A multi-intelligent reflective surface assisted UAV network communication and control system

Abstract

This invention considers the full range of phase shift, transmit power allocation, decoding, and flight control factors, and jointly designs a multi-intelligent reflector-assisted UAV network communication and control system. This system divides the complex original problem of solving phase shift, transmit power allocation, decoding, and flight control into three subproblems for alternating optimization, applying different methods to each subproblem to ultimately achieve the optimal result. This effectively solves the multivariable joint design problem and achieves stable convergence with low complexity. It can achieve the design goal of maximum rate, maximizing the system communication rate while meeting the minimum communication rate requirements of each ground user, allowing the system to complete communication tasks as efficiently as possible to better meet the communication needs of ground users. Furthermore, by adjusting the phase shift of multiple IRS reflections, the system's achievable rate is further increased.

Description

Technical Field

[0001] The present invention belongs to the field of mobile communication technology, and in particular relates to a multi-intelligent reflective surface-assisted unmanned aerial vehicle (UAV) network communication and control system. Background Art

[0002] With the rise of the Internet of Things (IoT), the number of diverse terminal devices, including cloud sensors, smartphones, and wearables, has continued to surge. Intelligent applications such as facial recognition, interactive gaming, and virtual reality are also emerging, rapidly increasing the demand for spectrum resources and data transmission rates. Furthermore, with the acceleration of urbanization, wireless communications face harsh wireless transmission environments, which severely degrades the communication quality for users on the ground. Against this backdrop, unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and intelligent reflective surfaces (IRS), as the latest technologies, can effectively address the challenges of spectrum resource shortages and harsh wireless transmission environments.

[0003] With the advancement and maturity of modern industrial technology, the performance of drones has significantly improved, playing a vital role in both military and civilian applications, performing tasks such as material transportation, rescue, and reconnaissance. In particular, in the communications sector, drones often act as flying base stations (BSs), aiming to improve network data transmission capabilities and expand service coverage. On the one hand, compared to terrestrial communications, drones offer the advantage of easily establishing air-to-ground (A2G) links based on line of sight (LoS), providing ground users with stable and high-speed communication links. On the other hand, drones possess excellent maneuverability and can be flexibly deployed near ground users, providing better channel conditions and thus improving communication quality. Next-generation communications systems aim to expand service coverage with high speed and low latency. With their many advantages, drones are considered one of the promising solutions to meet these requirements and are receiving increasing attention. However, with the surge in the number of communication devices and the emergence of various new applications and services, the demand for spectrum resources and data transmission rates is rapidly increasing.

[0004] Non-orthogonal multiple access allows multiple ground users to use the same frequency resources at the same time, which greatly improves spectrum utilization and increases the communication capacity of the system. Therefore, it is considered to be a very promising technical solution and has attracted widespread attention from academia and industry. UAVs and NOMA each have their own advantages. A novel idea is to further integrate the two into a UAV-NOMA structure. On the one hand, UAVs can move flexibly to make up for the lack of coverage of traditional base stations. On the other hand, NOMA can greatly improve spectrum efficiency and system capacity. This joint utilization optimizes the allocation of spectrum resources, improves network coverage and data transmission efficiency, especially in high-density ground user areas and remote areas. This structure also enhances the flexibility and responsiveness of the system, enabling it to adapt to rapidly changing communication needs and environmental conditions, providing strong support for future smart cities and Internet of Things (IoT) applications.

[0005] In addition, wireless communications also face a harsh transmission environment. The presence of a large number of obstructions in urban environments seriously hinders signal transmission. Fortunately, smart reflective surfaces are considered to be a promising solution to this problem and have been widely studied by academia and industry. Specifically, IRS is a 2D plane composed of many inexpensive passive reflective elements. By adjusting these independent elements, the incident signal can be changed, thereby reshaping the wireless channel. In addition, IRS is an almost passive device that can be quickly installed on the surface of a building to reconfigure the wireless channel at a very low cost, thereby improving communication quality. In this sense, the application of IRS to UAV-NOMA networks can inspire new technological applications and advantages. On the other hand, it can support large-scale connections while reducing communication delays and costs, providing a strong impetus for future communication development.

[0006] Although drone-assisted design can further enhance the flexibility and communication speed of traditional communication systems, currently, both academia and industry rarely consider the simultaneous application of IRS and NOMA technologies in drone-assisted communication scenarios. Even less research has been conducted on communication and control methods in multi-IRS-assisted UAV-NOMA networks. However, in reality, multi-IRS-assisted UAV communication systems using NOMA technology are more general and better suited to IoT application scenarios. On the one hand, according to the NOMA protocol, the decoding order of ground users is determined by their channel conditions. Dynamic UAVs inevitably lead to dynamic NOMA decoding orders, resulting in a high degree of coupling between multi-IRS phase shifts, UAV trajectories, and the NOMA decoding order, making this issue particularly challenging to address. On the other hand, for multi-IRS and multi-ground user scenarios, the optimal phase shift is no longer simply a coherent combination of the reflected and direct signals. Because the reflective elements need to serve multiple ground users simultaneously, IRS phase shift optimization becomes even more challenging. Therefore, in-depth research on phase shift, power, decoding, and control methods for multi-IRS-assisted UAV-NOMA networks is crucial. Summary of the Invention

[0007] In order to solve the above problems existing in the prior art, the present invention provides a multi-intelligent reflective surface-assisted UAV network communication and control system. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0008] A multi-intelligent reflective surface assisted UAV network communication and control system includes:

[0009] The initialization module is used to design system parameters according to the distribution of UAVs, ground users and IRS in the target area, and initialize them to obtain the initialization system parameters;

[0010] A multi-IRS phase shift control module is used to construct an initial first optimization problem for solving the reflection phase shift using the initialized system parameters, convert the initial optimization problem from a non-convex form to a convex form, and solve the convex form of the first optimization problem to obtain the reflection phase shift of each IRS;

[0011] The power allocation module is used to construct the initial second optimization problem for solving the transmission power using the reflection phase shift of each IRS, and convert it from a non-convex form to a convex form. The convex form of the second optimization problem is solved to obtain the transmission power of each UAV in each time slot;

[0012] The decoding order and trajectory planning module is used to construct the initial third optimization problem using the UAV's transmission power and the IRS reflection phase shift, and convert it from a non-convex form to a convex form. The convex form of the third optimization problem is solved to obtain the decoding order and trajectory planning results of the NOMA of each UAV;

[0013] The output module is used to determine whether the output condition is met according to the decoding order and trajectory planning result of the NOMA. If not, the execution process of the multi-IRS phase shift control module, power allocation module, decoding order and trajectory planning module is repeated until the output condition is met, and the optimal reflection phase shift, optimal transmission power, optimal decoding order and optimal trajectory planning result are obtained.

[0014] Beneficial effects:

[0015] The present invention studies the UAV-NOMA communication network assisted by multiple IRSs, taking into account multiple factors such as multiple IRS phase shift, UAV transmission power, NOMA decoding order and UAV control party of the multi-IRS assisted UAV-NOMA communication system. By jointly optimizing multiple IRS phase shift, UAV transmission power, NOMA decoding order and UAV trajectory, the defects of traditional UAV communication such as low efficiency and severe channel fading are overcome, and the purposes of multi-IRS phase shift control, UAV transmission power allocation, NOMA decoding order and UAV trajectory planning are achieved. A multi-intelligent reflector-assisted UAV network communication and control system with a complete joint design of phase shift, transmission power allocation, decoding and flight control is proposed.

[0016] The multi-intelligent reflector-assisted UAV network communication and control system provided by this invention can achieve a system that divides the complex original problem into three sub-problems for alternating optimization, applying different methods to each sub-problem to ultimately obtain the optimal result. This effectively solves the multivariable joint design problem and achieves stable convergence with low complexity. It can achieve the design goal of maximizing the rate, enabling the system to complete communication tasks as efficiently as possible to better meet the communication needs of ground users. Furthermore, by adjusting the phase shift of multiple IRSs, the system's achievable rate is further increased.

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of a multi-intelligent reflective surface-assisted UAV network communication and control system provided by the present invention;

[0019] Figure 2 It is a flow chart of the multi-IRS phase shift control module provided by the present invention;

[0020] Figure 3 It is a schematic diagram of the flow of the power distribution module provided by the present invention;

[0021] Figure 4 It is a flowchart of the decoding sequence and trajectory planning module provided by the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0023] The present invention mainly includes five modules: information initialization, multi-IRS phase shift control, UAV transmit power allocation, NOMA decoding sequence and UAV trajectory planning, and result output. The present invention adopts a joint design method based on an alternating optimization structure. During each iteration, the multi-IRS phase shift control module is first designed separately, then the UAV transmit power allocation is jointly designed, and finally the NOMA decoding sequence and UAV trajectory are jointly planned. The final output is the maximum rate that the system can achieve, as well as the optimal multi-IRS reflection phase shift, UAV transmit power, NOMA decoding sequence, and UAV trajectory.

[0024] like Figure 1 As shown, the present invention provides a multi-intelligent reflective surface assisted UAV network communication and control system, comprising:

[0025] The initialization module is used to design system parameters according to the distribution of UAVs, ground users and IRS in the target area, and initialize them to obtain the initialization system parameters;

[0026] Before the system is run, the present invention first designs the system initialization parameters through the information initialization module. The initialization system parameters include: multi-IRS reflection phase shift range, the initial transmission power of the UAV, the constraint relationship of the NOMA decoding order, and the initial trajectory of the UAV.

[0027] The present invention first defines the relevant parameters of the UAV, ground user and IRS in the target area; the relevant parameters include: represents all terrestrial users, Indicates the number of reflection units of a single IRS, represents all smart reflective surfaces, Θ l [n] represents the reflection phase shift matrix of the lth IRS, w k represents the location of ground user k; w r Indicates the location of the IRS; represents the trajectory of the drone; Indicates the total time slot of the system; I k,j [n] represents the decoding order of NOMA; p k [n] represents the transmission power of the UAV; the system communication bandwidth and white noise power are represented by B and σ respectively 2 ;

[0028] The present invention considers a finite time period T. For analysis, the time period T is divided into N equal time slots, and the interval between each time slot is δ t Therefore, the horizontal trajectory of the UAV can be expressed as q[n]=(x[n],y[n]),n=1,...,N, and the UAV flies at a fixed height H. The starting point and the end point of the UAV are denoted by q I and q F Therefore, the UAV maneuverability constraint is

[0029] q I =q[1], (1)

[0030] q F =q[N], (2)

[0031] ||q[n+1]-q[n]|| 2 ≤(V max δ t ) 2 ,n=1,...,N-1, (3)

[0032] Among them, V max is the maximum flight speed of the UAV. For the convenience of analysis, the present invention uses h l [n], denote the channels from the UAV to ground user k, the UAV to IRS1, and the IRS1 to ground user k, respectively. In this paper, the channels involved are considered to be quasi-static flat fading. By applying the channel estimation algorithm, the UAV can fully understand the channel state information.

[0033] Therefore, the channel from the UAV to IRS1 is expressed as

[0034]

[0035] Where β0 represents the average channel power gain when the reference distance is 1m, represents the IRS1 array response, expressed as:

[0036]

[0037] where g y and g z denote the element spacing along the y-axis and z-axis, respectively, λ denotes the carrier wavelength, In addition, θ l [n] and denote the vertical and horizontal arrival angles of the signal from the UAV to IRS l, sinθ l [n]=(h u [n]-h l )||q[n]-wl || -1 ,

[0038] According to the previous analysis, the channel from the UAV to the ground user k is given by

[0039]

[0040] in α u,k and κ1 are the path loss exponent and Rice factor of the k-channel of the UAV ground user, respectively. u,k [n]=||q[n]-w k ||,

[0041] The channel between the UAV and IRS1 is expressed as

[0042]

[0043] where γ and κ2 are the path loss exponent and Ricean factor of the channel between IRS1 and ground user k, respectively. Represents the LOS component:

[0044]

[0045] in, In addition, θ l [n] and denote the vertical and horizontal arrival angles of the signal from the UAV to IRS l, sinθ l,k =h l ||w k -w l || -1 ,

[0046] The communication rate function of the system is defined using the relevant parameters and is expressed as:

[0047]

[0048] Among them, R k [n] is the receiving task bit of the kth access point in the nth time slot, expressed as:

[0049] R k [n] = log2(1 + γ k [n])(10)

[0050] in, is the direct channel gain between the UAV and the ground user, For the reflection channel between UAV-multiple IRS-ground user, is the channel between IRS1 and ground user k, h l [n] is the channel from the drone to IRS1.

[0051] The signal sent by the UAV can reach the ground user through multiple paths, but due to severe signal attenuation, this paper assumes that the signal reflected twice or more by the IRS is ignored. Therefore, the combined channel power gain from the UAV to the ground user k is:

[0052] In the information initialization module, according to the location distribution of ground users, a regular trajectory initialization method can be used to initialize the UAV trajectory, such as using a straight line uniform speed flight method to fly from a given starting point to an end point within a total time T. In addition, the initialization obtains the UAV transmission power distribution and NOMA decoding order I k,j [n].

[0053] Using the NOMA decoding order I k,j [n] The constraints that determine the NOMA decoding order are expressed as:

[0054]

[0055] Among them, d u,k [n]=||q[n]-w k || 2 represents the distance from the UAV to the kth ground user, d u,j [n]=||q[n]-w j || 2 represents the distance from the UAV to the jth ground user, w j represents the coordinates of the jth ground user.

[0056] According to the location distribution of ground users, the trajectory of the UAV and the UAV power distribution are initialized, and the NOMA decoding order I is obtained according to the initialized UAV trajectory. k,j [n].

[0057] A multi-IRS phase shift control module is used to construct an initial first optimization problem for solving the reflection phase shift using the initialized system parameters, convert the initial optimization problem from a non-convex form to a convex form, and solve the convex form of the first optimization problem to obtain the reflection phase shift of each IRS;

[0058] In the multi-IRS phase shift control module, given the UAV's transmit power, NOMA decoding order, and UAV trajectory, the problem is transformed into a semi-definite program (SDP) problem using the semidefinite relaxation (SDR) technique. The reflected phase shift of the UAV can be obtained by using the convex-concave procedure (CCCP) and successive convex approximation (SCA) methods.

[0059] In the target area (such as a field scene), there are There are multiple intelligent reflective surfaces to assist a UAV to communicate with a ground user. Both the ground user and the UAV are equipped with a single antenna. Indicates the total time slot of the system; I k,j [n] represents the decoding order; the horizontal trajectory of the drone is represented by The height is fixed at H. The transmission power of the UAV in each time slot is expressed as p k [n]; the system communication bandwidth and white noise power are expressed as B and σ respectively 2 .

[0060] refer to Figure 2 As shown, the multi-IRS phase shift control module is used to:

[0061] a, using the drone’s initial transmit power P and NOMA decoding order I k,j [n], UAV trajectory q[n] and reflection phase shift Θ of multiple IRS l [n] Construct the initial first optimization problem, expressed as:

[0062]

[0063] Where R min is the minimum communication quality constraint for ground users, r k [n] is the first slack variable introduced, R k [n] is the receiving task bit of the kth ground user in the nth time slot, θ l,m [n] is the reflection phase shift of the lth IRS in the nth time slot;

[0064] b. Using the semidefinite relaxation (SDR) technique, the initial first optimization problem is converted into a semidefinite programming (SDP) problem. During the conversion process, the rank-one constraint is ignored to obtain a convex form of the first optimization problem.

[0065] Since the initial first optimization problem has non-convex constraints (13a) and (13b), the initial first optimization problem (SP1) is non-convex, and the present invention can apply the SDR method to deal with it. For analysis, the present invention can be H k [n] The matrix can be rewritten as follows:

[0066]

[0067] in,

[0068] Based on the above transformation, H k [n] is further converted into the form of trace additive constant, that is: in, V[n]f 0,rank(V[n])=1.

[0069] Therefore, (13b) can be rewritten as:

[0070]

[0071] in, α k [n] is another slack variable introduced, satisfying:

[0072]

[0073] The right side of formula (15) is the difference of two concave functions. The present invention can apply CCCP method. Specifically, by Performing a first-order Taylor expansion, the present invention can obtain The upper bound of can be expressed as

[0074]

[0075] in is the α in the lth SCA iteration k The value of [n].

[0076] In summary, the initial first optimization problem (SP1) can be reformulated as

[0077]

[0078] Where, α k [n] is the second slack variable introduced, p j [n] is the power allocated by the UAV to ground user j, U[n], V[n], V m,m [n]、O k [n] is an intermediate variable in the process of transforming the initial first optimization problem into a convex problem. is the channel from the UAV to the ground user k, σ 2 is the white noise power of the system, yes The upper bound of L is the Lth IRS, M is the number of reflection units in a single IRS;

[0079] c. Use a convex optimization tool to solve the first optimization problem to obtain the reflection phase shift of each IRS.

[0080] Due to the rank-one constraint, the problem (SP1-1) is still non-convex. Ignoring the rank-one constraint, the first optimization problem (SP1-1) is a standard semidefinite programming problem that can be solved efficiently using convex optimization tools such as CVX. The solution can later be made to satisfy the rank-one constraint by applying a Gaussian randomization procedure.

[0081] The power allocation module is used to construct the initial second optimization problem for solving the transmission power using the reflection phase shift of each IRS, and convert it from a non-convex form to a convex form. The convex form of the second optimization problem is solved to obtain the transmission power of each UAV in each time slot;

[0082] In the power allocation module, the optimal power allocation of the UAV can be obtained by using CCCP and SCA methods given the multi-IRS phase shift, NOMA decoding order and UAV trajectory.

[0083] refer to Figure 3 As shown, the power distribution module is specifically used for:

[0084] a, using NOMA decoding order I k,j [n], UAV trajectory q[n], reflection phase shift Θ l [n] Construct the initial second optimization problem to solve the transmit power, which can be expressed as:

[0085]

[0086] Where r 2k [n] is the third slack variable introduced, p j [n] is the power allocated to ground user j, P max is the maximum transmission power of the UAV, p k [n] is the power allocated to terrestrial user k.

[0087] b. Using the semidefinite relaxation (SDR) technique, the initial second optimization problem is transformed into a semidefinite programming (SDP) problem to obtain a convex second optimization problem.

[0088] Due to the non-convex constraint (19b), the initial second optimization problem (SP2) is also non-convex. The structure of constraint (19b) is similar to that of (13b), so the same method can be used for constraint (19b). Therefore, constraint (19b) can be transformed into the following form:

[0089]

[0090] in

[0091] where α 2k [n] is the fourth slack variable introduced, satisfying:

[0092]

[0093] The right side of constraint (20) is the subtraction form of two concave functions, and the present invention uses the CCCP method again. Similarly, we can get The upper bound of is expressed as:

[0094]

[0095] Therefore, the non-convex initial second optimization problem can be transformed into a convex second optimization problem, expressed as:

[0096]

[0097] Where H k [n] is the combined channel power gain from the UAV to the ground user k.

[0098] c. Use the CVX solver to solve the convex form of the second optimization problem and obtain the transmission power of the UAV.

[0099] The decoding order and trajectory planning module is used to construct the initial third optimization problem using the UAV's transmit power and the IRS's reflected phase shift, and then convert it from a non-convex form to a convex form. The convex form of the third optimization problem is solved to obtain the decoding order and trajectory planning results of each UAV's NOMA.

[0100] In the decoding order and trajectory planning module, by giving the multi-IRS phase shift and the UAV's transmit power, the penalty function method and SCA method can be used to plan the optimal NOMA decoding order and UAV trajectory.

[0101] refer to Figure 4 , decoding sequence and trajectory planning module, specifically used for:

[0102] a, using the reflection phase shift matrix Θ l [n] and the transmission power P construct the initial third optimization problem for solving the UAV trajectory and NOMA decoding order, which is expressed as:

[0103]

[0104] The NOMA decoding order of ground users is determined by the following formula

[0105]

[0106] Among them, d u,k [n]=||q[n]-w k || 2 represents the distance from the UAV to the kth ground user, d u,j [n]=||q[n]-w j || 2 represents the distance from the UAV to the jth ground user.

[0107] The initial third optimization problem (SP3) is still intractable because the objective function is non-convex and includes integer constraints (11)-(12).

[0108] Can H k [n] After analysis and transformation, we can get:

[0109]

[0110] in C k,l(l-1) / 2 [n] No specific expression, by H k [n] is obtained by expanding the square.

[0111] Next, the present invention introduces the fifth relaxation variable {u k [n]>0} means d u,k [n]Upper bound, {u l [n]>0} means d l The upper bound of [n]. We can get:

[0112]

[0113] Substituting (25) and (26) into (24), the present invention can obtain the lower limit of the combined channel power gain of the UAV to its ground user:

[0114]

[0115] Note that A k [n],B l,k [n],C k,l(l-1) / 2 [n],D l,kThe arrival angle variable of the signal sent by the UAV to the IRS in [n] is related to the position of the UAV, which makes the problem difficult to handle. Therefore, the following constraints are introduced to approximate the problem:

[0116]

[0117] where d m It represents the maximum displacement allowed by the UAV after each iteration of SCA. m is small enough, the arrival angle between the first iteration and the next iteration can be considered to remain unchanged.

[0118] By the previous approximation and the additional constraint (28), the convexity of the right side of (27) depends only on u k [n] and u l [n], the present invention defines the right side of (27) as h k [n].

[0119] The present invention takes into account the coefficient A k [n],B l,k [n] is always greater than zero, and the coefficient C k,l(l-1) / 2 [n],D l,k [n] is less than zero. h k The convexity of [n] depends on the sign of these coefficients.

[0120] When A k [n],B l,k [n]、C k,l(l-1) / 2 [n],D l,k [n],C k,l(l-1) / 2 [n],D l,k When [n] are both greater than zero, h k [n] relative to u k [n] and u l [n] is jointly convex, because h k The Hessian matrix of [n] is greater than zero. In this case, we can k [n] and u l [n] Perform a first-order Taylor expansion to obtain h k The lower bound of [n] is

[0121] When the coefficient C k,l(l-1) / 2 [n],D l,k [n] exists less than zero h k [n] Two convex functions are subtracted. At this time, the present invention can obtain the first-order Taylor expansion of the previous convex function

[0122] Therefore, given and The value of can be deduced Therefore, constraint (27) can be transformed into

[0123]

[0124] Next, the sixth slack variable Ψ is introduced k [n], satisfying:

[0125]

[0126] Therefore, the lower bound of the objective function of the initial third optimization problem (SP3) can be expressed as

[0127]

[0128] For the non-convex constraints (25) and (26), the present invention notes that their left side (LHS) is a convex function. Therefore, by and Applying the first-order Taylor expansion at , we can obtain the lower limits as follows:

[0129]

[0130] Then we analyze the constraint condition (11) related to NOMA decoding order. k,j [n], relax it to a continuous variable of 0-1, satisfying:

[0131]

[0132] The following two formulas should be satisfied at the same time:

[0133]

[0134] where π j [n] is the seventh slack variable introduced. Combined with equation (11), equations (34), (35), (36) and (37) are equivalent to equations (11) and (12). Adding equation (35) as a penalty term to the objective function, the new calculation rate function is where λ p represents the penalty factor, when λ p When it approaches infinity, the new calculation capacity function is the same as the original calculation capacity function. The second term at a given point as well as Perform a first-order Taylor expansion and we can get The conversion into a convex function is as follows:

[0135]

[0136] where r 3k [n] is the eighth slack variable introduced.

[0137] Then, we analyze formula (37) and express it in the following form:

[0138]

[0139] Using the SCA method, the second term on the left side of Equation (39) is calculated at a given point. and Performing a first-order Taylor expansion can ensure that the left side of Equation (39) is convex. Similarly, using the SCA method, the right side of Equation (39) at a given local point q (l) [n] Performing a first-order Taylor expansion can ensure that the right side of (39) is non-convex. This can transform (39) into a convex constraint as follows:

[0140]

[0141] Among them are

[0142] b. Using a convex optimization method, the initial third optimization problem is converted into a convex third optimization problem, which can be expressed as:

[0143]

[0144] Where r 3k [n] is the eighth slack variable introduced, q I is the starting point of the drone, q F is the end point of the drone. The horizontal trajectory of the drone is expressed as q[n]=(x[n],y[n]),n=1,...,N,V max is the maximum flight speed of the UAV, δ t is the interval between each time slot, d m is the maximum allowed displacement of the drone after each iteration of SCA, It is h k The lower bound of [n], h k [n] is the intermediate parameter used for presentation, u k [n] is the third slack variable introduced, Ψ k [n] is the sixth slack variable introduced, is the lower bound of the combined channel power gain from UAV to ground users, is u in the rth SCA iteration k The value of [n], is u in the rth SCA iteration l The value of [n], w l are the coordinates of IRS1, It is an intermediate variable in the process of converting the initial third optimization problem into a convex third optimization problem.

[0145] c. Use convex optimization tools to solve the convex form of the third optimization problem to obtain the NOMA decoding order and trajectory planning results of each drone.

[0146] The output module is used to determine whether the output condition is met according to the decoding order and trajectory planning result of the NOMA. If not, the execution process of the multi-IRS phase shift control module, power allocation module, decoding order and trajectory planning module is repeated until the output condition is met, and the optimal reflection phase shift, optimal transmission power, optimal decoding order and optimal trajectory planning result are obtained.

[0147] After an iteration, the result output module updates the system's communication rate, multi-IRS phase shift, the drone's transmit power, the NOMA decoding order, and the drone's trajectory. If the system's communication rate converges to a certain accuracy (this accuracy can be pre-set based on actual conditions), the iteration terminates and the optimal result is output. If the module records that the number of system iterations exceeds the maximum allowed number, the iteration also terminates. Otherwise, the next iteration begins.

[0148] The output condition is a preset convergence accuracy or a maximum number of iterations.

[0149] This invention considers the full range of phase shift, transmit power allocation, decoding, and flight control factors, and jointly designs a multi-intelligent reflector-assisted UAV network communication and control system. This system divides the complex original problem of solving phase shift, transmit power allocation, decoding, and flight control into three subproblems for alternating optimization, applying different methods to each subproblem to ultimately achieve the optimal result. This effectively solves the multivariable joint design problem and achieves stable convergence with low complexity. It can achieve the design goal of maximum rate, maximizing the system communication rate while meeting the minimum communication rate requirements of each ground user, allowing the system to complete communication tasks as efficiently as possible to better meet the communication needs of ground users. Furthermore, by adjusting the phase shift of multiple IRS reflections, the system's achievable rate is further increased.

[0150] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0151] Although the present application is described herein with reference to various embodiments, those skilled in the art will be able to understand and implement other variations of the disclosed embodiments in practicing the claimed application by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality.

[0152] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A multi-intelligent reflective surface assisted UAV network communication and control system, characterized in that: include: The initialization module is used to design system parameters according to the distribution of UAVs, ground users and IRS in the target area, and initialize them to obtain the initialization system parameters; The initialization module is specifically used to: Define the relevant parameters of UAVs, ground users, and IRS within the target area; the relevant parameters include: represents all terrestrial users, Indicates the number of reflection units of a single IRS, Represents all smart reflective surfaces, Θ l [n] represents the reflection phase shift matrix of the lth IRS, w k represents the location of ground user k; w r Indicates the location of the IRS; represents the trajectory of the drone; Indicates the total time slot of the system; I k,j [n] represents the decoding order of NOMA; p k [n] represents the transmission power of the UAV; the system communication bandwidth and white noise power are represented by B and σ respectively 2 ; A multi-IRS phase shift control module is used to construct an initial first optimization problem for solving the reflection phase shift using the initialized system parameters, convert the initial optimization problem from a non-convex form to a convex form, and solve the convex form of the first optimization problem to obtain the reflection phase shift of each IRS; The multi-IRS phase shift control module is used to: Using the drone’s initial transmission power P and NOMA decoding order I k,j [n], UAV trajectory q[n] and reflection phase shift Θ of multiple IRS l [n] Construct the initial first optimization problem, expressed as: Where R min is the minimum communication quality constraint for ground users, r k [n] is the first slack variable introduced, R k [n] is the receiving task bit of the kth ground user in the nth time slot, θ l,m [n] is the reflection phase shift of the lth IRS in the nth time slot; The power allocation module is used to construct the initial second optimization problem for solving the transmission power using the reflection phase shift of each IRS, and convert it from a non-convex form to a convex form. The convex form of the second optimization problem is solved to obtain the transmission power of each UAV in each time slot; Power distribution module, specifically used for: Using NOMA decoding order I k,j [n], UAV trajectory q[n], reflection phase shift Θ l [n] Construct the initial second optimization problem to solve the transmit power, which can be expressed as: Where r 2k [n] is the third slack variable introduced, P max is the maximum transmission power of the UAV, p k [n] is the power allocated to terrestrial user k; p j [n] is the power allocated to terrestrial user j; The decoding order and trajectory planning module is used to construct the initial third optimization problem using the UAV's transmission power and the IRS reflection phase shift, and convert it from a non-convex form to a convex form. The convex form of the third optimization problem is solved to obtain the decoding order and trajectory planning results of the NOMA of each UAV; Decoding sequence and trajectory planning module, specifically used for: Using the reflection phase shift matrix Θ l [n] and the initial transmission power P construct the initial third optimization problem for solving the UAV trajectory and NOMA decoding order, which can be expressed as: Among them, d u,k [n]=||q[n]-w k || 2 represents the distance from the UAV to the kth ground user, d u,j [n]=||q[n]-w j || 2 represents the distance from the UAV to the jth ground user; w j represents the coordinates of the jth ground user; The output module is used to determine whether the output condition is met according to the decoding order and trajectory planning result of the NOMA. If not, the execution process of the multi-IRS phase shift control module, power allocation module, decoding order and trajectory planning module is repeated until the output condition is met, and the optimal reflection phase shift, optimal transmission power, optimal decoding order and optimal trajectory planning result are obtained.

2. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 1 is characterized in that: The initialization system parameters include: the initialization system parameters include: multi-IRS reflection phase shift range, the initial transmission power of the drone, the constraint relationship of the NOMA decoding order and the initial trajectory of the drone.

3. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 2 is characterized in that: The initialization module is specifically used to: The communication rate function of the system is defined using the relevant parameters and is expressed as: Among them, R k [n] is the receiving task bit of the kth access point in the nth time slot, expressed as: R k [n]=log2(1+γ k [n]); in, is the direct channel gain between the UAV and the ground user, is the reflection channel gain between UAV-multiple IRS-ground user, is the channel between IRS1 and ground user k, h l [n] is the channel from the UAV to the lth IRS; According to the location distribution of ground users, the trajectory of the UAV and the UAV power distribution are initialized, and the NOMA decoding order I is obtained according to the initialized UAV trajectory. k,j [n]; The constraints of NOMA decoding order are expressed as:

4. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 3 is characterized in that: The multi-IRS phase shift control module is used to: The initial first optimization problem is transformed into a semidefinite programming (SDP) problem using the semidefinite relaxation (SDR) technique. During the transformation, the rank-one constraint is ignored to obtain the convex form of the first optimization problem, which can be expressed as: Where, α k [n] is the second slack variable introduced, p j [n] is the power allocated to ground user j, U[n], V[n], V m,m [n]、O k [n] are all intermediate variables introduced in the process of transforming the non-convex first optimization problem into a convex problem. is the channel from the UAV to the ground user k, σ 2 is the white noise power of the system, yes The upper bound of L is the Lth IRS, M is the number of reflection units in a single IRS; The first optimization problem is solved using a convex optimization tool to obtain the reflection phase shift of each IRS.

5. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 4 is characterized in that: Power distribution module, specifically used for: The semidefinite relaxation SDR technique is used to transform the initial second optimization problem into a semidefinite programming SDP problem to obtain a convex second optimization problem, which is expressed as: Where, H k [n] is the combined channel power gain from the UAV to the ground user; The convex form of the second optimization problem is solved using the CVX solver to obtain the UAV's transmission power.

6. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 5 is characterized in that: Decoding sequence and trajectory planning module, specifically used for: Using the convex optimization method, the initial third optimization problem is converted into a convex third optimization problem, which is expressed as: Where r 3k [n] is the eighth slack variable introduced, q I is the starting point of the drone, q F is the end point of the drone. The horizontal trajectory of the drone is expressed as q[n]=(x[n],y[n]),n=1,...,N,V max is the maximum flight speed of the UAV, δ t is the interval between each time slot, d m is the maximum allowed displacement of the drone after each iteration of SCA, It is h k The lower bound of [n], h k [n] is the intermediate parameter used for presentation, u k [n] is the third slack variable introduced, Ψ k [n] is the sixth slack variable introduced, is the lower bound of the combined channel power gain from UAV to ground users, is the given u in the rth iteration k The value of [n], is the given u in the rth iteration l The value of [n], w l are the coordinates of IRSl; It is an intermediate variable in the process of converting the initial third optimization problem into a convex third optimization problem; The convex optimization tool is used to solve the third optimization problem in convex form to obtain the NOMA decoding order and trajectory planning results of each drone.

7. The multi-intelligent reflective surface assisted UAV network communication and control system according to claim 1 is characterized in that: The output condition is a preset convergence accuracy or a maximum number of iterations.

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