previous -H(f)·X(f) previous Among them, S previous is the frequency domain representation of the signal strength of the previous cycle; X(f) previous is the frequency domain representation of the transmission signal of the previous cycle; ΔS is the transmission signal deviation estimate of the current cycle.

7. The signal processing-based anti-interference method for a self-organizing network radio station according to claim 6, characterized in that: The deadbeat filtering equation in step S5 is: S next =S desired -(ΔS+H(f)·N(f)) Where N(f) is the frequency domain representation of the noise signal estimated according to the interference model.

8. The signal processing-based anti-interference method for a self-organizing network radio station according to claim 7, characterized in that: In step S5, the modulated signal of the current period is subjected to inverse Fourier transformation and then processed based on the current channel conditions, including using the following conversion formula: x(t)=IFFT(S next (f)) Among them, S next (f) is the frequency domain representation of the modulated signal; x(t) is the time domain signal generated for the next cycle.

9. A signal processing-based ad hoc network radio station anti-interference system, characterized in that: Includes the following modules: A signal recording module is used to record the received signal strength, spectrum information and interference signal characteristics in each operation cycle; A Fourier transform module, used to convert the received signal and the interference signal from the time domain to the frequency domain through Fourier transform; An interference analysis module is used to construct characteristic parameters of the interference signal according to the frequency domain signal, including frequency, amplitude and phase information, and use this information to update the interference model; The adaptive filtering module is used to calculate the optimal transmission path and frequency based on the adaptive filtering algorithm according to the set communication requirements and the current channel conditions, and generate the expected transmission signal; Constructing a transmission preprocessing equation according to the expected transmission signal and the currently received signal, and obtaining an expected signal for the next cycle; The deviation estimation module is used to construct a transmission signal deviation estimation equation based on the received signal strength and spectrum information of the previous cycle and the transmission signal of the previous cycle, and calculate and obtain the transmission signal deviation estimation of the current cycle; A deadbeat filtering module is used to construct a deadbeat filtering equation according to the expected signal, the actual received signal and the interference model, and use the transmission signal deviation estimation as feedforward compensation to calculate the modulation signal of the next cycle; The inverse Fourier transform module is used to process the modulation signal of the next cycle based on the current channel status after inverse Fourier transform, generate the transmission control signal of the next cycle, and finally transmit it to the radio station.

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