Method for predicting beam forming through mobile sensing

Abstract

The invention discloses a method for predicting beam forming through mobile sensing, relates to wireless communication, and provides a scheme for solving the problem that beam forming in the prior art is not efficient enough. The method comprises the following steps: S1, constructing a novel perception-assisted SWIPT system model; s2, constructing a perception prediction and tracking model of a multi-antenna hybrid access point (HAP) to the motion state of a mobile energy user MEU; s3, constructing an online beam forming framework based on extended Kalman filtering (EKF); s4, constructing an optimization problem model in which the ratio of the energy collection power of the mobile energy user MEU to the total power consumption at the multi-antenna hybrid access point HAP is maximized; and S5, converting the non-convex optimization problem to convex optimization processing based on Dinkelbach and SDR transformation, and realizing wireless energy transmission WPT efficiency maximization of the perception-assisted SWIPT system. The method has the advantages that the constructed multi-antenna hybrid access point HAP of the perception-assisted SWIPT system provides services for a plurality of single-antenna communication users IU and a mobile energy user MEU, is closer to an actual application scene, and realizes accurate tracking precision and efficient power transmission.

Description

Technical Field

[0001] The present invention relates to wireless communication technology, and in particular to a method for motion sensing prediction beamforming. Background Art

[0002] As the popularity of the Internet of Things increases, a large number of emerging IoT applications and scenarios such as unmanned factories and smart homes have begun to attract public attention. These scenarios usually require the deployment of a large number of low-power sensors to achieve sustainable environmental monitoring, data collection and intelligent control. As a promising technology, wireless power communication SWIPT has become one of the most promising solutions, enabling base stations BS or access points AP to provide information transmission and wireless charging at the same time. In particular, sensors are able to obtain energy from remote sources through radio frequencies RF to achieve true wireless power supply, thus opening up new ways for the widespread deployment and sustainable operation of IoT devices.

[0003] Wireless energy transmission faces a series of new challenges in dynamic environments due to the emerging applications of sensors mounted on moving objects, such as unmanned transport vehicles and patrol robots. Especially in highly dynamic scenarios, it becomes critical to obtain accurate sensor location information. However, the frequent acquisition of channel state information (CSI) exacerbates the energy loss caused by the above feedback mechanism. Therefore, it is crucial to achieve timely perception and positioning of mobile sensors.

[0004] ISAC, which integrates communication and perception, enables base stations to wirelessly perceive and characterize the environment, becoming a key technology for future 6G networks. Recent studies have shown that ISAC can simplify high-dynamic wireless communications, thereby bypassing cumbersome feedback mechanisms and providing a possible solution for the positioning of mobile sensors. The efficiency of wireless energy transmission (WPT) in SWIPT perception-assisted SWIPT systems has been significantly improved.

[0005] However, existing technologies are limited to beamforming design in static scenarios, without considering mobile energy receivers or specific channel state information (CSI) acquisition. Once the receiver leaves a stable static state, frequent channel state information (CSI) feedback will lead to a reduction in the actual collected energy. Therefore, how to design an efficient beamforming algorithm to ensure that the real-time and accurate prediction and tracking of mobile energy users (MEUs) is achieved while meeting the requirements of information transmission and energy collection is a challenge that SWIPT technology still faces in practical applications. Summary of the invention

[0006] The present invention aims to provide a method for motion sensing prediction beamforming to solve the problems existing in the above-mentioned prior art.

[0007] The method for motion sensing prediction beamforming described in the present invention comprises the following steps:

[0008] S1. Construct a new perception-assisted SWIPT system model;

[0009] S2. Construct a perception prediction and tracking model of the multi-antenna hybrid access point HAP for the motion state of the mobile energy user MEU;

[0010] S3. Construct an online beamforming framework based on the extended Kalman filter (EKF);

[0011] S4. Construct an optimization problem model for maximizing the ratio of the power collected by the mobile energy user MEU to the total power consumption at the multi-antenna hybrid access point HAP;

[0012] S5. The non-convex optimization problem is transformed into a convex optimization process based on Dinkelbach and SDR transform to maximize the efficiency of wireless power transmission (WPT) of the perception-assisted SWIPT system.

[0013] The method of mobile sensing prediction beamforming described in the present invention has the advantage that the constructed sensing-assisted SWIPT system multi-antenna hybrid access point HAP provides services for multiple single-antenna communication users IU and one mobile energy user MEU, which is closer to the actual application scenario. Wave velocity shaping is performed based on the dynamic prediction of the extended Kalman filter EKF, the dynamic mobile energy user MEU is tracked by using the echo signal, and the channel state information CSI is inferred by predicting the motion parameters of the mobile energy user MEU, thereby achieving accurate tracking accuracy and efficient power transmission. The beamforming formula is formulated as a problem of maximizing the efficiency of wireless energy transmission WPT, while meeting the service quality requirements of single-antenna communication users IU and mobile energy users MEU. By applying Dinkelbach and SDR transforms, the original non-convex optimization problem is simplified and effectively solved, which greatly reduces the complexity of the calculation and meets the real-time processing requirements of the communication sensing-assisted SWIPT system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a method for motion sensing prediction beamforming described in the present invention.

[0015] Figure 2 It is a structural schematic diagram of the perception-assisted SWIPT system model described in the present invention.

[0016] Figure 3 It is a flowchart of the beamforming algorithm described in the present invention.

[0017] Figure 4It is a schematic diagram of the geometric distribution of a single-antenna communication user IU and a multi-antenna hybrid access point HAP in a single motion state.

[0018] Figure 5 It is a schematic diagram of the geometric distribution of a single-antenna communication user IU and a multi-antenna hybrid access point HAP in multiple motion states.

[0019] Figure 6 It is a schematic diagram of the real-time estimated position of the mobile energy user MEU.

[0020] Figure 7 It is a schematic diagram when the real-time estimated position of the mobile energy user MEU is decomposed into independent X-axis components.

[0021] Figure 8 It is a schematic diagram when the real-time estimated position of the mobile energy user MEU is decomposed into independent Y-axis components.

[0022] Fig. 9 It is a real-time energy transmission efficiency curve diagram of the method described in the present invention under different tracking upper limits.

[0023] Fig.10 It is a real-time average energy transmission efficiency curve diagram of the method described in the present invention under different tracking upper limits. DETAILED DESCRIPTION

[0024] The method of motion sensing prediction beamforming described in the present invention is as follows: Figures 1 to 3 As shown, the following steps are included:

[0025] S1. Construct a new perception-assisted SWIPT system model.

[0026] Construct a Figure 2 The sensing-assisted SWIPT system shown in the figure includes a multi-antenna hybrid access point HAP that sends communication signals to K single-antenna communication users IU, where the serial number of the single-antenna communication user IU is represented by k∈K={1,…,K}. At the same time, the multi-antenna hybrid access point HAP provides wireless charging for point-like mobile energy users MEU moving within a certain reachable range. The multi-antenna hybrid access point HAP is equipped with a uniform linear array, including M t transmit antennas and M r The mobile energy user MEU is equipped with a radio frequency energy collection module and moves along an unknown preset route of the multi-antenna hybrid access point HAP within the total mission time T. The multi-antenna hybrid access point HAP is fixed at a known position and is expressed in a two-dimensional coordinate system as: The single-antenna communication user IU is fixed at another known position, which is expressed in a two-dimensional coordinate system as:

[0027] in, Indicates the position of the multi-antenna hybrid access point HAP on the x-axis, Indicates the position of the multi-antenna hybrid access point HAP on the y-axis; x k represents the position of the kth single-antenna communication user IU on the x-axis, y k represents the position of the kth single-antenna communication user IU on the y-axis; the superscript (·) T Defined as a transpose operation, the value range of k is k∈{1,…,K}, K <M t .

[0028] S2. Construct a perception, prediction and tracking model of the multi-antenna hybrid access point HAP for the motion status of the mobile energy user MEU.

[0029] The multi-antenna hybrid access point HAP service time T is divided into L equal lengths δ, ie, T = Lδ. In each frame, the multi-antenna hybrid access point HAP first predicts the motion parameters of the mobile energy user MEU, and then directly infers the channel state information CSI based on the motion parameters.

[0030] The channel in the sensing-assisted SWIPT system is a line-of-sight channel, so it follows the free space path loss model. The channel from the multi-antenna hybrid access point HAP to the single-antenna communication user IU in the first frame is expressed as: The channel from the multi-antenna hybrid access point HAP to the mobile energy user MEU in the lth frame is expressed as:

[0031] β0 is the channel gain at the reference distance, d k is the Euclidean distance between the multi-antenna hybrid access point HAP and the kth single-antenna communication user IU, d E,l is the Euclidean distance between the multi-antenna hybrid access point HAP and the mobile energy user MEU, is the azimuth angle between the multi-antenna hybrid access point HAP and the kth single-antenna communication user IU, is the azimuth angle between the multi-antenna hybrid access point HAP and the mobile energy user MEU. The transmit array steering vector is modeled as:

[0032]

[0033] Indicates the azimuth.

[0034] In the first frame, the downlink signal transmitted by the multi-antenna hybrid access point HAP is Indicates, specifically: in, Represents M t ×1 complex matrix space, w k,lis the beamforming vector of the kth single-antenna communication user IU, v l is the beamforming vector of the mobile energy user MEU, the information carrying signal Expressed as express It follows a normal distribution with a mean of 0 and a variance of 1. represents any element of the symbol set K. The dedicated energy carries the signal is composed of E{|s0| 2 An arbitrary distribution with}=1 is generated.

[0035] Total transmit power P t The power budget P of the perception-assisted SWIPT system should not be exceeded max ,Right now:

[0036]

[0037] Among them, ‖·‖ represents the norm of the vector.

[0038] Therefore, the received signal at the kth single-antenna communication user IU in the lth frame is expressed as:

[0039]

[0040] where z k ~CN(0,σ k 2 ) is additive Gaussian white noise with mean 0 and variance σ k 2 The normal distribution of .

[0041] The received signal-to-interference-to-noise ratio γ corresponding to the kth single-antenna communication user IU k,l (w k,l ,v l ) is expressed as:

[0042]

[0043] Here, |·| means taking the absolute value.

[0044] For energy harvesting at the mobile energy user MEU, since all received RF signals are beneficial, the instantaneous RF power at the mobile energy user MEU is expressed as:

[0045]

[0046] where μ∈[0,1] is the energy conversion constant of the sensing-assisted SWIPT system, and the superscript (·) H Defined as taking the conjugate transpose operation.

[0047] The wireless energy transmission (WPT) efficiency of the sensing-assisted SWIPT system is defined as the power E collected by the mobile energy user MEU and the total power consumption P at the multi-antenna hybrid access point HAP. tot The ratio is expressed as:

[0048]

[0049] In the formula, is the power amplifier efficiency, P C is the circuit power consumption.

[0050] The multi-antenna hybrid access point HAP operates in full-duplex mode. While transmitting the downlink SWIPT signal, it also transmits the echo signal reflected by the mobile energy user MEU. l (t) Perform radar perception.

[0051]

[0052] υ l represents the Doppler shift of the echo, τ l represents the delay of the echo, z r,l Receiver noise representing the echo.

[0053] Round-trip channel model The reflection coefficient is The radar cross section is denoted as κ, and the receiving array steering vector is Transmit array steering vector

[0054]

[0055] vector represents the estimated parameters obtained by signal processing methods such as matched filtering, then the corresponding estimated variance The signal-to-noise ratio of the echo at the receiving end is Γ l Inversely proportional, as expressed by the following formula:

[0056]

[0057] where α MF is the intermediate frequency gain, Receiver noise for a multi-antenna hybrid access point (HAP).

[0058] The time-varying motion parameters of the mobile energy user MEU are defined as

[0059] in, represents the position of the mobile energy user MEU in the two-dimensional Cartesian coordinate system,

[0060] Represents the velocity of the mobile energy user MEU in a two-dimensional Cartesian coordinate system.

[0061] Therefore, the state transition model from the current frame to the next frame is composed of X l =A·X l-1 +z pl Given, where A = [I 2×2 ,δ·I 2×2 ; 0 2×2 ,I 2×2 ] represents the linear state transfer matrix, I 2×2 is a 2×2 identity matrix, 0 2×2 is a 2×2 zero matrix, z pl is the process noise vector, z pl ~CN(0,Q Z ), z pl Subject to mean 0 and covariance Q Z The complex Gaussian distribution, Q Z is the process noise.

[0062] S3. Construct an online beamforming framework based on extended Kalman filter (EKF).

[0063] In order to achieve closed-loop target prediction and tracking, a framework based on the extended Kalman filter (EKF) is adopted. The extended Kalman filter (EKF) is an effective state estimation method that can fuse measurements from multiple sensors to reduce uncertainty and error. In addition, the radar observation model that characterizes the relationship between the estimated parameters and the motion parameters is:

[0064] O l =f(X l )+z sl ;

[0065] Where f(·) is the nonlinear observation operator, z sl is the radar estimation noise vector, z sl ~CN(0,Q S ), where the estimated noise covariance matrix

[0066] The steps for fusing the estimated and predicted data are as follows:

[0067] At the beginning of each frame, the state prediction is Prediction covariance matrix Superscript represents the variable based on prediction, and the superscript ~ represents the variable corrected after prediction and perception fusion. The Kalman filter gain is then calculated as in represents the Jacobian matrix of the observation model. diag(x) is defined as a diagonal matrix where each diagonal element is the corresponding element in x. Therefore, the updated motion state is expressed as:

[0068] The forecast covariance matrix is ​​expressed as:

[0069] Iterate and update the motion state and the prediction covariance matrix M l ,The multi-antenna hybrid access point HAP can realize the positioning and tracking of the mobile energy user MEU.

[0070] S4. Construct an optimization problem model to maximize the ratio of the power collected by the mobile energy user MEU to the total power consumption at the multi-antenna hybrid access point HAP.

[0071] The goal of this embodiment is to maximize the efficiency of real-time wireless energy transmission WPT per frame by optimizing the transmit beamforming, while meeting the service quality requirements of the perception-assisted SWIPT system, including the signal-to-interference-to-noise ratio requirements of the single-antenna communication user IU and the energy collection and perception service quality of the mobile energy user MEU.

[0072] The trace of the prediction covariance matrix represents the mean square error after state tracking, so the instantaneous tracking performance is defined as S = tr(M l ), which is an implicit function of the beamforming vector v, and tr(·) is defined as the trace of the matrix. The corresponding beamforming problem is formulated as follows:

[0073]

[0074] stP(w k ,v)≤P max ,,

[0075] SINR k (w k )≥γ min ,,

[0076]

[0077] S(v)≤C max ..

[0078] Where st is defined as subject to the constraints; P(w k ,v)≤P max represents the transmit power budget; SINR k (w k )≥γ min Ensure the minimum signal-to-interference-to-noise ratio of the single-antenna communication user IU; for the harvested energy constraints, Indicates that beamforming is performed while constraining SINR k (w k )≥γ min The predicted energy under min Indicates the minimum power requirement at the first frame; predefined parameter C max Indicates the upper limit of tracking performance.

[0079] Combined estimated variance The relationship is inversely proportional to the received signal-to-noise ratio, and the estimated variance update is further expressed as in Constant a Ω Determined by the perception-assisted SWIPT system configuration and the specific estimation method, the estimated noise covariance matrix is ​​expressed as:

[0080]

[0081] S5. The non-convex optimization problem is transformed into a convex optimization process based on Dinkelbach and SDR transform to maximize the efficiency of wireless power transmission (WPT) of the perception-assisted SWIPT system.

[0082] In order to solve the non-convex objective function, a nonlinear fractional programming based on Dinkelbach is adopted, where the objective function The corresponding subtraction form is given by Given.

[0083] In the case of using the unique zero solution, the problem P0 is transformed into the following equivalent form:

[0084]

[0085] Introduce auxiliary variable W = ww H and V = vv H , the constraint condition is W k ≥0,V≥0,rank(W k )=1 and rank(V)=1. rank(·) is defined as the rank of the matrix. Therefore, the above optimization problem can be rewritten as:

[0086]

[0087] where g represents the channel from the predicted base station to the mobile energy user MEU. However, due to the rank-one constraint, the problem is still non-convex and can be solved by the classical semi-definite relaxation method. Therefore, we only need to remove the rank constraint, Problem P1 can be transformed into a semidefinite relaxation and effectively solved by a standard solver such as CVX. CVX is a convex optimization toolkit developed based on the Matlab environment and can be used to solve linear programming, quadratic programming, semidefinite programming, and geometric programming problems. Among them, the feasible rank-one solution of the original problem can be obtained by eigenvalue decomposition or Gaussian randomization procedure.

[0088] The effectiveness of the method proposed in the present invention is demonstrated by numerical results below.

[0089] The multi-antenna hybrid access point HAP is set at the origin and equipped with 32 transmitting antennas and 2 receiving antennas to provide services for 3 single-antenna communication users IU and 1 mobile energy user MEU, namely M t =32,M r =2, K =3. The sensing-assisted SWIPT system operates in the 5.5 GHz frequency band, and the reference channel power gain is calculated using the free space propagation model. The minimum signal-to-interference-to-noise ratio required by the single-antenna communication user IU is γ min =5dB, the energy collection requirement for the mobile energy user MEU is e min =1.1μW. The remaining simulation parameters are set as follows:

[0090] δ=0.5s,P max =40dBm,σ R =-70dBm,σ k =-80dBm,P C =0.8W,

[0091] a Ω =[0.085,0.9×10 6 ,45],α MF =10 4 ,κ=0.9,η=0.75,

[0092] Figure 4 and Figure 5 The geometric distribution of single-antenna communication users IU and multi-antenna hybrid access points HAP is shown, as well as the real-time tracking results under different target motions based on the proposed scheme.

[0093] The process noise z of the mobile energy user MEU in a single motion state pl The process noise experienced by the mobile energy user MEU in multiple motion states is relatively large. It can be observed that the real-time tracked trajectory usually matches the actual curves in different motion situations, proving that the present invention has good tracking performance.

[0094] In the multi-motion state, trajectory deviations are observed when the mobile energy user MEU undergoes sharp turns resulting in a sudden increase in process noise. However, these deviations gradually decrease after a few frames of real-time tracking adjustments, allowing the trajectory to quickly realign with the actual path.

[0095] To further demonstrate the performance of the present invention, we follow the setup in multiple motion states and evaluate the tracking performance of the instantaneous position mean square error during the entire service time. Figure 6 As shown in Figure 1, the present invention maintains a lower mean square error than the prior art throughout the entire operation time. In addition, this improvement gap is very obvious around frames 22 and 44, because the mobile energy user MEU experiences turns that the state transition model cannot accurately model. However, the solution of the present invention significantly reduces the positioning error and exhibits superior tracking performance. This positioning improvement is Figure 7 and Figure 8 It is more clearly illustrated in FIG, where the position of the mobile energy user MEU is decomposed into independent X-axis and Y-axis. The results show that the present invention is closer to the actual trajectory than the prior art without perception assistance.

[0096] The real-time wireless energy transmission WPT efficiency of the present invention under different tracking upper limits is as follows: Fig. 9 and Fig.10 As shown in Figure 2, the UB scheme represents the ideal optimal value based on the accurate mobile energy user MEU state prior, without any resource consumption. The three obvious fluctuations partially reflect the motion state of the mobile energy user MEU, because the RF signal strength changes with the distance d. E,l decreases with the increase of d, and the three peaks correspond exactly to d E,l The present invention is close to the ideal upper bound in overall performance, but due to the real-time positioning error caused by the sudden change of the movement of the mobile energy user MEU, there will be obvious gaps and fluctuations.

[0097] As the constraint value increases, the performance degradation becomes more obvious. This is due to the insufficient perception accuracy, which leads to significant channel prediction errors in subsequent frames, thus degrading the performance of the perception-assisted SWIPT system.

[0098] The results also reveal that stricter sensing accuracy constraints do not necessarily lead to higher wireless power transfer (WPT) efficiency because excessive sensing-assisted SWIPT system resources will be consumed.

[0099] In summary, the method of mobile sensing prediction beamforming described in the present invention can achieve efficient power transmission. Beamforming is formulated as a problem of maximizing the efficiency of wireless power transmission WPT, while meeting the service quality requirements of single-antenna communication users IU and mobile energy users MEU. By applying Dinkelbach and semi-definite relaxation methods, the original non-convex optimization problem is simplified and effectively solved. Simulation results are provided to verify the superiority of the proposed scheme in improving the performance of the sensing-assisted SWIPT system.

[0100] For those skilled in the art, various other corresponding changes and deformations can be made according to the technical solutions and concepts described above, and all of these changes and deformations should fall within the protection scope of the claims of the present invention.

Claims

1. A method for motion sensing prediction beamforming, characterized in that: The following steps are involved: S1. Construct a new perception-assisted SWIPT system model; S2. Construct a perception prediction and tracking model of the multi-antenna hybrid access point HAP for the motion state of the mobile energy user MEU; S3. Construct an online beamforming framework based on the extended Kalman filter (EKF); S4. Construct an optimization problem model for maximizing the ratio of the power collected by the mobile energy user MEU to the total power consumption at the multi-antenna hybrid access point HAP; S5. The non-convex optimization problem is transformed into a convex optimization process based on Dinkelbach and SDR transform to maximize the efficiency of wireless power transmission (WPT) of the perception-assisted SWIPT system.

2. The method for motion-aware prediction beamforming according to claim 1, characterized in that: In the step S1, the constructed sensing-assisted SWIPT system includes a multi-antenna hybrid access point HAP sending a communication signal to K single-antenna communication users IU, where the serial number of the single-antenna communication user IU is represented by k∈K={1,…,K}; the multi-antenna hybrid access point HAP provides wireless charging for point-like mobile energy users MEU moving within a certain reachable range; the multi-antenna hybrid access point HAP is equipped with a uniform linear array, including M t transmit antennas and M r The mobile energy user MEU is equipped with a radio frequency energy collection module and moves along an unknown preset route of the multi-antenna hybrid access point HAP within the total mission time T. The multi-antenna hybrid access point HAP is fixed at a known position and is expressed in a two-dimensional coordinate system as: The single-antenna communication user IU is fixed at another known position, which is expressed in a two-dimensional coordinate system as: in, Indicates the position of the multi-antenna hybrid access point HAP on the x-axis, Indicates the position of the multi-antenna hybrid access point HAP on the y-axis; x k represents the position of the kth single-antenna communication user IU on the x-axis, y k represents the position of the kth single-antenna communication user IU on the y-axis; the superscript (·) T Defined as a transpose operation, the value range of k is k∈{1,…,K}, K <M t .

3. The method for motion sensing prediction beamforming according to claim 2, characterized in that: In the step S2, the multi-antenna hybrid access point HAP service time T is divided into L equal lengths δ, that is, T=Lδ; in each frame, the multi-antenna hybrid access point HAP first predicts the motion parameters of the mobile energy user MEU, and then directly infers the channel state information CSI based on the motion parameters; The channel in the sensing-assisted SWIPT system is a line-of-sight channel that follows the free-space path loss model; the channel from the multi-antenna hybrid access point HAP to the single-antenna communication user IU in the first frame is expressed as: The channel from the multi-antenna hybrid access point HAP to the mobile energy user MEU in the lth frame is expressed as: β0 is the channel gain at the reference distance, d k is the Euclidean distance between the multi-antenna hybrid access point HAP and the kth single-antenna communication user IU, d E,l is the Euclidean distance between the multi-antenna hybrid access point HAP and the mobile energy user MEU, is the azimuth angle between the multi-antenna hybrid access point HAP and the kth single-antenna communication user IU, is the azimuth angle between the multi-antenna hybrid access point HAP and the mobile energy user MEU; The transmit array steering vector is modeled as: Indicates azimuth; In the first frame, the downlink signal transmitted by the multi-antenna hybrid access point HAP is expressed as: Among them, w k,l is the beamforming vector of the kth single-antenna communication user IU, v l is the beamforming vector of the mobile energy user MEU, the information carrying signal Expressed as express It follows a normal distribution with a mean of 0 and a variance of 1. represents any element of the symbol set K; and the dedicated energy carries the signal is composed of E{|s0| 2 } = 1 is generated by any distribution; Total transmit power P t The power budget P of the perception-assisted SWIPT system should not exceed max : Among them, ‖·‖ represents the norm of the vector; The received signal at the kth single-antenna communication user IU in the lth frame is expressed as: where z k ~CN(0,σ k 2 ) is additive Gaussian white noise with mean 0 and variance σ k 2 Normal distribution; corresponding to the received signal-to-interference-noise ratio γ of the kth single-antenna communication user IU k,l (w k,l ,v l ) is expressed as: Among them, |·| means taking the absolute value; The instantaneous RF power at the mobile energy user MEU is expressed as: where μ∈[0,1] is the energy conversion constant of the sensing-assisted SWIPT system, and the superscript (·) H Defined as taking the conjugate transpose operation; The wireless energy transmission WPT efficiency of the sensing-assisted SWIPT system is expressed as: In the formula, is the power amplifier efficiency, P C is the circuit power consumption; The multi-antenna hybrid access point HAP operates in full-duplex mode. While transmitting the downlink SWIPT signal, it also transmits the echo signal reflected by the mobile energy user MEU. l (t) Perform radar sensing; υ l represents the Doppler shift of the echo, τ l represents the delay of the echo, z r,l receiver noise representing the echo; Round-trip channel model The reflection coefficient is The radar cross section is denoted as κ, and the receiving array steering vector is Transmit array steering vector vector represents the estimated parameters obtained by signal processing methods such as matched filtering, then the corresponding estimated variance The signal-to-noise ratio of the echo at the receiving end is Γ l Inversely proportional, as expressed by the following formula: where α MF is the intermediate frequency gain, is the receiver noise of multi-antenna hybrid access point HAP; The time-varying motion parameters of the mobile energy user MEU are defined as in, represents the position of the mobile energy user MEU in the two-dimensional Cartesian coordinate system, represents the velocity of the mobile energy user MEU in a two-dimensional Cartesian coordinate system; The state transition model from the current frame to the next frame is represented by X l =A·X l-1 +z pl Given, where A = [I 2×2 ,δ·I 2×2 ; 0 2×2 ,I 2×2 ] represents the linear state transfer matrix, I 2×2 is a 2×2 identity matrix, 0 2×2 is a 2×2 zero matrix, z pl is the process noise vector, z pl ~CN(0,Q Z ), z pl Subject to mean 0 and covariance Q Z The complex Gaussian distribution, Q Z is the process noise.

4. The method for motion sensing prediction beamforming according to claim 3, characterized in that: In step S3, the radar observation model based on the extended Kalman filter EKF framework characterizing the relationship between the estimation parameters and the motion parameters is: O l =f(X l )+z sl ; Where f(·) is the nonlinear observation operator, z sl is the radar estimation noise vector, z sl ~CN(0,Q S ), where the estimated noise covariance matrix The steps for fusing the estimated and predicted data are as follows: At the beginning of each frame, the state prediction is Prediction covariance matrix Superscript represents the variable based on prediction, and the superscript ~ represents the variable modified after prediction and perception fusion; the Kalman filter gain is then calculated as in represents the Jacobian matrix of the observation model; diag(x) is defined as a diagonal matrix whose diagonal elements are the corresponding elements in x; The updated motion state is expressed as: The forecast covariance matrix is ​​expressed as: Iterate and update the motion state and the prediction covariance matrix M l , the multi-antenna hybrid access point HAP realizes the positioning and tracking of mobile energy users MEU.

5. The method for motion sensing prediction beamforming according to claim 4, characterized in that: In step S4, the trace of the prediction covariance matrix represents the mean square error after state tracking, and the instantaneous tracking performance is defined as S = tr(M l ), which is an implicit function of the beamforming vector v, and tr(·) is defined as the trace of the matrix; The corresponding beamforming problem formula is as follows: Where st is defined as subject to the constraints; P(w k ,v)≤P max represents the transmit power budget; SINR k (w k )≥γ min Ensure the minimum signal-to-interference-to-noise ratio of the single-antenna communication user IU; for the harvested energy constraints, Indicates that beamforming is performed while constraining SINR k (w k )≥γ min The predicted energy under min Indicates the minimum power requirement at the first frame; predefined parameter C max Indicates the upper limit of tracking performance; Combined estimated variance The estimated variance update is inversely proportional to the received signal-to-noise ratio. in Constant a Ω Determined by the perception-assisted SWIPT system configuration and the specific estimation method, the estimated noise covariance matrix is ​​expressed as:

6. The method for motion-aware prediction beamforming according to claim 5, characterized in that: In step S5, a non-convex objective function is solved by using a Dinkelbach-based nonlinear fractional programming, where the objective function The corresponding subtraction form is: In the case of using the unique zero solution, the problem P0 is transformed into the following equivalent form: Introduce auxiliary variable W = ww H and V = vv H , the constraints are Wk≥0,V≥0, rank(W k )=1 and rank(V)=1; rank(·) is defined as the rank of the matrix; Rewrite the optimization problem as: S(V)≤C max , IN k ≥0,V≥0, Where g represents the channel from the predicted base station to the mobile energy user MEU; Remove the rank constraint, rank(W k )=1, Problem P1 is transformed into a semidefinite relaxation and solved by a standard solver.

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