Resource allocation method and system of BackCom network based on EH under assistance of unmanned aerial vehicle

Abstract

The invention relates to the technical field of backscatter communication, particularly discloses an unmanned aerial vehicle assisted EH-based BackCom network resource allocation method and system, and provides a robust resource allocation problem which aims at maximizing the total throughput of all unmanned aerial vehicles. Key parameters (BackCom time allocation, transmitting power, master user interference protection and reflection coefficient) are jointly optimized. In order to reduce adverse effects of uncertainty of CSI parameters and reflection coefficients, a robust learning algorithm based on a polyhedron is introduced when a resource allocation problem is solved, and an outage probability constraint is converted into a deterministic constraint. And finally, decomposing the non-convex optimization problem into two sub-problems by adopting an alternating optimization method, and solving by adopting a Lagrange multiplier method. Simulation results show that the method achieves relatively high network throughput rate and energy utilization rate in an EH-based BackCom network assisted by the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of backscatter communication technology, and in particular to a resource allocation method and system for an EH-based BackCom network assisted by a drone. Background Art

[0002] With the rapid development of wireless communication technology and the exponential growth of Internet of Things (IoT) devices, the large-scale deployment of IoT devices has not only generated unprecedented data traffic, but also led to significant energy consumption and growing energy demand due to the inevitable continuous communication and frequent network interactions between devices. Therefore, how to effectively improve the energy efficiency of IoT devices, reduce power consumption, and ensure the reliability of data transmission has become a key issue that needs to be urgently addressed.

[0003] Backscatter communication (BackCom), an emerging low-power communication paradigm, demonstrates significant advantages in improving energy efficiency (EE). In a BackCom network, backscatter nodes effectively utilize unused spectrum resources for data transmission, while integrated energy harvesting (EH) technology harvests radio frequency energy to power node operation, thereby reducing reliance on traditional batteries and lowering energy consumption. Consequently, EH-based BackCom networks have attracted widespread attention and provide a solid foundation for further exploration of related resource allocation issues.

[0004] While existing research has made significant contributions to the resource allocation (RA) problem in EH-based BackCom networks, most studies have focused on the case of perfect channel state information (CSI) and deterministic reflection coefficients. In practical deployments, imperfect CSI and uncertainty in reflection coefficients pose significant challenges to system performance. These factors can lead to reduced data transmission rates and lower energy harvesting efficiency, thus affecting the overall stability and reliability of the network. Summary of the Invention

[0005] The present invention provides a resource allocation method and system for an EH-based BackCom network assisted by a drone, and solves the technical problem that the imperfect CSI and uncertainty of the reflection coefficient may lead to a decrease in data transmission rate and energy collection efficiency, thereby affecting the overall stability and reliability of the network.

[0006] To solve the above technical problems, the present invention provides a resource allocation method for an EH-based BackCom network assisted by a drone, comprising the following steps:

[0007] Construct a communication model for the EH-based BackCom network assisted by drones, where EH refers to energy harvesting and BackCom network refers to backscatter communication.

[0008] With the goal of maximizing the total throughput of all UAVs, under the constraints of network SINR constraints, EH requirements, transmit power limits, interference limits, and time limits, a resource allocation problem is constructed with the UAV's transmit power, BackCom time, direct transmission time, and reflection coefficient as optimization parameters;

[0009] Solve the resource allocation problem and obtain the optimal solution of the optimization parameters.

[0010] Furthermore, the communication model includes a primary network and a secondary network; the primary network consists of a base station and M primary users, and the base station sends wireless signals to the primary users; the secondary network consists of N drones serving as secondary users and information receivers; each drone is equipped with an EH circuit and a backscatter unit; the drone uses its EH circuit to collect radio frequency energy from the signal sent by the base station, which takes BackCom time, and then uses the collected energy to send information to the information receiver, which takes direct transmission time; the available spectrum is divided into K orthogonal sub-channels, and each sub-channel is allocated to a primary user.

[0011] Furthermore, N=M, the first secondary user shares the subchannel with the first primary user, the second secondary user shares the subchannel with the second primary user, and so on until the Nth secondary user shares the subchannel with the Mth primary user.

[0012] Furthermore, the total throughput of all UAVs is equal to the sum of the throughput of all UAVs during the BackCom time and the throughput at the information receiver during the direct transmission time.

[0013] Furthermore, the network's SINR constraints, EH requirements, transmit power limitations, interference limitations, and time limitations include C1 to C6, where constraint C1 is the SINR constraint for each UAV during the direct transmission time, constraint C2 is the SINR interruption probability constraint for each UAV during the BackCom phase, constraint C3 is the energy interruption probability constraint for each UAV, and constraint C4 represents the time allocation constraint for all UAVs; constraint C5 is the interference constraint for each primary user, and constraint C6 is the reflection coefficient constraint for each UAV.

[0014] Furthermore, constraint C2 is that the probability that the SINR of each UAV is not greater than its set minimum value is not greater than the SINR threshold probability; constraint C3 is, The probability is not less than 1-τ n , τ n is the EH limit threshold probability, T is the total time slot for the base station to send wireless signals to the primary user, t a For direct transmission time, is the BackCom time of the nth UAV, is the reflection coefficient of the nth UAV, Energy collected for the nth drone, is the minimum energy required by the nth UAV.

[0015] Furthermore, constraint C1 is that the SINR of each UAV at the information receiver during the direct transmission time is not less than its set minimum value; constraint C4 is that t a and Not greater than T; constraint C5 is that the sum of the average interference power received by each primary user during the BackCom time and the direct transmission time is not greater than the interference power threshold I th,m ; Constraint C6 is that the reflection coefficient of each drone is in the range of [0,1].

[0016] Furthermore, solving the resource allocation problem to obtain the optimal solution of the optimization parameters specifically includes the following steps:

[0017] Collect channel gain data sample set And by channel gain data sample set Performing shape learning and size calibration to determine channel gain parameters in the resource allocation problem;

[0018] Fixing the transmission power and reflection coefficient of the UAV, the resource allocation problem is transformed into the first sub-problem of optimizing the BackCom time and direct transmission time of the UAV;

[0019] Solve the first sub-problem using a linear programming solver to obtain a BackCom time solution and a direct transmission time solution;

[0020] Substituting the BackCom time solution and the direct transmission time solution into the resource allocation problem, the resource allocation problem is transformed into a second sub-problem of optimizing the transmission power and reflection coefficient of the UAV;

[0021] The Lagrange multiplier is introduced to transform the objective function of the second subproblem into a Lagrangian function, and the Lagrange multiplier is updated through recursive solution and subgradient method to obtain the transmission power solution and reflection coefficient solution;

[0022] The first subproblem and the second subproblem are solved alternately until the iteration converges, and the optimal solution for BackCom time, the optimal solution for direct transmission time, the optimal solution for transmission power, and the optimal solution for reflection coefficient are obtained.

[0023] Furthermore, Lagrange multipliers are introduced to transform the objective function of the second sub-problem into a Lagrangian function, which specifically includes the steps of:

[0024] Write each constraint of the second subproblem in standard form;

[0025] The Lagrangian function is obtained by multiplying the left-hand side of each standard inequality constraint by a non-negative Lagrangian multiplier and then adding the sum to the total throughput of all UAVs.

[0026] The present invention also provides a resource allocation system for EH-based BackCom network assisted by drones, which mainly includes a model construction module, a problem construction module and a problem solving module;

[0027] The model building module is used to build a communication model of the EH-based BackCom network assisted by drones;

[0028] The problem construction module is used to construct a resource allocation problem with the goal of maximizing the total throughput of all drones, under the constraints of the network's SINR constraints, EH requirements, transmit power limits, interference limits, and time limits, using the drone's transmit power, BackCom time, direct transmission time, and reflection coefficient as optimization parameters;

[0029] The problem solving module is used to solve the resource allocation problem and obtain the optimal solution of the optimization parameters.

[0030] The present invention provides a resource allocation method and system for an EH-based BackCom network assisted by a drone, which proposes a robust resource allocation problem that aims to maximize the total throughput of all drones and jointly optimizes key parameters (BackCom time allocation, transmission power, primary user interference protection, and reflection coefficient). In order to reduce the adverse effects of the uncertainty of CSI parameters and reflection coefficients, a polyhedron-based robust learning algorithm is introduced when solving the resource allocation problem, and the interruption probability constraint is converted into a deterministic constraint. Finally, the alternating optimization method is used to decompose the non-convex optimization problem into two sub-problems, and the Lagrange multiplier method is used to solve them. Simulation results show that the present invention achieves higher network throughput and energy utilization in the EH-based BackCom network assisted by a drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a diagram of the EH-based BackCom network structure with the assistance of a drone provided by an embodiment of the present invention;

[0032] Figure 2 Graphs showing changes in input power for the ideal linear EH model and the nonlinear EH model provided by an embodiment of the present invention;

[0033] Figure 3 is the total transmission power P of different algorithms provided in the embodiment of the present invention sum The relationship diagram with the total throughput of SU;

[0034] Figure 4 is the noise power σ of different algorithms provided in the embodiment of the present invention 2 The relationship diagram with the total throughput of SU;

[0035] Figure 5 is the interference threshold value I of different algorithms provided in the embodiment of the present invention th,m The relationship diagram with the total throughput of SU;

[0036] Figure 6 The minimum SINR threshold of different algorithms provided by the embodiment of the present invention is Relationship graph with total drone throughput. DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and are not to be construed as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of protection of the present invention. Many changes may be made to the present invention without departing from the spirit and scope of the present invention.

[0038] The embodiment of the present invention first provides a resource allocation method for an EH-based BackCom network assisted by a drone, which includes the following steps:

[0039] S1. Construct a communication model of the EH-based BackCom network assisted by drones, where EH refers to energy harvesting and BackCom network refers to backscatter communication.

[0040] S2, with the goal of maximizing the total throughput of all UAVs, under the constraints of network SINR constraints, EH requirements, transmit power limits, interference limits and time limits, the resource allocation problem is constructed with the UAV's transmit power, BackCom time, direct transmission time and reflection coefficient as optimization parameters;

[0041] S3. Solve the resource allocation problem and obtain the optimal solution of the optimization parameters.

[0042] (1) Step S1: Constructing a network model

[0043] In step S1, the communication model of the EH-based BackCom network assisted by drones is as follows: Figure 1 As shown in Figure 1, the network consists of a primary network and a secondary network. The primary network consists of a base station (BS) and M primary users (PUs), where the BS sends wireless signals to the PUs. The secondary network consists of N unmanned aerial vehicles (UAVs) operating as secondary users (SUs) and information receivers (IRs). The set of PUs is defined as: C PU ={PU 1,PU 2,…,PU M}, and the set of UAVs is defined as C SU={SU 1,SU 2,…,SUN}.

[0044] The available spectrum is divided into K orthogonal subchannels, each assigned to a PU. Without loss of generality, assume that the kth subchannel is assigned to PU m (the mth PU), and each subchannel can be shared simultaneously by a PU and a SU. In this secondary network, each UAV is equipped with an EH circuit and a backscatter unit. The UAV uses its EH circuit to harvest radio frequency (RF) energy from the signal transmitted by the base station (BS) and then uses the harvested energy to transmit information to the IR.

[0045] In this embodiment, it is assumed that the BS transmits wireless signals to the PUs throughout the entire time frame T. Set N = M, so that the first SU (SU 1) shares with the first PU (PU 1), the second SU (SU 2) shares with the second PU (PU 2), and so on, until the Nth SU (SU N) shares with the Mth PU (PU M). To balance the requirements of energy collection and data transmission, the secondary network divides the time frame T into two phases:

[0046] 1) BackCom stage: The total duration of this phase, of which = represents the BackCom time of SU n (nth SU). Each SU collects the RF signal for EH and also backscatters its own signal to IR through time division multiple access (TDMA). SU n performing BackCom sends its data to IR via the channel of PU m, which is shared under the underlying spectrum sharing paradigm. Therefore, the average interference power received by PU m at this stage can be expressed as:

[0047]

[0048] in, is the reflection coefficient of SU n performing BackCom, P sum is the total transmit power of the BS, f n represents the channel gain from BS to SUn, h n,m represents the channel gain from SU n to PU m.

[0049] In the BackCom phase, the SINR of SU n can be written as:

[0050]

[0051] Among them, h n represents the channel gain from SU n to IR, h B represents the channel gain from BS to IR, σ 2is the noise power.

[0052] Therefore, the throughput of SU n in the BackCom phase can be obtained:

[0053]

[0054] Where B is the bandwidth.

[0055] 2) Direct transmission (DT) phase: All UAVs use the harvested energy to transmit at the transmission time t a The data is transmitted to the IR within 1 second. At this stage, the nth SU introduces interference to PU m. Therefore, the average interference power received by PU m can be formulated as:

[0056]

[0057] Among them, p n is the transmit power of SU n.

[0058] Therefore, in order to ensure that the communication quality of PU m is not damaged, the total average interference power in the entire time frame T should meet the following conditions:

[0059]

[0060] Among them, I th,m is the interference power threshold of PU m.

[0061] (2) Step S2: Constructing the optimization problem

[0062] In a drone-assisted EH-based BackCom network, the performance of the primary network is degraded due to multiple SUs transmitting signals simultaneously. To address this issue, this embodiment proposes the HTT (Handle Before Transmit) mode and SIC (Successive Interference Cancellation) technology, ultimately resulting in an optimization problem.

[0063] A. Capture first, transmit later (HTT) mode

[0064] The HTT mode is used during the BackCom phase because it enables each SU to initially harvest sufficient energy, ensuring that subsequent backscattered data transmission is both reliable and energy-efficient. The HTT mode consists of two phases: energy harvesting (EH) and data transmission. During the EH phase, the SU harvests energy from the RF energy source's signal. The SU then uses the harvested energy to transmit data during the data transmission phase.

[0065] 1) Energy harvesting

[0066] The total energy harvested by a SU during the EH phase is usually described by the following linear model:

[0067]

[0068] Among them, 0≤η≤1 represents the energy conversion efficiency, represents the input power of SU n, which can be expressed as:

[0069]

[0070] Where θ is the power allocation factor. An appropriate power allocation factor is selected to balance EH and data transmission to maximize the EE and data transmission efficiency of the network.

[0071] However, in practice, the EH circuit exhibits nonlinear EH characteristics. Therefore, the energy harvested by the nth SU is modeled as:

[0072]

[0073] Among them, P M It represents the maximum harvested power when the actual EH circuit reaches saturation. Both α and β are constants determined by the characteristics of the EH circuit. Figure 2 The figure shows the variation of harvested energy with input power for the ideal linear EH model and the nonlinear EH model. This figure reveals the limitations of the ideal linear EH model in the nonlinear EH circuit in equation (8).

[0074] In the considered network, the harvested energy should be greater than the consumed energy to ensure that the SU can continue to operate and complete its communication tasks. Therefore, the following constraints exist:

[0075]

[0076] in, Indicates the minimum energy required by SU n.

[0077] 2) Data transmission

[0078] After harvesting energy, the SU uses the harvested energy to transmit data. Assuming that the circuit energy consumption of the SU is negligible, the throughput of the SU n during the DT phase can be obtained using the following successive interference cancellation (SIC) technique.

[0079] B.SIC Technology

[0080] In a multi-user BackCom network, it is assumed that all channels experience fast fading and that the CSI remains constant within each time frame. To address the interference challenge, this embodiment uses SIC technology. SIC gradually reduces the interference in the composite signal by decoding and removing strong signals one by one, thereby improving overall system performance.

[0081] In the DT phase, all SUs send signals to the IR, so the received signal at the IR can be expressed as:

[0082]

[0083] Among them, P m represents the transmission power from BS to PU m, c n is the data symbol sent by the nth SU, s m is the data symbol sent by PU m, represents statistical expectation; σ n ~CN(0,σ 2 ), and obeys a circularly symmetric complex Gaussian distribution with a mean of 0 and a variance of σ 2 , assuming its normalized energy is 1.

[0084] Arrange the channel gains of N SUs in descending order, that is:

[0085] h1≥h2≥…≥h N (11)

[0086] At the beginning, all signals are transmitted without channel coding, so the SINR of the nth SU is:

[0087]

[0088] When decoding the signal from the first user (i.e., the SU with the highest channel gain), subtract To get the remaining signal:

[0089]

[0090] Similarly, the SINR of the nth SU can be obtained:

[0091]

[0092] Therefore, the SINR at IR on SU n is as follows:

[0093]

[0094] The corresponding throughput of SU n at IR is:

[0095]

[0096] C. Optimization Problem

[0097] Based on the above, the resource allocation problem constructed by the present invention is to maximize the total network throughput R of all drones, and jointly optimize the transmit power, BackCom time and reflection coefficient under the constraints of SINR requirements, EH requirements, transmit power limit, interference limit and time limit. This embodiment formulates the resource allocation problem as follows:

[0098]

[0099] in, yes The minimum setting value, yes The minimum value of ε n is the SINR threshold probability, τ n is the EH limit threshold probability, and Pr{} represents the probability of the event within the brackets {}. C1 to C6 are six constraints. C1 defines the SINR constraint for Sun during the DT phase. C2 represents the SINR outage probability constraint for Sun during the BackCom phase. C3 represents the energy outage probability constraint for Sun. C4 represents the time allocation constraint for Sun. C5 represents the interference constraint for PU m. C6 represents the reflection coefficient constraint for Sun.

[0100] (3) Step S3: Model solution

[0101] In this step, this embodiment designs an alternating optimization framework. Based on the polyhedral nature of CSI uncertainty and combined with polyhedral uncertainty modeling, this framework solves the robust optimization problem that considers reflection coefficient uncertainty and imperfect CSI. Finally, the optimization framework is constructed based on the alternating strategy, and the Lagrange multiplier method is used to obtain a robust solution.

[0102] A. Learning Theory

[0103] This embodiment proposes a polyhedron-based robust learning algorithm (PLRA) to deal with the imperfect CSI problem. The details are as follows.

[0104] First, uncertainty is represented using a high probability region (HPR). To determine the HPR, samples of D imperfect CSI channels (iid) are collected. This helps characterize the uncertainty set and derive the HPR for the CSI error. Modeling using polyhedral ensembles provides a general framework that can be combined with a wide range of optimization tools and techniques. Building on this strength, this example uses polyhedral ensembles to encapsulate and model CSI-related uncertainty. Therefore, this example first models the user channel gains in the network.

[0105] This example uses a polyhedron set to model uncertainty. The polyhedron model is parameterized as:

[0106]

[0107] in is the center of the polyhedron, and s n >0 is the size of the polyhedron shape, and S represents the uncertain CSI region learned from iid samples. There are D iid samples to learn the uncertainty model, where the dth sample

[0108] This embodiment proposes a statistical learning method to determine the parameters of the polyhedron, which includes two steps: shape learning and size calibration. First, the data sample is divided into two parts, namely and This embodiment uses to approximate the shape of the HPR and use to calibrate the size of the uncertainty set.

[0109] Shape learning: This example uses To approximate the shape of HPR. In this embodiment, h n and h B As the origin of the polyhedron uncertainty set. is calculated as the sample mean, that is:

[0110]

[0111] Size calibration: This example uses a dataset To calibrate the polyhedral uncertainty sets so that they contain the confidence level The core method involves estimating the quantiles of the transformed data samples. Specifically, this embodiment defines a transformation function as follows:

[0112]

[0113] a(1) and a(2) represent the first and second data values of each sample data a.

[0114] where t p (a) Random vector space Map to This transform quantizes the components of a to a reference value and deviation.

[0115] Then, this example uses the sample data set The derived transformation tp (a) Estimated The quantile determines the size of the HPR. Therefore, the size of S can be set based on the sample data set t p (a) Estimation of the distribution of To formalize it, this embodiment defines The quantiles are as follows:

[0116]

[0117] Then calculate the dataset The t of each sample in p (a) and arrange these values in ascending order as t p (1) ≤…≤t p (D) . The n*th value, where ( means round up), which can be considered as t p (a) Given this upper bound, this embodiment can set the size of the set S as follows:

[0118]

[0119] After obtaining the size of the polyhedron uncertainty set S, the learned channel gain is obtained through formula (18).

[0120] Table 1 shows the pseudocode for the polyhedron-based learning robust optimization algorithm in Algorithm 1. The specific steps are as follows. The algorithm first collects independent and identically distributed samples under incomplete channel state information. These samples are then divided into two parts: one for shape learning and the other for size calibration. Next, this embodiment sets the size of the HPR and performs shape learning and size calibration steps to learn the channel gain parameters.

[0121] Table 1

[0122]

[0123] B. Two-stage alternating optimization method

[0124] The above algorithm achieves robust channel gain estimation for optimization by sampling from iid.

[0125] Due to the non-convexity and coupling constraints of the original problem, this embodiment decomposes it into two sub-problems and uses an alternating optimization method to solve them. First, fix p n and Optimize time variable t a and Therefore, the original problem is transformed into the first subproblem

[0126]

[0127] Therefore, the first sub-problem is a linear programming (LP) problem that can be solved directly using standard LP solvers (such as the simplex algorithm or the interior point method) to obtain The optimal solution of .

[0128] Next, fix t a and Optimize p n and Transform the resource allocation problem into the second sub-problem

[0129]

[0130] Subproblems The non-convex objective function and the nonlinear constraints coupled by multiplication make it difficult to solve directly. To this end, the Lagrange multiplier method is introduced to transform the original problem into a more tractable form.

[0131] To facilitate analysis, each constraint is first written in standard form. This example defines:

[0132]

[0133]

[0134]

[0135]

[0136] Introduce non-negative Lagrange multipliers for each of the above inequality constraints. Let:

[0137]

[0138] Therefore, the Lagrangian function is:

[0139]

[0140] Among them, λ n 、μ n 、v n 、 Respectively The Lagrange multiplier of .

[0141] In order to obtain the optimal solution, the following Karush-KuhnTucker (KKT) conditions must be met:

[0142] 1) Primal feasibility: All constraints in the original problem must be satisfied:

[0143]

[0144] 2) Dual feasibility: All Lagrange multipliers must be non - negative:

[0145]

[0146] 3) Complementary slackness: For each constraint, we have:

[0147]

[0148] 4) Stationarity: Calculate the gradient of each primal variable. Thus, the gradient of p n can be calculated as follows:

[0149]

[0150] where,

[0151] Similarly, the gradient can be calculated as follows:

[0152]

[0153] Due to the coupling of the optimal solution of p n (the power of subsequent users is included in the denominator), therefore, a backward - forward recursive method is adopted to solve it, and the specific steps are as follows.

[0154] Solve the closed - form expression of p from the gradient condition n Because:

[0155]

[0156] Considering the coupling terms in the constraints, the optimal transmit power is:

[0157]

[0158] where, Δ n represents the coupling effect caused by the Lagrange multiplier terms introduced by the dependency relationship and interference constraints among the previous users p i (i < n), and [·] + represents taking the non - negative part, that is, max{0, ·}. is the value of the optimal λ n .

[0159] Similarly, the closed - form expression of the optimal reflection coefficient can be obtained as:

[0160]

[0161] After verifying all KKT conditions, in order to ensure the optimality and effectiveness of the solution, this embodiment will further check the convergence of the algorithm to ensure that the changes in all variables and Lagrange multipliers are less than the set small threshold and that the iterative process has indeed reached a stable state.

[0162] In order to solve the dual problem, the subgradient method is usually used to iteratively update the Lagrange multiplier. represents the original variable, Denotes the corresponding Lagrange multiplier at the kth iteration. The update rule is given by:

[0163]

[0164]

[0165]

[0166]

[0167] Where l(k) represents the step size of the kth iteration.

[0168] During the entire alternating optimization process, each iteration consists of the following two steps:

[0169] 1) Solve the subproblem Under fixed conditions Under this condition, the time variable is obtained by solving the linear programming (Equation (23)) The optimal solution of

[0170] 2) Solve the subproblem Under the condition of fixed time variables, the power p is updated by recursive solution (combining equations (34)-(38)) and subgradient method to update the dual variables (see equations (39)-(42)). n and reflection coefficient

[0171] The objective function is expressed as Since solving each subproblem ensures that the objective value does not decrease with each iteration, after the kth iteration, this embodiment has:

[0172]

[0173] When the changes of all primal and dual variables are below the preset convergence threshold χ (i.e., This indicates that the algorithm has reached a stable state and has converged to a set of local optimal solutions that meet the KKT conditions.

[0174] The pseudo code of the alternating optimization method is shown in Algorithm 2 shown in Table 2.

[0175] Table 2

[0176]

[0177] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This embodiment is not limited here.

[0178] Based on the resource allocation method of the EH-based BackCom network assisted by the above-mentioned drone, an embodiment of the present invention also provides a corresponding resource allocation system, including a model construction module, a problem construction module and a problem solving module. The model construction module is used to construct a communication model of the EH-based BackCom network assisted by the drone. The problem construction module is used to maximize the total throughput of all drones, and under the constraints of the network's SINR constraints, EH requirements, transmission power limitations, interference limitations and time limitations, use the drone's transmission power, BackCom time, direct transmission time and reflection coefficient as optimization parameters to construct a resource allocation problem. The problem solving module is used to solve the resource allocation problem and obtain the optimal solution for the optimization parameters.

[0179] The present invention provides a resource allocation method and system for an EH-based BackCom network assisted by a drone. This system proposes a robust resource allocation problem that aims to maximize the total throughput of all drones and jointly optimizes key parameters (BackCom time allocation, transmit power, primary user interference protection, and reflection coefficient). To reduce the adverse effects of uncertainty in CSI parameters and reflection coefficients, a polyhedron-based robust learning algorithm is introduced to solve the resource allocation problem, converting the interruption probability constraint into a deterministic constraint. Finally, an alternating optimization method is used to decompose the non-convex optimization problem into two subproblems, which are then solved using the Lagrange multiplier method. This results in a high network throughput and energy utilization rate in the drone-assisted EH-based BackCom network.

[0180] The embodiments described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0181] Computer programs for implementing the methods and systems of the present invention can be written in any combination of one or more programming languages and stored in a computer-readable storage medium. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0182] Computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be a machine-readable signal medium. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, compact disc read-only memories (CD ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0183] (4) Simulation verification

[0184] This example provides numerical results to demonstrate the effectiveness of the proposed method and system (PLRA). Its effectiveness is verified by comparing it with an ellipse-based learning algorithm (ELRA), a non-robust algorithm (NRA), and a greedy algorithm (GA). The base station's coverage area is a circle with a radius of 600 meters, the base station bandwidth B is 5 MHz, the number of state units is 3, and the number of data sets is 1000. The remaining simulation parameters are shown in Table 3.

[0185] Table 3 Simulation parameter settings

[0186]

[0187] Figure 3 The relationship between the total transmission power of the BS and the total throughput of the SU is shown. Figure 3 It can be seen that as the total transmission power P sum increases, and the total throughput of the SU decreases. It is obvious that the polyhedron-based robust learning algorithm proposed in this embodiment has better performance than the comparison algorithm. In the BackCom stage of the EH-based UAV-assisted BackCom network, the UAV obtains energy from the base station through the EH. However, high-power signals may saturate the energy harvesting circuit, making it difficult to effectively convert more RF energy into usable electrical energy. This limits the energy that the UAV can use in the subsequent DT stage, thereby affecting its transmission capacity and throughput. The PLRA proposed in this embodiment has strong adaptability and robustness in dealing with complex interference environments caused by high power. Through precise modeling and optimization, PLRA effectively eliminates the adverse effects of high-power transmission on system performance, ensuring more stable and efficient communication quality.

[0188] Figure 4 represents the noise power σ 2 The relationship between the total throughput of the UAV and the noise power. As the noise power increases, the total throughput of the UAV tends to decrease. Figure 4 It can be clearly seen that the PLRA proposed in this embodiment outperforms the comparison algorithm in maintaining higher throughput under different interference thresholds from the PU. A decrease in signal quality requires more retransmissions and error correction processes, which increases overall energy consumption. As a result, network throughput decreases as more resources are consumed to achieve reliable data transmission. Figure 4 It is shown that PLRA effectively mitigates these adverse effects and ensures better performance in terms of energy efficiency than other algorithms despite higher noise power. This highlights the effectiveness of the method proposed in this example in optimizing network performance under challenging conditions.

[0189] Figure 5 The interference threshold I in the UAV-assisted EH-based BackCom network is shown. th,mThe relationship between and the total throughput of SU. Figure 5 It can be seen that as the interference threshold increases, the total throughput of SU tends to rise. This is because as the interference threshold I th,m This means the PU can tolerate more interference. Therefore, the SU can further increase its transmission power to achieve higher transmission throughput. The PLRA proposed in this embodiment demonstrates significant advantages in this process, outperforming other schemes. This result demonstrates that PLRA significantly improves system flexibility and adaptability through precise learning and dynamic adjustment of uncertainty sets, maximizing network performance and efficiency while satisfying PU interference tolerance constraints.

[0190] Figure 6 Figure 2 shows the SINR threshold in a UAV-assisted EH-based BackCom network. The relationship between and SU throughput. Figure 6 It can be seen that as the SINR threshold As the SINR threshold increases, the SU throughput also increases. This shows that a higher SINR threshold forces the system to optimize resource allocation to meet more stringent communication quality requirements, thereby enabling each SU to send data under better channel conditions and reducing the need for transmission errors and retransmissions. Therefore, although a higher SINR threshold may appear to impose stricter requirements on the system, it actually encourages more efficient transmission strategies, improves overall network performance, and ultimately leads to a significant increase in SU throughput. The algorithm proposed in this embodiment shows significant advantages in optimizing resource allocation and handling imperfect CSI, maintaining high energy efficiency even in the presence of incomplete CSI information. This feature not only improves the overall performance of the network, but also optimizes energy usage, indicating that the algorithm has significant effectiveness and robustness in complex network environments.

[0191] In summary, the PLRA proposed in this embodiment outperforms the comparison algorithm in terms of total throughput of the drone in all environments. This shows that PLRA outperforms the comparison algorithm in terms of performance and is effective in maximizing the network throughput of the BackCom network based on drone-assisted EH.

[0192] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A resource allocation method for an EH-based BackCom network assisted by a drone, characterized in that: Including steps: Construct a communication model for the EH-based BackCom network assisted by drones, where EH refers to energy harvesting and BackCom network refers to backscatter communication. With the goal of maximizing the total throughput of all UAVs, under the constraints of network SINR constraints, EH requirements, transmit power limits, interference limits, and time limits, a resource allocation problem is constructed with the UAV's transmit power, BackCom time, direct transmission time, and reflection coefficient as optimization parameters; Solve the resource allocation problem and obtain the optimal solution of the optimization parameters.

2. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 1, characterized in that: The communication model includes a primary network and a secondary network; the primary network consists of a base station and M primary users, and the base station sends wireless signals to the primary users; the secondary network consists of N drones serving as secondary users and information receivers; each drone is equipped with an EH circuit and a backscatter unit; the drone uses its EH circuit to collect radio frequency energy from the signal sent by the base station, which takes BackCom time, and then uses the collected energy to send information to the information receiver, which takes direct transmission time; the available spectrum is divided into K orthogonal subchannels, each of which is allocated to a primary user.

3. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 2, characterized in that: N=M, the first secondary user shares the subchannel with the first primary user, the second secondary user shares the subchannel with the second primary user, and so on until the Nth secondary user shares the subchannel with the Mth primary user.

4. The resource allocation method for an EH-based BackCom network assisted by a drone according to any one of claims 1 to 3, characterized in that: The total throughput of all UAVs is equal to the sum of the throughput of all UAVs during the BackCom time and the throughput at the information receiver during the direct transmission time.

5. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 4, characterized in that: The network's SINR constraints, EH requirements, transmit power limits, interference limits, and time limits include C1 to C6. Constraint C1 is the SINR constraint for each UAV during the direct transmission time, constraint C2 is the SINR interruption probability constraint for each UAV during the BackCom phase, constraint C3 is the energy interruption probability constraint for each UAV, and constraint C4 represents the time allocation constraint for all UAVs; constraint C5 is the interference constraint for each primary user, and constraint C6 is the reflection coefficient constraint for each UAV.

6. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 5, characterized in that: Constraint C2 is that the probability that the SINR of each drone is not greater than its set minimum value is not greater than the SINR threshold probability; constraint C3 is, The probability of is not less than 1-τn, τn is the EH limit threshold probability, T is the total time slot for the base station to send wireless signals to the primary user, ta is the direct transmission time, is the BackCom time of the nth UAV, is the reflection coefficient of the nth UAV, Energy collected for the nth drone, is the minimum energy required by the nth UAV.

7. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 6, characterized in that: Constraint C1 is that the SINR of each UAV at the information receiver during the direct transmission time is not less than its set minimum value; Constraint C4 is, ta and is no greater than T; constraint C5 is that the sum of the average interference power received by each primary user during the BackCom time and the direct transmission time is no greater than the interference power threshold Ith,m; constraint C6 is that the reflection coefficient of each UAV is in the range of [0,1].

8. The resource allocation method for an EH-based BackCom network assisted by a drone according to any one of claims 5 to 7, characterized in that: Solving the resource allocation problem and obtaining the optimal solution of the optimization parameters specifically includes the following steps: Collect channel gain data sample set And by channel gain data sample set Performing shape learning and size calibration to determine channel gain parameters in the resource allocation problem; Fixing the transmission power and reflection coefficient of the UAV, the resource allocation problem is transformed into the first sub-problem of optimizing the BackCom time and direct transmission time of the UAV; Solve the first sub-problem using a linear programming solver to obtain a BackCom time solution and a direct transmission time solution; Substituting the BackCom time solution and the direct transmission time solution into the resource allocation problem, the resource allocation problem is transformed into a second sub-problem of optimizing the transmission power and reflection coefficient of the UAV; The Lagrange multiplier is introduced to transform the objective function of the second subproblem into a Lagrangian function, and the Lagrange multiplier is updated through recursive solution and subgradient method to obtain the transmission power solution and reflection coefficient solution; The first subproblem and the second subproblem are solved alternately until the iteration converges, and the optimal solution for BackCom time, the optimal solution for direct transmission time, the optimal solution for transmission power, and the optimal solution for reflection coefficient are obtained.

9. The resource allocation method for the EH-based BackCom network assisted by a drone according to claim 8, characterized in that: Introducing Lagrange multipliers to transform the objective function of the second sub-problem into a Lagrange function, specifically comprising the steps of: Write each constraint of the second subproblem in standard form; The Lagrangian function is obtained by multiplying the left-hand side of each standard inequality constraint by a non-negative Lagrangian multiplier and then adding the sum to the total throughput of all UAVs.

10. The resource allocation system based on EH BackCom network assisted by UAV is characterized by: Includes model building module, problem building module and problem solving module; The model building module is used to build a communication model of the EH-based BackCom network assisted by drones; The problem construction module is used to construct a resource allocation problem with the goal of maximizing the total throughput of all drones, under the constraints of the network's SINR constraints, EH requirements, transmit power limits, interference limits, and time limits, using the drone's transmit power, BackCom time, direct transmission time, and reflection coefficient as optimization parameters; The problem solving module is used to solve the resource allocation problem and obtain the optimal solution of the optimization parameters.

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