Method for multi-user communication in cognitive wireless network assisted by UAV acting as relay

Abstract

The application belongs to the technical field of wireless communication, and particularly relates to a multi-user communication method of a cognitive wireless network assisted by a UAV serving as a relay, comprising the following steps: constructing a cognitive wireless network system in which a UAV serves as a relay to assist secondary users in communication; calculating an initial flight trajectory of the UAV, and discretely processing the initial flight trajectory to obtain a set of discrete flight trajectories of the UAV; based on the set of discrete flight trajectories of the UAV, constructing a total optimization problem model P1 with the maximum system throughput as a target; decomposing the problem model P1 into three sub-problems P2, P3 and P4 by using a block coordinate descent algorithm; and iteratively solving the three sub-problems P2, P3 and P4 to obtain a result. The application can effectively avoid interference to primary users, guarantee the service quality of secondary users, and obtain greater system throughput.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a multi-user communication method in which a UAV acts as a relay to assist a cognitive wireless network. Background Technology

[0002] The explosive growth in broadband service demand and the massive increase in mobile devices have led to a severe spectrum scarcity problem. To alleviate spectrum constraints and meet the ever-growing demand for high data rates, cognitive radio (CR)-based communication networks have attracted significant attention due to their outstanding role in improving spectrum efficiency. CR technology applied to wireless communication networks can provide more use and deployment of underutilized spectrum licensed to legitimate users. Specifically, in situations with limited spectrum resources, secondary users (SUs) can dynamically access licensed spectrum while satisfying primary user (PU) interference constraints. SUs and PUs coexist on the same spectrum, ensuring that PUs can tolerate interference caused by SUs. However, in CR networks, the transmit power of SUs should be limited to guarantee the quality of service for PUs. Furthermore, the severe channel fading conditions of terrestrial communication networks further degrade the performance of SUs. Therefore, improving the performance of SUs in CR networks is crucial.

[0003] In recent years, UAV-assisted communication has been widely studied in military and civilian applications due to its advantages such as flexible deployment, rapid response, and strong environmental adaptability. Compared with traditional ground-based relay networks (CR), UAVs have a high probability of having line-of-sight (LAS) links, so the performance of the relay unit (SU) in UAV-supported CR networks is significantly less affected by channel fading. However, due to the presence of strong Loss-of-Sight (LoS) links from UAVs to relay units (PUs) in UAV-assisted CR networks, the interference caused by UAV transmitters to PUs is also significantly increased. Therefore, in CR networks where UAVs act as relays, reasonable design of UAV trajectories and transmission power is necessary. Trajectory planning and adaptive communication are two closely coupled and important design aspects. On the one hand, adaptive communication techniques such as transmission power allocation should make reasonable use of predictable channel changes caused by relay movement. On the other hand, optimal trajectory design needs to strike a balance between source-relay and relay-destination throughput, while avoiding interference to PUs during communication, which depends on the power allocation of the source / relay transmitter. Therefore, how to balance trajectory planning and adaptive power allocation is a challenging problem. In addition, current research on UAVs as communication relays mainly focuses on serving single-pair ground nodes. In practical applications, such as disaster relief, military scenarios, and communication between base stations and edge users in cell networks, there are often situations where multiple users simultaneously request relay services. Therefore, introducing UAVs into CR networks and leveraging their unique advantages to assist multi-user communication is of significant research value and can further improve network communication performance. Summary of the Invention

[0004] To address the above problems, this invention provides a multi-user communication method for UAVs acting as relays in cognitive wireless networks, comprising the following steps:

[0005] S1. Construct a cognitive wireless network system that uses UAVs as relays to assist secondary user pair communication; the cognitive wireless network system includes UAVs, primary users, and secondary user pairs, wherein the total number of secondary user pairs is [missing information]. M is a positive integer, and each secondary user pair includes a source secondary user and a destination secondary user; all source secondary users range from 1 to M. By assigning index numbers, we obtain the source sub-user set μ = {1, 2, ..., S}. This represents the number of source users, and the position of the i-th source user is denoted as... For all target users from 1 to By assigning index numbers, we obtain the target user set υ = {1, 2, ..., D}. This represents the number of destination users, and the position of the j-th destination user is denoted as... Both the source secondary user and the destination secondary user are secondary users;

[0006] S2. Calculate the initial flight trajectory of the UAV, and discretize the initial flight trajectory to obtain a set of discrete flight trajectories of the UAV;

[0007] S3. Based on the set of discrete flight trajectories of UAVs, construct the overall optimization problem model P1 with the goal of maximizing system throughput;

[0008] S4. Given the UAV flight trajectory and transmission power allocation strategy, transform problem model P1 into subproblem P2 and solve it to obtain the secondary user communication scheduling strategy;

[0009] S5. Based on the given UAV flight trajectory and the secondary user communication scheduling strategy obtained from sub-problem P2, transform problem model P1 into sub-problem P3 and solve it to obtain the transmit power allocation strategy;

[0010] S6. Based on the secondary user communication scheduling strategy obtained from subproblem P2 and the transmit power allocation strategy obtained from subproblem P3, transform problem model P1 into subproblem P4 and solve it to obtain the UAV flight trajectory;

[0011] S7. Determine whether the results have converged based on the system throughput. If so, output the solved secondary user communication scheduling strategy, transmit power allocation strategy, and UAV flight trajectory. Otherwise, substitute the UAV flight trajectory into step S4 for another round of calculation.

[0012] Beneficial effects of this invention:

[0013] This invention, in a multi-user cognitive wireless network, aims to maximize system throughput by jointly optimizing UAV trajectories, UAV and source / secondary user transmit power, and communication scheduling between source and destination secondary users, constructing a total optimization problem model P1. To satisfy the specific information causality constraints in model P1, the access order of UAVs is first obtained using the classic Pickup-and-Delivery Problem (PDP). Given a task time T, the initial flight trajectory is discretized into a finite number of trajectory points using time-interval discretization techniques. This invention effectively solves the spectrum shortage problem, improves spectrum utilization, effectively avoids interference with primary users, ensures service quality for multiple user pairs, and achieves greater system throughput and improved communication performance within limited time and energy constraints. Attached Figure Description

[0014] Figure 1 This is a system model diagram of multi-user communication in the UAV-assisted cognitive wireless network of the present invention;

[0015] Figure 2 This is a flowchart of the iterative optimization algorithm of the present invention;

[0016] Figure 3 The flight trajectory of the UAV in this embodiment of the invention;

[0017] Figure 4 This invention provides a comparison of the total throughput of a UAV-assisted multi-user communication system under different flight energy consumption constraints.

[0018] Figure 5 This invention relates to the communication scheduling of ground secondary users by UAV in different time slots;

[0019] Figure 6 This invention relates to the power allocation between the source user and the UAV in different time slots;

[0020] Figure 7 The interference of the SSU / UAV transmit power on the PU in this invention;

[0021] Figure 8 The causal relationship between the UAV relay information acquisition and transmission and time T in this invention;

[0022] Figure 9 This invention provides a comparison of system throughput under different flight times T. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention constructs a cognitive wireless network system that uses UAVs as relays to assist secondary users in communication, such as... Figure 1 As shown; the cognitive wireless network system includes UAVs, primary users, and secondary user pairs, wherein the total number of secondary user pairs is [number missing]. M is a positive integer, and each secondary user pair includes a source secondary user and a destination secondary user. A UAV is deployed as a relay to assist communication between multiple secondary user pairs. A decode-and-forward (DF) strategy is used to forward information received from the source secondary user (SSU) to the corresponding destination secondary user (DSU). This invention assigns all source secondary users numbers from 1 to... By assigning index numbers, we obtain the source sub-user set μ = {1, 2, ..., S}. This represents the number of source secondary users, corresponding to all destination secondary users from 1 to... By assigning index numbers, we obtain the target user set υ = {1, 2, ..., D}. This represents the number of destination secondary users. Assuming the location of each ground node is known, the location of the i-th source secondary user is represented as... i∈μ; the position of the j-th destination user is represented as j∈υ; the position of the main user k={1,2,…,K} is represented by w. pu,k This indicates that K represents the number of primary users. Both source and destination secondary users are considered secondary users.

[0025] Based on the above system, this method proposes a UAV-assisted communication approach based on TDMA. In the multi-user communication process within a cognitive wireless network where the UAV acts as a relay, the following aspects are mainly considered:

[0026] 1) When UAVs act as relays to assist communication between secondary users, since SU and PU share the spectrum, it is necessary to maximize the communication performance of SU in the CR network and meet the QoS of SU while ensuring that PU can tolerate the interference caused by SU.

[0027] 2) UAVs used as mobile relays can only forward information bits previously collected from the source secondary user to the corresponding destination secondary user, and the transmission of data must follow the information-causality relationship.

[0028] 3) The energy of the UAV is limited, and it is necessary to ensure that the UAV can reach the designated location before the energy is exhausted.

[0029] 4) By adopting a PDP-based trajectory initialization method, the information-causal relationship constraints that the UAV needs to satisfy are fully considered in the initial flight trajectory, thereby obtaining a larger system throughput.

[0030] Based on the above analysis, this invention proposes a multi-user communication method for a cognitive wireless network in which a UAV acts as a relay, such as... Figure 2 As shown, it includes the following steps:

[0031] S1. Calculate the initial flight trajectory of the UAV, and discretize the initial flight trajectory to obtain a set of discrete flight trajectories of the UAV.

[0032] Specifically, in order to meet the specific information causal constraints and communication requirements, the initial design, as in step S2, is first performed, including:

[0033] S21. Construct an access path planning problem based on the locations of all secondary users, and solve the access path planning problem using the PDP method to obtain the optimal access order in which the UAV visits all secondary users; determine the initial flight trajectory and minimum flight time T of the UAV based on the optimal access order. PDP .

[0034] Specifically, this embodiment is based on the initial position of the UAV, Location of each source user The order of the destination user's position and the UAV's ending position starts from 0, and each position is assigned a number to obtain the set of key position labels φ={0,1,2,...,S,S+1,...,S+D,S+D+1} traversed by the UAV during flight; where g0 represents the initial position coordinates of the UAV, g S+D+1 Indicates the coordinates of the UAV's end position, g k Let C represent the position coordinates of a secondary user labeled k∈{1,2,..,S+D}. For any two distinct positions labeled a,b∈φ and a≠b, define the direct flight distance between them as C. a,b =||g a -g b ||, then the flight time for the UAV to cover this direct flight distance is V max Represents the maximum flight speed of the drone; define a binary variable x. a,b If the UAV traverses the connecting edge between position a and position b (i.e., the direct flight distance C of the UAV along the distance between position a and position b), a,b (flight), then x a,b =1, otherwise x a,b =0. The total distance traveled by the UAV is defined as L = ∑ a∈φ ∑ b∈φ,a≠b C a,b x a,b Then, the optimal access sequence for all SUs is planned using the PDP solution.

[0035] Based on the above analysis, step S21 constructs an access path planning problem according to the locations of all secondary users, which is expressed as:

[0036] min L({x a,b ,a,b∈φ anda≠b})

[0037]

[0038]

[0039] T0≤T k ≤T S+D+1 k∈φ (1c)

[0040]

[0041] T m ≤T S+m m = 1…M / 2 (1e)

[0042]

[0043] Among them, constraints (1a) and (1b) ensure that each position can only be accessed by the UAV once, which also indicates that each secondary user can only be accessed by the UAV once; constraint (1c) determines that the UAV first accesses the initial position of the UAV, and finally reaches the end position of the UAV after traversing all secondary users, T0 and T S+D+1 T represents the time when the UAV reaches its initial position and the time when it reaches its final position, respectively. k Let T represent the time when the UAV arrives at the k-th secondary user; constraint (1d) ensures time consistency, that is, the arrival time of the location previously visited by the UAV plus the next flight time is less than the arrival time of the location to be visited by the UAV next time. a,b This represents the flight time of the UAV as it travels along the connection between position a and position b; constraint (1e) is an information causality constraint, ensuring that the UAV has visited its corresponding source user before visiting a destination user. For a user with source user index number m and destination user index number S+m, T m T represents the time when the UAV arrives at the source secondary user with index number m. S+m Let m represent the time when the UAV arrives at the destination secondary user with index number m; constraint (1f) ensures that there is at most one UAV trajectory between any two secondary users. This problem is a mixed integer optimization problem, which can be solved using the CPLEX CP optimizer, thus obtaining the optimal access order of the UAVs.

[0044] S22. Given a task time T, use the time-interval discretization method to discretize the time range [0,T] into N equal time slots;

[0045] Specifically, assuming the UAV's flight altitude is H and the mission time is T, according to the three-dimensional Euclidean coordinate representation, the UAV's position at time t is represented as q(t) = (x(t), y(t), H), where 0 ≤ t ≤ T. Since the UAV trajectory is a continuous function of time, involving a large number of optimization variables, this embodiment first uses a time-interval discretization method to discretize the time range [0, T] into N equal time slots, thus obtaining T = Nδ t , where δ t δ represents the length of each time slot. t The value is small enough that the distance between the UAV and the ground user remains approximately constant within each time slot. Therefore, the continuous UAV trajectory over a continuous time period is discretized into N time slot trajectories, which can also be viewed as discretized into N+1 trajectory points; thus, the position of the UAV in the nth time slot can be represented as:

[0046] q(n)=(x(n),y(n),H),n∈{1,2,…,N}

[0047] Based on the above expression, in the UAV trajectory, the initial position and the final position of the UAV are represented as q(0) = q initial ,q(N)=q final Meanwhile, UAVs have a maximum flight speed V. max Therefore, during UAV flight, the flight speed within each time slot follows these constraints:

[0048]

[0049] Where v(n) represents the flight speed of the UAV in the nth time slot.

[0050] Based on throughput requirements, a corresponding initial hovering time is allocated for each secondary user. During hovering, the UAV communicates directly above the secondary user. Therefore:

[0051]

[0052]

[0053] in, This represents the initial hovering time of the UAV of the s-th source user. This represents the minimum throughput requirement for the s-th source user to send data to the UAV; This represents the transmit power of the s-th source user sending data to the UAV; This represents the initial hovering time of the UAV for the d-th destination user. This represents the minimum throughput requirement for the UAV to forward data to the d-th destination user; γ represents the transmit power when the UAV forwards data to the d-th destination user; B represents the channel bandwidth; H represents the UAV flight altitude; and γ0 represents the signal-to-interference plus-noise ratio at the reference distance (generally, the reference distance refers to a unit distance equal to 1m). Where λ0 represents the channel power gain per unit distance, σ 2 The noise power is represented as the total power of receiver noise and main network interference at the UAV and SSU.

[0054] S23. Based on the UAV's initial hovering time, mission time T, and minimum flight time T... PDP The hovering time of the UAV for different users is calculated and expressed as:

[0055]

[0056] in, This represents the hover time of the s-th source user. This represents the hover time for the d-th destination user. This represents the initial hover time for the m-th user;

[0057] S24. Based on the optimal access order and hovering time set of the UAV, the initial flight trajectory is discretized into multiple trajectory points within the mission time T using the time-equal interval discretization technique, thus obtaining the UAV discrete flight trajectory set.

[0058] Specifically, in the initial flight trajectory calculation phase of this invention, the default flight speed of the UAV is the maximum flight speed V. max First, the time range [0,T] is discretized into N equal time slots using the time equal interval discretization method, resulting in N+1 time points. These N+1 time points are then used as N+1 trajectory points, and the coordinates of the UAV at these N+1 trajectory points can be obtained by multiplying the velocity by the time, thus obtaining the set of discrete flight trajectories of the UAV.

[0059] S2. Based on the time slots divided in step S1 and the obtained set of discrete flight trajectories of UAVs, construct the overall optimization problem model P1 with the goal of maximizing system throughput.

[0060] Specifically, the process of constructing the overall optimization problem model P1 includes:

[0061] First, binary variables are used. and Let represent the communication scheduling of SSU and DSU, respectively. Wherein, if the UAV receives information from the i-th source sub-user in the n-th time slot, then α i (n) = 1, otherwise α i (n) = 0. Similarly, if the UAV transmits information to the j-th destination user in the n-th time slot, then β j (n) = 1, otherwise β j (n) = 0. Each UAV can only communicate with one ground user per time slot, which leads to the following constraint:

[0062]

[0063] Meanwhile, the UAV used as a relay can only transmit data previously from SSU. m The information collected was forwarded to SSU. m Corresponding DSU m This means that in any time slot n, the UAV sends data to the DSU. m The amount of information must be less than or equal to that from the corresponding SSU m The amount of information collected. Additionally, regarding the DF strategy, from SSU...m After receiving the information, the UAV needs some time to decode it before it begins sending it to the DSU. m Sending, which is defined as the processing delay. Without loss of generality, assume the processing delay is one time slot. Therefore, in time slot n, the information-causality constraint can be expressed as:

[0064]

[0065] β m (w) represents the communication scheduling of the destination user with index number m in the w-th time slot. This represents the downlink transmission rate of the signal sent by the UAV to the destination secondary user with index number m in the nth time slot; α m (w) represents the communication scheduling of the source secondary user with index number m in the w-th time slot. This represents the uplink transmission rate of the signal sent from the source secondary user with index number m to the UAV in the nth time slot; This represents the amount of information received by the destination user from the UAV from the w-th time slot to the n-th time slot, with index number m. This represents the amount of information sent from the source secondary user with index number m to the UAV from the w-th time slot to the (n-1)-th time slot.

[0066] Preferably, in this system, since secondary users and primary users share the spectrum, the goal is to maximize the communication performance of the SU in the CR network while ensuring that the PU can tolerate interference caused by the SU, and simultaneously meet the SU's QoS. This is achieved by adopting a low-level spectrum sharing method, whereby UAVs and SUs can continuously access the licensed spectrum as long as the interference to the PU does not exceed a threshold. Secondary users and PUs can share the spectrum under interference temperature (IT) constraints. Assuming the channel model between the SSU and PU follows independent Rayleigh fading, the IT constraint for each PU to avoid average interference from the SSU is expressed as:

[0067]

[0068] Where θ and ζ represent the path loss exponent and a random variable with a mean of 1 that follows an exponential distribution, respectively. ζ [] indicates taking the average of the random variable ζ; ||s i -w pu,k || indicates SSU i and PU k The 2-norm of distance; Γ S This indicates the IT threshold for primary users to deal with interference from SSU.

[0069] The PU receives interference information from the UAV relay station, assuming its channel model is based on the LoS channel model. In time slot n, the IT constraints for each PU to avoid UAV interference can be expressed as:

[0070]

[0071] Among them, Γ U This represents the IT threshold for the primary user in response to interference from UAVs.

[0072] Preferably, each secondary user must meet a minimum total throughput to guarantee QoS; otherwise, due to the maximization of total system throughput, secondary user pairs with poor channel gain will never be scheduled. It is assumed that the wireless channel between the UAV and the secondary user pairs is dominated by line-of-sight (LoS) links. Furthermore, it is assumed that the Doppler effect caused by UAV flight can be perfectly compensated at the receiver. Within time slot n, the time-varying channel gains of the uplink and downlink can be expressed as:

[0073]

[0074]

[0075] in, This represents the time-varying channel gain between the UAV and the i-th source sub-user in the n-th time slot; Let q(n)-s represent the time-varying channel gain between the UAV and the j-th destination secondary user in the n-th time slot; λ0 represents the channel power gain per unit distance. i || and ||q(n)-d j || represent UAV and SSU respectively in time slot n. i and DSU j The L2 norm of the distance.

[0076] make This represents the transmit power of the signal sent by the i-th source sub-user to the UAV in the n-th time slot; This represents the transmit power of the UAV sending a signal to the j-th destination user in the nth time slot; therefore, the achievable transmission rates of the uplink and downlink in the nth time slot are expressed as follows:

[0077]

[0078]

[0079] Where B is the channel bandwidth; σ 2 The noise power is represented as the total power of receiver noise and main network interference at the UAV and SSU.

[0080] Preferably, within time T, each secondary user must meet a minimum total throughput to guarantee QoS. In the uplink of the source secondary user transmitting information to the UAV, the following is adopted: This represents the minimum throughput requirement of the i-th source secondary user; in the downlink transmission of information from the UAV to the destination secondary user, the following is adopted: Let represent the minimum throughput requirement for the j-th destination user, then:

[0081]

[0082]

[0083] In this system, a rotorcraft UAV is used. The UAV's energy consumption includes flight energy consumption and communication energy consumption. Since the UAV's communication energy consumption is much smaller than its flight energy consumption, it is assumed that the UAV's communication energy consumption can be ignored. By discretizing the UAV's flight time into equal intervals, the UAV's flight energy consumption model is as follows:

[0084]

[0085] Where E({q(n)}) represents the total energy consumed by the UAV when performing the task, P0 represents the airfoil power of the UAV in the hovering state, and P i This represents the induced power during UAV hovering. d0 represents the UAV blade tip velocity, s represents the airframe drag ratio, A represents the rotor robustness, v0 represents the rotor induced velocity when the UAV is hovering, and ρ is the air density.

[0086] Assuming the maximum energy that a UAV battery can store is The UAV needs to fly back to its destination before running out of energy:

[0087]

[0088] Based on the above analysis, this invention formulates the optimization problem as optimizing the UAV flight trajectory {q(n)}, the source and secondary users, and the UAV's transmit power allocation strategy. Communication scheduling strategy with secondary users {α i (n),β j To maximize the system throughput, we use (n)}.

[0089] Specifically, the overall optimization problem model P1 is expressed as:

[0090] P1:

[0091] st C1:

[0092] C2:

[0093] C3:

[0094] C4:

[0095] C5:

[0096] C6:

[0097] C7:

[0098] C8:

[0099] C9:

[0100] C10:

[0101] C11:

[0102] C12:||q(n+1)-q(n)|| 2 ≤(V max δ t ) 2 n = 1, 2, ..., N-1

[0103] C13:q(0)=q initial ,q(N)=q final

[0104] Among them, C1 is the minimum throughput requirement constraint for the source secondary user in the uplink, and C2 is the minimum throughput requirement constraint for the destination secondary user in the downlink; C3 is the transmit power constraint from the source secondary user to the UAV in the uplink, C4 is the transmit power constraint from the UAV to the destination secondary user in the downlink, C5 is the communication scheduling constraint for the source secondary user, C6 is the communication scheduling constraint for the destination secondary user, C7 is the joint scheduling constraint for the source and destination secondary users, which means that the UAV communicates with only one secondary user in each time slot; C8 is the information causality constraint, C9 is the source secondary user interference constraint for the primary user, C10 is the UAV interference constraint for the primary user, C11 is the UAV flight energy consumption constraint, C12 is the UAV flight speed constraint, and C13 is the UAV position constraint.

[0105] S3. The block coordinate descent algorithm is used to decompose the problem model P1 into three subproblems: P2, P3, and P4.

[0106] Specifically, in problem P1, the UAV flight trajectory {q(n)} is a function of time slot n, and the secondary user communication scheduling strategy {α}... i(n),β j All variables (n) are binary variables, and constraints C1, C2, C8, and C10-C12 are non-convex constraints. Therefore, problem P1 is a mixed-integer non-convex problem, which is difficult to solve directly. Thus, this invention uses a block coordinate descent algorithm to decompose problem P1 into three subproblems for separate solving.

[0107] Specifically, given the UAV flight trajectory {q(n)} and the transmit power allocation strategy In this case, optimize the secondary user communication scheduling strategy {α} i (n),β j Since the communication scheduling of SU in problem P1 is an integer constraint, the binary variables in constraints C5 and C6 can be relaxed to continuous variables. Therefore, the optimization of user communication scheduling in P1 can be expressed by the following subproblem P2:

[0108] P2:

[0109] st C1,C2,C7,C8

[0110] C14:

[0111] C15:

[0112] Wherein, C14 is the relaxed source secondary user communication scheduling constraint, and C15 is the relaxed destination secondary user communication scheduling constraint.

[0113] Subproblem P2 is a linear programming problem that can be solved using linear programming tools or the simplex method.

[0114] Based on the solution of P2, the slack variables should be reconstructed as binary variables. To this end, we define the following rule: within time slot n, for any α i (n),β j (n), according to arg max(α) i (n),β j (n)) Get the corresponding secondary user, and set the communication scheduling variable of this user in the nth time slot to 1, and the communication scheduling variable of other secondary users to 0.

[0115] Preferably, the specific process of transforming problem model P1 into subproblem P2 and solving it includes:

[0116] S301. Combine the optimization items UAV flight trajectory {q(n)} and UAV / SSU transmit power allocation strategy in problem model P1. Fixed, the problem model P1 is transformed into one that only computes the secondary user communication scheduling policy {α}. i (n),βj Subproblems of (n)};

[0117] S302. Binary Variable Relaxation: Given the UAV flight trajectory {q(n)} and the UAV / SSU transmit power allocation strategy In this case, relaxation is introduced to transform the subproblem of step S301 into a convex problem, and the SU communication scheduling strategy under the current case is obtained by solving it through the CVX toolbox.

[0118] S303. Binary variable reconstruction: In each time slot, according to arg max(α) i (n),β j (n)) Obtain the corresponding secondary user, and set the communication scheduling of that secondary user to 1, and the other secondary users to 0;

[0119] S304. Finally, the secondary user communication transmission schedule {α} is obtained. i (n),β j (n)}.

[0120] Specifically, the secondary user communication scheduling strategy {α} is obtained by solving the given UAV flight trajectory {q(n)} and subproblem P2. i (n) * ,β j (n) *}, transform problem model P1 into subproblem P3′:

[0121] P3′:

[0122] st C1,C2,C3,C4,C8,C9,C10

[0123] However, the information-causal constraint C8 in the above equation is non-convex. To solve this non-convex problem, we define slack variables. m = 1, 2, ..., M / 2 pairs Perform scaling. Subproblem P3′ is restated as follows:

[0124] P3:

[0125] st C1,C2,C3,C4,C9,C10

[0126] C16:

[0127] C17:

[0128] C18:

[0129] m = 1, 2, ..., M / 2, n = 2, 3, ..., N

[0130] In subproblem P3, slack variables are introduced. Subsequently, new constraints C16 and C17 were added. However, the optimal solution to subproblem P3 can only be obtained if the equality of constraint C17 holds. Therefore, subproblem P3′ is equivalent to subproblem P3. For subproblem P3, its objective function and all constraints satisfy the conditions of standard convex optimization, so the above convex problem can be solved using the MatlabCVX toolbox.

[0131] Specifically, the optimal secondary user communication scheduling strategy {α} is obtained through subproblems P2 and P3. i (n) * ,β j (n) * and optimal transmit power allocation strategy Then, substitute it into problem model P1, thereby transforming problem model P1 into subproblem P4′:

[0132] P4′:

[0133] st C1,C2,C8,C10,C11,C12,C13

[0134] In the above equation, the objective function and constraints C1, C2, C8, and C10 are all non-convex with respect to q(n). Furthermore, constraint C11 is also non-convex with respect to v(n). To address this non-convexity, a suboptimal solution is obtained iteratively using successive convex approximation techniques and the relaxation function method.

[0135] for Given the position q of the UAV in time slot n during the l-th iteration. (l) (n), obtained from the concave function of the first-order Taylor expansion. The lower bound is found and replaced. Specifically, ||q(n)-s i || 2 Consider it as a variable, for a given local point q in the l-th iteration (l) (n), The concave function of the first-order Taylor expansion can be expressed as:

[0136]

[0137] make In the above formula:

[0138]

[0139]

[0140] Similarly: for Its first-order Taylor expansion is a concave function:

[0141]

[0142] in,

[0143]

[0144] For constraint C10: Can be converted Where ||q(n)-w pu,k || 2 +H 2 The following inequalities are satisfied:

[0145] ||q(n)-w pu,k || 2 +H 2 ≥||q (l) (n)-w pu,k || 2 +2||q (l) (n)-w pu,k || T ||q(n)-q (l) (n)||+H 2

[0146] According to the above formula, constraint C10 can be transformed into a convex constraint, that is...

[0147]

[0148] For constraint C11, introduce a slack variable {y(n)|y(n)≥0}, let By scaling the slack variables, we have:

[0149]

[0150] The above equation remains a non-convex function. By expressing the lower bound using a first-order Taylor expansion on the right-hand side of the above equation, we have:

[0151] Therefore, the optimization subproblem P4′ can be reformulated as:

[0152] P4:

[0153] st C12,C13

[0154] C19:

[0155] C20:

[0156] C21:

[0157] C22:

[0158] C23:

[0159] C24:

[0160] C25:

[0161]

[0162] As shown in the above derivation, subproblem P4 is a convex optimization problem, which can be efficiently handled using existing convex optimization tools (such as CVX). Since the trajectory optimization problem P4′ is non-convex, it is transformed into a convex problem P4 using the SCA method. Based on the transformation of P4′, the solution to P4 provides a lower bound for P4′, which means that the solution to P4′ can be approximately obtained by solving P4.

[0163] S4. Solve the three subproblems P2, P3 and P4 in sequence to obtain the results. Determine whether the system throughput result obtained at this time has converged. If so, output the current secondary user communication scheduling strategy, transmit power allocation strategy and UAV flight trajectory. Otherwise, substitute the current UAV flight trajectory and repeat step S4 to solve the three subproblems P2, P3 and P4.

[0164] Specifically, the method flow of the present invention is shown in Figure 2, and the specific algorithm is shown in the table below:

[0165] Table 1

[0166]

[0167]

[0168] In one embodiment, a simulation experiment was conducted using 3 secondary user pairs, 3 primary users, and one UAV, assuming that the communication throughput requirements of all SUs are equal, i.e. Furthermore, the initial transmit power of the SSU and UAV is set to be equal to their average power, i.e.

[0169] Figure 3 The impact of different IT thresholds on UAV flight trajectories was plotted. It can be observed that when the UAV flies through the PU's area, the smaller the IT threshold, the more sensitive the PU is to interference from the UAV. The UAV reduces interference to the PU by moving away from it, thus ensuring PU communication quality. Secondly, when the IT threshold is in the range of Γ... S Set to different values ​​and ΓU When set to equal values, the two path curves of the UAV relay can be observed to be close to each other; when Γ S Set to the same value and Γ U When set to different values, the two path curves of the UAV relay are far apart. This indicates that the PU is more sensitive to interference from the UAV relay than to interference from the source secondary user.

[0170] Figure 4 The comparison of total system throughput under different flight energy consumption conditions is presented. The results show that, with a constant IT threshold, the total system throughput increases with propulsion energy. It increases with the increase of propulsion energy. When the propulsion energy is low, the UAV, due to energy limitations, cannot fly over all secondary users, resulting in poor channel gain and reduced system throughput. When the propulsion energy... When the channel size is large, UAVs can fully utilize their flexible maneuverability to achieve better channel gain, thereby increasing the overall system throughput. However, when... When the size is large enough, the system throughput will no longer increase due to the limitation of average power of the UAV. It grows with growth. Meanwhile, the impact of different IT thresholds on system throughput can be observed. The smaller the IT threshold, the lower the system throughput. This is because, to ensure communication for the primary user, IT constraints limit the transmit power of the source secondary user, and UAVs move away from the PU to protect it from interference, thus reducing system throughput.

[0171] Figure 5 and Figure 6 The following are examples showing the IT threshold settings: Γ S = -60dBm, Γ U With a resolution of -59 dBm and a secondary user QoS of 30 Mbits, the communication scheduling of the ground secondary user and the optimized power of the source secondary user / UAV change over time T. It can be observed that whether the source secondary user is sending data to the UAV or the UAV is forwarding data to the destination secondary user, it will communicate at maximum power to improve the overall system throughput, provided the interference IT threshold is met. When the UAV relay flies away from the secondary user, its power decreases rapidly due to the constraint of average power.

[0172] Figure 7 The study verified that the proposed solution satisfies the IT threshold constraint, demonstrating the interference to the PU caused by data transmission from the source secondary user to the UAV and data forwarding from the UAV to the destination secondary user at different times. It can be observed that when the IT threshold of the UAV to the PU is set to: Γ UAt -59dBm, the interference value of the UAV to each PU is less than or equal to -59dBm. For the source and secondary users, since the locations of the source and secondary users and the PU are fixed, if the transmit power of the source and secondary users is known, it is only necessary to verify whether the interference of the source and secondary users to the nearest PU meets the interference threshold. This is because, at the same power, the closer the distance between the source and secondary users and the PU, the greater the interference. As can be seen from the figure, when the IT threshold of the source and secondary users to the PU is set to: Γ U At -60dBm, the interference from the source sub-user to each PU satisfies the IT constraint. This proves the effectiveness of the proposed algorithm.

[0173] exist Figure 8 The study verified that the proposed scheme satisfies the information-causality constraint. The IT threshold is set as follows: Γ S = -60dBm, Γ U = -59dBm, QoS for secondary users = 30Mbits. The cumulative amount of information bits received by the UAV from each source secondary user and the cumulative amount of information bits transmitted via UAV relay to the corresponding destination secondary user are plotted against time. By comparing the amount of information bits, it is observed that each secondary user pair satisfies the QoS. Furthermore, the UAV always first collects information bits from the source secondary user and then sends them to the corresponding destination secondary user; the two curves for information collection and transmission by the UAV relay for each secondary user pair eventually intersect at the same point. This proves that the information-causality relationship is satisfied for each pair of ground secondary users.

[0174] Figure 9 The relationship between throughput and total flight time T for different system schemes was plotted. Overall, the system throughput of all four schemes increases with time T. It can also be seen that the throughput of the joint optimization scheme is significantly better than the baseline scheme and the optimal relay location scheme. This is because the joint optimization scheme has more design freedom to optimize the total system throughput. These results indicate that throughput can be further improved by jointly optimizing UAV trajectories, user communication scheduling, and transmit power.

[0175] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," "fixing," "rotation," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal connection of two components or the interaction between two components. Unless otherwise explicitly limited, those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0176] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-user communication method for a UAV acting as a relay in an auxiliary cognitive wireless network, characterized in that, Includes the following steps: S1. Construct a cognitive wireless network system that uses UAVs as relays to assist secondary user pair communication; the cognitive wireless network system includes UAVs, primary users, and secondary user pairs, wherein the total number of secondary user pairs is [missing information]. M is a positive integer, and each secondary user pair includes a source secondary user and a destination secondary user; all source secondary users range from 1 to M. Assign index numbers to obtain the source sub-user set. S= This represents the number of source users, and the position of the i-th source user is denoted as... , For all target users from 1 to Assign index numbers to obtain the target sub-user set. D= This represents the number of destination users, and the position of the j-th destination user is denoted as... , Both the source secondary user and the destination secondary user are considered secondary users. S2. Calculate the initial flight trajectory of the UAV, and discretize the initial flight trajectory to obtain a set of discrete flight trajectories for the UAV, including: S21. Construct an access path planning problem based on the locations of all secondary users, and solve the access path planning problem using the PDP method to obtain the optimal access order in which the UAV visits all secondary users; determine the initial flight trajectory and minimum flight time T of the UAV based on the optimal access order. PDP ; S22. Given a task time T, the time range [0, T] is discretized into N equal time slots using a time-interval discretization method; based on throughput requirements, a corresponding initial hovering time for the UAV is allocated for each secondary user, expressed as: in, This represents the initial hovering time of the UAV of the s-th source user. This represents the minimum throughput requirement for the s-th source user to send data to the UAV; This represents the transmit power of the s-th source user sending data to the UAV; This represents the initial hovering time of the UAV for the d-th destination user. This represents the minimum throughput requirement for the UAV to forward data to the d-th destination user; B represents the transmit power when the UAV forwards data to the d-th destination user; B represents the channel bandwidth; and H represents the UAV's flight altitude. This represents the signal-to-interference-plus-noise ratio at the reference distance; S23. Based on the UAV's initial hovering time, mission time T, and minimum flight time T... PDP The hovering time of the UAV for different users is calculated and expressed as: in, This represents the hover time of the s-th source user. This represents the hover time for the d-th destination user; S24. Based on the optimal access order and hovering time set of the UAV, the initial flight trajectory is discretized into multiple trajectory points within the mission time T using the time-equal interval discretization technique, thus obtaining the UAV discrete flight trajectory set; S3. Based on the set of discrete flight trajectories of UAVs, construct the overall optimization problem model P1 with the goal of maximizing system throughput; S4. Given the UAV flight trajectory and transmission power allocation strategy, transform problem model P1 into subproblem P2 and solve it to obtain the secondary user communication scheduling strategy; S5. Based on the given UAV flight trajectory and the secondary user communication scheduling strategy obtained from sub-problem P2, transform problem model P1 into sub-problem P3 and solve it to obtain the transmit power allocation strategy; S6. Based on the secondary user communication scheduling strategy obtained from subproblem P2 and the transmit power allocation strategy obtained from subproblem P3, transform problem model P1 into subproblem P4 and solve it to obtain the UAV flight trajectory; S7. Determine whether the result has converged. If so, output the secondary user communication scheduling strategy, transmit power allocation strategy and UAV flight trajectory. Otherwise, substitute the UAV flight trajectory into step S4 and perform another round of calculation.

2. The multi-user communication method for a UAV acting as a relay in a cognitive wireless network according to claim 1, step S21 involves constructing an access path planning problem based on the locations of all secondary users, and solving the access path planning problem using the PDP method. The access path planning problem is expressed as: in, This represents the set of key location markers traversed by the UAV during flight, where 0 and S+D+1 represent the initial and final location markers of the UAV, respectively, S represents the number of source secondary users, and D represents the number of destination secondary users; a binary variable is defined. If the UAV traverses the connecting edge between position a and position b, then x a,b =1, otherwise x a,b =0; T0 and T represents the time when the UAV reaches its initial position and the time when the UAV ends its position, respectively. k T represents the time when the UAV arrives at the k-th secondary user; a,b T represents the flight time of the UAV traversing the edge connecting position a and position b. m T represents the time when the UAV arrives at the source secondary user with index number m. S+m This indicates the time when the UAV arrives at the destination user with index number m.

3. In the multi-user communication method of a UAV acting as a relay to assist a cognitive wireless network according to claim 1, step S3 constructs a total optimization problem model P1 based on the set of discrete flight trajectories of UAVs, with the goal of maximizing system throughput, as follows: in, N represents the number of time slots. This represents the communication scheduling of the i-th source secondary user in the n-th time slot; q(n) represents the communication scheduling of the j-th destination user in the n-th time slot; q(n) represents the position of the UAV in the n-th time slot; This represents the transmit power of the signal sent by the i-th source sub-user to the UAV in the n-th time slot; This represents the transmission power of the UAV sending a signal to the j-th destination user in the nth time slot; This represents the uplink transmission rate of the signal sent from the i-th source secondary user to the UAV in the n-th time slot; This represents the downlink transmission rate at which the UAV sends a signal to the j-th destination user in the nth time slot; This represents the minimum throughput requirement of the i-th source user. This represents the minimum throughput requirement for the j-th destination user. This represents the length of the nth time slot. This represents the maximum transmit power of any source user sending signals to the UAV. This represents the average transmit power of the signal sent from the source secondary user to the UAV. This represents the maximum transmit power of the UAV to send signals to any destination secondary user. This represents the average transmit power of the signal sent by the UAV to the target secondary user; This represents the signal-to-interference-plus-noise ratio at the reference distance. This indicates the position of the k-th main user. This represents the IT threshold for interference from the source secondary user to the primary user. This represents the IT threshold for interference from UAVs to the primary user, where H represents the UAV's flight altitude. This represents the total flight energy consumption of the UAV. V represents the maximum energy that a UAV battery can store. max q represents the maximum flight speed of the UAV. initial Indicates the initial position of the UAV, q final Indicates the end position of the UAV.

4. A multi-user communication method for a UAV acting as a relay in a cognitive wireless network according to claim 3, wherein, given the UAV flight trajectory and transmit power allocation strategy, the problem model P1 is transformed into a sub-problem P2, and the sub-problem P2 is solved using linear programming tools or the simplex method to obtain the secondary user communication scheduling strategy; wherein, Subproblem P2 is represented as: Wherein, C14 is the relaxed source secondary user communication scheduling constraint, and C15 is the relaxed destination secondary user communication scheduling constraint.

5. A multi-user communication method for a UAV acting as a relay-assisted cognitive wireless network according to claim 4, wherein, based on the given UAV flight trajectory and the secondary user communication scheduling strategy solved for sub-problem P2, problem model P1 is transformed into sub-problem P3, and the transmit power allocation strategy is obtained by solving it using the Matlab CVX toolbox; wherein, Subproblem P3 is represented as: in, For the set of downlink transmission rate relaxation variables, These are slack variables.

6. According to claim 5, a multi-user communication method using a UAV as a relay-assisted cognitive wireless network, based on the secondary user communication scheduling strategy obtained from sub-problem P2 and the transmit power allocation strategy obtained from sub-problem P3, transforms problem model P1 into sub-problem P4, and obtains the UAV flight trajectory by solving it using the Matlab CVX toolbox; wherein, Subproblem P4 is represented as: in, for First-order Taylor expansion; for First-order Taylor expansion; These are slack variables; Indicates the first In the next iteration, the UAV in the time slot The location inside; Indicates the duration of each time slot; These are slack variables; and These represent the airfoil power and induced power of the UAV in hovering state, respectively; d0 represents the blade tip velocity of the UAV, and d0 represents the airframe drag ratio of the UAV. This indicates the rotor robustness of the UAV, A represents the rotor disk area of ​​the UAV, and v0 represents the rotor induced speed when the UAV is hovering; This indicates air density.

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