A method to maximize the total throughput of multi-UAV-assisted Internet of Things

Abstract

The present invention belongs to the field of wireless transmission technology and discloses a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things (IoT). The method comprises obtaining location information of cellular edge users and D2D devices; constructing a total throughput maximization problem for the multi-UAV-assisted IoT and decomposing the maximization problem into two subproblems; solving the relay selection and time allocation subproblems of the D2D pairs; and solving the three-dimensional deployment subproblem of the UAVs. The two subproblems are alternately optimized until the objective function of the total throughput maximization problem for the multi-UAV-assisted IoT converges to obtain a final optimization result. Based on the final optimization result, the total throughput of the multi-UAV-assisted IoT is maximized. The present invention achieves a higher total throughput for the multi-UAV-assisted IoT.

Description

Technical Field

[0001] The present invention belongs to the field of wireless transmission technology, and in particular relates to a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things. Background Art

[0002] With the rapid development of the Internet of Things (IoT), the number of IoT devices connected to cellular networks has grown significantly, and IoT devices have been widely used in society. However, due to the long communication distance and the obstacles of dense buildings, the service obtained by IoT devices at the cellular edge is not satisfactory.

[0003] To improve IoT coverage and throughput, drones (UAVs) have attracted widespread attention due to their high maneuverability and flexibility. Specifically, deploying multiple UAVs in the air as relays increases the probability of line-of-sight transmission with IoT devices on the ground. Furthermore, to support large-scale connections between IoT devices and UAVs, non-orthogonal multiple access (NOMA) technology is employed. This allows multiple users to transmit data on the same spectrum with varying receive power levels, further improving IoT performance.

[0004] In multi-UAV-assisted IoT, the number of drones acting as relays is limited, necessitating consideration of relay contention among IoT devices. Furthermore, the three-dimensional deployment of drones can impact channel fading across multiple links simultaneously. Therefore, optimizing relay selection, resource allocation, and the three-dimensional deployment of drones is crucial to maximize the overall throughput of multi-UAV-assisted IoT. Summary of the Invention

[0005] The object of the present invention is to provide a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things (IoT) while satisfying the minimum throughput constraint of IoT devices and the channel signal-to-noise ratio constraint.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for maximizing the total throughput of a multi-drone-assisted Internet of Things (IoT) system, comprising a multi-antenna base station, U single-antenna cell edge users, M single-antenna drones, and N pairs of direct-to-direction (D2D) pairs, each of which includes a D2D transmitter and a D2D receiver. The drones function as relay devices, relaying data from the base station to the cell edge users and simultaneously relaying data from the D2D transmitters to the D2D receivers. The method for maximizing the total throughput of the multi-drone-assisted IoT system comprises:

[0008] Step 1: The base station obtains the location information of U cell edge users and N pairs of D2D pairs, divides the cell edge users into M clusters based on the location information, and assigns one drone to each cluster to relay data sent by the base station to the cell edge users in that cluster;

[0009] Step 2: Construct the total throughput maximization problem for multi-UAV-assisted IoT and decompose it into two sub-problems: the total throughput maximization problem for multi-UAV-assisted IoT given the three-dimensional deployment of UAVs and the total throughput maximization problem for multi-UAV-assisted IoT given the D2D relay selection and time allocation.

[0010] Step 3: Solve the problem of maximizing the total throughput of multi-UAV-assisted IoT under a given 3D UAV deployment. Based on the current 3D UAV deployment, optimize the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase.

[0011] Step 4: Solve the problem of maximizing the total throughput of multi-UAV-assisted IoT given the D2D relay selection and time allocation. Optimize the three-dimensional deployment of UAVs based on the current D2D relay selection and time allocation.

[0012] Step 5: Repeat steps 3 and 4, and optimize the two sub-problems alternately until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT converges;

[0013] Step 6: Maximize the total throughput of the multi-UAV-assisted IoT based on the final optimization results, which include the three-dimensional deployment of UAVs, the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase.

[0014] Several optional methods are also provided below, but they are not intended to be additional limitations on the above-mentioned overall solution. They are merely further supplements or optimizations. Under the premise that there are no technical or logical contradictions, each optional method can be combined separately for the above-mentioned overall solution, or multiple optional methods can be combined.

[0015] Preferably, the total throughput maximization problem of constructing a multi-UAV assisted Internet of Things includes:

[0016] The throughput R of the transmission from the base station to the mth drone B,m yes:

[0017]

[0018] where m∈{1,2,...M}, τm,n represents the time for the nth D2D transmitter to transmit data to the mth UAV in the NOMA transmission phase, that is, the time allocation of the D2D transmitter in the NOMA transmission phase, W is the wireless channel bandwidth, P B is the base station's transmit power, h B,m is the channel gain between the base station and the mth UAV, P N is the transmit power of the D2D transmitter, h n,m is the channel gain between the nth D2D transmitter and the mth UAV, σ 2 is the additive white Gaussian noise power;

[0019] The throughput R of the transmission from the nth D2D transmitter to the mth UAV n,m yes:

[0020]

[0021] The throughput R of the transmission from the mth UAV to the kth cell edge user of the corresponding cluster m,k yes:

[0022]

[0023] where k∈{1,2,...K m}, K m is the total number of cell edge users in the mth cluster, t m,k represents the transmission time of the mth UAV to the kth cell edge user of the corresponding cluster, P u Indicates the transmission power of the UAV, h m,k represents the channel gain between the mth UAV and the kth cell edge user of the corresponding cluster;

[0024] The transmission throughput R from the mth UAV to the nth D2D receiver m,n+N yes:

[0025]

[0026] Where n+n represents the nth D2D receiver, represents the time for the mth UAV to transmit data to the nth D2D receiver in the D2D transmission phase, i.e., the time allocation of the D2D receiver in the D2D transmission phase, h m,n+M represents the channel gain between the mth UAV and the nth D2D receiver;

[0027] The throughput R of the transmission from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone is B,m,k yes:

[0028]

[0029] The throughput R of the transmission from the nth D2D transmitter to the nth D2D receiver via the relay of the mth UAV is n,m,n+N yes:

[0030] R n,m,n+N =min{R n,m ,R m,n+N}=R n,m

[0031] The total throughput of multi-UAV-assisted IoT R sum for:

[0032]

[0033] Then the objective function of the total throughput maximization problem of multi-UAV assisted IoT is maxR sum .

[0034] Preferably, the method of optimizing the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase according to the current three-dimensional deployment of drones includes:

[0035] Step 3.1: Based on the location information of the D2D pairs and the drones, calculate the channel gains between all D2D pairs and all drones. If the channel gain between the nth D2D transmitter or the nth D2D receiver and the mth drone does not meet the minimum channel gain constraint, the nth D2D pair cannot select the mth drone as a relay device, and update the channel gain h between the nth D2D transmitter and the mth drone. n,m =0;

[0036] Step 3.2: Set a probability vector P for each D2D transmitter n =(p n,1 ,p n,2 ,...,p n,M ), where p n,m represents the probability that the nth D2D transmitter selects the mth drone as a relay, initialized

[0037] Step 3.3: Each D2D transmitter transmits a signal according to the probability vector P n Select the drone as the relay device and obtain the relay selection information;

[0038] Step 3.4: Based on the relay selection information obtained in step 3.3, use the greedy time allocation method to optimize the time allocation of D2D transmitters in the NOMA transmission phase: In the NOMA transmission phase, allocate time that meets the minimum throughput constraint to all D2D transmitters. For the mth UAV, if If it is less than the NOMA transmission phase time T1, the remaining time in the NOMA transmission phase time T1 is allocated to the D2D transmitter with the largest channel gain with the m-th UAV; otherwise, the time allocation τ of all D2D transmitters of the m-th UAV is updated. m,n =0, where τ m,n represents the time when the nth D2D transmitter transmits data to the mth UAV in the NOMA transmission phase;

[0039] Step 3.5: In the D2D transmission phase, according to R n,m =R n,m,n+N , calculate the time allocation of the D2D receiver, if If it is greater than the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is updated m,n =0; if If the D2D transmission phase time θT1 is less than or equal to the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is selected. m,n remains unchanged; among them, R n,m R represents the throughput of the transmission from the nth D2D transmitter to the mth UAV. n,m,n+N represents the throughput of transmission from the nth D2D transmitter to the nth D2D receiver via the relay of the mth UAV, It represents the time when the mth UAV transmits data to the nth D2D receiver during the D2D transmission phase;

[0040] Step 3.6: Calculate the total throughput R of multi-UAV-assisted IoT under the current relay selection and time allocation scheme sum (t), and according to the current total throughput R sum (t) Update the probability vector of all D2D transmitters, t is the current iteration number: use R max Indicates the maximum throughput, the initial value is 0, if the current total throughput R sum (t) is greater than the maximum throughput R max , update R max =R sum (t), and the probability vector at the t+1th iteration is updated as:

[0041]

[0042] where p n,m(t+1) represents the probability that the nth D2D transmitter selects the mth UAV as a relay after the update, p n,m (t) represents the probability that the nth D2D transmitter selects the mth UAV as a relay before the update, a n (t) represents the UAV selected by the nth D2D transmitter in the tth iteration, ω1 represents the first learning step, represents the normalized reward;

[0043] Otherwise, the probability vector at the t+1th iteration is updated as:

[0044]

[0045] Where ω2 represents the second learning step, m n,max represents the optimal relay selection for the nth D2D transmitter;

[0046] Step 3.7: Repeat steps 3.3 to 3.6 until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT under the given three-dimensional deployment of UAVs converges.

[0047] Preferably, the optimizing the three-dimensional deployment of drones based on the current D2D relay selection and time allocation includes:

[0048] The sub-problem of maximizing the total throughput of a given multi-UAV-assisted IoT by selecting and allocating D2D relays is equivalently formulated as a three-dimensional deployment optimization problem for each UAV. The following optimization steps are performed for each UAV:

[0049] Step 4.1: Initialize the whales based on the Bernoulli shift chaos according to the population size S and the chaotic iteration step size. The 3D position of each whale represents the 3D deployment position of the UAV.

[0050] Step 4.2: Calculate the total throughput of the relay transmission through the drone as the fitness value of each whale. Take the whale with the highest fitness value as the optimal whale and record the 3D position of the optimal whale. The fitness value calculation formula is as follows:

[0051]

[0052] where R sum,m represents the total throughput of relay transmission through the mth UAV, K m is the total number of cell edge users in the mth cluster, R B,m,k R represents the throughput of transmission from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone. n,m represents the throughput of transmission from the nth D2D transmitter to the mth UAV;

[0053] Step 4.3: Update the adaptive convergence factor a according to the current number of iterations t:

[0054]

[0055] where a max and a mim They represent the maximum and minimum values ​​of the adaptive convergence factor, w represents the adjustment factor, and t max Indicates the maximum number of iterations;

[0056] Update coefficients A and C according to the adaptive convergence factor a:

[0057] C=2r

[0058] A=2ar-a

[0059] Where r∈[0,1] is a random number;

[0060] Step 4.4: Generate a random number p∈[0,1). If p<0.5 and A≤1, use the shrinking and surrounding strategy to update the three-dimensional position of the whale:

[0061] X(t+1)=X * (t)-A|CX * (t)-X(t)|

[0062] Where X(t+1) represents the updated three-dimensional position of the whale, X * (t) represents the 3D position of the optimal whale in the tth iteration, and X(t) represents the 3D position of the whale before the update;

[0063] If p < 0.5 and A > 1, the prey search strategy based on the Levy flight mechanism is used to update the whale's three-dimensional position:

[0064]

[0065] where X rand (t) represents the three-dimensional position of the randomly selected whale in the t-th iteration, β represents the step size control parameter, represents point multiplication, Levy(s) represents the random search path of the prey search strategy based on the Levy flight mechanism and follows the Levy distribution;

[0066] If p ≥ 0.5, the bubble net attack strategy is used to update the whale's position:

[0067] X(t+1)=|X * (t)-X(t)|e bl cos(2πl)+X * (t)

[0068] Where b is a constant representing the shape of the spiral, and l∈[-1,1] is a random number;

[0069] Step 4.5: Calculate the reverse solution for all whales:

[0070] X′ i,j =λ(x min,j +x max,j )-X i,j

[0071] where X′ i,j represents the reverse solution of the position of whale i in dimension j, λ∈[0,1] is a random number, X i,j represents the position of whale i in dimension j, x min,j and x max,j They represent the lower and upper bounds of the whale’s three-dimensional position in dimension j;

[0072] Sort the three-dimensional positions of all whales and their reverse solutions according to their fitness values, and select the top S whales as the next generation population;

[0073] Step 4.6: Repeat steps 4.2 to 4.5 until the total throughput of the relay transmission through the UAV is R sum,m convergence.

[0074] Preferably, the step 5 alternately optimizes the two sub-problems until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT converges, including:

[0075] The two sub-problems are optimized alternately until the convergence values ​​of the objective functions of the two sub-problems are consistent, and then the alternating optimization of the two sub-problems is terminated.

[0076] The present invention provides a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things. D2D devices select relay devices through a probability-based relay selection and greedy time allocation method, thereby solving the relay competition problem among multiple devices. The relay selection probability vector of the D2D devices is updated based on the total throughput, thereby achieving a higher total throughput. The UAVs are deployed in three dimensions through an improved whale optimization method that introduces Bernoulli shift initialization, an adaptive convergence factor, a Levy flight mechanism, and an elite reverse learning mechanism. Compared with traditional optimization methods, the improved whale optimization method can achieve a higher total throughput. By alternately optimizing two sub-problems, the total throughput maximization problem of the multi-UAV-assisted Internet of Things can be well solved, achieving a higher total throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the structure of the multi-UAV assisted Internet of Things of the present invention;

[0078] Figure 2 Schematic diagram of the time slot structure of the present invention;

[0079] Figure 3 This is a flow chart of a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things according to the present invention. DETAILED DESCRIPTION

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0081] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0082] like Figure 1 As shown, the multi-drone-assisted IoT of this embodiment includes a multi-antenna base station (BS), U single-antenna cell edge users, M single-antenna unmanned aerial vehicles (UAVs), and N device-to-device (D2D) pairs. Each D2D pair includes a D2D transmitter and a D2D receiver. The UAVs act as relay devices, relaying data from the BS to the cell edge users and simultaneously relaying data from the D2D transmitters to the D2D receivers. To avoid mutual interference between the relay transmissions of the M UAVs, the wireless channel bandwidth is divided into M orthogonal channels with a bandwidth of W.

[0083] like Figure 2 As shown in the figure, each time slot T includes a NOMA transmission phase (duration T1), a cell edge user transmission phase (duration εT1), and a D2D transmission phase (duration θT1). During the NOMA transmission phase, the base station transmits data destined for cell edge users to M drones via M orthogonal channels. At the same time, the D2D transmitters take turns transmitting data to the drones. During the cell edge user transmission phase, the drones relay data received from the base station to the cell edge users. During the D2D transmission phase, the drones relay data received from the D2D transmitters to the D2D receivers.

[0084] like Figure 3 As shown, a method for maximizing the total throughput of a multi-UAV-assisted Internet of Things in this embodiment includes the following steps:

[0085] Step 1: The base station obtains the location information of U cell edge users and N pairs of D2D pairs, and divides the cell edge users into M clusters according to the location information. The mth cluster has K m Cell edge users are assigned to each cluster, and one drone is assigned to relay data sent by the base station to the cell edge users of the cluster.

[0086] Step 2: Construct the total throughput maximization problem of multi-UAV-assisted IoT and decompose it into two sub-problems: the total throughput maximization problem of multi-UAV-assisted IoT under given three-dimensional deployment of UAVs and the total throughput maximization problem of multi-UAV-assisted IoT under given D2D relay selection and time allocation.

[0087] Set the binary variable α m,n Represents the relay selection of the D2D pair. If the nth D2D pair selects the mth drone as the relay device, then α m,n =1, otherwise α m,n = 0. Time variable τ m,n represents the time when the nth D2D transmitter transmits data to the mth UAV in the NOMA transmission phase; represents the time for the mth UAV to transmit data to the nth D2D receiver in the D2D transmission phase. If the nth D2D pair selects other UAVs except the mth UAV, then τ m,n =0, In the NOMA transmission phase, the base station transmits the data of the cell edge user in the mth cluster to the mth drone via one antenna. The D2D transmitter that selects the mth drone as a relay also transmits data to the mth drone in turn. Since the received signal strength from the base station is usually greater than the received signal strength from the D2D transmitter, the mth drone first decodes the signal from the base station and then decodes the signal from the D2D transmitter. The total throughput maximization problem of the multi-drone-assisted IoT is constructed as follows:

[0088] The throughput R of the transmission from the base station to the mth drone B,m yes:

[0089]

[0090] where m∈{1,2,...M}, τ m,n represents the time for the nth D2D transmitter to transmit data to the mth UAV in the NOMA transmission phase, that is, the time allocation of the D2D transmitter in the NOMA transmission phase, W is the wireless channel bandwidth, P B is the base station's transmit power, h B,m is the channel gain between the base station and the mth UAV, P Nis the transmit power of the D2D transmitter, h n,m is the channel gain between the nth D2D transmitter and the mth UAV, σ 2 is the additive white Gaussian noise power.

[0091] The throughput R of the transmission from the nth D2D transmitter to the mth UAV n,m yes:

[0092]

[0093] The throughput R of the transmission from the mth UAV to the kth cell edge user of the corresponding cluster m,k yes:

[0094]

[0095] where k∈{1,2,...K m}, K m is the total number of cell edge users in the mth cluster, t m,k represents the transmission time of the mth UAV to the kth cell edge user of the corresponding cluster, P U Indicates the transmission power of the UAV, h m,k represents the channel gain between the mth UAV and the kth cell edge user of the corresponding cluster.

[0096] The transmission throughput R from the mth UAV to the nth D2D receiver m,n+N yes:

[0097]

[0098] Where n+N represents the nth D2D receiver, represents the time for the mth UAV to transmit data to the nth D2D receiver in the D2D transmission phase, i.e., the time allocation of the D2D receiver in the D2D transmission phase, h m,n+N represents the channel gain between the m-th UAV and the n-th D2D receiver.

[0099] According to the barrel theory, the transmission throughput R from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone is B,m,k yes:

[0100]

[0101] In order to successfully relay the data of the nth D2D transmitter to the nth D2D receiver, the throughput of the transmission from the nth D2D transmitter to the mth UAV must not exceed the throughput of the transmission from the mth UAV to the nth D2D receiver. The throughput R of the transmission from the nth D2D transmitter to the nth D2D receiver through the relay of the mth UAV is n,m,n+N yes:

[0102] R n,m,n+N =min{R n,m ,R m,n+N}=R n,m

[0103] The total throughput of multi-UAV-assisted IoT R sum for:

[0104]

[0105] Then the objective function of the total throughput maximization problem of multi-UAV assisted IoT is maxR sum .

[0106] Step 3: To solve the problem of maximizing the total throughput of multi-UAV-assisted IoT under a given three-dimensional deployment of UAVs, the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase are optimized according to the current three-dimensional deployment of UAVs to solve the relay selection and time allocation problems of D2D pairs.

[0107] This embodiment determines the relay selection and time allocation of D2D pairs under the constraints of the minimum throughput of D2D devices and the channel signal-to-noise ratio. According to the current geographical location of the drone, a relay selection and greedy time allocation method based on random learning is proposed.

[0108] Step 3.1: Based on the location information of the D2D pairs and the drones, calculate the channel gains between all D2D pairs and all drones. If the channel gain between the nth D2D transmitter or the nth D2D receiver and the mth drone does not meet the minimum channel gain constraint, the nth D2D pair cannot select the mth drone as a relay device, and update the channel gain h between the nth D2D transmitter and the mth drone. n,m =0.

[0109] Step 3.2: Set a probability vector P for each D2D transmitter n =(p n,1 ,p n,2 ,...,p n,M ), where p n,m represents the probability that the nth D2D transmitter selects the mth drone as a relay, initialized

[0110] Step 3.3: Each D2D transmitter transmits a signal according to the probability vector P n Select the drone as the relay device and get the relay selection information, which completes the binary variable α m,n Assignment.

[0111] Step 3.4: Based on the relay selection information obtained in step 3.3, use the greedy time allocation method to optimize the time allocation of D2D transmitters in the NOMA transmission phase: In the NOMA transmission phase, allocate time that meets the minimum throughput constraint to all D2D transmitters. For the mth UAV, if If it is less than the NOMA transmission phase time T1, the remaining time in the NOMA transmission phase time T1 is allocated to the D2D transmitter with the largest channel gain with the m-th UAV; otherwise, the time allocation τ of all D2D transmitters of the m-th UAV is updated. m,n =0.

[0112] Step 3.5: In the D2D transmission phase, according to R n,m =R n,m,n+N , calculate the time allocation of the D2D receiver, if If it is greater than the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is updated m,n =0; if If the D2D transmission phase time θT1 is less than or equal to the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is selected. m,n Remain unchanged.

[0113] Step 3.6: Calculate the total throughput R of multi-UAV-assisted IoT under the current relay selection and time allocation scheme sum (t), and according to the current total throughput R sum (t) Update the probability vector of all D2D transmitters, t is the current iteration number: use R max Indicates the maximum throughput, the initial value is 0, if the current total throughput R sum (t) is greater than the maximum throughput R max , update R max =R sum (t), and the probability vector at the t+1th iteration is updated as:

[0114]

[0115] where p n,m (t+1) represents the probability that the nth D2D transmitter selects the mth UAV as a relay after the update, p n,m(t) represents the probability that the nth D2D transmitter selects the mth UAV as a relay before the update, a n (t) represents the UAV selected by the nth D2D transmitter in the tth iteration, ω1 represents the first learning step, represents the normalized reward.

[0116] Otherwise, the probability vector at the t+1th iteration is updated as:

[0117]

[0118] Where ω2 represents the second learning step, m n,max It represents the optimal relay selection of the nth D2D transmitter. The relay selection under the current maximum throughput is the current optimal relay selection.

[0119] Step 3.7: Repeat steps 3.3 to 3.6 until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT under the given three-dimensional deployment of UAVs converges.

[0120] Step 4: To solve the problem of maximizing the total throughput of multi-UAV-assisted IoT under the given D2D relay selection and time allocation, optimize the three-dimensional deployment of UAVs based on the current D2D relay selection and time allocation to solve the three-dimensional deployment problem of UAVs.

[0121] Because transmissions between drones and IoT devices do not interfere with transmissions between other drones and IoT devices, the D2D problem of maximizing the total throughput of a multi-drone-assisted IoT system, given relay selection and time allocation, can be equivalently expressed as a three-dimensional deployment optimization problem for each drone. Therefore, this embodiment proposes an improved whale optimization method that performs the following operations for the three-dimensional deployment of each drone:

[0122] Step 4.1: Initialize the whales based on Bernoulli shift chaos according to the population size S and the chaotic iteration step size. The 3D position of each whale represents the 3D deployment position of a UAV.

[0123] Step 4.2: Calculate the total throughput of the relay transmission through the drone as the fitness value of each whale, and optimize the objective function R through a single drone. sum,m To evaluate the quality of the solution, the objective function R sum,m The larger the value of , the higher the quality of the solution. Therefore, the whale with the highest fitness value is taken as the optimal whale, and the three-dimensional position of the optimal whale is recorded. The fitness value calculation formula is as follows:

[0124]

[0125] where R sum,mrepresents the total throughput of relay transmission through the mth UAV, K m is the total number of cell edge users in the mth cluster, R B,m,k R represents the throughput of transmission from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone. n,m represents the throughput of transmission from the nth D2D transmitter to the mth UAV.

[0126] Step 4.3: Update the adaptive convergence factor a according to the current number of iterations t:

[0127]

[0128] where a max and a mim They represent the maximum and minimum values ​​of the adaptive convergence factor, w represents the adjustment factor, and t max Indicates the maximum number of iterations;

[0129] Update coefficients A and C according to the adaptive convergence factor a:

[0130] C=2r

[0131] A=2ar-a

[0132] Where r∈[0,1] is a random number;

[0133] Step 4.4: Generate a random number p∈[0,1). If p<0.5 and A≤1, use the shrinking and surrounding strategy to update the three-dimensional position of the whale:

[0134] X(t+1)=X * (t)-A|CX * (t)-X(t)|

[0135] Where X(t+1) represents the updated three-dimensional position of the whale, X * (t) represents the 3D position of the optimal whale in the tth iteration, and X(t) represents the 3D position of the whale before the update;

[0136] If p < 0.5 and A > 1, the prey search strategy based on the Levy flight mechanism is used to update the whale's three-dimensional position:

[0137]

[0138] where X rand (t) represents the three-dimensional position of the randomly selected whale in the t-th iteration, β represents the step size control parameter, represents point multiplication, Levy(s) represents the random search path of the prey search strategy based on the Levy flight mechanism and follows the Levy distribution;

[0139] If p ≥ 0.5, the bubble net attack strategy is used to update the whale's position:

[0140] X(t+1)=|X * (t)-X(t)|e bl cos(2πl)+X * (t)

[0141] Where b is a constant representing the shape of the spiral, and l∈[-1,1] is a random number;

[0142] Step 4.5: Calculate the reverse solution for all whales:

[0143] X′ i,j =λ(x min,j +x max,j )-X i,j

[0144] where X′ i,j represents the reverse solution of the position of whale i in dimension j, λ∈[0,1] is a random number, X i,j represents the position of whale i in dimension j, x min,j and x max,j They represent the lower and upper bounds of the whale's three-dimensional position in dimension j, respectively.

[0145] The three-dimensional positions of all whales and the reverse solutions of whales are sorted according to their fitness values, and the top S whales are selected as the next generation population.

[0146] Step 4.6: Repeat steps 4.2 to 4.5 until the objective function R sum,m convergence.

[0147] Step 5: Repeat steps 3 and 4, and optimize the two sub-problems alternately until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT converges.

[0148] When the two sub-problems are optimized alternately, the optimization ends until the convergence values ​​of the objective functions of the two sub-problems are consistent. The convergence value of the objective function of the total throughput maximization problem of multi-UAV assisted Internet of Things under the given three-dimensional deployment of UAVs is the total throughput R of the multi-UAV assisted Internet of Things. sum , and the convergence value of the objective function of the total throughput maximization problem of multi-UAV assisted IoT under the given relay selection and time allocation of D2D is If the two are equal and no longer change, the overall optimization is considered complete.

[0149] Step 6: Maximize the total throughput of the multi-UAV-assisted IoT based on the final optimization results, which include the three-dimensional deployment of UAVs, the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase.

[0150] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for maximizing the total throughput of a multi-UAV-assisted Internet of Things, characterized in that: The multi-drone-assisted Internet of Things includes a multi-antenna base station, U single-antenna cell edge users, M single-antenna drones, and N pairs of D2D pairs, each D2D pair including a D2D transmitter and a D2D receiver. The drones act as relay devices, relaying data from the base station to the cell edge users and simultaneously relaying data from the D2D transmitters to the D2D receivers. The method for maximizing the total throughput of the multi-drone-assisted Internet of Things includes: Step 1: The base station obtains the location information of U cell edge users and N pairs of D2D pairs, divides the cell edge users into M clusters based on the location information, and assigns one drone to each cluster to relay data sent by the base station to the cell edge users in that cluster; Step 2: Construct the total throughput maximization problem for multi-UAV-assisted IoT and decompose it into two sub-problems: the total throughput maximization problem for multi-UAV-assisted IoT given the three-dimensional deployment of UAVs and the total throughput maximization problem for multi-UAV-assisted IoT given the D2D relay selection and time allocation. Step 3: Solve the problem of maximizing the total throughput of multi-UAV-assisted IoT under a given 3D UAV deployment. Based on the current 3D UAV deployment, optimize the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase. Step 4: Solve the problem of maximizing the total throughput of multi-UAV-assisted IoT given the D2D relay selection and time allocation. Optimize the three-dimensional deployment of UAVs based on the current D2D relay selection and time allocation. Step 5: Repeat steps 3 and 4, and optimize the two sub-problems alternately until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT converges; Step 6: Maximize the total throughput of the multi-UAV-assisted IoT based on the final optimization results, which include the three-dimensional deployment of UAVs, the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase.

2. The method for maximizing the total throughput of a multi-UAV-assisted Internet of Things according to claim 1, characterized in that: The total throughput maximization problem of constructing a multi-UAV assisted Internet of Things includes: The throughput R of the transmission from the base station to the mth drone B，m yes: where m∈{1, 2, ...M}, τ m，n represents the time for the nth D2D transmitter to transmit data to the mth UAV in the NOMA transmission phase, that is, the time allocation of the D2D transmitter in the NOMA transmission phase, W is the wireless channel bandwidth, P B is the base station's transmit power, h B,m is the channel gain between the base station and the mth UAV, P N is the transmit power of the D2D transmitter, h n，m is the channel gain between the nth D2D transmitter and the mth UAV, σ 2 is the additive white Gaussian noise power; The throughput R of the transmission from the nth D2D transmitter to the mth UAV n，m yes: The throughput R of the transmission from the mth UAV to the kth cell edge user of the corresponding cluster m,k yes: where k∈{1, 2, ...K m }, K m is the total number of cell edge users in the mth cluster, t m，k represents the transmission time of the mth UAV to the kth cell edge user of the corresponding cluster, P U Indicates the transmission power of the UAV, h m,k represents the channel gain between the mth UAV and the kth cell edge user of the corresponding cluster; The transmission throughput R from the mth UAV to the nth D2D receiver m，n+N yes: Where n+N represents the nth D2D receiver, represents the time for the mth UAV to transmit data to the nth D2D receiver in the D2D transmission phase, i.e., the time allocation of the D2D receiver in the D2D transmission phase, h m,n+N represents the channel gain between the mth UAV and the nth D2D receiver; The throughput R of the transmission from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone is B，m，k yes: The throughput R of the transmission from the nth D2D transmitter to the nth D2D receiver via the relay of the mth UAV is n，m，n+N yes: R n，m，n+N =min{R n，m ,R m,n+N }=R n，m The total throughput of multi-UAV-assisted IoT R sum for: Then the objective function of the total throughput maximization problem of multi-UAV assisted IoT is maxR sum .

3. The method for maximizing the total throughput of a multi-UAV-assisted Internet of Things according to claim 1, characterized in that: The optimization of the relay selection of D2D pairs, the time allocation of D2D transmitters in the NOMA transmission phase, and the time allocation of D2D receivers in the D2D transmission phase based on the current three-dimensional deployment of drones includes: Step 3.1: Based on the location information of the D2D pairs and the drones, calculate the channel gains between all D2D pairs and all drones. If the channel gain between the nth D2D transmitter or the nth D2D receiver and the mth drone does not meet the minimum channel gain constraint, the nth D2D pair cannot select the mth drone as a relay device, and update the channel gain h between the nth D2D transmitter and the mth drone. n，m =0; Step 3.2: Set a probability vector P for each D2D transmitter n =(p n，1 , p n，2 ,...,p n，M ), where p n，m represents the probability that the nth D2D transmitter selects the mth drone as a relay, initialized Step 3.3: Each D2D transmitter transmits a signal according to the probability vector P n Select the drone as the relay device and obtain the relay selection information; Step 3.4: Based on the relay selection information obtained in step 3.3, use the greedy time allocation method to optimize the time allocation of D2D transmitters in the NOMA transmission phase: In the NOMA transmission phase, allocate time that meets the minimum throughput constraint to all D2D transmitters. For the mth UAV, if If it is less than the NOMA transmission phase time T1, the remaining time in the NOMA transmission phase time T1 is allocated to the D2D transmitter with the largest channel gain with the m-th UAV; otherwise, the time allocation τ of all D2D transmitters of the m-th UAV is updated. m，n =0, where τ m，n represents the time when the nth D2D transmitter transmits data to the mth UAV in the NOMA transmission phase; Step 3.5: In the D2D transmission phase, according to R n，m =R n，m，n+N , calculate the time allocation of the D2D receiver, if If it is greater than the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is updated m，n =0; if If the D2D transmission phase time θT1 is less than or equal to the D2D transmission phase time θT1, the time allocation τ of all D2D transmitters of the mth UAV is selected. m，n remains unchanged; among them, R n，m R represents the throughput of the transmission from the nth D2D transmitter to the mth UAV. n，m，n+N represents the throughput of transmission from the nth D2D transmitter to the nth D2D receiver via the relay of the mth UAV, It represents the time when the mth UAV transmits data to the nth D2D receiver during the D2D transmission phase; Step 3.6: Calculate the total throughput R of multi-UAV-assisted IoT under the current relay selection and time allocation scheme sum (t), and according to the current total throughput R sum (t) Update the probability vector of all D2D transmitters, t is the current iteration number: use R max Indicates the maximum throughput, the initial value is 0, if the current total throughput R sum (t) is greater than the maximum throughput R max , update R max =R sum (t), and the probability vector at the t+1th iteration is updated as: where p n,m (t+1) represents the probability that the nth D2D transmitter selects the mth UAV as the relay after the update, P n,m (t) represents the probability that the nth D2D transmitter selects the mth UAV as a relay before the update, a n (t) represents the UAV selected by the nth D2D transmitter in the tth iteration, ω1 represents the first learning step, represents the normalized reward; Otherwise, the probability vector at the t+1th iteration is updated as: Where ω2 represents the second learning step, m n,max represents the optimal relay selection for the nth D2D transmitter; Step 3.7: Repeat steps 3.3 to 3.6 until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT under the given three-dimensional deployment of UAVs converges.

4. The method for maximizing the total throughput of a multi-UAV-assisted Internet of Things according to claim 1, characterized in that: The optimization of the three-dimensional deployment of drones based on the current D2D relay selection and time allocation includes: The sub-problem of maximizing the total throughput of a given multi-UAV-assisted IoT by selecting and allocating D2D relays is equivalently formulated as a three-dimensional deployment optimization problem for each UAV. The following optimization steps are performed for each UAV: Step 4.1: Initialize the whales based on the Bernoulli shift chaos according to the population size S and the chaotic iteration step size. The 3D position of each whale represents the 3D deployment position of the UAV. Step 4.2: Calculate the total throughput of the relay transmission through the drone as the fitness value of each whale. Take the whale with the highest fitness value as the optimal whale and record the 3D position of the optimal whale. The fitness value calculation formula is as follows: where R sum,m represents the total throughput of relay transmission through the mth UAV, K m is the total number of cell edge users in the mth cluster, R B，m，k R represents the throughput of transmission from the base station to the kth cell edge user of the corresponding cluster through the relay of the mth drone. n，m represents the throughput of transmission from the nth D2D transmitter to the mth UAV; Step 4.3: Update the adaptive convergence factor a according to the current number of iterations t: where a max and a mim They represent the maximum and minimum values ​​of the adaptive convergence factor, w represents the adjustment factor, and t max Indicates the maximum number of iterations; Update coefficients A and C according to the adaptive convergence factor a: C＝2r A=2ar-a Where r∈[0,1] is a random number; Step 4.4: Generate a random number p∈[0,1). If p<0.5 and A≤1, use the shrinking and surrounding strategy to update the three-dimensional position of the whale: X(t+1)=X*(t)-A|CX*(t)-X(t)| Where X(t+1) represents the updated 3D position of the whale, X*(t) represents the 3D position of the optimal whale in the t-th iteration, and X(t) represents the 3D position of the whale before the update; If p < 0.5 and A > 1, the prey search strategy based on the Levy flight mechanism is used to update the whale's three-dimensional position: where X rand (t) represents the three-dimensional position of the randomly selected whale in the t-th iteration, β represents the step size control parameter, represents point multiplication, Levy(s) represents the random search path of the prey search strategy based on the Levy flight mechanism and follows the Levy distribution; If p ≥ 0.5, the bubble net attack strategy is used to update the whale's position: X(t+1)=|X * (t)-X(t)|e bl cos(2πl)+X * (t) Where b is a constant representing the shape of the spiral, and l∈[-1,1] is a random number; Step 4.5: Calculate the reverse solution for all whales: X′ i，j =λ(x min，j +x max，j )-X i，j where X′ i，j represents the reverse solution of the position of whale i in dimension j, λ∈[0,1] is a random number, X i，j represents the position of whale i in dimension j, x min，j and x max,j They represent the lower and upper bounds of the whale’s three-dimensional position in dimension j; Sort the three-dimensional positions of all whales and their reverse solutions according to their fitness values, and select the top S whales as the next generation population; Step 4.6: Repeat steps 4.2 to 4.5 until the total throughput of the relay transmission through the UAV is R sum，m convergence.

5. The method for maximizing the total throughput of a multi-UAV-assisted Internet of Things according to claim 1, characterized in that: Step 5, alternately optimizing the two sub-problems until the objective function of the total throughput maximization problem of multi-UAV-assisted IoT converges, includes: The two sub-problems are optimized alternately until the convergence values ​​of the objective functions of the two sub-problems are consistent, and then the alternating optimization of the two sub-problems is terminated.

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