Dynamic interference suppression method of multipath channel phase focusing and harmonic anti-phase cancellation

Abstract

The application discloses a dynamic interference suppression method of multipath channel phase focusing and harmonic reverse phase cancellation, relates to the technical field of interference suppression, and comprises a path characteristic extraction module, which extracts sampling data of a received signal, constructs a received signal matrix, generates a pilot signal, carries out filter processing on a noise matrix, calculates a channel frequency response matrix, and extracts path characteristics by using a peak value detection method. The method can comprehensively calculate the time delay component contribution of the channel time domain response by discretizing the channel frequency response matrix through limited sampling points and combining all frequency components in the frequency bandwidth, can enhance the dynamic response capability of the channel model by introducing time-varying environmental characteristics through a dynamic path time delay function, can improve the signal characteristic correction precision through dynamic compensation of path time delay error phase rotation, and can realize the channel description process with path grouping as the core.

Description

Technical Field

[0001] This invention relates to the field of interference suppression technology, and in particular to a dynamic interference suppression method that combines phase focusing and harmonic phase cancellation in multipath channels. Background Technology

[0002] In multipath channels, due to the influence of factors such as reflection, scattering and refraction during signal propagation, the received signal is usually composed of the superposition of signals from multiple propagation paths. Each path has independent time delay, gain and phase characteristics. This multipath effect not only leads to channel frequency selective fading and inter-symbol interference, but also seriously affects the stability and reliability of communication signals. In the field of existing channel optimization, common methods include path delay estimation, channel characteristic modeling and interference path suppression.

[0003] However, while common methods can extract the delay and gain of multipath channels, they are still insufficient in phase information processing and path phase focusing optimization, failing to effectively achieve signal phase consistency and thus preventing further improvement in the quality of the main path signal. Secondly, traditional interference path suppression methods rely more on simple static responses based on power differences, making it difficult to track and dynamically adjust the characteristics of interference paths, resulting in weak adaptability in dynamically changing multipath channel environments. At the same time, existing channel modeling and grouping techniques fail to accurately capture the dependencies between paths, ignoring the interaction of associated paths by modeling through static gain or power characteristics, thus reducing the accuracy of channel description. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation to address the shortcomings in phase information processing and path phase focusing optimization, which cannot effectively achieve signal phase consistency, resulting in the inability to further improve the signal quality of the main path. Secondly, traditional interference path suppression methods are mostly based on simple static responses to power differences, making it difficult to track and dynamically adjust the characteristics of interference paths, thus making them less adaptable to dynamically changing multipath channel environments. At the same time, existing channel modeling and grouping techniques fail to accurately capture the dependencies between paths, ignoring the interaction of associated paths by modeling through static gain or power characteristics, thus reducing the accuracy of channel description.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation, comprising:

[0008] Extract the sampled data of the received signal, construct the received signal matrix, generate the pilot signal, filter the noise matrix, calculate the channel frequency response matrix, and extract the path characteristics using the peak detection method.

[0009] Based on the path characteristics, a dependency graph between multiple paths in the channel is constructed. The path dependencies are traversed to form a multi-path grouping structure. Dynamic weight analysis is performed on each group of paths based on the path power, and the channels are combined into a channel description matrix and the channel is dynamically corrected.

[0010] Signal decomposition is performed based on the channel description matrix, and amplitude and phase information are extracted as time-frequency signals. Feature extension is performed on the time-frequency signals of the path. Paths are selected based on phase offset, and main paths and interference paths are selected based on path weights. The amplitude gain of the interference path and the phase component of the interference path signal are extracted to form a set of interference path characteristics. An anti-cancellation signal is generated and superimposed with the main path signal to form an optimized signal.

[0011] Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, define the control factor and adjust the strength of the anti-cancellation signal of the interference path and dynamically adjust the anti-cancellation signal channel matrix;

[0012] Perform residual power monitoring and save key parameters recorded during the dynamic feedback optimization process for cloud backup.

[0013] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, the steps of constructing a received signal matrix, generating pilot signals, filtering the noise matrix, calculating the channel frequency response matrix, and extracting path characteristics using peak detection include:

[0014] The received signal sampling data is extracted using the multi-channel antenna array to construct the received signal matrix;

[0015] Pilot signals are obtained using the Zadoff-Chu sequence generation rules based on the full-length bandwidth, number of sampling points, and sampling interval in the frequency domain.

[0016] The pilot transmission signal matrix is ​​composed based on the pilot signals. The pilot transmission signals are collected by the receiving signal matrix and the pilot signal matrix is ​​composed. The noise matrix is ​​filtered by a low-pass filter, and the channel frequency response matrix is ​​calculated.

[0017] A Fourier transform is performed on the channel frequency response matrix in the time domain, where the Fourier transform integral is discretized using a finite number of sampling points.

[0018] Peak detection method is used to extract the main path delay, path gain and path contribution power of each path.

[0019] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, the step of forming a multipath grouping structure, performing dynamic weight analysis on each group of paths according to the path power magnitude, combining them into a channel description matrix, and dynamically correcting the channel includes:

[0020] Construct a dependency graph between channel multipaths to calculate the dependency relationship between paths. Use the sum of the mean and standard deviation of the delay difference between different paths as the delay threshold. If the delay difference between different path pairs is less than or equal to the delay threshold, it is determined that there is a dependency relationship, and the path pair is marked as having a dependency relationship.

[0021] The path pairs with strong dependencies are traversed to form a multi-path grouping structure. Dynamic weight analysis is performed on the path grouping structure, and priority is assigned according to the path power.

[0022] The channel description matrix is ​​synthesized by grouping along the path, and the path gain is adjusted using real-time observation data to dynamically correct the channel modeling.

[0023] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, the step of performing feature expansion on the time-frequency signal of the path and filtering the path based on the phase offset includes:

[0024] The channel description matrix is ​​decomposed into signals to extract amplitude and phase information as time-frequency signals;

[0025] The time-frequency signal of the path is extended by the ASC mechanism, and the signal is processed by ASC convolution for each path;

[0026] The contribution weight of path features is calculated based on feature extension, and phase offset analysis is performed. The sum of the mean and standard deviation of historical phase offsets is used as the offset threshold to filter paths with phase offsets less than the offset threshold.

[0027] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the set of interference path characteristics is used to generate an anti-cancellation signal and superimpose it with the main path signal to form an optimized signal, including:

[0028] Based on historical experience values, paths with a large phase offset from the main path are selected as a set of interference paths.

[0029] For each interference path, extract the interference path amplitude gain and the phase component of the interference path signal to form an interference path characteristic set;

[0030] By extracting the characteristics of the interference path from the interference path feature set, the anti-cancellation signal is generated and superimposed with the main path signal to form an optimized signal.

[0031] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the calculation of the residual between the current optimized channel matrix and the reference matrix of the main path signal includes,

[0032] Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, and calculate the residual power based on the residual signal amplitude. Sum the power of all frequency components to obtain the total residual power over the entire frequency domain.

[0033] As a preferred embodiment of the dynamic interference suppression method of multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the definition of a control factor, and the adjustment of the anti-cancellation signal of the interference path using the control factor to obtain the anti-cancellation signal channel matrix, includes,

[0034] The control factor is defined based on the residual power. The anti-cancellation signal of the interference path is adjusted using the control factor. The dynamically adjusted anti-cancellation signal is then superimposed on the original channel matrix to obtain the anti-cancellation signal channel matrix.

[0035] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the residual power monitoring includes,

[0036] The total residual power is calculated based on the inverse cancellation signal channel matrix, and the residual power is compared with the residual power before the initial optimization. The residual power attenuation rate is calculated, and residual power monitoring is performed.

[0037] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the key parameters recorded during the dynamic feedback optimization process are backed up in the cloud, including,

[0038] The anti-cancellation signal channel matrix is ​​saved as a data file, along with key parameters recorded during the dynamic feedback optimization process. These key parameters include the main path set, the interference path set, and the residual power attenuation rate. The resulting file is then backed up to the cloud via wireless transmission technology.

[0039] As a preferred embodiment of the dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation described in this invention, wherein: the extraction of sampled data of the received signal and the construction of the received signal matrix include,

[0040] By extending the MIMO antenna system, channel state information is extracted, and the sampled data of the received signal is extracted using the multi-channel antenna array to construct the received signal matrix.

[0041] The beneficial effects of this invention are as follows: By discretizing the channel frequency response matrix through a finite number of sampling points and combining all frequency components within the frequency bandwidth, the contribution of the time delay component to the channel time domain response can be comprehensively calculated. The dynamic response capability of the channel model is enhanced by introducing time-varying environmental characteristics through a dynamic path delay function. The accuracy of signal characteristic correction is improved by dynamically compensating for the phase rotation of path delay error. The channel description process with path grouping as the core, through feature expansion weighting and dynamic feedback path correction, improves the long-term adaptability of the channel model under dynamic environments. By optimizing the signal matrix based on the enhancement of the main path signal and the suppression of the interference path signal, it can dynamically adapt to environmental changes and reduce the impact of transient changes in channel characteristics on system performance. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the dynamic interference suppression method of multipath channel phase focusing and harmonic phase cancellation in Example 1.

[0044] Figure 2 This is a flowchart illustrating the dynamic interference suppression method of multipath channel phase focusing and harmonic phase cancellation in Example 1. Detailed Implementation

[0045] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0046] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0048] Example 1, referring to Figures 1 to 2This is the first embodiment of the present invention, which provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation, including the following steps:

[0049] Extract the sampled data of the received signal, construct the received signal matrix, generate the pilot signal, filter the noise matrix, calculate the channel frequency response matrix, and extract the path characteristics using the peak detection method.

[0050] Preferably, the sampled data of the received signal is extracted to construct a received signal matrix, including:

[0051] By extending the MIMO (Multiple-Input Multiple-Output) antenna system, channel state information is extracted (and the basic parameters required for path modeling are initialized). The sampled data of the received signal is extracted using the multiple channels of the antenna array, and a received signal matrix is ​​constructed, represented as follows:

[0052] Y(t,f)=H'(t,f)·X(t,f)+N(t,f);

[0053] Where Y(t,f) represents the received signal matrix with dimension N. r ×T,N r H'(t,f) represents the number of receiving antennas, T represents the number of time-domain sampling points, and H′(t,f) represents the initial channel matrix with dimension N. r ×N t N t Let N represent the number of transmit antennas, and let X(t,f) represent the transmit signal matrix with dimension N. r ×T, N(t,f) represents the noise matrix with dimension N. r ×T.

[0054] Furthermore, a received signal matrix is ​​constructed, pilot signals are generated, the noise matrix is ​​filtered, the channel frequency response matrix is ​​calculated, and path characteristics are extracted using peak detection, including...

[0055] The received signal sampling data is extracted using the multi-channel antenna array to construct the received signal matrix;

[0056] Based on the full-length bandwidth, number of sampling points, and sampling interval in the frequency domain, the pilot signal is obtained using the Zadoff-Chu sequence generation rule, and is represented as follows:

[0057]

[0058] Among them, X pilot(n) represents the pilot signal value at the nth discrete sampling point, v represents the imaginary unit, pn(n+1) represents adding a quadratic phase characteristic to the signal to ensure that the generated signal has a smooth and uniform spectrum in the frequency domain, where p represents the rank cardinality, usually called the generation cardinality of the Zadoff-Chu sequence, n and n+1 represent the discrete sampling point indices in the nth and n+1th Zadoff-Chu sequences, respectively, N polot This represents the total length of the pilot signal, i.e., the total number of sample points in the sequence;

[0059] Based on the pilot signals, a pilot transmission signal matrix is ​​constructed. The pilot transmission signals are acquired through the receiving signal matrix and then assembled into a pilot signal matrix. A low-pass filter is used to filter the noise matrix, and the channel frequency response matrix is ​​calculated, expressed as:

[0060] Y pilot (t,f)=H'(t,f)·X pilot (t,f)+N(t,f);

[0061]

[0062] Where H(t,f) represents the channel frequency response matrix, Y pilot (t,f) represents the pilot signal matrix, Y′ pilot (t,f) represents the pilot signal matrix after filtering. The Moore-Penrose pseudo-inverse matrix representing the pilot transmit signal matrix is ​​used to calculate the channel response, X. pilot (t,f) represents the pilot transmit signal matrix. This represents the conjugate transpose of the pilot transmit signal matrix;

[0063] The channel frequency response matrix is ​​subjected to a Fourier transform in the time domain, where the Fourier transform integral is discretized using a finite number of sampling points, as shown below:

[0064]

[0065] Where H(t,τ) represents the time-delay domain channel response, represents the channel gain associated with the delay τ, and B represents the frequency bandwidth. This indicates that all frequency components of the signal within the frequency bandwidth B are considered, used to calculate the combined contribution of the time delay component to the signal, τ. k τ represents the discrete point of time delay. k = k·Δτ, where k represents the index and Δτ represents the time delay resolution. f m This represents the m-th frequency sampling point. N f This represents the total number of sampling points, and Δf represents the sampling interval.

[0066] The peak detection method is used to extract the main path delay, path gain, and path contribution power of each path, which are expressed as follows:

[0067] τ i =argmax|H(t,τ)|;

[0068] h i =H(t,τ) i );

[0069] P i =|h i | 2 ;

[0070] Where, τ i The main path delay is represented by H(t,τ). k The main peaks are selected from the frequency domain energy distribution of the signal. These peaks correspond to the main path delay of the signal, h. i The complex gain of the path, containing signal amplitude and phase information (e.g., gain at the scattering center or along the reflection path), |h i | 2 P represents the squared modulus of the complex gain, i.e., the energy contribution of the path signal. i Let N represent path power, i represent the path index number, and the total number of valid paths be N. p .

[0071] By using the Zadoff-Chu sequence to generate pilot signals, which has unique quadratic phase characteristics, the spectrum of the pilot signal is kept smooth and uniformly distributed in the frequency domain, making the channel frequency response calculation more accurate. At the same time, it reduces the impact of spectral non-uniformity on measurement error in complex channel scenarios, and significantly improves the stability and resolution of the channel frequency response matrix.

[0072] By utilizing the synchronous transmission characteristics of the pilot transmit signal matrix and the receive signal matrix, and processing the noise matrix through a low-pass filter, high-frequency noise is eliminated to the maximum extent in complex electromagnetic environments. By utilizing the Moore-Penrose pseudo-inverse of the pilot matrix and combining it with the conjugate transpose of the pilot signal, the stability of solving the channel frequency response matrix is ​​significantly improved.

[0073] Discretizing the channel frequency response matrix using a finite number of sampling points, and combining all frequency components within the frequency bandwidth, allows for the comprehensive calculation of the time delay component contribution of the channel's time domain response. Discretization simplifies computational complexity, enabling rapid extraction of time delay domain information at high resolution. By effectively identifying the peak points of the main energy distribution paths of the channel, the energy contribution of each path to signal transmission can be clearly mapped. Furthermore, in multipath interference environments, the main propagation paths are highlighted, significantly improving the efficiency and accuracy of path classification.

[0074] Example 2, refer to Figures 1 to 2 This is the second embodiment of the present invention, which provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation, including the following steps:

[0075] Based on the path characteristics, a dependency graph between multiple paths in the channel is constructed. The path dependencies are traversed to form a multi-path grouping structure. Dynamic weight analysis is performed on each group of paths based on the path power, and the channels are combined into a channel description matrix and the channel is dynamically corrected.

[0076] Preferably, a multi-path grouping structure is formed, and dynamic weight analysis is performed on each path group according to the path power, combining them into a channel description matrix and dynamically correcting the channel, including:

[0077] A dependency graph between channel multipaths is constructed by depth-first search (DFS), where the delay of each main path is regarded as a node in the graph, and the dependency between paths is calculated. The sum of the mean and standard deviation of the delay difference between different paths based on historical experience is used as the delay threshold. If the delay difference between different path pairs is less than or equal to the delay threshold, it is determined that there is a dependency, and the path pair is marked as having a dependency.

[0078] The path pairs with strong dependencies are traversed to form a multi-path grouping structure. Dynamic weight analysis is performed on the path grouping structure, and priority is assigned according to the path power.

[0079] The channel description matrix is ​​synthesized by grouping along the path, and the path gain is adjusted using real-time observation data of the received signal to dynamically correct the channel model, as shown below:

[0080]

[0081] Where H(t,f) represents the channel description matrix, and G ι This indicates that each strongly correlated path group was obtained through the path dependency graph, where ι represents the group number, L represents the total number of groups, and i∈G ι Indicates in group G ι For all paths (nodes), calculate the signal contribution of each path in turn. The phase rotation effect corresponding to the path delay error is used to correlate the dynamic delay of the path with the signal frequency f and τ. i (t) represents the dynamic function of path delay, reflecting the influence of time-varying environment on signal propagation.

[0082] By dynamically adjusting the adaptability of the dependency formation process through the setting of path delay threshold, the path grouping dependency can adapt to the dynamic characteristics of delay changes in the time-varying channel environment. By traversing the path dependency graph to generate a multi-path grouping structure, the accuracy of path grouping is improved. After the path is grouped, its position information can reflect the local correlation of the main transmission paths in the channel, while avoiding interference from weakly dependent paths, and enhancing the sensitivity of the channel model to the main signal channels.

[0083] Optimizing priority allocation within path groups through dynamic weighting analysis of path power not only reflects the signal strength contributed by each path but also directly introduces a measure of the importance of power to the channel description, thereby increasing the contribution of the main path groups to the channel description matrix. By optimizing the dynamic correction effect of the channel description matrix based on path grouping, the gain of the path group can be dynamically adjusted through real-time observation data.

[0084] By introducing time-varying environmental characteristics through a dynamic path delay function, the dynamic response capability of the channel model is enhanced. Dynamic compensation for path delay error phase rotation improves the accuracy of signal characteristic correction. The channel description process, with path grouping as its core, combined with dynamic correction of path gain and delay function, ultimately forms a time-varying channel description matrix. This not only improves the dynamic adaptation function of the channel model but also enhances the robustness of path characteristic extraction in complex environments, enabling the scheme to effectively cope with multipath transmission and rapidly changing delay characteristics in actual wireless communication scenarios.

[0085] Example 3, referring to Figures 1 to 2 This is the third embodiment of the present invention, which provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation, including the following steps:

[0086] Signal decomposition is performed based on the channel description matrix, and amplitude and phase information are extracted as time-frequency signals. Feature extension is performed on the time-frequency signals of the path. Paths are selected based on phase offset, and main paths and interference paths are selected based on path weights. The amplitude gain of the interference path and the phase component of the interference path signal are extracted to form a set of interference path characteristics. An anti-cancellation signal is generated and superimposed with the main path signal to form an optimized signal.

[0087] Preferably, the time-frequency signal of the path is feature-extended, and the path is filtered based on the phase offset, including:

[0088] The channel description matrix is ​​decomposed into a signal, and amplitude and phase information are extracted as time-frequency signals. Components with small phase shifts and significant contributions to the main path are amplified. Based on this, the signal characteristics of the main path are optimized, and the interference effects of secondary paths are suppressed. This can be expressed as:

[0089] A i (t)=|h i |;

[0090] Φ i =arg(h i );

[0091] ω i =2πf i ;

[0092] x i (t)=A i (t)cos(ω i t+Φ i );

[0093] Among them, A i (t) represents the amplitude of the path on path i, Φ i ω represents the phase component of the signal along path i. i This represents the signal frequency component of the i-th path, originating from the center frequency component f of the signal. i f i x represents the center frequency component of path i. i (t) represents the time-domain signal component of path i;

[0094] The time-frequency signal of the path is extended using the ASC mechanism, and each path is processed using ASC convolution, as shown below:

[0095]

[0096] Among them, F asc (i) represents the high-resolution feature value of path i, which serves as the feature extension of path i. Ka represents the total number of convolution kernel indices ka. The signal characteristics of path i are decomposed into g by ASC convolution. ka (f) and h ka (Φ), g ka (f) represents the frequency domain kernel weight, which handles amplitude characteristics, h ka (Φ) represents the phase kernel weight, which processes the path phase. This indicates that path i is at frequency point f. k The power, N represents the total number of paths, Φ j The phase component of the signal at path j is represented by δ, which represents the normalization factor.

[0097] The path contribution is calculated based on feature extension, and the phase offset of the path pair is determined, expressed as:

[0098]

[0099] ΔΦ ij =|Φ i -Φ j |;

[0100] Among them, W i F represents the weight contribution of path i, used to indicate the main contribution of the path. asc (j) represents the high-resolution feature value of path j, ΔΦ ij This represents the phase offset between path i and path j;

[0101] The path with a phase offset less than the sum of the mean and standard deviation of historical phase offsets is selected as the offset threshold.

[0102] By extracting amplitude and phase information in signal decomposition, signal features are optimized and interference effects are suppressed. The decomposition of amplitude and phase information directly enhances the important signal characteristics of the main path, where amplitude characterizes the signal strength of the path and phase captures the time synchronization and propagation characteristics of the signal.

[0103] By using dual-kernel weighting with ASC convolutional feature expansion, high-resolution feature extraction of the path is enhanced. The path signal is decomposed into two key dimensions, frequency power and phase characteristics, by utilizing the analytical effect of the convolutional kernel. The high-resolution feature values ​​of the path after feature expansion map the actual contribution of the path in complex channel scenarios.

[0104] By using a convolution kernel normalization factor to ensure the stability and anti-interference of feature expansion, the negative impact of inter-path phase offset on feature expansion results is effectively suppressed, and the dynamic compression characteristics of phase kernel weights on high-offset paths are enhanced. The path separation effect is optimized by setting the threshold of phase offset analysis, and signal distortion is reduced by optimizing the combination of amplitude and phase signal characteristics. Amplitude optimization in signal decomposition enhances the signal strength of the main path, while phase expansion combined with the ASC convolution mechanism optimizes phase consistency and low-interference characteristics, thereby reducing signal distortion effects in multipath channels. The quality of the channel description matrix is ​​improved by combining path weights and feature expansion.

[0105] By combining signal decomposition, amplitude and phase characteristic optimization, and ASC mechanism, and through feature expansion weight screening and dynamic feedback path correction, a complete system for phase focusing and path contribution optimization in multipath channels is formed. This not only reduces the interference of multipath transmission on the signal, but also improves the long-term adaptability of the channel model in dynamic environments, making the model robust and highly accurate in complex scenarios.

[0106] Example 4, refer to Figures 1 to 2 This is the fourth embodiment of the present invention, which provides a dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation, including the following steps:

[0107] Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, define the control factor, use the control factor to adjust the anti-cancellation signal of the interference path, obtain the anti-cancellation signal channel matrix, perform residual power monitoring, and save the key parameters recorded during the dynamic feedback optimization process for cloud backup.

[0108] Preferably, a set of interference path characteristics is formed, an anti-cancellation signal is generated and superimposed on the main path signal to form an optimized signal, including:

[0109] The path weights are recalculated for the set of paths with consistent phase, and the main path is selected based on historical experience values. Simultaneously, paths with significant phase offsets from the main paths are selected as the set of interference paths, as follows:

[0110] ξ gr ={i|ΔΦ i ,z>θ z};

[0111] Where, ξ gr Denotes the set of interference paths, ΔΦ i,z θ represents the phase offset between path i and the main path z. z The phase offset threshold representing the interference path can be set to π / 3 based on historical experience.

[0112] For each interference path, extract the interference path amplitude gain and the phase component of the interference path signal to form an interference path characteristic set;

[0113] By extracting the characteristics of the interference path from the interference path feature set, generating a cancellation signal and superimposing it with the main path signal to form an optimized signal, represented as:

[0114]

[0115] Among them, H inv,z (t) represents the cancellation signal of the interference path z, h gr,z (t) represents the complex gain of the interference path z, Φ z H represents the phase component of the interference path z. yh (t,f) represents the optimized channel matrix.

[0116] By selecting paths with large phase offset from the main path to form a set of interference paths, the signal interference separation effect is improved. A mechanism for dynamic adjustment of the phase offset threshold is adopted to make the interference path identification process adapt to dynamic channel scenarios and environmental changes, reduce the loss of important paths due to misjudgment, and strengthen the independence of interference path signals from the main signal.

[0117] The amplitude gain and phase components of the interference path are extracted and a characteristic set is constructed to provide a high-precision data foundation for the subsequent generation of the anti-cancellation signal. The anti-cancellation signal is generated based on the complex gain and phase components of the interference path. Relying on the data in the interference path characteristic set, the anti-cancellation signal can completely counteract the interference path signal in terms of amplitude and phase, effectively improving the dynamic adjustment capability of the channel matrix and ensuring that the interference signal suppression process is not affected by the complex characteristics of the secondary path.

[0118] By optimizing the signal matrix based on main path signal enhancement and interference path signal suppression, the system can dynamically adapt to environmental changes and reduce the impact of transient changes in channel characteristics on system performance. Through the comprehensive application of main path screening, interference path characteristic extraction, anti-cancellation signal generation, and optimized signal matrix, the system ultimately achieves multi-angle collaboration between main path enhancement and interference path suppression, reduces the impact of interference paths on the performance of the main path, significantly improves the dynamic adaptation performance of the channel description matrix, and optimizes the stability and robustness of the overall communication link.

[0119] Furthermore, the residual between the current optimized channel matrix and the reference matrix of the main path signal is calculated, including:

[0120] Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, and calculate the residual power based on the residual signal amplitude. Summarize the power of all frequency components to obtain the total residual power over the entire frequency domain, expressed as:

[0121]

[0122] P cc (t,f)=|H yh (t,f)-H zh (t,f)| 2 ;

[0123]

[0124] Among them, P cc (t,f) represents the residual power, H zh (t,f) represents the reference matrix of the main path signal, P ch (t) represents the total residual power, F represents the set of all frequency points, and ξ az h represents the set of main paths qτ represents the complex gain of the main path q. q (t) represents the delay of the main path q, H yh (t,f)-H zh (t,f) represents the residual signal matrix.

[0125] By constructing the residual signal matrix, the matching degree between the current optimized channel matrix and the main path characteristics is measured in real time, improving the enhancement accuracy of the main path. By calculating the residual signal amplitude, the standard for interference path elimination effect is refined, improving the accuracy of interference path suppression. By combining the time delay characteristics and gain of the main path set, the generation of the reference matrix is ​​optimized, improving the dynamic adaptation capability of the optimized signal matrix. Through the construction of the main path reference matrix, real-time monitoring of the residual signal matrix, and dynamic adjustment of residual power, a comprehensive feedback optimization mechanism is established. In complex channel scenarios, this significantly improves the stability and anti-interference capability of the communication system performance, while reducing the impact of residual power on channel transmission quality.

[0126] Furthermore, a control factor is defined, and the cancellation signal of the interference path is adjusted using the control factor to obtain the cancellation signal channel matrix, including:

[0127] Based on the residual power, a control factor is defined. The anti-cancellation signal of the interference path is adjusted using the control factor. The dynamically adjusted anti-cancellation signal is then superimposed onto the original channel matrix to obtain the anti-cancellation signal channel matrix, which is expressed as:

[0128]

[0129] |H fx,z (t)|=γ(t·|H inv,z (t)|·P ch (t);

[0130]

[0131] Where γ(t) represents the dynamic control factor, P zj H represents the initial residual power at the start of optimization. fx,z (t) indicates that H fxv,z (t) represents the adjusted interference path z cancels out the signal strength, H dtf (t,f) represents the dynamically adjusted anti-cancellation signal channel matrix.

[0132] By dynamically monitoring residual power, a control factor is constructed to optimize the accuracy of interference path suppression. By superimposing dynamic cancellation signals, the channel characteristics are corrected in real time, enabling the signal optimization to have dynamic response performance. By connecting the control factor with residual power in real time, a closed-loop optimization is constructed, so that the interference path intensity adjustment and channel optimization can achieve an adaptive balance.

[0133] Furthermore, residual power monitoring is performed, including...

[0134] Based on the dynamically adjusted decancellation signal channel matrix, the total residual power is finally calculated, and compared with the residual power before the initial optimization. The residual power attenuation rate is calculated, and residual power monitoring is performed, as expressed as:

[0135]

[0136] Among them, R sj P represents the residual power attenuation rate. cy This represents the final residual power based on the dynamically adjusted decancellation signal channel matrix.

[0137] By calculating the total residual power through the dynamically adjusted anti-cancellation signal channel matrix, the interference suppression effect can be quantitatively evaluated in real time, improving the system optimization accuracy. The global efficiency of the optimization process is described by the residual power attenuation rate, and the performance improvement of different channel correction processes is clarified. This not only demonstrates the efficiency of the optimization system in handling complex interference paths, but also quantitatively evaluates the performance improvement effect of the feedback optimization process. It clarifies the interference signal suppression effect after the implementation of the algorithm at different stages, thus providing core evaluation indicators for the global adjustment of the optimization strategy.

[0138] Furthermore, key parameters recorded during the dynamic feedback optimization process are backed up to the cloud, including...

[0139] The final dynamically adjusted anti-cancellation signal channel matrix is ​​saved as a data file, along with key parameters recorded during the dynamic feedback optimization process, including the main path set, interference path set, and residual power attenuation rate. The resulting file is then backed up to the cloud via wireless transmission technology.

[0140] In summary, this invention discretizes the channel frequency response matrix using a finite number of sampling points and combines all frequency components within the frequency bandwidth to comprehensively calculate the contribution of the time delay component to the channel's time-domain response. It enhances the dynamic response capability of the channel model by introducing time-varying environmental characteristics through a dynamic path delay function, improves the accuracy of signal characteristic correction through dynamic compensation of path delay error phase rotation, and enhances the long-term adaptability of the channel model under dynamic environments through feature expansion weighting and dynamic feedback path correction. Furthermore, by optimizing the signal matrix based on main path signal enhancement and interference path signal suppression, it can dynamically adapt to environmental changes and reduce the impact of transient channel characteristic changes on system performance.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A dynamic interference suppression method based on phase focusing and harmonic phase cancellation in multipath channels, characterized in that, include: S1, extract the sampled data of the received signal, construct the received signal matrix, generate the pilot signal, filter the noise matrix, calculate the channel frequency response matrix, and extract the path characteristics using the peak detection method; S2, construct a dependency graph between multiple paths of the channel based on path characteristics, traverse the path dependency relationship to form a multi-path grouping structure, perform dynamic weight analysis on each group of paths based on the path power, combine them into a channel description matrix and dynamically correct the channel. S3. Based on the channel description matrix, the signal is decomposed to extract the amplitude and phase information as time-frequency signals. The time-frequency signals of the path are extended in terms of features. The path is selected according to the phase offset. The main path and the interference path are selected according to the path weight. The amplitude gain of the interference path and the phase component of the interference path signal are extracted to form the interference path characteristic set. The anti-cancellation signal is generated and superimposed with the main path signal to form the optimized signal. S4. Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, define the control factor, and use the control factor to adjust the anti-cancellation signal of the interference path to obtain the anti-cancellation signal channel matrix. S5 performs residual power monitoring and saves key parameters recorded during the dynamic feedback optimization process for cloud backup.

2. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 1, characterized in that: In step S1, the received signal matrix is ​​constructed, pilot signals are generated, the noise matrix is ​​filtered, the channel frequency response matrix is ​​calculated, and path characteristics are extracted using the peak detection method. Specifically, this includes the following sub-steps: S101. Extract the sampled data of the received signal using the multi-channel antenna array and construct the received signal matrix; S102. Based on the full-length bandwidth, number of sampling points, and sampling interval in the frequency domain, the pilot signal is obtained using the Zadoff-Chu sequence generation rule; S103. Based on the pilot signals, a pilot transmission signal matrix is ​​formed. The pilot transmission signals are collected through the receiving signal matrix and formed into a pilot signal matrix. The noise matrix is ​​filtered through a low-pass filter, and the channel frequency response matrix is ​​calculated, as follows: in, Represents the channel frequency response matrix. Represents the pilot signal matrix. This represents the pilot signal matrix after filtering. The Moore-Penrose pseudo-inverse matrix, representing the pilot transmit signal matrix, is used to calculate the channel response. This represents the pilot transmit signal matrix. This represents the conjugate transpose of the pilot transmit signal matrix; S104. Perform a Fourier transform on the channel frequency response matrix in the time domain, wherein the Fourier transform integral is discretized through a finite number of sampling points; S105. Use peak detection method to extract the main path delay, path gain and path contribution power of each path.

3. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 2, characterized in that: In step S2, a multi-path grouping structure is formed. Dynamic weight analysis is performed on each group of paths based on their power, and the groups are combined to form a channel description matrix and the channel is dynamically corrected. This specifically includes the following sub-steps: S201. Construct a dependency graph between channel multipaths, calculate the dependency relationship between paths, and use the sum of the mean and standard deviation of the delay difference between different paths as the delay threshold. If the delay difference between different path pairs is less than or equal to the delay threshold, it is determined that there is a dependency relationship, and the path pair is marked as having a dependency relationship. S202. Traverse the path pairs with strong dependencies to form a multi-path grouping structure. Perform dynamic weight analysis on the path grouping structure and assign priorities according to the path power. S203. Synthesize the channel description matrix by path grouping, adjust the path gain using real-time observation data, and dynamically correct the channel, as shown below: in, Represents the channel description matrix. This indicates that each strongly related path group was obtained through the path dependency graph. L represents the group number, and L represents the total number of groups. Indicates grouping For each path in the dataset, calculate the signal contribution of each path sequentially. The phase rotation effect corresponding to the delay error of the path, the dynamic delay of the associated path, and the signal frequency. A dynamic function representing path delay, reflecting the impact of time-varying environments on signal propagation. The complex gain of the path is represented by a number that includes signal amplitude and phase information, where v represents the imaginary unit.

4. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 3, characterized in that: In step S3, the time-frequency signal of the path is feature-extended, and the path is filtered based on the phase offset. This specifically includes the following sub-steps: S301. Perform signal decomposition on the channel description matrix, extract amplitude and phase information as time-frequency signals, represented as: in, This represents the amplitude of the path on path i. This represents the phase component of the signal along path i. This represents the signal frequency component of the i-th path, originating from the center frequency component of the signal. , This represents the center frequency component of path i. Represents the time-domain signal component of path i; S302. Feature extension of the time-frequency signal of the path is performed using the ASC mechanism. Signal processing is performed on each path using ASC convolution, as shown below: in, Represents the high-resolution feature value of path i, as a feature extension of path i. Represents the kernel index The total number is decomposed into the signal characteristics of the ASC convolution path i. as well as , This represents the frequency domain kernel weights and handles amplitude characteristics. Represents the phase kernel weights, used to process path phase. This indicates that path i is at the frequency point. The power, where N represents the total number of paths. This represents the phase component of the signal along path j. Indicates the normalization factor; S303. Calculate the contribution weight of path features based on feature extension, and perform phase offset analysis. Use the sum of the mean and standard deviation of historical phase offsets as the offset threshold to filter paths with phase offsets less than the offset threshold.

5. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 4, characterized in that: In step S3, the interference path characteristic set is formed, an anti-cancellation signal is generated and superimposed with the main path signal to form an optimized signal, which specifically includes the following sub-steps: S311. Based on historical experience values, select paths with a large phase offset from the main path as a set of interference paths; S312. Extract the interference path amplitude gain and the phase component of the interference path signal for each interference path to form an interference path characteristic set; S313. By extracting the characteristics of the interference path from the interference path characteristic set, generating a cancellation signal and superimposing it with the main path signal to form an optimized signal, represented as: in, This represents the cancellation signal of the interference path z. The complex gain of the interference path z is represented. This represents the phase component of the interference path z. This represents the optimized channel matrix. This represents the set of interference paths.

6. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 5, characterized in that: In step S4, the specific process for calculating the residual between the current optimized channel matrix and the reference matrix of the main path signal is as follows: Calculate the residual between the current optimized channel matrix and the reference matrix of the main path signal, and calculate the residual power based on the residual signal amplitude. Sum the power of all frequency components to obtain the total residual power over the entire frequency domain.

7. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 6, characterized in that: In step S4, the specific process of defining a control factor and using the control factor to adjust the anti-cancellation signal of the interference path to obtain the anti-cancellation signal channel matrix is ​​as follows: The control factor is defined based on the residual power. The anti-cancellation signal of the interference path is adjusted using the control factor. The dynamically adjusted anti-cancellation signal is then superimposed on the original channel matrix to obtain the anti-cancellation signal channel matrix.

8. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 7, characterized in that: In step S5, the specific process for residual power monitoring is as follows: The total residual power is calculated based on the inverse cancellation signal channel matrix, and the residual power is compared with the residual power before the initial optimization. The residual power attenuation rate is calculated, and residual power monitoring is performed.

9. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 8, characterized in that: In step S5, the specific process for saving the key parameters recorded during the dynamic feedback optimization process and backing them up to the cloud is as follows: The anti-cancellation signal channel matrix is ​​saved as a data file, along with key parameters recorded during the dynamic feedback optimization process. These key parameters include the main path set, the interference path set, and the residual power attenuation rate. The resulting file is then backed up to the cloud via wireless transmission technology.

10. The dynamic interference suppression method for multipath channel phase focusing and harmonic phase cancellation as described in claim 2, characterized in that: In step S101, the specific process for extracting the sampled data of the received signal and constructing the received signal matrix is ​​as follows: By extending the MIMO antenna system, channel state information is extracted, and the sampled data of the received signal is extracted using the multi-channel antenna array to construct the received signal matrix.

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