Multipath channel enhancement method and system based on AoA filtering in synaesthesia integrated system

Abstract

The present invention relates to the field of communications, and specifically to a multipath channel enhancement method and system based on AoA filtering in a synaesthesia integrated system; constructing an uplink communication scenario, generating a transmit signal, a receive signal, and a channel model based on the uplink communication scenario; performing channel estimation on the transmit signal and the receive signal based on a least squares algorithm to obtain a channel response estimate; sensing and acquiring AoA information of each communication path, constructing a channel transfer factor based on the AoA information and the channel model; performing Kalman filtering on the channel response estimate based on the channel transfer factor, and outputting a filtered channel vector. The present invention improves the estimation accuracy and real-time performance of channel state information in a multipath environment by combining the sensed communication path angle information with Doppler information and dynamically optimizing the channel response estimate using Kalman filtering.

Description

Technical Field

[0001] The present invention relates to the field of communications, and in particular to a multipath channel enhancement method and system based on AoA filtering in a synaesthesia integration system. Background Art

[0002] The accuracy of channel estimation is particularly important for the performance of communication systems, especially in high-speed mobile and complex and changing wireless environments. In today's era of rapid development of communication technology, with the increasing research and construction of communication networks, perception-assisted communication technology is becoming increasingly critical.

[0003] Channel estimation technology is crucial in wireless communications, serving as the foundation for accurate demodulation and decoding. When signals are transmitted over wireless channels, they are subject to various noise and interference, which can severely distort the signal's original characteristics. Consequently, the channel estimation process often produces large errors, which interfere with the communication system's ability to accurately restore the original signal, severely impacting the system's overall performance, such as causing a decrease in data transmission rate and an increase in bit error rate.

[0004] In traditional multipath channel estimation systems, multipath refers to the phenomenon in which signals, when propagating, are reflected, scattered, and refracted by various obstacles, forming multiple propagation paths to the receiver. The superposition of signals from these different paths complicates the received signal and makes it difficult to process. Multipath results in a loss of orthogonality between subcarriers. Once this orthogonality is lost, signals interfere with each other, severely degrading transmission quality and hindering channel performance. This makes it difficult for communication systems to achieve efficient and stable signal transmission in multipath environments.

[0005] In addition, accurately estimating the channel state in a multipath environment is more complex, further increasing the difficulty of channel estimation. Due to the superposition and interference of multipath signals, it is difficult to obtain the estimated channel state. As the communication system's requirements for communication quality continue to increase, higher requirements are also placed on the accuracy and real-time performance of channel estimation, which undoubtedly makes the channel estimation task in a multipath environment more arduous. Summary of the Invention

[0006] In response to the problems mentioned in the prior art, the present invention proposes a multipath channel enhancement method and system based on AoA filtering in a synaesthesia integrated system. By combining the perceived angle information and Doppler information, Kalman filtering is used to process the initial channel estimation results to reduce noise and enhance the accuracy of channel state information.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention proposes a multipath channel enhancement method based on AoA filtering in a synaesthesia integrated system, comprising the following steps:

[0009] Build an uplink communication scenario and generate transmit signals, receive signals, and channel models based on the uplink communication scenario;

[0010] Performing channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value;

[0011] Sense and obtain the AoA information of each communication path, and construct the channel transfer factor based on the AoA information and channel model;

[0012] The channel response estimate is subjected to Kalman filtering based on the channel transfer factor, and a filtered channel vector is output.

[0013] As a further improvement of the present invention, the transmission signal is , represents the baseband symbol at the mth OFDM symbol of the nth subcarrier, where is the number of subcarriers, is the number of OFDM symbols;

[0014] The received signal As shown in the following formula:

[0015]

[0016] Where: Indicates the number of subcarriers; Indicates the number of OFDM symbols; Indicates the n subcarrier m OFDM symbols, the dimension is ; is the transmission power of the signal; for The square root of is the noise matrix composed of additive Gaussian white noise of different antenna units;

[0017] The channel model The expression is as follows:

[0018]

[0019] Where: L is the number of multipaths, when represents the direct path, represents an indirect path, L Take 2; is the duration of OFDM symbols; is the subcarrier spacing; is the Doppler shift of each communication path between the receiving end and the output end; is the delay of each communication path between the receiving end and the output end; It is channel l The attenuation coefficient of the path; Indicates the l horizontal angle of arrival of the diameter; represents the receiving antenna steering vector; Represents the phase rotation or phase shift of the propagating wave; Express The sum of the paths.

[0020] As a further improvement of the present invention, the channel response estimation value is expressed as follows:

[0021]

[0022] Where: For the subcarrier OFDM symbol channel response estimate; To transmit a signal; To receive signals; Yes Perform inverse operation; Yes Perform the Hermitian transpose.

[0023] As a further improvement of the present invention, the channel transfer factor is expressed as follows:

[0024]

[0025] Where: represents the channel transfer factor; is the LoS path channel fading coefficient, is the NLoS path channel fading coefficient; is the number of antenna array elements; Represents the phase rotation or phase shift of the propagating wave; Indicates angle The sine value of Indicates angle The sine value of Indicates the spacing between antenna elements; express The variable name of the concept; Indicates wavelength; Indicates the transfer relationship between adjacent antenna arrays after multipath superposition. arrive The accumulation of .

[0026] As a further improvement of the present invention, the Kalman filter processing process is as follows:

[0027] a. Let the channel response estimate , Represents the vector before filtering, the channel response estimate Filter to get the filtered vector ;

[0028] b. For antenna elements, , calculate the current predicted value, the formula is as follows:

[0029]

[0030] Where: is the filtering result of the previous moment; is the channel transfer factor; Current forecast value;

[0031] c. Perform a weighted correction on the current prediction value based on the Kalman gain, as shown in the following formula:

[0032]

[0033] Where: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final prediction filtering result at the current moment;

[0034] The Kalman gain is obtained as follows:

[0035]

[0036] Where: is the power of interference plus noise, is the predicted value of the current error covariance; For Perform complex conjugate transpose; For Take the inverse of the sum;

[0037] d. Update the error covariance according to the following formula:

[0038]

[0039] Where: is the actual value of the current error covariance;

[0040] e. Repeat steps b to d until all The antenna array elements are processed and the filtered channel vector is output.

[0041] As a further improvement of the present invention, the filtered channel vector is decoded to obtain an information bit stream, and a bit error rate is calculated based on the information bit stream to verify the signal performance obtained by the method.

[0042] In a second aspect, the present invention proposes a multipath channel enhancement system based on AoA filtering in a sensing integrated system, comprising:

[0043] The first construction module is used to construct an uplink communication scenario and generate a transmit signal, a receive signal and a channel model based on the uplink communication scenario;

[0044] a channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value;

[0045] The second building module is used to sense and obtain the AoA information of each communication path, and build a channel transfer factor based on the AoA information and the channel model;

[0046] The output module is used to perform Kalman filtering on the channel response estimation value based on the channel transfer factor and output the filtered channel vector.

[0047] In a third aspect, the present invention proposes a multipath channel enhancement device based on AoA filtering in a synaesthesia integration system, comprising a processor and a memory, wherein when the processor executes a computer program stored in the memory, it implements the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system as described above.

[0048] In a fourth aspect, the present invention proposes a computer-readable storage medium for storing a computer program, wherein when the computer program is executed by a processor, the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system as described above is implemented.

[0049] Compared with the prior art, the present invention has achieved the following technical effects:

[0050] The present invention combines the communication path angle information acquired through perception with Doppler information and adopts Kalman filtering to dynamically optimize the channel response estimation value, thereby improving the estimation accuracy and real-time performance of the channel state information in a multipath environment. The present invention constructs a channel transfer factor through the communication path angle information and utilizes the iterative mechanism of Kalman filtering to dynamically correct the deviation between the predicted value and the observed value of the channel response estimation value, thereby effectively suppressing noise interference and enhancing the multipath signal analysis capability, effectively solving the problems of subcarrier orthogonality destruction and signal distortion caused by the multipath effect in traditional systems, and significantly improving the data transmission rate and bit error rate performance of the communication system in a highly dynamic and complex environment.

[0051] The present invention uses a least squares algorithm to perform channel estimation, which can quickly generate a channel response estimate value, provide basic channel state information in a low-complexity manner, lay the foundation for subsequent Kalman filter iterations, and avoid excessive consumption of system resources by a complex initialization process. By combining AoA information with a channel model to generate a channel transfer factor, the channel transfer factor is used as a Kalman filter parameter, and the channel response estimate is processed using a Kalman filter. This not only reduces the impact of noise on channel estimation, but also reduces the impact of multipath separation on channel estimation, thereby improving the accuracy of channel estimation. The present invention verifies the channel estimation effect by decoding the filtered channel vector and calculating the bit error rate, providing a basis for system parameter optimization and ensuring the reliability and scalability of the method in real scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the process of the present invention;

[0053] Figure 2 This is a diagram of a transmission signal structure in an embodiment of the present invention;

[0054] Figure 3 A schematic diagram of a scenario constructed in an embodiment of the present invention;

[0055] Figure 4 This is a simulation result diagram comparing the method of the present invention with other filtering algorithms;

[0056] Figure 5 This is a simulation result diagram showing the impact of the angle estimation error of the present invention on the performance of the method of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0058] like Figure 1 As shown, the present invention proposes a multipath channel enhancement method based on AoA filtering in a synaesthesia integrated system, comprising the following steps:

[0059] Build an uplink communication scenario and generate transmit signals, receive signals, and channel models based on the uplink communication scenario;

[0060] Performing channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value;

[0061] Sense and obtain the AoA (Angle of Arrival) information of each communication path, and construct the channel transfer factor based on the AoA information and channel model;

[0062] The channel response estimate is subjected to Kalman filtering based on the channel transfer factor, and a filtered channel vector is output.

[0063] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments:

[0064] Step 1: See Figure 3 , which is the uplink communication scenario of this embodiment. In the uplink communication scenario, the base station is equipped with a uniform equidistant linear array, and the antenna array dimension is , is the number of antenna array elements, the user has a single antenna and is assumed to be a point target.

[0065] Step 2: In the uplink communication scenario of the integrated interawareness system in this implementation, OFDM (Orthogonal Frequency Division Multiplexing) signals are used, such as Figure 2 The figure shows the structure of the transmission signal. The transmission signal consists of training symbols and data symbols. The transmission signal is , represents the baseband symbol at the mth OFDM symbol of the nth subcarrier, where is the number of subcarriers, is the number of OFDM symbols.

[0066] In the embodiment, the received signal corresponding to the mth symbol of the nth subcarrier is As shown in the following formula:

[0067] (1)

[0068] Where: Indicates the current number of subcarriers, Represents the current number of OFDM symbols, then Indicates the n subcarrier m OFDM symbols, the dimension is ; is the transmission power of the signal; for The square root of is the noise matrix composed of additive Gaussian white noise of different antenna units, each element has a mean of 0 and a variance of Complex Gaussian distribution .

[0069] This embodiment takes into account the time-varying and multipath effects of the channel. The channel model expression of the uplink communication channel at the mth OFDM symbol of the nth subcarrier is as follows:

[0070] (2)

[0071] Where: L is the number of multipaths, when Indicates the direct (LoS Line-of-Sight) path, Indicates a non-line-of-sight (NLoS) path. This embodiment assumes that there is a LoS path and an NLoS path between the user equipment and the base station, that is, L Take 2; is the duration of OFDM symbol; is the subcarrier spacing; is the Doppler shift of each communication path between the user equipment and the base station; is the delay of each communication path between the user equipment and the base station; It is channel l The fading coefficient of the path, the embodiment assumes that it obeys the mean of 0 and the variance of Complex Gaussian distribution ; Indicates the l horizontal angle of arrival of the diameter; represents the receiving antenna steering vector; Represents the phase rotation or phase shift of the propagating wave; Express The sum of the paths.

[0072] in The specific expressions are as follows:

[0073] (3)

[0074] Where: Indicates the antenna spacing; Indicates wavelength; Indicates the l The sine of the horizontal angle of arrival of the beam; Indicates the number of antenna array elements.

[0075] Step 3: Perform channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimate. The channel response estimate is expressed as follows:

[0076] (4)

[0077] Where: For the subcarrier OFDM symbol channel response estimate; To transmit a signal; To receive signals; Yes Perform inverse operation; Yes Perform the Hermitian transpose.

[0078] Due to the presence of noise, the above channel estimation method has errors, which affects the performance of the communication system. In the integrated system of this embodiment, the AoA of each path is equivalent to Can be obtained through perception.

[0079] Step 4: This embodiment uses AoA information to construct a transfer factor , the transfer factor As a Kalman filter factor, Kalman filtering can be used to reduce the impact of noise on channel estimation results. It can reflect the channel coefficient relationship between different antenna elements.

[0080] Since this embodiment is a two-path scenario with L=2, the transfer factor The expression is updated as follows:

[0081] (5)

[0082] Where: represents the channel transfer factor; is the LoS path channel fading coefficient, is the NLoS path channel fading coefficient; is the number of antenna array elements; Represents the phase rotation or phase shift of the propagating wave; Indicates angle The sine value of Indicates angle The sine value of Indicates the spacing between antenna elements; express The variable name of the concept; Indicates the transfer relationship between adjacent antenna arrays after multipath superposition. arrive The accumulation of .

[0083] LoS path channel fading coefficient in the embodiment and NLoS path channel fading coefficient The calculation formula is as follows:

[0084] (6)

[0085] Where: 、 The receiving antenna array is Subcarrier No. The received signal at the OFDM symbol and the Subcarrier No. OFDM symbols, and , ; Represents vector mean calculation.

[0086] in The expression is as follows:

[0087] (7)

[0088] Where: Indicates the first OFDM symbol; Indicates the second OFDM symbol; Indicates the first subcarrier; Indicates the second subcarrier.

[0089] Step 5: This embodiment uses the channel transfer factor as the Kalman filter factor and uses the Kalman filter to estimate the channel response. Perform filtering.

[0090] First, let represents the vector before filtering, Represents the filtered vector, in the embodiment , Indicates the antenna array elements, in this embodiment , indicating that the first antenna array element does not perform Kalman filtering.

[0091] Calculate the current forecast value using the following formula:

[0092] (8)

[0093] Where: is the filtering result of the previous moment; is the channel transfer factor; Current forecast value.

[0094] According to the Kalman gain Make a weighted correction to the current forecast value, as shown in the following formula:

[0095] (9)

[0096] Where: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final prediction filtering result at the current moment.

[0097] The Kalman gain can dynamically adjust the weights of the predicted value and the observed value in the state estimation to minimize the estimation error. In the embodiment, the Kalman gain is obtained as follows:

[0098] (10)

[0099] Where: is the power of interference plus noise, is the predicted value of the current error covariance; For Perform complex conjugate transpose; For Take the inverse of the sum.

[0100] In the embodiment The calculation formula is as follows:

[0101] (11)

[0102] Where: for The conjugate transpose of .

[0103] The filtered value in the embodiment is represented by the prior estimated predicted value and the actual value, and is updated by the Kalman gain. , then update according to the following formula ,make , and repeat the above process.

[0104] (12)

[0105] Where: is the actual value of the current error covariance;

[0106] like , the iteration ends and the filtered channel is output .

[0107] Step 6: The bit error rate can measure the accuracy of the channel estimation. The filtered channel vector is decoded according to the estimated channel response value to recover the information bit stream. The received information bit stream is compared with the originally transmitted information bit stream, and the bit error rate is obtained by counting the number of error bits. The specific calculation formula is as follows:

[0108]

[0109] The following is the verification of the method of the present invention:

[0110] In the MATLAB (Matrix Laboratory) simulation environment, the multipath channel estimation method based on AoA filtering proposed in the present invention is analyzed and verified.

[0111] During the simulation, the subcarrier spacing =480kHz. Number of OFDM symbols =32, number of subcarriers =64. The user's transmitting antenna is a single antenna, and the base station's receiving antenna size is .

[0112] In order to verify the performance of the method of the present invention, see Figure 4 The figure shows a comparison of the demodulation bit error rate performance of the method of the present invention and various channel estimation methods under different signal-to-noise ratios. The horizontal axis is the signal-to-noise ratio (unit: dB) and the vertical axis is the bit error rate (unit: logarithmic scale). The figure shows five methods for comparison:

[0113] 1. Initial channel estimation: The least squares method is used to directly obtain the rough CSI (Channel State Information) by comparing the received signal with the pilot symbols without any filtering.

[0114] 2. The filtering method of the present invention: The method of the present invention can fuse multipath angle information through the processing of formula (5) in step 4, and can introduce the Kalman filter recursive update mechanism through the processing of formula (6) to formula (12) in step 5, thereby realizing dynamic optimization and precise enhancement of the initial CSI estimation.

[0115] 3. LOS information filtering: The antenna direction factor of the direct path is extracted as the state transfer factor, and only one round of Kalman filtering is performed on the direct path.

[0116] 4. NLOS information filtering: Extract the direction information of the indirect path as the state transfer factor, and perform one round of Kalman filtering only on the indirect path.

[0117] 5. Two-path sequential filtering: Two rounds of serial Kalman filtering are performed on the CSI using the directional information of the direct path and the indirect path, further suppressing multipath interference and transient errors.

[0118] Depend on Figure 4 It can be seen that as the signal-to-noise ratio increases, the bit error rate of all methods decreases because the channel estimation accuracy improves with the increase of the signal-to-noise ratio.

[0119] In order to more intuitively compare the performance differences of different estimation methods in complex channels, this embodiment uses the signal-to-noise ratio of 0dB as a representative observation point for analysis. Figure 4 As can be seen from the curve, the bit error rate of the initial channel estimation is the highest, about 10 -1, which shows that the initial channel estimation performance is the worst because the influence of noise is not eliminated by filtering; the bit error rates of the LoS path filtering channel estimation and the NLoS path filtering channel estimation are approximately 10 -2 and 8×10 -3 , the bit error rate of the two-path sequential filtering channel estimation is about 4×10 -3 , indicating that even using partial path information filtering can effectively enhance the estimation accuracy, but since the construction of the transfer factor only uses phase information, the filtering effect is limited; while the bit error rate of the method of the present invention is reduced to 10 -3 Below, thereby eliminating the influence of noise to the greatest extent, so the performance is the best.

[0120] See also Figure 5 To verify the impact of perception parameter accuracy on the performance of the proposed algorithm, the effect of angle error on system performance is shown, with the horizontal axis representing the signal-to-noise ratio and the vertical axis representing the bit error rate. The accompanying figure shows that as the perception parameter accuracy decreases, the AoA angle error increases, and the channel estimation performance also continues to degrade.

[0121] Based on the same inventive concept, an embodiment of the present invention also provides a multipath channel enhancement system based on AoA filtering in a synaesthesia integration system. Since the principle of solving the problem by the multipath channel enhancement system based on AoA filtering in the synaesthesia integration system is similar to the multipath channel enhancement method based on AoA filtering in the aforementioned synaesthesia integration system, the implementation of the multipath channel enhancement system based on AoA filtering in the synaesthesia integration system can refer to the implementation of the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system, and the repeated parts will not be repeated.

[0122] In a specific implementation, the multipath channel enhancement system based on AoA filtering in the synaesthesia integration system provided by the embodiment of the present invention specifically includes:

[0123] The first construction module is used to construct an uplink communication scenario and generate a transmit signal, a receive signal and a channel model based on the uplink communication scenario;

[0124] a channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value;

[0125] The second building module is used to sense and obtain the AoA information of each communication path, and build a channel transfer factor based on the AoA information and the channel model;

[0126] The output module is used to perform Kalman filtering on the channel response estimation value based on the channel transfer factor and output the filtered channel vector.

[0127] Accordingly, an embodiment of the present invention also provides a multipath channel enhancement device based on AoA filtering in a synaesthesia integration system, comprising a processor and a memory, wherein when the processor executes the computer program stored in the memory, it implements the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system provided by the embodiment of the present invention.

[0128] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0129] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, wherein, when the computer program is executed by a processor, the multipath channel enhancement method based on AoA filtering in the above-mentioned synaesthesia integration system provided by an embodiment of the present invention is implemented.

[0130] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar portions of the various embodiments will be sufficient. The systems, devices, and storage media disclosed in the embodiments are described briefly because they correspond to the methods disclosed in the embodiments. For relevant details, refer to the method descriptions.

[0131] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0133] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0134] The above is a detailed introduction to the multipath channel enhancement method, system, device and storage medium based on AoA filtering in the synaesthesia integrated system provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A multipath channel enhancement method based on AoA filtering in a synaesthesia integrated system, characterized in that: The following steps are involved: Build an uplink communication scenario and generate transmit signals, receive signals, and channel models based on the uplink communication scenario; Performing channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value; The AoA information of each communication path is sensed and acquired, and a channel transfer factor is constructed based on the AoA information and the channel model. The expression of the channel transfer factor is as follows: Where: represents the channel transfer factor; is the LoS path channel fading coefficient, is the NLoS path channel fading coefficient; is the number of antenna array elements; Represents the phase rotation or phase shift of the propagating wave; Indicates angle The sine value of Indicates angle The sine value of Indicates the spacing between antenna elements; express The variable name of the concept; Indicates wavelength; Indicates the transfer relationship between adjacent antenna arrays after multipath superposition. arrive The accumulation of The channel response estimate is subjected to Kalman filtering based on the channel transfer factor, and a filtered channel vector is output.

2. The multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1 is characterized in that: The transmission signal is , represents the baseband symbol at the mth OFDM symbol of the nth subcarrier, where is the number of subcarriers, is the number of OFDM symbols; The received signal As shown in the following formula: Where: Indicates the number of subcarriers; Indicates the number of OFDM symbols; Indicates the n subcarrier m OFDM symbols, the dimension is ; is the transmission power of the signal; for The square root of is the noise matrix composed of additive Gaussian white noise of different antenna units; The channel model The expression is as follows: Where: L is the number of multipaths, when represents the direct path, represents an indirect path, L Take 2; is the duration of OFDM symbol; is the subcarrier spacing; is the Doppler shift of each communication path between the receiving end and the output end; is the delay of each communication path between the receiving end and the output end; It is channel l The attenuation coefficient of the path; Indicates the l horizontal angle of arrival of the diameter; represents the receiving antenna steering vector; Represents the phase rotation or phase shift of the propagating wave; Express The sum of the paths.

3. The multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1 is characterized in that: The channel response estimation value is expressed as follows: Where: For the subcarrier OFDM symbol channel response estimate; To transmit a signal; To receive signals; Yes Perform inverse operation; Yes Perform the Hermitian transpose.

4. The multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1 is characterized in that: The Kalman filter processing process is as follows: a. Let the channel response estimate , Represents the vector before filtering, the channel response estimate Filter to get the filtered vector ; b. For antenna elements, , calculate the current predicted value, the formula is as follows: Where: is the filtering result of the previous moment; is the channel transfer factor; Current forecast value; c. Perform a weighted correction on the current prediction value based on the Kalman gain, as shown in the following formula: Where: is the Kalman gain; is the actual value of the channel coefficient at the current moment; is the final prediction filtering result at the current moment; The Kalman gain is obtained as follows: Where: is the power of interference plus noise, is the predicted value of the current error covariance; For Perform complex conjugate transpose; For Take the inverse of the sum; d. Update the error covariance according to the following formula: Where: is the actual value of the current error covariance; e. Repeat steps b to d until all The antenna array elements are processed and the filtered channel vector is output.

5. The multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to claim 1 is characterized in that: The filtered channel vector is decoded to obtain an information bit stream, and the bit error rate is calculated based on the information bit stream to verify the signal performance obtained by this method.

6. A multipath channel enhancement system based on AoA filtering in the synaesthesia integrated system, characterized by: include: The first construction module is used to construct an uplink communication scenario and generate a transmit signal, a receive signal and a channel model based on the uplink communication scenario; a channel estimation module, configured to perform channel estimation on the transmitted signal and the received signal based on a least squares algorithm to obtain a channel response estimation value; The second building block is used to sense and obtain the AoA information of each communication path, and construct a channel transfer factor based on the AoA information and the channel model. The expression of the channel transfer factor is as follows: Where: represents the channel transfer factor; is the LoS path channel fading coefficient, is the NLoS path channel fading coefficient; is the number of antenna array elements; Represents the phase rotation or phase shift of the propagating wave; Indicates angle The sine value of Indicates angle The sine value of Indicates the spacing between antenna elements; express The variable name of the concept; Indicates wavelength; Indicates the transfer relationship between adjacent antenna arrays after multipath superposition. arrive The accumulation of The output module is used to perform Kalman filtering on the channel response estimation value based on the channel transfer factor and output the filtered channel vector.

7. A multipath channel enhancement device based on AoA filtering in the synaesthesia integrated system, characterized in that: The system comprises a processor and a memory, wherein when the processor executes the computer program stored in the memory, the system implements the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the multipath channel enhancement method based on AoA filtering in the synaesthesia integration system according to any one of claims 1 to 5 is implemented.

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