Channel reconstruction and optimal port selection system and method for a fluid antenna system

Abstract

This invention relates to a channel reconstruction and optimal port selection system and method for a fluid antenna system, comprising: a transmitter, a multipath channel module, a fluid antenna system, and a receiver; the transmitter sends a modulated signal to the multipath channel module, which simulates multipath propagation effects and sends the signal to each port of the fluid antenna system; the receiver performs channel estimation based on the received signal, determines the optimal receiving port through an optimal port selection algorithm, and sends a control signal to the fluid antenna system to activate the port; the fluid antenna system sends the signal from the selected port to the receiver for demodulation and decoding, and the receiver can simultaneously feed back the channel state to the transmitter to achieve adaptive transmission adjustment.

Description

Technical Field

[0001] This invention belongs to the technical field and relates to a channel reconstruction and optimal port selection system and method, particularly a channel reconstruction and optimal port selection system and method for a fluid antenna system. Background Technology

[0002] With the widespread deployment of 5G communication technology in power systems, smart grids are placing higher demands on the reliability and stability of wireless links in complex environments. Traditional fixed antennas are prone to problems such as unstable received signal quality, insufficient spatial diversity, and difficulty in effectively suppressing interference in ultra-dense networking and scenarios with significant multipath interference, and can no longer meet the high-reliability communication requirements of smart grid services.

[0003] In summary, traditional fixed antennas have insufficient channel adaptability and weak spatial diversity capability in complex environments, making it difficult to meet the service requirements for high-reliability parameter monitoring.

[0004] To address the aforementioned problems, this invention proposes a channel reconstruction and optimal port selection system and method for a fluid antenna system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a channel reconstruction and optimal port selection system and method for fluid antenna systems, which can solve problems such as uncontrollable channels, increased interference, and limited performance of fixed antennas in smart grids under 5G ultra-dense networking environments.

[0006] The above-mentioned objective of this invention is achieved through the following technical solution: A channel reconstruction and optimal port selection system for a fluid antenna system includes: a transmitter, a multipath channel module, a fluid antenna system, and a receiver; the transmitter sends a modulated signal to the multipath channel module, which simulates multipath propagation effects and sends the signal to each port of the fluid antenna system; the receiver performs channel estimation based on the received signal, determines the optimal receiving port through an optimal port selection algorithm, and sends a control signal to the fluid antenna system to activate the port; the fluid antenna system sends the signal from the selected port to the receiver for demodulation and decoding, and the receiver can simultaneously feed back the channel state to the transmitter to achieve adaptive transmission adjustment.

[0007] A method for channel reconstruction and optimal port selection in a fluid antenna system includes the following steps: Step 1: Based on the multipath channel model, mathematically model the complex channel coefficients of different ports; Step 2: Based on the mathematical modeling of the complex channel coefficients obtained in Step 1, estimate the number of paths using the MDL criterion. The direction angle is estimated using the MUSIC algorithm. and elevation angle Path gain is estimated using the least squares regression method. The channel response of each port in the two-dimensional array is reconstructed, thereby completing the channel reconstruction; Step 3: After completing the channel reconstruction in Step 2, select the optimal port for communication based on the amplitude information of the reconstructed channel.

[0008] Furthermore, the specific steps of step 1 include: In a two-dimensional FAS, the receiver has a total of Ports, distributed in On the plane. The channel is on the . The complex channel coefficients at each port are expressed as follows: in: The direction cosine, and The first The azimuth and elevation angles of the path, For path complex gain, It is additive white Gaussian noise.

[0009] Furthermore, the specific steps of step 2 include: (1) Based on the mathematical modeling of the complex channel coefficients obtained in step 1, the number of paths is estimated using the MDL criterion. To determine the number of paths in the channel Assuming a narrowband stationary model: in time The array receive vector is: in For array manifold matrix, These are signal vectors that are not perfectly correlated. With zero mean and variance The spatial white noise is assumed, and {x(t)} is assumed to be independent and identically distributed (i.i.d.). The snapshots are stacked in columns to form a data matrix. The sample (space) covariance matrix is ​​defined as: right Perform eigenvalue decomposition to obtain One real nonnegative eigenvalue Theoretically, the covariance matrix can be written as in For signal covariance, if If the signal is a real signal and the signal and noise satisfy the above assumptions, then The eigenvalues ​​of the front a larger value and One equals The repetitive noise characteristic value.

[0010] Based on this model, the likelihood function of the observed data can be written, and the model selection criterion can be constructed: In the formula, The total observation dimension of the array (i.e., the number of array elements), The number of signal sources to be estimated. For the number of samples, Let f be the likelihood function, and (2) Estimating the direction angle using the MUSIC algorithm and elevation angle Determining the number of paths Then, the system further uses the MUSIC algorithm to estimate the angle of arrival AoA for each path; For a two-dimensional reconfigurable port array, its array steering vector can be expressed as: Based on noise subspace The MUSIC spatial spectral function is defined as follows: By searching for the peak of this spectral function on a two-dimensional angular grid, the AoA estimates for all paths can be obtained. .

[0011] (3) Estimate path gain using least squares regression method The system only from Select from the ports Observation is performed on each port; The observation ports are spaced on a two-dimensional grid. Uniform distribution: , in ,and .

[0012] Observation channel vector It can be represented as: , in: To receive channel observations, For the path gain to be estimated, To receive noise samples, for The observation matrix, its first... Behavior: .

[0013] make , ,but The goal is to minimize the squared error: It can be estimated using the following formula: , in for The pseudo-rebellion, specifically, , It is the conjugate transpose operator.

[0014] At this point, the number of paths... Angle of arrival for each path and the corresponding path gain All of these have been determined. Based on these estimates, the system can reconstruct the channel response of each port in the two-dimensional array, thereby completing the channel reconstruction.

[0015] Furthermore, the specific steps of step 3 include: Using the estimated path gain Reconstruct the channel gain of all ports: , Select the port with the highest channel gain as the optimal port: , The system receives data signals at the optimal port to complete the communication process.

[0016] The advantages and beneficial effects of this invention are as follows: 1. This invention proposes a channel reconstruction and optimal port selection system and method for a fluid antenna system. Based on a multipath channel model, the complex channel coefficients of different ports are mathematically modeled. The number of paths is estimated using the MDL criterion, the azimuth and elevation angles are estimated using the MUSIC algorithm, and the path gain is estimated using the least squares regression method. The channel response of each port in the two-dimensional array is reconstructed, thus completing the channel reconstruction. After the channel reconstruction is completed, the optimal port is selected for communication based on the amplitude information of the reconstructed channel. Simulation analysis shows that the method of this invention can still achieve near-full-port scanning performance even when only about 10% of the ports are observed. This invention effectively improves the communication reliability of smart grids in 5G ultra-dense networks, reduces the channel estimation cost, and provides higher accuracy and higher security wireless transmission guarantees for monitoring key grid parameters. This invention has the advantages of low cost, high efficiency, and high accuracy, and can significantly improve the communication reliability and key parameter monitoring capabilities of smart grids in complex scenarios.

[0017] 2. This invention achieves efficient sampling of a small number of observation ports by constructing a reconfigurable antenna array. It then reconstructs the channel distribution across all ports using least-squares estimation, ultimately selecting the port with the strongest channel gain for data reception. This results in stable and reliable communication performance even in complex electromagnetic environments. This method effectively improves the communication reliability of smart grids in 5G ultra-dense networks, reduces channel estimation costs, and provides higher accuracy and security for wireless transmission of key grid parameters. Simulation analysis shows that this method achieves near-full-port scanning performance with only a small number of observation ports, while also possessing advantages such as low complexity and high efficiency. As the demand for dynamically reconfigurable communication capabilities in future smart grids continues to increase, this type of fluid antenna technology will gradually replace traditional single-point antenna structures, becoming an important technological direction for new smart grid communication. Attached Figure Description

[0018] Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a flowchart of the processing of the present invention. Detailed Implementation

[0019] The structure of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that these embodiments are descriptive and not limiting.

[0020] A channel reconstruction and optimal port selection system for a fluid antenna system, such as Figure 1As shown, it includes: a transmitter, a multipath channel module, a fluid antenna system, and a receiver; the transmitter sends the modulated signal to the multipath channel module, which simulates the multipath propagation effect and sends the signal to each port of the fluid antenna system; the receiver performs channel estimation based on the received signal, determines the optimal receiving port through an optimal port selection algorithm, and sends a control signal to the fluid antenna system to activate the port; the fluid antenna system sends the signal from the selected port to the receiver for demodulation and decoding, and the receiver can feed back the channel status to the transmitter to achieve adaptive transmission adjustment.

[0021] The entire process forms a closed loop of "channel estimation - port selection - signal reception - feedback control", thereby dynamically optimizing system performance in complex multipath environments.

[0022] In this embodiment, the channel reconstruction and optimal port selection of the fluid antenna system of the present invention consists of a transmitter and a receiver. The receiver is equipped with a reconfigurable antenna array. This specific embodiment takes a two-dimensional antenna as an example, whose ports are uniformly distributed on a two-dimensional plane. The system maximizes channel gain by selecting the optimal receiver port, thereby improving the reliability and spectral efficiency of millimeter-wave communication.

[0023] A method for channel reconstruction and optimal port selection in a fluid antenna system, such as Figure 2 As shown, it includes the following steps: Step 1: Based on the multipath channel model, mathematically model the complex channel coefficients of different ports; The specific steps of step 1 include: In a two-dimensional FAS, the receiver has a total of Ports, distributed in On the plane. The channel is on the . The complex channel coefficients at each port are expressed as follows: in: The direction cosine, and The first The azimuth and elevation angles of the path, For path complex gain, It is additive white Gaussian noise.

[0024] Step 2: Based on the mathematical modeling of the complex channel coefficients obtained in Step 1, estimate the number of paths using the MDL criterion. The direction angle is estimated using the MUSIC algorithm. and elevation angle Path gain is estimated using the least squares regression method. The channel response of each port in the two-dimensional array is reconstructed, thereby completing the channel reconstruction; The specific steps of step 2 include: (1) Based on the mathematical modeling of the complex channel coefficients obtained in step 1, the number of paths is estimated using the MDL criterion. To determine the number of paths in the channel Assuming a narrowband stationary model (or each snapshot satisfies a linear Gaussian model): in time... The array receive vector is in For array manifold matrix, These are signal vectors that are not perfectly correlated. With zero mean and variance The spatial white noise is assumed, and {x(t)} is assumed to be independent and identically distributed (i.i.d.). The snapshots are stacked in columns to form a data matrix. The sample (space) covariance matrix is ​​defined as follows: right Perform eigenvalue decomposition to obtain One real nonnegative eigenvalue Theoretically, the covariance matrix can be written as in For signal covariance, if If the signal is a real signal and the signal and noise satisfy the above assumptions, then The eigenvalues ​​of the front A larger value (related to signal energy) and One equals The repetitive noise characteristic value. Based on this model, the likelihood function of the observed data can be written, and the model selection criterion can be constructed: In the formula The total observation dimension of the array (i.e., the number of array elements), The number of signal sources to be estimated. The number of samples, Let f be the likelihood function, and In practice, the system iterates through all possible sources and selects the source number that minimizes the MDL criterion function. This serves as the final estimation result. This process accurately distinguishes between the signal subspace and the noise subspace, thereby reliably determining the number of paths in the channel. This provides a correct model order basis for subsequent channel parameter estimation.

[0025] (2) Estimating the direction angle using the MUSIC algorithm and elevation angle Determining the number of paths Then, the system further uses the MUSIC (Multiple Signal Classification) algorithm to estimate the angle of arrival (AoA) of each path, that is... The MUSIC algorithm is based on the orthogonality principle between the signal subspace and the noise subspace, and searches in different directions by constructing a spatial spectrum function.

[0026] For a two-dimensional reconfigurable port array, its array steering vector can be expressed as: Based on noise subspace The MUSIC spatial spectral function is defined as follows: By searching for the peak of this spectral function on a two-dimensional angular grid, the AoA estimates for all paths can be obtained. These angular parameters will be used to construct the path steering matrix, providing the necessary spatial feature information for subsequent path gain estimation and two-dimensional channel reconstruction.

[0027] (3) Estimate path gain using least squares regression method To reduce channel estimation overhead, the system only... Select from the ports Observations are performed at individual ports. The observation ports are spaced at intervals on a two-dimensional grid. Uniform distribution: , in ,and .

[0028] Observation channel vector It can be represented as: , in: To receive channel observations, For the path gain to be estimated, To receive noise samples, for The observation matrix, its first... Behavior: .

[0029] make , ,but The goal is to minimize the squared error: (The noise is very small and can be ignored) Determine what minimizes (15) A common method for calculating the value is to use least squares regression, therefore It can be estimated using the following formula: , in for The pseudo-rebellion, specifically, , It is the conjugate transpose operator.

[0030] At this point, the number of paths... Angle of arrival for each path and the corresponding path gain All of these have been determined. Based on these estimates, the system can reconstruct the channel response of each port in the two-dimensional array, thereby completing the channel reconstruction.

[0031] Step 3: After completing the channel reconstruction in Step 2, select the optimal port for communication based on the amplitude information of the reconstructed channel.

[0032] The specific steps of step 3 include: Using the estimated path gain Reconstruct the channel gain of all ports: , Select the port with the highest channel gain as the optimal port: , The system receives data signals at the optimal port to complete the communication process.

[0033] Example 1: To facilitate understanding of the channel reconstruction and optimal port selection method proposed in this invention, the method flow is illustrated below with a specific example. The parameter scale used in this example is moderate and can clearly demonstrate all the processing steps of this invention.

[0034] Example scenario setting Consider a two-dimensional fluid antenna array with the following size: Total number of ports The port spacing is set to half a wavelength. To reduce observation overhead, samples are selected uniformly from the array only: Channel measurements were performed on each port.

[0035] Assume there are two main propagation paths in the channel. Its complex channel model is: in: The noise is additive white Gaussian noise, and the SNR is set to 20 dB.

[0036] The calculation steps corresponding to the method of this invention are as follows: Step 1: Mathematical Modeling of Complex Channels For a two-dimensional port ((m,n)), its complex channel coefficient model is: in: Based on the above model, the observed values ​​are calculated for the selected (M=12) known ports, forming the observed signal vector. Step 2: Channel Reconfiguration (1) Estimate the number of paths based on the MDL criterion. structure Sample covariance matrix: Sort the feature values ​​and calculate the MDL values ​​corresponding to different numbers of candidate paths (n=0,1,2,3,4): Finally, in the number of candidate paths The minimum MDL value obtained is: (2) MUSIC algorithm estimates azimuth and elevation angles Constructing the noise subspace Calculate the MUSIC spatial spectrum: The estimation results were obtained by searching for spectral peaks in a two-dimensional angular grid (0–180°):

[0037] The estimation errors are all less than .

[0038] (3) Least squares estimation of path gain Construct the observation matrix based on the estimated direction angle. : The path gain is estimated as follows: Substituting the data, we calculate:

[0039] (4) Reconstruct the channels of all 100 ports. For any port (m, n): Finally, a reconstructed channel matrix with 100 ports is obtained. .

[0040] Step 3: Optimal Port Selection Calculate the amplitude for each port: Find the port where the maximum value is located: In this example, the optimal port index is found to be port 56, and the reconstructed channel gain is approximately 1.18. Comparison with the actual channel shows that the actual optimal port is also port 56. This demonstrates that the method of this invention achieves high channel reconstruction accuracy and precise port selection.

[0041] The computational complexity and measurement overhead of this invention will be analyzed below to demonstrate its superiority.

[0042] (1) Computational complexity analysis The computational complexity of this invention mainly lies in two stages: channel parameter estimation and full-port channel reconstruction. Assume the total number of ports in the system is... The number of observation ports is The estimated number of multipath paths is In the path number estimation stage, the MDL criterion is used to select the model order, with a complexity of O(n). In the AoA estimation stage, the MUSIC algorithm is used, with a complexity of O(n log n). ,in Let be the number of grid points in the 2D angle search. Path gain estimation is achieved through least squares regression, with a complexity of O(n log n). Because in actual systems and the number of paths Typically much smaller Therefore, the overall computational complexity is much lower than that required for all N A full-port scan method that measures each port.

[0043] (2) Measurement cost analysis Traditional full-port scanning requires scanning all ports. N While measurements are performed on multiple ports, this invention only requires measurements on sparsely distributed... High-precision channel reconstruction can be achieved by observing multiple ports. This significantly reduces the time overhead and hardware resource consumption of channel estimation, making it particularly suitable for smart grid communication scenarios with high real-time requirements. This invention achieves a good balance between complexity, overhead, and performance, possessing comprehensive advantages such as low complexity, low cost, high precision, and strong adaptability. It is especially suitable for high-reliability, low-latency wireless parameter monitoring and data transmission scenarios in smart grids, providing a feasible technical path for future power Internet of Things communication systems.

[0044] Although embodiments and drawings of the present invention have been disclosed for illustrative purposes, those skilled in the art will understand that various substitutions, variations and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims

1. A channel reconstruction and optimal port selection system for a fluid antenna system, characterized in that: include: Transmitter, multipath channel module, fluid antenna system, and receiver; The transmitter sends the modulated signal into the multipath channel module, which simulates the multipath propagation effect and sends the signal to each port of the fluid antenna system. The receiver performs channel estimation based on the received signal, determines the optimal receiving port through the optimal port selection algorithm, and sends a control signal to the fluid antenna system to activate the port. The fluid antenna system sends the signal from the selected port to the receiver for demodulation and decoding. At the same time, the receiver can feed back the channel status to the transmitter to achieve adaptive transmission adjustment.

2. A method for channel reconstruction and optimal port selection in a fluid antenna system, characterized in that: Includes the following steps: Step 1: Based on the multipath channel model, mathematically model the complex channel coefficients of different ports; Step 2: Based on the mathematical modeling of the complex channel coefficients obtained in Step 1, estimate the number of paths using the MDL criterion. The direction angle is estimated using the MUSIC algorithm. and elevation angle Path gain is estimated using the least squares regression method. The channel response of each port in the two-dimensional array is reconstructed, thereby completing the channel reconstruction; Step 3: After completing the channel reconstruction in Step 2, select the optimal port for communication based on the amplitude information of the reconstructed channel.

3. The channel reconstruction and optimal port selection method for a fluid antenna system according to claim 2, characterized in that: The specific steps of step 1 include: In a two-dimensional FAS, the receiver has a total of Ports, distributed in On the plane; the channel is on the . The complex channel coefficients at each port are expressed as follows: ; in: The direction cosine, and The first The azimuth and elevation angles of the path, For path complex gain, It is additive white Gaussian noise.

4. The channel reconstruction and optimal port selection method for a fluid antenna system according to claim 2, characterized in that: The specific steps of step 2 include: (1) Based on the mathematical modeling of the complex channel coefficients obtained in step 1, the number of paths is estimated using the MDL criterion. ; To determine the number of paths in the channel Assuming a narrowband stationary model: in time The array receive vector is: ; in For array manifold matrix, These are signal vectors that are not perfectly correlated. With zero mean and variance Spatial white noise, and assume that {x(t)} are independent and identically distributed (i.i.d.); The snapshots are stacked in columns to form a data matrix. ; The sample (space) covariance matrix is ​​defined as: ; right Perform eigenvalue decomposition to obtain One real nonnegative eigenvalue Theoretically, the covariance matrix can be written as ; in For signal covariance, if If the signal is a real signal and the signal and noise satisfy the above assumptions, then The eigenvalues ​​of the front a larger value and One equals The repetitive noise characteristic value; Based on this model, the likelihood function of the observed data can be written, and the model selection criterion can be constructed: ; In the formula, The total observation dimension of the array (i.e., the number of array elements), The number of signal sources to be estimated. For the number of samples, Let f be the likelihood function, and ; (2) Estimating the direction angle using the MUSIC algorithm and elevation angle ; Determining the number of paths Then, the system further uses the MUSIC algorithm to estimate the angle of arrival AoA for each path; For a two-dimensional reconfigurable port array, its array steering vector can be expressed as: ; Based on noise subspace The MUSIC spatial spectral function is defined as follows: ; By searching for the peak of this spectral function on a two-dimensional angular grid, the AoA estimates for all paths can be obtained. ; (3) Estimate path gain using least squares regression method ; The system only from Select from the ports Observation is performed on each port; The observation ports are spaced on a two-dimensional grid. Uniform distribution: ， in ,and ; Observation channel vector It can be represented as: ， in: To receive channel observations, For the path gain to be estimated, To receive noise samples, for The observation matrix, its first... Behavior: ； make , ,but ; The goal is to minimize the squared error: ; It can be estimated using the following formula: ， in for The pseudo-rebellion, specifically, , It is the conjugate transpose operator; At this point, the number of paths... Angle of arrival for each path and the corresponding path gain All of these have been determined. Based on these estimates, the system can reconstruct the channel response of each port in the two-dimensional array, thereby completing the channel reconstruction.

5. The channel reconstruction and optimal port selection method for a fluid antenna system according to claim 2, characterized in that: The specific steps of step 3 include: Using the estimated path gain Reconstruct the channel gain of all ports: ， Select the port with the highest channel gain as the optimal port: ， The system receives data signals at the optimal port to complete the communication process.

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