An aerial dynamic intelligent reflecting surface assisted network parameter configuration method

Abstract

This invention proposes a method for configuring network parameters using an airborne dynamic intelligent reflector-assisted network. It utilizes an unmanned aerial vehicle (UAV)-borne reconfigurable intelligent surface (RIS-UAV) to reflect signals from a local first-type transceiver (RFT1), providing data transmission services to a second-type transceiver (RFT2). Specifically, this invention proposes a communication flow between RIS-UAV, RFT1, and RFT2, with RFT1 as the computing and control center. A three-dimensional air-to-ground channel model incorporating angular errors is established. An optimization method for the RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position is designed based on iterative optimization and first-order Taylor expansion. Furthermore, a decision signaling flow and an update mechanism for the RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position based on the "maximum percentage of user performance degradation" are presented.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to the 3D dynamic deployment of Aerial Reconfigurable Intelligent Surfaces (ARISs) in wireless communication networks and the configuration of anti-shake robust transmission parameters, including ARIS position optimization protocols, processes and methods. Background Technology

[0002] In recent years, airborne reconfigurable intelligent surface (RIS)-assisted wireless communication on unmanned aerial vehicles (UAVs) has become a research hotspot in academia because RIS, supported by the agility of UAVs, possesses extensive high-gain service capabilities. If ground-based RIS cannot be deployed in locations with high line-of-sight (LoS) links to both the base station (BS) and the user, the probability of outages is extremely high. Furthermore, UAVs can not only provide high-LoS links for RIS but also reduce reflection path loss through location optimization. Combining these advantages and disadvantages, the combination of UAV and RIS, i.e., RIS-UAV, is a promising new network element in future 6G networks, with crucial applications.

[0003] However, many problems still need to be solved in the actual deployment and application of RIS-UAV-assisted networks. On the one hand, the communication performance of RIS-UAV is greatly affected by the accuracy of air-to-ground (A2G) channel modeling. Accurate models are the foundation of high-performance network deployment solutions. Currently, most literature uses the following three channel models: LoS channel model, probabilistic LoS channel model, and Ricean fading channel model. However, in order to facilitate solving optimization problems, most existing literature uses fixed channel parameters and does not consider the changes in channel parameters caused by the three-dimensional motion of RIS-UAV, thus failing to accurately characterize the real channel conditions of RIS-UAV-assisted networks.

[0004] On the other hand, most existing RIS-UAV communication work assumes that the Channel State Information (CSI) for A2G is perfect. However, unlike fixed cellular base stations on the ground, airflow and motor vibrations can cause random deviations in the flight trajectory of UAV platforms in the horizontal or vertical directions, leading to channel estimation errors, communication beam mismatch, and reduced communication rates. Therefore, establishing an accurate three-dimensional A2G channel model with UAV jitter and further researching RIS-UAV-assisted network deployment schemes remains an open and important problem. Summary of the Invention

[0005] This invention proposes a method for configuring parameters of an airborne dynamic intelligent reflector-assisted network. It utilizes a RIS-UAV to reflect signals from a local first-kind radio frequency transceiver (RFT1) to provide data transmission services to a second-kind radio frequency transceiver (RFT2). Specifically, this invention proposes a communication flow between the RIS-UAV, RFT1, and RFT2, establishes a 3D air-to-ground channel model including angular errors, designs an RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position optimization method based on iterative optimization and first-order Taylor expansion, and provides an update mechanism and decision signaling flow for the RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position based on the "maximum percentage of user performance degradation."

[0006] The method for configuring parameters of an aerial dynamic intelligent reflector-assisted network proposed in this invention is attached. Figure 1 As shown, it mainly includes steps 200-240.

[0007] Step 200, the proposed communication flow between RIS-UAV, RFT1, and RFT2 is attached. Figure 2 As shown.

[0008] The communication process consists of two phases: an initialization phase and a normalization phase. In the initialization phase, RFT2 reports its own location information; RFT1 calculates network parameters, including its beamforming matrix, RIS phase matrix, and UAV 3D position, based on the 3D air-to-ground channel model (including angle errors) established in step 210; then, RFT1 updates its own beamforming matrix and sends control information to the RIS-UAV to update the RIS phase matrix and UAV 3D position; subsequently, it enters the normalization phase. In the normalization phase, RFT2 reports its location information and current rate information; RFT1 evaluates whether network parameters need updating, including its beamforming matrix, RIS phase matrix, and UAV 3D position, based on the rate information of all RFT2s; if an update is needed, it calculates the network parameters, including its beamforming matrix, RIS phase matrix, and UAV 3D position, based on the 3D air-to-ground channel model (including angle errors) established in step 210; if no update is needed, no processing is performed. Then, RFT2 reports its location and current rate information, repeating the above process. RFT1 and RFT2 communicate with the RIS-UAV via wireless means.

[0009] Step 210: The established 3D air-to-ground channel model, including angle errors, is shown below.

[0010] It should be noted that this invention ignores the direct link between RFT1 and RFT2, and only considers the reflection channels between RFT1 and RIS-UAV, and between RIS-UAV and RFT2. Furthermore, the channel model of this invention considers the impact of positional movement and UAV jitter-induced angle errors on channel parameters; therefore, the calculation results are more consistent with reality and more robust.

[0011] At time t, the channel H between RFT1 and RIS-UAV BR [t] can be calculated as follows:

[0012]

[0013]

[0014]

[0015]

[0016] Where β0 is the path loss per unit distance, d BR [t] represents the distance between RFT1 and RIS-UAV; α is the path loss exponent between RFT1 and RIS-UAV. max and α minThese represent the maximum and minimum values ​​of the path loss exponent. a1 and b1 are constants determined by the environment; K is the Rice factor of the channel between RFT1 and RIS-UAV. max and K min These are the maximum and minimum values ​​of the Rice factor; The independent and identically distributed matrices that follow a circularly symmetric complex Gaussian distribution; H BR The nth term in [t] x +1)×(n y +1),m+1) elements are n x =0,1,…,N x -1, n y =0,1,…,N y -1,m=0,2,…,M-1,N x N represents the number of units in the horizontal dimension of the RIS. y Let M be the number of elements in the vertical dimension of RIS, M be the number of antennas in RFT1, and θ be the number of elements in the vertical dimension of RIS. BR [t] represents the pitch angle of RFT1 relative to the RIS-UAV. RFT1 is the azimuth angle relative to RIS-UAV; 1N ×M It is an N×M dimension matrix of all 1s; A BR [t] of ((n) x +1)×(n y +1),m+1) elements are Δθ BR [t] represents the pitch jitter error of RFT1 relative to RIS-UAV; B BR [t] of ((n) x +1)×(n y +1),m+1) elements are Let RFT1 be the azimuth jitter error relative to RIS-UAV; define

[0017] The channel between RIS-UAV and RFT2 at time t The following can be calculated:

[0018]

[0019]

[0020]

[0021]

[0022] in, The distance between RIS-UAV and RFT2; The path loss exponent between RIS-UAV and RFT2. The Rice factor for the channel between RIS-UAV and RFT2; These are independent and identically distributed vectors that follow a circularly symmetric complex Gaussian distribution; H BR The nth term in [t] x +1)×(n y +1)) elements are n x =0,1,…,N-1,n y =0,1,…,N y -1, RFT2 is the pitch angle relative to the RIS-UAV. This is the azimuth angle of RFT2 relative to RIS-UAV; The (n) x +1)×(n y +1)) elements are This refers to the pitch jitter error of RFT2 relative to RIS-UAV; The (n) x +1)×(n y +1)) elements are Let RFT2 be the azimuth jitter error relative to RIS-UAV; define

[0023] Step 220 optimizes the RFT1 beamforming, RIS phase, and UAV 3D position based on iterative optimization and first-order Taylor expansion. In this step, we focus on the optimization at a specific moment, therefore the time variable t is omitted.

[0024] In a K RFT2 device, the objective of this invention is to maximize the transmission rate of the minimum RFT2 device by designing optimal RFT1 beamforming, RIS phase, and UAV three-dimensional position.

[0025] The RFT1 beamforming matrix V is obtained by solving the following problem using the convex optimization toolbox.

[0026]

[0027] Where, λ k It is a slack variable. v kIt is the beamforming vector of RFT1 on the k-th RFT2. Let φ be the phase matrix of RIS, φ = [φ1, ..., φ2]. N ] T ,φ n Let N be the phase of the nth RIS unit, and N be the number of RIS units, where N = Nn. x N y , Blkdiag{·} is a block diagonal operation, P max This represents the maximum transmit power of RFT1, where the superscript [r] indicates the value of the variable in the r-th iteration. It is noise power. Let x represent the real part of x.

[0028] The RIS phase matrix φ is obtained by solving the following problem using the convex optimization toolbox.

[0029]

[0030] Where, γ k It is a slack variable. Γ n For φ n The amplitude, ρ n For φ n The phase.

[0031] The three-dimensional position of the UAV obtained by solving the following problem using the convex optimization toolbox

[0032]

[0033]

[0034] in, and f k,l (H R They are respectively about and H R The lower bound, τ k It is a slack variable. and These are the actual deployable space for the UAV's horizontal position and height, respectively.

[0035] By iteratively solving “P1→P2→P3→P4→P1→P2→…”, until the increment of η is less than a set threshold, the RFT1 beamforming, RIS phase, and UAV three-dimensional position at this point can be considered as configurable optimal network parameters.

[0036] Step 230: Based on the principle of "maximum percentage of user performance degradation", determine whether it is necessary to update RFT1 beamforming, RIS phase and UAV position.

[0037] Based on the current transmission rate reported by each RFT2, RFT1 calculates its rate decrease relative to the previous Q time slots and calculates the average rate decrease percentage. If more than X% of the RFT2s have an average rate decrease percentage exceeding Y%, then the beamforming, RIS phase, and UAV position of RFT1 are updated, and step 220 is performed; otherwise, no updates are performed, and no operation is performed.

[0038] The values ​​of Q, X, and Y can be preset according to the actual situation.

[0039] Step 240, the decision signaling process for RFT1 beamforming, RIS phase, and UAV position update is attached. Figure 3 As shown.

[0040] After calculating the optimal RFT1 beamforming, RIS phase, and UAV position based on the position information reported by all RFT2 units in the previous time step, RFT1 sends the calculated RIS phase and UAV position information to RIS-UAV. Upon receiving the information, RIS-UAV sends an ACK message to RFT1 and updates its RIS phase and its own position information. If RIS-UAV does not receive the message, RFT1 will continuously retransmit the information to RIS-UAV. After updating its RIS phase and its own position, RIS-UAV will send an ACK message to RFT1 again to ensure information synchronization.

[0041] Beneficial effects

[0042] This invention discloses a method for configuring network parameters using an aerial dynamic intelligent reflector-assisted network. It introduces the basic device components and a novel network element: the UAV-borne intelligent reflector surface (RIS-UAV), which consists of a RIS and a UAV. By reporting its current transmission rate using RFT2 and calculating the average rate decrease using RFT1, the system can determine whether to update network parameters, including RFT1 beamforming, RIS phase, and UAV position.

[0043] Furthermore, this invention designs a communication process between RFT1, RFT2, and UAV-RIS, deploying the central control and computing unit in RFT1 to ensure the network's computational processing and real-time control capabilities. Furthermore, the 3D channel model established in this invention considers channel angle errors, providing more accurate input data for network parameter updates.

[0044] Furthermore, this invention designs a decision signaling process for RFT1 beamforming, RIS phase, and UAV position update. Through the RIS-UAV dual ACK feedback mechanism, the execution of signaling and the synchronization of information can be guaranteed.

[0045] The method for configuring parameters of an aerial dynamic intelligent reflector-assisted network according to the present invention can be applied to various outdoor communication systems, including UAV-RIS assisted networks, UAV assisted networks, and other aerial communication systems. Attached Figure Description

[0046] To clearly explain the technical steps of this invention, all the accompanying drawings used in this description will be briefly described below. It should be noted that the drawings described below are merely examples of embodiments of this invention, and those skilled in the art can still obtain other drawings in different scenarios based on these drawings.

[0047] Appendix Figure 1 This is the implementation process of the present invention;

[0048] Appendix Figure 2 This invention describes the communication process between RIS-UAV, RFT1, and RFT2.

[0049] Appendix Figure 3 This invention relates to the decision signaling process for RFT1 beamforming, RIS phase, and UAV position update.

[0050] Appendix Figure 4 This is an example scenario provided by the present invention;

[0051] Appendix Figure 5 This relates the minimum and maximum transmission rates to the number of iterations in the practical application scenario of this invention.

[0052] Appendix Figure 6 This is the optimized result of the proposed solution and the baseline solution under the actual scenario of this invention;

[0053] Appendix Figure 7 This relates the minimum and maximum transmission rates to the angle jitter in the actual scenario of this invention.

[0054] Appendix Figure 8 This relates the minimum and maximum transmission rates to the number of RIS units under the practical application scenario of this invention. Detailed Implementation

[0055] The steps and processes of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the examples described in this application are merely one application scenario of the present invention, and other results based on the content of the present invention without making substantial changes are within the protection scope of the present invention.

[0056] Appendix Figure 4 This is an example scenario provided by the present invention, demonstrating all the devices included in the invention and some parameters that will be used subsequently. In this example scenario, the aerial reconfigurable smart surface consists of a smart reflective surface and a drone, which are deployed over an area that has no direct transmission link with a ground base station. (Appendix) Figure 4 The base station (BS) in this invention is the RFT1 device, which has a wireless transmission link with the RIS-UAV to control the phase of the RIS and the position of the UAV. The user equipment (UE) is the RFT2 device in this invention.

[0057] Appendix Figure 4 One example scenario provided is a single base station multi-user scenario. The communication between the user and the base station adopts the RIS-UAV, RFT1 and RFT2 communication process proposed in this invention, which includes an initialization phase and a normalization phase. It is based on iterative optimization and first-order Taylor RFT1 beamforming, RIS phase and UAV three-dimensional position optimization, as well as the decision signaling process for RFT1 beamforming, RIS phase and UAV position update.

[0058] According to the appendix Figure 4 The example scenario has the following parameters: BS coordinates w BS =(2400,0,20) T The number of UEs is K = 5, and their coordinates are u1 = (-300, 150, 2). T u2 = (-150, -200, 2) T u3 = (0, 0, 2) T u4 = (250, -250, 2) T u5 = (300, 200, 2) T RIS-UAV initial and termination positions q I =q F = (450, 0, 80), Number of BS antennas M = 8, Maximum BS transmit power P max =20dBm, process duration T D =160s, RIS unit number N=100, unit path loss β0=-60dBm, noise power spectral density σ 2 = -174dBm / Hz, minimum value of Rice factor K min =0dB and maximum value K max=30dB, environmental constants a1=11.95, b1=0.14, the UAV's flight area is a rectangular space with x distance [-450, 450], y distance [-300, 300], and z distance [80, 200]. All distances are in meters.

[0059] Step 300: Establish the communication system model and target optimization problem in the example scenario.

[0060] In time slot t, the achievable rate R of UE k k [t] is calculated as

[0061]

[0062] The 3D channel considering UAV jitter error is represented as follows (the time variable t is omitted for simplicity).

[0063]

[0064]

[0065] The definitions of the relevant parameters in (13)-(15) can be found in (1)-(12). The optimization problem of maximizing the minimum user rate in each time slot can be expressed as:

[0066]

[0067] The first constraint is the minimum rate requirement of the UE, the second constraint is the power budget of the BS, the third constraint is the optimization space of the RIS reflection coefficient, and the fourth constraint is the feasible deployment space of the UAV.

[0068] Step 310, based on P5, solve for the beamforming matrix {v} of BS. k}

[0069] Given the RIS phase {φ n} and UAV three-dimensional position w R The solution to P5 can be simplified to the following problem.

[0070]

[0071] The definitions of the relevant parameters can be found in (9). This problem is a convex problem and can be quickly solved by convex optimization toolboxes, such as CVX.

[0072] Step 320, based on P5, solve for the RIS phase {φ n}

[0073] Given the beamforming matrix {v} of the BS k} and UAV three-dimensional position wR The solution to P5 can be simplified to the following problem.

[0074]

[0075] The definitions of the relevant parameters can be found in (10). This problem is a convex problem and can be quickly solved by convex optimization toolboxes, such as CVX.

[0076] Step 330: Based on P5, solve for the three-dimensional position of the UAV.

[0077] Given the beamforming matrix {v} of the BS k} and RIS phase {φ n Solving P5 can be simplified to solving the following two subproblems alternately.

[0078]

[0079]

[0080] The definitions of the relevant parameters can be found in (11) and (12). However, (11) and (12) do not provide the definitions. and f k,l (H R The specific expression for ). Here, let's take Taking f as an example, we can derive its specific expression. This method is also applicable to obtaining f. k,l (H R The specific expression for ).

[0081] Extend By focusing on solving the nonconvexity problem caused by channel angle dependence, we can obtain the following expression.

[0082]

[0083] Among them, H B H is the height of BS. U For UE height, For BS two-dimensional location, Let k be the two-dimensional position of user k.

[0084] Z 1,k,l ≤(X 1,k ΦX2+X 1,k ΦY2+Y 1,k ΦX2+Y 1,k ΦY2)v l (20)

[0085]

[0086]

[0087]

[0088]

[0089] Then we can find that, Compared to Z2 and Z 3,k It is not jointly convex; therefore, in the r-th iteration, we can use the following approximation.

[0090]

[0091] Applying the same convex approximation operation to the right side of equations (21) and (23), we obtain the following inequality.

[0092]

[0093]

[0094] Although (22) and (24) are respectively relative to δ BR and The convex function, but δ BR and Neither The convex function. Therefore, we adopt the following approximation.

[0095]

[0096] in,

[0097]

[0098] Similarly, we have

[0099]

[0100] in,

[0101]

[0102] The detailed derivations of (29) and (31) will be given below.

[0103] At this point, only the right side of (20) is relative to... It is non-convex. Focusing on the right side of (20), we can find that its value is X. 1,k ΦX2,X 1,k φY2, Y 1,k ΦX2, Y 1,k The affine transformation of all elements in ΦY2, due to its relation with vl The dot product. And the radial transformation does not change the convexity. Therefore, as long as X... 1,k ΦX2,X 1,k φY2, Y 1,k ΦX2, Y 1,k All elements in ΦY2 are A convex function is sufficient. Here, we take X as an example. 1,k Let's take the elements in ΦX2 as an example to illustrate how to convert them. The convex function. This method also applies to X. 1,k ΦY2,Y 1,k ΦX2, Y 1,k Elements in ΦY2.

[0104] It's easy to know, The m-th element can be written as (its phase can be obtained through {v k} and {φ n Alignment (omitted here)

[0105]

[0106] Among them, K r =K max / K min To address the nonconvexity of (32), we introduce a series of slack variables.

[0107]

[0108] Where, μ 2,k μ and μ3 are defined in (31) and (29) respectively.

[0109] Therefore, we can obtain

[0110]

[0111] in, It is μ 1,k The convexity of the function is due to the angle dependence of the channel. We use a first-order Taylor expansion for a convex approximation.

[0112]

[0113]

[0114]

[0115] therefore, All relative to The constraints are all transformed into convex constraints, where each element can be obtained using the method described above. Similarly, f k,l (H R The calculation method can also be used to obtain it.

[0116] Step 340: Iteratively solve “P1→P2→P3→P4→P1→P2→…” until the increment of η is less than a set threshold. At this point, the RFT1 beamforming, RIS phase, and UAV three-dimensional position can be considered as configurable optimal network parameters.

[0117] Step 350: Based on the principle of "maximum percentage decrease in user performance", determine whether it is necessary to update RFT1 beamforming, RIS phase, and UAV position. At this time, the values ​​of Q, X, and Y are 10, 20, and 30, respectively.

[0118] Based on the current transmission rate reported by each RFT2, RFT1 calculates its rate decrease relative to the previous Q time slots and calculates the average rate decrease percentage. If more than X% of the RFT2s have an average rate decrease percentage exceeding Y%, then the beamforming, RIS phase, and UAV position of RFT1 are updated, and step 220 is performed; otherwise, no updates are performed, and no operation is performed.

[0119] Step 360: Repeat the above process in chronological order.

[0120] The following explains some of the resulting images obtained in this example scenario.

[0121] In the appendix Figure 5-8 In our study, we compared six different approaches: 1) 3D movement with jitter: 3D trajectory optimization considering drone jitter and 3D reflection, which is our proposed approach; 2) 3D movement without jitter: 3D trajectory optimization with 3D reflection but without considering drone jitter; 3) 2D movement with jitter: 2D trajectory optimization considering drone jitter when the drone altitude is fixed at the minimum altitude; 4) 3D static with jitter: Static deployment considering drone jitter and 3D reflection; 5) 3D static without jitter: Static deployment with 3D reflection, but without considering drone jitter; 6) 2D static with jitter: Static deployment considering drone jitter when the drone altitude is fixed at the minimum altitude.

[0122] Appendix Figure 5The convergence of the proposed RFT1 beamforming, RIS phase, and UAV 3D position iterative optimization algorithms is demonstrated. The convergence of all schemes is similar. Furthermore, it is evident that the proposed scheme achieves a higher minimum rate than other schemes. This is because the use of RFT1 beamforming, RIS phase, and UAV 3D position update procedures can fully utilize the high maneuverability of the UAV. Moreover, it can be seen that with increasing iterations, the impact of UAV jitter and altitude optimization on performance becomes increasingly prominent. Ultimately, compared to the other two mobility solutions, the proposed mobility solution achieves performance improvements of 17.65% and 29.87%, respectively; in the case of static RIS-UAV, these figures reach 28.89% and 45.00%, respectively.

[0123] Appendix Figure 6 (a) Visually demonstrates the three-dimensional position and trajectory of the RIS-UAV after optimization using six different schemes. We have also included... Figure 6 (b) shows the time-varying UE scheduling results of the mobility scheme, and in Figure 6 (c) illustrates the fixed power allocation results under the static scheme. (See Appendix) Figure 6 (b) and appendix Figure 6 (c) It can be seen that the transmission time allocation for different users in all mobile schemes and the power allocation for different users in the static scheme are directly proportional to the distance from the user to the base station, which is in line with our expectations. In practical applications, the ground computing center... Figure 6 All results are calculated, and then the flight trajectory, RIS phase, and BS beamforming of the RIS-UAV are controlled based on these results.

[0124] Appendix Figure 7 The impact of angular errors caused by UAV jitter on the maximum and minimum speeds under different schemes is presented. As the azimuth and elevation errors of the BS and the UAV increase, the performance of all schemes degrades to varying degrees. However, our proposed scheme shows a significant advantage for both mobile and static UAVs. This is because we consider altitude optimization under UAV jitter, further compensating for the performance loss caused by elevation errors, which has not been studied in other benchmark schemes. Furthermore, we observe that when θ... BR When the maximum change in θ is small, the performance gain obtained by optimizing the drone altitude is lower than the performance gain obtained by considering drone jitter. However, as θ... BR The greatest change will occur, and this phenomenon will reverse.

[0125] Appendix Figure 8The relationship between the maximum and minimum rates and the number of RIS units N is shown. It can be seen that deploying more units in the passive RIS improves system performance. This performance gain gradually saturates as the number of RIS elements increases, especially for static UAV schemes, because interference caused by passive RIS reflections also increases with N. Furthermore, UAV jitter significantly reduces the gain provided by the RIS, while UAV 3D trajectory planning and consideration of 3D angle errors can minimize the impact of UAV jitter, which is why other baseline schemes perform poorly. Increasing the number of antennas can improve system performance, but it also makes the system more sensitive to angle errors. This is because the more antennas there are, the narrower the beam is formed, and the greater the performance loss caused by UAV jitter.

Claims

1. A method for configuring parameters of an aerial dynamic intelligent reflector-assisted network, characterized in that, include: By utilizing a UAV-borne reconfigurable smart surface (RIS-UAV) to reflect signals from a local Type 1 transceiver (RFT1), data transmission services are provided to a remote Type 2 transceiver (RFT2). RFT1 serves as the computing and control center. Considering the impact of location movement and UAV jitter-induced angular errors on channel parameters, a 3D channel model between RFT1 and the RIS-UAV at time t is established. ，（1） ， （2） in, For path loss per unit distance, The distance between RFT1 and RIS-UAV. The path loss exponent between RFT1 and RIS-UAV. The Rice factor is the channel factor between RFT1 and RIS-UAV. To form independent and identically distributed matrices that follow a circularly symmetric complex Gaussian distribution, for A matrix of all 1s in dimension The The elements are , This represents the pitch jitter error of RFT1 relative to RIS-UAV. The The elements are , The azimuth jitter error of RFT1 relative to RIS-UAV is defined; a 3D channel between RIS-UAV and RFT2 is established at time t. ，（3） ， （4） in, The distance between RIS-UAV and RFT2. The path loss exponent between RIS-UAV and RFT2. The Rice factor for the channel between RIS-UAV and RFT2. For independent and identically distributed vectors that follow a circularly symmetric complex Gaussian distribution, The The elements are , This represents the pitch jitter error of RFT2 relative to RIS-UAV. The The elements are , To address the azimuth jitter error of RFT2 relative to RIS-UAV, an optimization method based on iterative optimization and first-order Taylor expansion is designed for the RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position. The RFT1 beamforming matrix, RIS phase matrix, and UAV three-dimensional position are updated according to the degree of degradation of the average rate performance of RFT2 within several time slots.

2. The method according to claim 1, characterized in that, The proposed communication process between RIS-UAV, RFT1, and RFT2 includes two phases: an initialization phase and a normalization phase. In the initialization phase, RFT2 reports its own position information, beamforming matrix, RIS phase matrix, and UAV 3D position. Then, RFT1 updates its own beamforming matrix. Based on a 3D air-to-ground channel model including angle errors, RFT1 calculates network parameters, including its beamforming matrix, and sends control information to RIS-UAV to update the RIS phase matrix and UAV 3D position. Then, the normalization phase begins. In the normalization phase, RFT2 reports its position information and current rate information. RFT1 evaluates whether to update network parameters, including its beamforming matrix, RIS phase matrix, and UAV 3D position, based on all RFT2 rate information. If an update is needed, the network parameters are recalculated; otherwise, no update is performed.

3. The method according to claim 1, characterized in that, The channel model considers the impact of angular errors caused by location movement and UAV jitter on the path loss exponent. Rice factor The influence of channel parameters is expressed by the following formula. （5） （6） in, and These represent the maximum and minimum values ​​of the path loss exponent. and A constant determined by the environment. and These are the maximum and minimum values ​​of the Rice factor. The pitch angle of RFT1 or RFT2 relative to the RIS-UAV. This refers to the pitch jitter error of RFT1 or RFT2 relative to the RIS-UAV.

4. The method according to claim 1, characterized in that, Based on iterative optimization and first-order Taylor expansion design, the RFT1 beamforming, RIS phase, and UAV 3D position were optimized. The UAV 3D position optimization was broken down into two sequential subproblems: 2D position optimization and height optimization. The RFT1 beamforming was solved iteratively. RIS phase UAV2-dimensional position UAV height RFT1 Beamforming RIS phase UAV2-dimensional position UAV height", up to the minimum user transmission rate. If the increment is less than a set threshold, the RFT1 beamforming, RIS phase, and UAV three-dimensional position are considered to be the configurable optimal network parameters.

5. The method according to claim 1, characterized in that, Based on the current transmission rate reported by each RFT2, RFT1 calculates the rate decrease relative to the previous Q time slots and calculates the average rate decrease percentage. If the average rate decrease percentage of more than X% of RFT2 exceeds Y%, then the beamforming, RIS phase, and UAV position of RFT1 are updated; otherwise, no updates are performed and no operation is performed. The values ​​of Q, X, and Y are preset according to the actual situation.

6. The method according to claim 1, characterized in that, In the decision signaling process of RFT1 beamforming, RIS phase and UAV position update, RFT1 sends the calculated RIS phase and UAV position information to RIS-UAV. After receiving the information, RIS-UAV sends an ACK message to RFT1 and updates the RIS phase and its own position information. If RIS-UAV does not receive a message, RFT1 will continue to retransmit the information to RIS-UAV; after RIS-UAV updates the RIS phase and its own position, it will send an ACK message to RFT1 to ensure information synchronization.

Images