Signal same-frequency interference suppression method used under formation condition

Abstract

The invention relates to the technical field of signal co-frequency interference suppression, and discloses a signal co-frequency interference suppression method used in a formation condition, and the method comprises the steps: obtaining a sending signal vector and a single-input multi-output mode; and calculating a transmission channel coefficient, analyzing a same-frequency interference mode, calculating an interference signal vector in the same-frequency interference mode, and projecting and eliminating the interference signal vector to obtain an actual output signal vector. The method comprises the following steps: constructing an equivalent channel matrix, calculating pseudo-inverse to obtain a weight vector, subtracting the weight vector from a received signal to obtain a same-frequency interference signal, and carrying out same-frequency interference suppression on the signal according to the same-frequency interference signal. The method can solve the problems that in the prior art, residual interference exists, and for formation transmission of multiple fixed nodes, the algorithm is too complex and the calculation complexity is high due to the fact that a traditional beam forming technology is applied.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal co-channel interference suppression, and particularly to a signal co-channel interference suppression method for platoon scenarios. BACKGROUND

[0002] At present, when solving the problem of co-channel interference suppression, the traditional method usually uses interference alignment technology, but this technology can only be applied in the scenario of one interference source to one receiving end, and the system needs to have enough degrees of freedom.

[0003] In one prior art, beamforming technology is used to suppress co-channel interference, and self-interference cancellation technology is used to suppress co-channel interference. Among them, beamforming technology is widely used in multi-node scenarios to suppress co-channel interference. However, the multi-user co-channel interference link cannot be completely eliminated, that is, there is residual interference.

[0004] In summary, in the prior art, the multi-user co-channel interference link cannot be completely eliminated, and there is residual interference. Moreover, for fixed multi-node platoon transmission, the application of traditional beamforming technology will make the algorithm too complex, and there is a problem of high computational complexity. SUMMARY

[0005] The present application provides a signal co-channel interference suppression method for platoon scenarios to solve the problem that in the prior art, the multi-user co-channel interference link cannot be completely eliminated, there is residual interference, and for fixed multi-node platoon transmission, the application of traditional beamforming technology will make the algorithm too complex, and there is a problem of high computational complexity.

[0006] In a first aspect, to solve the above technical problems, the present application provides a signal co-channel interference suppression method for platoon scenarios, comprising:

[0007] obtaining a sending signal vector of a plurality of sending nodes and a single-input multi-output channel mode of a receiving end;

[0008] performing receiving signal calculation according to the sending signal vector to obtain a receiving signal vector;

[0009] performing calculation according to the receiving signal vector to obtain a transmission channel coefficient;

[0010] performing analysis and judgment according to the transmission channel coefficient to obtain a co-channel interference mode in which signal co-channel interference exists;

[0011] performing interference signal calculation according to the sending signal vector in the co-channel interference mode to obtain an interference signal vector;

[0012] projecting the interference signal vector onto a space perpendicular to the receiving signal vector and performing interference cancellation processing to obtain an actual output signal vector;

[0013] performing a matrix construction operation on the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix;

[0014] performing pseudo-inverse calculation according to the equivalent channel matrix to obtain an equivalent channel pseudo-inverse;

[0015] performing a suppression signal calculation according to the equivalent channel pseudo-inverse to obtain a weight vector of the co-channel interference suppression vector;

[0016] subtracting the weight vector from the received signal vector to obtain a co-channel interference signal of the receiving node, and performing co-channel interference suppression of the signal according to the co-channel interference signal.

[0017] In an optional implementation, the obtaining of the feature 1 and the feature 2 comprises:

[0018] The receiving signal calculation according to the sending signal vector to obtain a received signal vector comprises:

[0019] The received signal vector is calculated by the following formula:

[0020]

[0021] wherein, r mi represents a received signal vector of an i-th receiving end on an m-th node, S k represents a sending signal vector of a k-th sending node, P k represents a transmission power of the k-th sending node, a mk represents a channel gain from the k-th sending node to the m-th node, I mi represents a sending signal of other adjacent nodes received at the i-th receiving end on the m-th node, and K represents a number of sending nodes.

[0022] In an optional implementation, the interference signal calculation according to the sending signal vector to obtain an interference signal vector comprises:

[0023] The interference signal vector is calculated by the following formula:

[0024] W mk = r mi - H mk S

[0025] wherein, W mk represents an interference signal vector from a k-th sending node to an m-th node, H mk represents a channel gain vector from the k-th sending node to the m-th node, S represents a sending signal vector, and r mi represents a received signal vector of an i-th receiving end on the m-th node.

[0026] In an alternative embodiment, the projecting the interference signal vector onto a space perpendicular to the received signal vector and performing interference cancellation to obtain an actual output signal vector comprises:

[0027] obtaining eigenvalues of the received signal vector;

[0028] performing decomposition according to the eigenvalues to obtain signal subspaces and noise subspaces which are perpendicular to each other;

[0029] projecting the received signal vector onto the signal subspaces to obtain a sub-received signal vector;

[0030] projecting the interference signal vector onto the signal subspaces to obtain a sub-interference signal vector;

[0031] subtracting the sub-interference signal vector from the sub-received signal vector to obtain an actual output signal vector.

[0032] In an alternative embodiment, performing matrix construction operation according to the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix comprises:

[0033] constructing the equivalent channel matrix by:

[0034]

[0035] wherein H m is the equivalent channel matrix, P T is the power of the node transmission signal, and B is the actual output signal vector.

[0036] In an alternative embodiment, performing pseudo-inverse calculation according to the equivalent channel matrix to obtain an equivalent channel pseudo-inverse comprises:

[0037] calculating the equivalent channel pseudo-inverse by:

[0038]

[0039] wherein, is the equivalent channel pseudo-inverse, denotes the conjugate transpose of the equivalent channel matrix, and H m is the equivalent channel matrix.

[0040] In an alternative embodiment, performing suppression signal calculation according to the equivalent channel pseudo-inverse to obtain a weight vector of the co-frequency interference suppression vector comprises:

[0041] calculating the weight vector of the co-frequency interference suppression vector by:

[0042]

[0043] wherein W m is a weight vector of the co-channel interference suppression vector, s m is a transmit signal vector of the mth node, is an equivalent channel pseudo-inverse.

[0044] In a second aspect, the present application provides a device for co-channel interference suppression of signals in a platoon scenario, comprising:

[0045] a data acquisition module configured to acquire transmit signal vectors of a plurality of transmitting nodes and a single-input multiple-output channel mode of a receiving end;

[0046] a received signal acquisition module configured to calculate a received signal vector based on the transmit signal vectors, to calculate transmission channel coefficients based on the received signal vector, and to determine a co-channel interference mode in which co-channel interference exists based on an analysis of the transmission channel coefficients;

[0047] an interference signal acquisition module configured to calculate an interference signal vector based on the transmit signal vectors in the co-channel interference mode;

[0048] an actual signal acquisition module configured to project the interference signal vector onto a space perpendicular to the received signal vector and to perform interference cancellation processing to obtain an actual output signal vector;

[0049] a matrix acquisition module configured to construct a matrix based on the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix;

[0050] a pseudo-inverse acquisition module configured to calculate a pseudo-inverse of the equivalent channel matrix to obtain an equivalent channel pseudo-inverse;

[0051] a weight vector acquisition module configured to calculate a weight vector of a co-channel interference suppression vector based on the equivalent channel pseudo-inverse;

[0052] a co-channel suppression module configured to subtract the weight vector from the received signal vector to obtain a co-channel interference signal of a receiving node, and to perform co-channel interference suppression of signals based on the co-channel interference signal.

[0053] In a third aspect, the present application further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for co-channel interference suppression of signals in a platoon scenario according to any one of the above aspects when executing the computer program.

[0054] In a fourth aspect, the present application also provides a computer readable storage medium comprising a stored computer program which, when executed by a device in which the computer readable storage medium is located, controls the device to perform the method for signal co-channel interference suppression in platoon scenario according to any one of the preceding aspects.

[0055] Compared with the prior art, the present application has the following beneficial effects:

[0056] The present application discloses a method for signal co-channel interference suppression in platoon scenario, comprising: obtaining a sending signal vector of a sending node and a single-input multiple-output channel mode of a receiving end; performing receiving signal calculation according to the sending signal vector to obtain a receiving signal vector; performing interference signal calculation according to the sending signal vector to obtain an interference signal vector; projecting the interference signal vector onto a space perpendicular to the receiving signal vector and performing interference cancellation processing to obtain an actual output signal vector; performing matrix construction operation according to the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix; performing pseudo-inverse calculation according to the equivalent channel matrix to obtain an equivalent channel pseudo-inverse; performing suppression signal calculation according to the equivalent channel pseudo-inverse to obtain a weight vector of a co-channel interference suppression vector; subtracting the weight vector from the receiving signal vector to obtain a co-channel interference signal of the receiving node, and performing signal co-channel interference suppression according to the co-channel interference signal. Compared with the prior art, the present application uses beamforming technology to suppress co-channel interference and uses self-interference cancellation technology to suppress co-channel interference. The beamforming technology is widely used in multi-node scenarios. However, the multi-user co-channel interference link cannot be completely eliminated, i.e. there is residual interference.

[0057] The present application introduces the idea of signal projection, uses power normalization method to process the equivalent channel matrix, and uses pseudo-inverse to calculate the co-channel interference suppression weight vector of the equivalent channel, which can solve the problem that the multi-user co-channel interference link cannot be completely eliminated in the prior art, i.e. there is residual interference. Moreover, for fixed multi-node platoon transmission, the application of traditional beamforming technology will make the algorithm too complex, i.e. there is a problem of high computational complexity. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a flowchart of the method for signal co-channel interference suppression in platoon scenario provided by the first embodiment of the present application;

[0059] Figure 2 is a schematic diagram of a sending data structure provided by the present application;

[0060] Figure 3 is a schematic diagram of the device structure for signal co-channel interference suppression in platoon scenario provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0062] At present, when solving the problem of same-frequency interference suppression, the traditional method usually uses interference alignment technology, but this technology can only be applied in the scene of one interference source to one receiving end, and the system needs to have enough degrees of freedom.

[0063] In one prior art, a same-frequency interference is suppressed by using a beamforming technology and a same-frequency interference is suppressed by using a self-interference cancellation technology. Among them, the beamforming technology suppresses the same-frequency interference and is widely used in multi-node scenarios. However, the multi-user same-frequency interference link cannot be completely eliminated, that is, there is residual interference. In summary, in the prior art, the multi-user same-frequency interference link cannot be completely eliminated, there is residual interference, and for fixed multi-node formation transmission, the application of the traditional beamforming technology will make the algorithm too complex, and there is a problem of high computational complexity.

[0064] To solve the above problems, with reference to Figure 1 The first embodiment of the present application provides a signal same-frequency interference suppression method for formation, comprising the following steps:

[0065] S11, obtaining a sending signal vector of a plurality of sending nodes and a single-input multi-output channel mode of a receiving end;

[0066] S12, performing receiving signal calculation according to the sending signal vector to obtain a receiving signal vector;

[0067] S13, performing calculation according to the receiving signal vector to obtain a transmission channel coefficient;

[0068] S14, performing analysis and judgment according to the transmission channel coefficient to obtain a same-frequency interference mode in which signal same-frequency interference exists;

[0069] S15, performing interference signal calculation according to the sending signal vector in the same-frequency interference mode to obtain an interference signal vector;

[0070] S16, projecting the interference signal vector onto a space perpendicular to the receiving signal vector and performing interference cancellation processing to obtain an actual output signal vector;

[0071] S17, performing a matrix construction operation in the single-input multiple-output channel mode according to the actual output signal vector, to obtain an equivalent channel matrix;

[0072] S18, performing pseudo-inverse calculation according to the equivalent channel matrix, to obtain an equivalent channel pseudo-inverse;

[0073] S19, performing suppression signal calculation according to the equivalent channel pseudo-inverse, to obtain a weight vector of the co-frequency interference suppression vector;

[0074] S20, subtracting the weight vector from the received signal vector, to obtain a co-frequency interference signal of the receiving node, and performing co-frequency interference suppression of the signal according to the co-frequency interference signal.

[0075] Compared with the prior art, the beam forming technology is used to suppress the co-frequency interference, and the self-interference cancellation technology is used to suppress the co-frequency interference. Among them, the beam forming technology is widely used in the multi-node scene. However, the multi-user co-frequency interference link cannot be completely eliminated, that is, there is residual interference. The present application introduces the idea of signal projection, adopts the power normalization method to process the equivalent channel matrix, and uses the pseudo-inverse to calculate the co-frequency interference suppression weight vector of the equivalent channel, which can solve the problem that the multi-user co-frequency interference link cannot be completely eliminated in the prior art, and there is residual interference. Moreover, for the fixed multi-node formation transmission, the application of the traditional beam forming technology will make the algorithm too complex, and there is a problem of high computational complexity.

[0076] In step S11, the sending signal vector of a plurality of sending nodes and the single-input multiple-output channel mode of the receiving end need to be obtained.

[0077] It should be noted that in a wireless communication system, a transmitting node (referring to a transmitting end or a base station) encodes and modulates information into a specific signal vector for transmission to a receiving node through a wireless channel. The transmitting signal vector is a sequence of modulated signals containing original information data. In obtaining the transmitting signal vector, a suitable modulation method is selected according to system requirements and channel characteristics, such as amplitude modulation (AM), frequency modulation (FM), phase modulation (PM), or more advanced digital modulation methods such as orthogonal frequency division multiplexing (OFDM), etc., and then one or more carrier signals are generated, which will be used to modulate the information data, map the information data to modulation symbols, and form the modulated signal vector. Before modulation, the original information data is subjected to forward error correction (FEC) encoding, such as convolutional encoding, Turbo encoding, or LDPC encoding, to increase redundancy, improve transmission reliability, or perform error detection and correction. In order to reduce the impact of continuous errors on system performance, the encoded data is subjected to interleaving processing. In order to reduce the bandwidth occupation of the signal and improve the spectral efficiency, the modulated signal is passed through a shaping filter, such as a root-raised cosine filter, and then the baseband signal is converted to a radio frequency signal for transmission through a wireless channel. The acquisition of the transmitting signal vector also needs to calculate the timing and synchronization problems, record the modulation method, symbol rate, constellation diagram, etc. Parameters, and assign a time stamp to each transmitted signal vector to facilitate synchronization at the receiving end, ensuring that the receiving end can correctly demodulate and decode the signal.

[0078] The single-input multiple-output channel mode describes the propagation environment of the signal from the transmitting node to the receiving node. In the acquisition process, first, design pilot signals, which have a known pattern and are easy to detect and demodulate at the receiving end. The pilot signals are transmitted before or between data transmission to enable channel estimation at the receiving end. Then the receiving end receives the pilot signals and estimates the channel response based on these signals, for example, using algorithms such as least squares, maximum likelihood estimation, or blind estimation to estimate the channel response. The gain and delay of each path are extracted from the channel response to identify and quantify the multipath effect, including the number of paths, the gain and phase of each path, and the single-input multiple-output channel mode is obtained based on the estimated path gain and delay.

[0079] In step S12, a receiving signal vector is calculated based on the transmitting signal vector.

[0080] In one implementation, the receiving signal vector is calculated by the following formula:

[0081]

[0082] wherein r mi represents the receiving signal vector of the i-th receiving end at the m-th node, S kis the received signal vector at the mth node, and k is the transmit power of the kth transmitting node, and mk is the channel gain from the kth transmitting node to the mth node, and mi is the received signal of other neighboring nodes at the i th receiving end on the mth node, and K is the number of transmitting nodes.

[0083] It should be noted that the received signal vector r mi can be calculated by the transmitted signal vector S k is a vector containing the signal of the kth transmitting node, which is a sequence of digitally modulated signals in digital communication. Data encoding and modulation can be achieved using a digital signal processor (DSP) or a field programmable gate array (FPGA), or using radio frequency front-end hardware, including DAC, mixer, filter and power amplifier (PA), to convert digital signals into wireless signals.

[0084] Channel gain refers to the channel gain from the kth transmitting node to the mth node, which describes the attenuation and distortion of the signal during transmission. Channel gain can be a complex number, with the real part representing the attenuation of the signal and the imaginary part representing the phase change of the signal. Channel gain is usually obtained through channel estimation, which includes: the transmitting end can send known pilot signals, the receiving end receives and demodulates these signals, and estimates the channel response according to the received quality of the pilot signals. It can also use least squares, maximum likelihood estimation or blind estimation algorithms to estimate the channel response according to the received power and phase information of the pilot signals; the estimation of channel gain requires complex signal processing at the receiving end, including signal acquisition, processing and analysis.

[0085] Transmit power P k refers to the power of the signal transmitted by the kth transmitting node. This parameter is the power of the actual transmitter output port, which determines the ability of the signal to propagate in the wireless channel, measured in watts (W) or milliwatts (mW). In the design phase of the communication system, the transmit power is pre-set according to the system coverage, device capability and regulatory requirements. In actual operation, the transmit power can be dynamically adjusted through power control algorithms to adapt to different communication conditions and reduce interference. The transmit power can be directly measured by using measuring equipment such as power meter, spectrum analyzer, etc. to measure the radio frequency output power of the transmitting end. Transmit power is used to determine the initial strength of the signal, which is a key parameter for calculating channel budget, link budget and signal coverage.

[0086] The transmission signals of other adjacent nodes represent the transmission signals of other adjacent nodes received at the kth receiving end of the mth node, which come from other users in the same network or adjacent communication networks. Techniques such as beamforming and multi-user detection can be used to distinguish and estimate signals from different transmission nodes, and signal processing techniques can be used to identify and estimate the source and characteristics of the signals.

[0087] In step S13, the transmission channel coefficients are calculated according to the received signal vector.

[0088] It should be noted that the transmission channel coefficients are parameters that describe the attenuation and phase changes experienced by signals in the channel. In a multipath propagation environment, signals can reach the receiving end through multiple paths, each path having its own specific channel coefficient. Using the known pilot signals transmitted by the transmitting end, the receiving end can estimate the response of the channel, and can use maximum likelihood estimation (MLE) to estimate the channel coefficients by maximizing the likelihood function of the received signal. In the signal detection and demodulation stage, the transmission channel coefficients are used to adjust the received signal to maximize the signal-to-noise ratio (SNR), and in array signal processing, the transmission channel coefficients are used for beamforming to enhance signals from the desired direction and suppress interference from other directions. In adaptive filter design, the transmission channel coefficients can be dynamically adjusted according to real-time channel changes to adapt to changes in channel conditions. In the case of co-channel interference, the received signal at the ith receiving end is the superposition of signals from multiple transmitting ends. Therefore, the transmission channel coefficients can reflect this superposition effect, i.e., the influence of the signal from the jth transmitting end on the ith receiving end, and can describe the time-varying nature, multipath effect, fading, and other characteristics of the channel. These characteristics affect the quality of signal transmission and thus affect the value of the transmission channel coefficients. In a multi-user environment, the transmission channel coefficients need to support multi-user detection techniques to effectively separate and identify signals from different transmitting ends in the presence of co-channel interference.

[0089] In step S14, the co-channel interference mode of the presence of signal co-channel interference is determined by analyzing and judging the transmission channel coefficients.

[0090] It should be noted that the co-channel interference pattern refers to the interference characteristics between signals transmitted on the same frequency. In wireless communication, the pattern of co-channel interference is determined based on the channel estimation results of the transmission channel coefficients. This includes determining the source of interference (such as other communication devices or co-channel wireless networks), the strength of the interference, and the degree of impact on the performance of the communication system. By analyzing the statistical characteristics of the channel estimation results, such as the distribution and variation of channel gain, the pattern and characteristics of the interference can be inferred. The purpose of channel estimation is to recover the original signal by estimating the characteristics and parameters of the channel using the received signal data. Channel estimation algorithms such as Minimum Mean Square Error (MMSE) estimation and Maximum Likelihood (ML) estimation are widely used. These algorithms can obtain the state information of the channel, including channel gain and phase, which can accurately identify whether there is co-channel interference in the signal.

[0091] In step S15, interference signal calculation is performed on the transmit signal vector under the co-channel interference pattern to obtain an interference signal vector.

[0092] In one implementation, the interference signal vector is calculated by the following formula:

[0093] W mk = r mi - H mk S

[0094] where W mk is the interference signal vector from the kth transmitting node to the mth node, H mk is the channel gain vector from the kth transmitting node to the mth node, S is the transmit signal vector, and r mi is the receive signal vector of the ith receiver at the mth node.

[0095] It should be noted that the interference signal vector refers to the sequence of interference signals received at the receiver, which come from other users in the same network or adjacent communication networks. The channel gain vector describes the channel effects from each kth transmitting node to the mth node, including path loss, shadow fading, and multipath fading, which is obtained through channel estimation. The basic process of channel estimation includes: using the channel matrix to relate the "transmit signal" and "receive signal", transmitting a pilot / reference signal, detecting the received signal, comparing the transmit signal and the received signal, and solving the elements in the channel matrix, i.e., obtaining the channel matrix, to complete the channel estimation. Channel estimation can be achieved through Least Squares (LS) algorithm or Minimum Mean Square Error (MMSE) algorithm. These algorithms use the known pilot sequence periodically inserted by the transmitting end into the data sequence to be transmitted, and the receiving end receives the signal containing the pilot sequence and performs sampling and demodulation, and then uses the received pilot sequence and the known pilot sequence to perform correlation operation to obtain the estimated value of the channel response.

[0096] The transmit signal vector S is a vector containing the signals of all transmitting nodes. Generating this transmit signal vector involves a digital signal processing (DSP) demarcation point. The bit stream is first mapped into a symbol sequence in groups. After symbol mapping, the modulator converts the digital symbols into the corresponding analog signal x(t) for transmission over the physical link. The entire process includes inputting the digital signal into a digital-to-analog converter, filtering, and transferring it to a high-frequency carrier. The receive signal vector represents the signal vector received by the i-th receiver at the m-th node. The receive signal vector is obtained through the following steps: The first module on the receiver side is the analog front end (AFE), which includes at least a filter for suppressing out-of-band noise, a crystal oscillator for timing, and an analog-to-digital converter for converting data into digital form. The received signal is the open-circuit voltage at the receiving antenna port, divided down. The open-circuit voltage is the sum of the incident electric field weighted by the receive antenna pattern gain. Receiving the receive signal vector involves structures such as a superheterodyne receiver or a direct down-conversion receiver. The signals received by these receivers from the antenna pass through the RF bandpass filter to filter out out-of-band interference and compress the image signal. Then, they are linearly amplified by the low-noise amplifier (LNA). The amplified signal is mixed with the local oscillator signal and down-converted to a fixed intermediate frequency signal. After filtering and intermediate frequency amplification, the useful signal is extracted for demodulation.

[0097] In step S16, the interference signal vector is projected onto a space perpendicular to the received signal vector and interference cancellation processing is performed to obtain an actual output signal vector.

[0098] In one implementation, the method includes: obtaining the eigenvalues ​​of a received signal vector; decomposing according to the eigenvalues ​​to obtain a signal subspace and a noise subspace that are spatially perpendicular to each other; projecting the received signal vector into the signal subspace to obtain a sub-received signal vector; projecting the interference signal vector into the signal subspace to obtain a sub-interference signal vector; and subtracting the sub-interference signal vector from the sub-received signal vector to obtain an actual output signal vector.

[0099] It should be noted that in signal processing, eigenvalues are important tools for describing linear transformation. For a received signal vector, its eigenvalues can represent the intrinsic structure and variation trend of the signal, which are obtained by solving a covariance matrix that describes the correlation between the components of the received signal vector. In actual implementation, a sufficient number of received signal samples are first collected, which need to cover the characteristics of the channel and the statistical characteristics of the interference. The collection of samples can be completed by sending a known pilot signal during the idle period of communication, and then the covariance matrix is constructed and eigenvalue decomposition is performed to obtain the eigenvalues. This process can use the linear algebra library in numerical calculation software (such as MATLAB) to calculate the eigenvalues and eigenvectors of the covariance matrix.

[0100] Through eigenvalue decomposition, the space in which the received signal vector is located can be decomposed into a signal subspace and a noise subspace. The signal subspace is spanned by the eigenvectors corresponding to the larger eigenvalues, and the noise subspace is spanned by the eigenvectors corresponding to the smaller eigenvalues. In actual application, the obtained eigenvalues are sorted from large to small, and the singular value decomposition (SVD) or eigenvalue decomposition (EVD) algorithm can be used to realize this decomposition process. In the process of projecting the received signal vector into the signal subspace, the sub-received signal vector related to the main eigenvalue in the signal is retained, so that the noise and other unimportant components can be removed. In practice, the eigenvector corresponding to the largest eigenvalue in the covariance matrix can be selected to construct a signal subspace eigenvector matrix, which is then applied to the original received signal vector. The sub-interference signal vector is the projection of the interference signal vector in the signal subspace, which represents the part of the interference signal that overlaps with the main signal component. Similar to the calculation of the sub-received signal vector, the same signal subspace eigenvector matrix is used to project the interference signal vector.

[0101] The actual output signal vector is the signal after removing the influence of the interference signal, which is closer to the original useful signal. In actual application, this step only involves a simple vector subtraction operation. First, the sub-received signal vector and the sub-interference signal vector are calculated, and then the sub-interference signal vector is subtracted from the sub-received signal vector.

[0102] In step S17, a matrix construction operation is performed on the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix.

[0103] In one implementation, the equivalent channel matrix is constructed by:

[0104]

[0105] where H m is the equivalent channel matrix, P T is the power of the node transmission signal, and B is the actual output signal vector.

[0106] The equivalent channel matrix describes the transformation relationship of the signal after the channel transmission from the sending node to the receiving node under the given sending power, contains the attenuation, phase change and other information of the channel, is the basic data of channel estimation and signal processing in the wireless communication system, integrates the characteristics of the channel and the characteristics of the sending signal, can help to predict the behavior of the signal in the channel, and thus design more effective signal processing algorithms such as equalizer, decoder, etc. The actual output signal vector is obtained by the interference cancellation processing in step S14, contains the useful signal received from the receiving node, and removes the influence of the interference signal. The power of the sending signal is set at the sending end, and can be dynamically adjusted by the power control algorithm to adapt to different communication conditions and reduce interference. The construction of the equivalent channel matrix is realized by calculating the actual output signal vector and the power of the sending signal, and the power normalization method is used to process the equivalent channel matrix. The equivalent channel matrix can be used to design the channel equalizer to compensate for the signal distortion caused by the channel. In the signal detection and demodulation stage, the equivalent channel matrix provides the necessary channel state information. The equivalent channel matrix can also be used for the design and optimization of the communication system, such as adjusting the sending power, selecting the modulation mode, etc.

[0107] In step S18, pseudo-inverse calculation is performed according to the equivalent channel matrix to obtain the equivalent channel pseudo-inverse.

[0108] In an implementation manner, the equivalent channel pseudo-inverse is calculated by the following formula:

[0109]

[0110] wherein, is the equivalent channel pseudo-inverse, denotes the conjugate transpose of the equivalent channel matrix, H m is the equivalent channel matrix.

[0111] It should be noted that the equivalent channel pseudo-inverse is a generalized inverse matrix of the equivalent channel matrix, and the pseudo-inverse is used in signal processing to solve the least square problem and the optimization problem, and in a single-input multiple-output system, can help to recover the transmitted signal or perform signal pre-equalization processing. The conjugate transpose of the equivalent channel matrix is to take the conjugate of the elements of the matrix and then perform the transpose operation. The equivalent channel pseudo-inverse provides a method for processing signals when the channel matrix is not invertible or non-square, reflecting a kind of "reversal" of the influence of the channel on the signal, so that the computer can recover the transmitted signal from the received signal. In specific practical applications, the inverse operation will encounter numerical stability problems, especially when the matrix is close to singular. In order to improve the numerical stability, when designing the algorithm, the number of operations needs to be reduced, the large number plus the small number needs to be avoided, the subtraction of two similar numbers needs to be avoided, and the small number as the divisor or the large number as the multiplier needs to be avoided. The equivalent channel pseudo-inverse can be used to recover the transmitted signal at the receiving end, especially in multi-user detection and signal demodulation. At the transmitting end, the equivalent channel pseudo-inverse can also be used to design a pre-equalizer to reduce the influence of the channel on the signal.

[0112] In step S19, the weight vector of the co-channel interference suppression vector is calculated according to the equivalent channel pseudo-inverse.

[0113] In an implementation manner, the weight vector of the co-channel interference suppression vector is calculated by the following formula:

[0114]

[0115] wherein, W m is the weight vector of the co-channel interference suppression vector, s m is the transmitted signal vector of the mth node, is the equivalent channel pseudo-inverse.

[0116] It should be noted that the weight vector of the co-channel interference suppression vector is a set of coefficients for separating the useful signal and the interference signal at the receiving end. In a multi-user environment, different signals will collide when transmitted at the same frequency, causing co-channel interference. The design goal of the weight vector is to suppress these interferences as much as possible while maintaining the integrity of the useful signal. The equivalent channel pseudo-inverse is obtained by step S16. The sending signal vector of the mth node is obtained, which contains all the signal information sent from the node, such as modulation information and coding information. Using the equivalent channel pseudo-inverse and the sending signal vector, the weight vector is calculated, which describes the best useful signal extracted from the received signal under given channel conditions. In specific application scenarios, the weight vector is used to adjust the received signal to maximize the signal-to-noise ratio (SNR) during signal detection and demodulation, thereby improving the detection accuracy of the signal; in array signal processing, the weight vector is used for beamforming to enhance the signal from the desired direction and suppress the interference from other directions; in adaptive filter design, the channel conditions are time-varying, and the weight vector can be dynamically adjusted according to the real-time channel changes to adapt to the changes in channel conditions; the weight vector can handle multipath effects.

[0117] In step S20, the co-channel interference signal of the receiving node is obtained by subtracting the weight vector from the received signal vector, and the co-channel interference suppression of the signal is performed according to the co-channel interference signal.

[0118] It should be noted that co-channel interference refers to the interference between signals transmitted at the same frequency. In a multi-user environment, such as urban centers or high-density network areas, different users' signals will collide at the same frequency, causing co-channel interference. This interference can reduce signal quality, increase bit error rate, and affect the performance of the communication system. Co-channel interference suppression can be achieved by using interference cancellation techniques, such as interference cancellation, interference suppression filters, etc., to process the co-channel interference signal. After the interference signal is suppressed, the useful signal is reconstructed to improve communication quality. Co-channel interference suppression can reduce or eliminate the impact of co-channel interference on the performance of the communication system. In specific applications, by suppressing co-channel interference, the reception quality of the signal can be improved, and the bit error rate can be reduced; effective co-channel interference suppression can improve the spectral utilization rate, allowing more users to communicate at the same frequency; co-channel interference suppression can increase the capacity of the system and improve the efficiency of the communication system.

[0119] In order to facilitate the understanding of the present application, some preferred embodiments of the present application will be further described below.

[0120] The working process of the present application will be described below with a more common scenario as an example. Please refer to Figure 2 , which is Figure 1A working scenario diagram of the method.

[0121] In the flight of UAV formation, multiple UAVs need to communicate on the same frequency to coordinate actions. Due to the close distance between UAVs, co-channel interference becomes a serious problem, affecting the communication quality and flight safety. In order to solve this problem, a signal co-channel interference suppression method for formation situation is adopted. First, each UAV needs to estimate the channel gain with other UAVs, use the estimated channel gain to construct the equivalent channel matrix, calculate the pseudo-inverse of the equivalent channel matrix, calculate the weight vector according to the equivalent channel pseudo-inverse and the sending signal vector, separate the co-channel interference signal from the received signal, and suppress the separated co-channel interference signal. Each UAV sends a known pilot signal before takeoff and regularly during flight, and other UAVs receive these pilot signals and use them to estimate the channel response. Channel estimation can use least squares or MMSE algorithm. Assuming there are 3 UAVs in the formation, each UAV has 2 antennas, forming a 3x2 equivalent channel matrix, which contains all the channel gain information between UAVs. Calculate the pseudo-inverse of the equivalent channel matrix. Each UAV calculates the weight vector according to its own sending signal vector and the equivalent channel pseudo-inverse. Each UAV uses the calculated weight vector to subtract from the received signal vector to get the co-channel interference signal, and suppresses the separated co-channel interference signal, for example, using a filter or machine learning algorithm to identify and eliminate interference. The suppressed signal is used for signal demodulation and data extraction. Through this method, the UAV formation can effectively suppress co-channel interference in complex wireless environment, improve the reliability of communication and the safety of flight. Experimental results show that after using this method, the communication bit error rate of UAV formation is reduced by 30%, and the communication distance is increased by 20%.

[0122] In summary, the application discloses a signal co-frequency interference suppression method for a platoon case, comprising the following steps: obtaining a sending signal vector of a sending node and a single-input multiple-output channel mode of a receiving end; performing receiving signal calculation according to the sending signal vector to obtain a receiving signal vector; performing interference signal calculation according to the sending signal vector to obtain an interference signal vector; projecting the interference signal vector onto a space perpendicular to the receiving signal vector and performing interference cancellation processing to obtain an actual output signal vector; performing matrix construction operation according to the actual output signal vector in the single-input multiple-output channel mode to obtain an equivalent channel matrix; performing pseudo-inverse calculation according to the equivalent channel matrix to obtain an equivalent channel pseudo-inverse; performing suppression signal calculation according to the equivalent channel pseudo-inverse to obtain a weight vector of a co-frequency interference suppression vector; subtracting the weight vector from the receiving signal vector to obtain a co-frequency interference signal of the receiving node, and performing co-frequency interference suppression of the signal according to the co-frequency interference signal. Compared with the prior art, the co-frequency interference is suppressed by using the beam forming technology and the co-frequency interference is suppressed by using the self-interference cancellation technology. The beam forming technology is widely used in the multi-node scene. However, the multi-user co-frequency interference link cannot be completely removed, that is, there is residual interference.

[0123] The application introduces the idea of signal projection, adopts the power normalization method to process the equivalent channel matrix, and uses the pseudo-inverse to calculate the co-frequency interference suppression weight vector of the equivalent channel, thereby solving the technical problem that the signal is transformed into other frequency bands to realize signal separation in the prior art, and the transmission information has a large loss, and the calculation complexity is low, the operation is simple, and the application is easy to realize.

[0124] Reference Figure 3 The second embodiment of the application provides a signal co-frequency interference suppression device for a platoon case, comprising:

[0125] A data acquisition module is configured to obtain sending signal vectors of multiple sending nodes and a single-input multiple-output channel mode of a receiving end.

[0126] A receiving signal acquisition module is configured to perform receiving signal calculation according to the sending signal vector to obtain a receiving signal vector, perform calculation according to the receiving signal vector to obtain transmission channel coefficients, and perform analysis and judgment according to the transmission channel coefficients to obtain a co-frequency interference mode in which signal co-frequency interference exists.

[0127] An interference signal acquisition module is configured to perform interference signal calculation according to the sending signal vector in the co-frequency interference mode to obtain an interference signal vector.

[0128] An actual signal acquisition module is configured to project the interference signal vector onto a space perpendicular to the receiving signal vector and perform interference cancellation processing to obtain an actual output signal vector.

[0129] a matrix obtaining module, configured to perform a matrix construction operation according to the actual output signal vector in the single-input multi-output channel mode, to obtain an equivalent channel matrix;

[0130] a pseudo-inverse obtaining module, configured to perform pseudo-inverse calculation according to the equivalent channel matrix, to obtain an equivalent channel pseudo-inverse;

[0131] a weight vector obtaining module, configured to perform suppression signal calculation according to the equivalent channel pseudo-inverse, to obtain a weight vector of a co-channel interference suppression vector;

[0132] a co-channel suppression module, configured to subtract the weight vector from the received signal vector, to obtain a co-channel interference signal of the receiving node, and perform co-channel interference suppression of the signal according to the co-channel interference signal.

[0133] It should be noted that the device for co-channel interference suppression of signals in a platoon provided in the embodiments of the present application is used to execute all the flow steps of the method for co-channel interference suppression of signals in a platoon provided in the embodiments, and the working principles and beneficial effects of the two are one-to-one corresponding, thus no longer being described in detail.

[0134] The embodiments of the present application further provide an electronic device. The electronic device comprises a processor, a memory, and a computer program, such as an algorithm program, stored in the memory and executable on the processor. The processor implements the steps in the above various embodiments of the method for co-channel interference suppression of signals in a platoon when executing the computer program, such as the step S11 shown in the figure. Figure 1 Alternatively, the processor implements the functions of the modules / units in the above various device embodiments, such as the co-channel suppression module, when executing the computer program.

[0135] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0136] The electronic device can be a desktop computer, a notebook, a palm computer, and a smart tablet, etc. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device, and do not constitute a limitation on the electronic device, and can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0137] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, and is a control center of the electronic device, which connects various parts of the electronic device through various interfaces and lines.

[0138] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created based on use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash storage device, or other volatile solid-state storage device.

[0139] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0140] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0141] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only for the specific embodiments of the present application and are not used to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for suppressing co-channel interference of signals in a formation situation, characterized in that: Executed by a computer, including: Obtaining the transmission signal vectors of multiple transmitting nodes and the single-input multiple-output channel mode of the receiving end; Performing a received signal calculation based on the transmitted signal vector to obtain a received signal vector; Performing calculations based on the received signal vector to obtain a transmission channel coefficient; Analyzing and judging according to the transmission channel coefficient, a co-channel interference pattern is obtained in which co-channel interference of signals exists; Performing interference signal calculation in the co-frequency interference mode according to the transmitted signal vector to obtain an interference signal vector; Projecting the interference signal vector onto a space perpendicular to the received signal vector and performing interference cancellation processing to obtain an actual output signal vector; Performing a matrix construction operation in the single-input multiple-output channel mode according to the actual output signal vector to obtain an equivalent channel matrix; Performing pseudo-inverse calculation according to the equivalent channel matrix to obtain an equivalent channel pseudo-inverse; Perform suppression signal calculation based on the equivalent channel pseudo-inverse to obtain a weight vector of the co-channel interference suppression vector; The weight vector is subtracted from the received signal vector to obtain a co-channel interference signal of the receiving node, and co-channel interference suppression of the signal is performed based on the co-channel interference signal.

2. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: The performing a received signal calculation according to the transmitted signal vector to obtain a received signal vector includes: The received signal vector is calculated using the following formula: Among them, r mi represents the received signal vector of the i-th receiver on the m-th node, S k is the signal vector sent by the kth sending node, P k is the transmission power of the kth sending node, α mk is the channel gain from the kth sending node to the mth node, I mi is the transmission signal of other adjacent nodes received at the i-th receiving end on the m-th node, and K is the number of transmitting nodes.

3. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: The performing interference signal calculation according to the transmitted signal vector to obtain the interference signal vector includes: The interference signal vector is calculated using the following formula: W mk =r mi -H mk S Among them, W mk is the interference signal vector from the kth sending node to the mth node, H mk is the channel gain vector from the kth sending node to the mth node, S is the sending signal vector, r mi is the received signal vector of the i-th receiver on the m-th node.

4. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: The projecting the interference signal vector onto a space perpendicular to the received signal vector and performing interference cancellation processing to obtain an actual output signal vector includes: Obtaining the eigenvalues ​​of the received signal vector; Decomposing the signal subspace and the noise subspace according to the eigenvalues ​​to obtain a signal subspace and a noise subspace that are perpendicular to each other in space; Projecting the received signal vector onto a signal subspace to obtain a sub-received signal vector; Projecting the interference signal vector onto the signal subspace to obtain a sub-interference signal vector; The sub-interference signal vector is subtracted from the sub-received signal vector to obtain an actual output signal vector.

5. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: Performing a matrix construction operation in the single-input multiple-output channel mode according to the actual output signal vector to obtain an equivalent channel matrix includes: The equivalent channel matrix is ​​constructed as follows: Among them, H m is the equivalent channel matrix, P T is the power of the signal sent by the node, and B is the actual output signal vector.

6. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: The performing pseudo-inverse calculation according to the equivalent channel matrix to obtain the equivalent channel pseudo-inverse includes: The equivalent channel pseudo-inverse is calculated by the following formula: in, is the equivalent channel pseudo-inverse, represents the conjugate transpose of the equivalent channel matrix, H m is the equivalent channel matrix.

7. The method for suppressing co-channel interference of signals in formation according to claim 1, characterized in that: The calculating of the suppression signal according to the equivalent channel pseudo-inverse to obtain the weight vector of the co-channel interference suppression vector includes: The weight vector of the co-channel interference suppression vector is calculated using the following formula: Among them, W m is the weight vector of the co-channel interference suppression vector, s m is the sending signal vector of the mth node, is the equivalent channel pseudo-inverse.

8. A signal co-channel interference suppression device for use in formation situations, characterized in that: include: A data acquisition module is used to obtain the transmission signal vectors of multiple transmitting nodes and the single-input multiple-output channel mode of the receiving end; A received signal acquisition module is configured to calculate a received signal based on the transmitted signal vector to obtain a received signal vector; calculate based on the received signal vector to obtain a transmission channel coefficient; and analyze and determine based on the transmission channel coefficient to obtain a co-channel interference pattern indicating the presence of co-channel interference. An interference signal acquisition module is used to calculate the interference signal in the co-frequency interference mode according to the transmitted signal vector to obtain an interference signal vector; an actual signal acquisition module, configured to project the interference signal vector onto a space perpendicular to the received signal vector and perform interference cancellation processing to obtain an actual output signal vector; A matrix acquisition module, configured to perform a matrix construction operation in the single-input multiple-output channel mode according to the actual output signal vector to obtain an equivalent channel matrix; a pseudo-inverse obtaining module, configured to perform pseudo-inverse calculation based on the equivalent channel matrix to obtain an equivalent channel pseudo-inverse; A weight vector acquisition module is used to calculate the suppression signal according to the equivalent channel pseudo inverse to obtain a weight vector of the co-channel interference suppression vector; The co-channel suppression module is used to subtract the weight vector from the received signal vector to obtain the co-channel interference signal of the receiving node, and suppress the co-channel interference of the signal according to the co-channel interference signal.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the signal co-channel interference suppression method for formation situations as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the signal co-channel interference suppression method for formation conditions as described in any one of claims 1 to 7.

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