A method for optimizing resource allocation in backscatter-assisted wireless energy supply networks

Abstract

The disclosed embodiment is about a method for optimizing resource allocation in a backscatter-assisted wireless power supply network. The method includes: establishing a wirelessly powered NOMA-MEC network, wherein the NOMA-MEC network includes an energy source, a MEC server, and a plurality of IoT nodes; the NOMA-MEC network sequentially performs an energy collection phase, a backscatter phase, an active transmission phase, a task calculation phase, and a data return phase; obtaining variables in the energy collection phase, the backscatter phase, the active transmission phase, the task calculation phase, and the data return phase to obtain a variable group, and establishing a non-convex optimization problem model based on the variable group; based on the non-convex optimization problem model, obtaining the best resource allocation method in the NOMA-MEC network. The disclosed embodiment can effectively ensure the fairness of the communication capacity of IoT nodes.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of wireless power communication technology, and in particular to a method for optimizing resource allocation in a backscatter-assisted wireless power supply network. Background Art

[0002] With the advent of the Internet of Everything era, in order to realize the interconnection between people, people and things, and things and things, a large number of devices will be connected to the Internet of Things in the future. This will not only bring opportunities to individuals, enterprises, and countries, but also bring severe challenges to communication quality requirements and energy consumption. Among them, from the overall perspective of the Internet of Things, the devices connected in the future will definitely be large and small. Faced with the problems of weak computing power of small devices and inability to work for a long time (for example: sensors), the wirelessly powered mobile edge computing (MEC) network, as an emerging technology of the Internet of Things, can not only provide energy for devices, but also enhance the computing power of devices, providing solutions to such problems. In addition, with the explosive growth of access devices, spectrum resources are becoming increasingly scarce, and wireless power supply makes energy very limited. The introduction of backscatter communication technology can improve the utilization of energy and spectrum. However, backscatter technology obtains radio frequency energy from the environment, and the transmission rate is affected by the dual channels, so the improvement of the transmission rate is very limited. Non-Orthogonal Multiple Access (NOMA) technology allows IoT nodes to share spectrum to improve spectrum utilization, and can effectively improve the transmission rate of the system.

[0003] Therefore, it is necessary to improve one or more problems existing in the above-mentioned related technical solutions.

[0004] It should be noted that this section is intended to provide background or context for the technical solutions of the present disclosure stated in the claims. The description herein is not admitted to be prior art by virtue of being included in this section. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a method for optimizing resource allocation in a backscatter-assisted wireless power supply network, thereby overcoming one or more problems caused by limitations and defects of related technologies, at least to a certain extent.

[0006] According to a first aspect of an embodiment of the present disclosure, a method for optimizing resource allocation in a backscatter-assisted wireless power supply network is provided, comprising:

[0007] Establishing a wirelessly powered NOMA-MEC network, wherein the NOMA-MEC network includes an energy source, a MEC server, and a plurality of IoT nodes;

[0008] The NOMA-MEC network sequentially performs energy collection phase, backscattering phase, active transmission phase, task calculation phase and data return phase;

[0009] Obtaining variables in the energy collection phase, the backscattering phase, the active transmission phase, the task calculation phase, and the data return phase to obtain a variable group, and establishing a non-convex optimization problem model according to the variable group;

[0010] Based on the non-convex optimization problem model, the optimal resource allocation method in the NOMA-MEC network is obtained.

[0011] In one embodiment of the present disclosure, the energy collection stage includes:

[0012] The energy source broadcasts an energy signal, and the IoT node collects energy;

[0013] The energy collection time is a first preset value.

[0014] In one embodiment of the present disclosure, the backscattering stage includes:

[0015] Using time division multiple access technology, the backscattering stage is divided into a number of time slots. In the time slots, the energy source sends the energy signal to the IoT node, and the IoT node sequentially backscatters the received energy signal to perform task offloading;

[0016] Among them, the number of time slots corresponds to the number of IoT nodes, the backscattering duration is a second preset value, the reflection coefficient of the backscattering is a third preset value, and the IoT nodes collect energy when not performing backscattering.

[0017] In one embodiment of the present disclosure, the active transmission phase includes:

[0018] The energy source stops broadcasting the energy signal, and the IoT node offloads the task to the MEC server based on NOMA technology;

[0019] Among them, the NOMA transmission duration is the fourth preset value, and the transmission power of the Internet of Things node is the fifth preset value.

[0020] In one embodiment of the present disclosure, the task calculation phase includes:

[0021] The MEC server decodes the offload task of the IoT node by using continuous interference cancellation technology to obtain decoded data, and performs task calculation on the decoded data, where the task calculation duration is a sixth preset value;

[0022] Among them, the IoT node with the largest channel gain is decoded first, and the signals of other IoT nodes are regarded as interference. The signals of the IoT nodes are eliminated from the received signals, and then the IoT node with the largest channel gain among the IoT nodes is decoded to solve the task information of all the IoT nodes decoded by the MEC server.

[0023] In one embodiment of the present disclosure, the data return phase includes:

[0024] The MEC server transmits the result of the task calculation back to each of the IoT nodes, and the transmission duration is a seventh preset value.

[0025] In one embodiment of the present disclosure, the IoT node includes:

[0026] An energy collection module, a backscattering module and a local computing module are used to enable the IoT node to perform task offloading and local computing simultaneously;

[0027] Among them, the local calculation duration of the local calculation module is an eighth preset value, and the local calculation frequency of the local calculation module is a ninth preset value.

[0028] In one embodiment of the present disclosure, the variables in the variable group include:

[0029] The energy collection duration, the backscattering duration, the reflection coefficient, the NOMA transmission duration, the transmission power, the task calculation duration, the return duration, the local calculation duration and the local calculation frequency.

[0030] In one embodiment of the present disclosure, establishing a non-convex optimization problem model includes:

[0031] Taking the maximum and worst number of bits of the IoT node as the goal, under the constraints of energy causality and computing power of the IoT nodes, a joint non-convex optimization problem of the energy collection duration, the backscattering duration, the reflection coefficient, the NOMA transmission duration, the transmit power, the task calculation duration, the return transmission duration, the local calculation duration and the local calculation frequency is established.

[0032] In one embodiment of the present disclosure, the optimal resource allocation method in the NOMA-MEC network is obtained based on the non-convex optimization problem model, including:

[0033] The non-convex optimization problem is processed by contradiction and auxiliary variable method to obtain a convex optimization problem, and then the convex optimization problem is processed by a preset iterative algorithm to obtain the optimal resource optimization allocation method.

[0034] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

[0035] In the embodiments of the present disclosure, the resource optimization allocation method in the wireless power supply network assisted by backscattering can effectively ensure the fairness of the communication capacity of the Internet of Things nodes on the one hand; on the other hand, it combines the advantages of backscattering and NOMA, and can utilize resources more effectively and flexibly. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0037] Figure 1 A flow chart showing a method for optimizing resource allocation in a backscatter-assisted wireless power supply network in an exemplary embodiment of the present disclosure is shown;

[0038] Figure 2 A schematic diagram of an MEC network in an exemplary embodiment of the present disclosure is shown;

[0039] Figure 3 A schematic diagram showing that the NOMA-MEC network in an exemplary embodiment of the present disclosure sequentially performs an energy collection phase, a backscattering phase, an active transmission phase, a task calculation phase, and a data return phase. DETAILED DESCRIPTION

[0040] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0041] In addition, the accompanying drawings are only schematic illustrations of the embodiments of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0042] This example implementation first provides a method for optimizing resource allocation in a backscatter-assisted wireless power supply network. Figure 1 As shown in , the method for optimizing resource allocation in the backscatter-assisted wireless power supply network may include: steps S101 to S104.

[0043] Step S101: Establishing a wirelessly powered NOMA-MEC network, wherein the NOMA-MEC network includes an energy source, a MEC server, and a plurality of IoT nodes;

[0044] Step S102: The NOMA-MEC network sequentially performs an energy collection phase, a backscattering phase, an active transmission phase, a task calculation phase, and a data return phase;

[0045] Step S103: acquiring variables in the energy collection phase, the backscattering phase, the active transmission phase, the task calculation phase, and the data return phase to obtain a variable group, and establishing a non-convex optimization problem model according to the variable group;

[0046] Step S104: Based on the non-convex optimization problem model, obtain the optimal resource allocation method in the NOMA-MEC network.

[0047] Through the above-mentioned backscatter-assisted wireless power supply network resource optimization allocation method, on the one hand, the fairness of the communication capacity of the Internet of Things nodes can be effectively guaranteed; on the other hand, it combines the advantages of backscattering and NOMA, and can utilize resources more effectively and flexibly.

[0048] Next, we will refer to Figures 1 to 3 The various steps of the above-mentioned method for optimizing resource allocation in a backscatter-assisted wireless power supply network in this example implementation are described in more detail.

[0049] Step S101: Establish a wirelessly powered NOMA-MEC network, wherein the NOMA-MEC network includes an energy source, a MEC server, and several IoT nodes.

[0050] For example, a backscatter-assisted wireless power supply MEC network is established as follows: Figure 2 As shown, it includes an energy source, a MEC server and K IoT nodes, where the channel gain between the energy source and the K IoT nodes is g 1 ,g 2 ,...,g K , the channel gain between the MEC server and K IoT nodes is h 1 ,h 2 ,...,h K .

[0051] Step S102: The NOMA-MEC network sequentially performs an energy collection phase, a backscattering phase, an active transmission phase, a task calculation phase, and a data return phase.

[0052] For example, Figure 3As shown in the figure, the total time is divided into five stages: the first stage is energy harvesting, the second stage is backscattering and energy harvesting, and the time division multiple access technology is used to divide the K IoT nodes into corresponding time slots. In the third stage, NOMA is used to actively offload tasks. In the fourth stage, the MEC server calculates the task results. In the fifth stage, the results are transmitted to the IoT nodes.

[0053] Step S103: Obtain variables in the energy collection phase, the backscattering phase, the active transmission phase, the task calculation phase, and the data return phase to obtain a variable group, and establish a non-convex optimization problem model according to the variable group.

[0054] For example, a non-convex allocation optimization problem model is established based on the IoT node backscatter coefficient, IoT node power, IoT node energy collection time, IoT node backscatter time, IoT node active transmission time, IoT node local computing time and IoT node local computing frequency.

[0055] Step S104: Based on the non-convex optimization problem model, obtain the optimal resource allocation method in the NOMA-MEC network.

[0056] For example, since the non-convex optimization problem model cannot be solved, it is necessary to transform the non-convex allocation optimization problem model into a convex optimization problem model, and use a preset iterative algorithm to update the optimal variables and optimal target values ​​in the model through the convex optimization problem model until convergence to obtain the optimal number of bits for the worst IoT node.

[0057] Optionally, in one embodiment, the energy collection stage includes: the energy source broadcasts an energy signal, and the Internet of Things node collects energy; wherein the energy collection duration is a first preset value.

[0058] For example, in the energy collection phase, the energy source sends an energy signal, and all IoT nodes collect energy using t c Represents the energy collection time at this stage. Using the linear energy collection model, the energy received by the kth IoT node at this stage is Where P t is the energy source transmission power, a k is the parameter of linear energy harvesting.

[0059] Optionally, in one embodiment, the backscattering stage includes: utilizing time division multiple access technology to divide the backscattering stage into a number of time slots, within the time slots, the energy source sends the energy signal to the Internet of Things node, and the Internet of Things node backscatters the received energy signal in turn to perform task offloading; wherein, the number of time slots corresponds to the number of Internet of Things nodes, the backscattering duration is a second preset value, the reflection coefficient of the backscattering is a third preset value, and the Internet of Things node collects energy when the backscattering is not performed.

[0060] For example, during the backscattering period, the time division multiple access technology is used to divide this period into K time slots, and the K time slots correspond to K IoT nodes respectively. During this period, the energy source sends energy signals to the K IoT nodes, and the K IoT nodes modulate and reflect the received energy signals in turn to unload the task, and each IoT node collects energy when it is not reflecting. represents the backscattering time of the kth IoT node in this stage, B represents the channel bandwidth, σ 2 represents the thermal noise power spectral density, ξ represents the performance gap between backscattering and active communication, and g k (k∈{1,2,...,K}) and h k They represent the channel gains from the energy source to the IoT node link and from the MEC server to each IoT node link, α k represents the reflection coefficient of backscattering. Then the number of bits unloaded by the kth IoT node during this time is At the same time, the kth IoT node The energy harvested during this time is Before the end of the backscattering phase, the total energy collected by the kth IoT node is in Let P c,k represents the power consumption of the backscattering circuit of the kth IoT node. In this stage, the energy consumption of the backscattering circuit of the kth IoT node is

[0061] Optionally, in one embodiment, the active transmission phase includes: the energy source stops broadcasting the energy signal, and the Internet of Things node offloads the task to the MEC server based on the NOMA technology; wherein, the NOMA transmission duration is a fourth preset value, and the transmission power of the Internet of Things node is a fifth preset value.

[0062] For example, the third stage is the active transmission stage. NOMA technology is used to transmit the signals of different IoT nodes to the MEC server at the corresponding power. Each IoT node uses the collected energy to offload the task. Let the NOMA transmission time be ta ,h k represents the channel gain between the kth IoT node and the MEC server, p k represents the transmission power of the kth IoT node. Assuming that the channel is quasi-static fading, the channel gains of each IoT node are ranked from large to small as h 1 >h 2 >…>h k >…>h K > 0, and the decoding order of IoT nodes on the MEC server is {1,2,...,K}. Therefore, the number of bits transmitted by the kth IoT node at this stage is The number of transmission bits of the kth IoT node is For the energy consumption of this stage, p c,k represents the circuit power consumption of the kth IoT node during active transmission, then the energy consumed by the IoT node in this stage is

[0063] Optionally, in one embodiment, the task calculation stage includes: the MEC server uses continuous interference elimination technology to decode the offload task of the IoT node to obtain decoded data, and performs task calculation on the decoded data, and the task calculation duration is a sixth preset value; wherein, the IoT node with the largest channel gain is decoded first, and the signals of other IoT nodes are regarded as interference, and the signal of the IoT node is eliminated from the received signal, and then the IoT node with the largest channel gain among the IoT nodes is decoded to solve the task information of all the IoT nodes decoded by the MEC server.

[0064] For example, in the task calculation phase, which is the MEC server calculation phase, the MEC server decodes the received signal to obtain decoded data and calculates the task data; specifically, the IoT node with the largest channel gain is first decoded, and the signals of other IoT nodes are regarded as interference. Then the signal of the IoT node is eliminated from the received signal, and then the IoT node with the largest channel gain among the remaining IoT nodes is decoded. And so on, until the MEC server decodes the task information of all IoT nodes.

[0065] Optionally, in one embodiment, the data feedback stage includes: the MEC server feedbacks the result of the task calculation to each of the IoT nodes, and the feedback duration is a seventh preset value.

[0066] For example, the data return phase is the MEC server download phase, and the MEC server downloads the calculation results to each IoT node. The MEC server is equipped with a high-performance CPU and its calculation results are usually small. Therefore, the time used in the calculation phase and the download phase of the MEC server can be ignored, that is, the sixth preset value and the seventh preset value are close to 0.

[0067] Optionally, in one embodiment, the Internet of Things node includes: an energy collection module, a backscattering module and a local computing module, which are used to enable the Internet of Things node to perform task offloading and local computing simultaneously; wherein, the local computing duration of the local computing module is an eighth preset value, and the local computing frequency of the local computing module is a ninth preset value.

[0068] For example, each IoT node is equipped with an energy harvesting module, a backscattering module, and a local computing module. Therefore, each IoT node can perform task offloading and local computing at the same time; for local computing, let f k and τ k (0≤τ k ≤T) represent the calculation frequency and time of the kth IoT node respectively, then the calculation bit number and energy consumption of the kth IoT node are expressed as and Among them C cpu The number of CPU cycles required to calculate 1 bit, ε k is the effective capacitance coefficient of the kth IoT node processor chip.

[0069] It can be seen that in the entire network, the total energy harvested by the kth IoT node is The total energy consumed by the kth IoT node is The total number of unloaded bits of the kth IoT node is

[0070] In the communication system, the IoT nodes collect energy in three time periods, namely, the energy collection phase, the time slot corresponding to the backscattering phase, and the backscattering period of other IoT nodes. Therefore, the total energy harvested by the kth IoT node is in P t : The transmission power of the energy source, g k : The channel gain between the energy source and the IoT node, a k : The linear energy harvesting model parameters;

[0071] In the communication system, the energy consumption of IoT nodes is consumed by backscattering circuit consumption, NOMA circuit consumption, signal transmission consumption and local computing consumption. The total energy consumed by the kth IoT node is where pc,k The power consumption of NOMA circuit is represented by P c,k represents the backscattering circuit power consumption of the kth IoT node, ε k is the effective capacitance coefficient of the processor chip at the kth IoT node;

[0072] In the communication system, the time periods for IoT nodes to offload tasks are backscattering, active transmission, and local calculation. The total number of bits of the kth IoT node is The signal-to-noise ratio of the IoT node during backscattering is During active transmission, due to the use of NOMA technology, the signal-to-noise ratio of the IoT node is Thus, the transmission rate of the IoT node during backscattering is obtained Transmission rate of IoT nodes during active transmission where α k represents the backscatter coefficient, B is the communication channel bandwidth, and ξ represents the performance gap between backscatter communication and active communication.

[0073] Optionally, in one embodiment, the variables in the variable group include: the energy collection duration, the backscattering duration, the reflection coefficient, the NOMA transmission duration, the transmission power, the task calculation duration, the backhaul duration, the local calculation duration and the local calculation frequency.

[0074] For example, the energy collection time, the backscattering time, the reflection coefficient, the NOMA transmission time, the transmission power, the task calculation time, the return time, the local calculation time and the local calculation frequency are all variables.

[0075] Optionally, in one embodiment, the establishing of the non-convex optimization problem model includes: taking the maximum and worst number of bits of the IoT node as the target, and under the constraints of energy causality and computing power of the IoT nodes, establishing a joint non-convex optimization problem of the energy collection time, the backscattering time, the reflection coefficient, the NOMA transmission time, the transmit power, the task calculation time, the backhaul time, the local calculation time and the local calculation frequency.

[0076] For example, according to the energy causality constraints of each IoT node, taking the number of bits of the largest and worst IoT node as the target, under the constraints of IoT node energy causality and computing power, a joint non-convex optimization problem of energy collection time, NOMA transmission time, backscattering coefficient of each IoT node, transmission power, backscattering time, local computing time and frequency is established:

[0077]

[0078] Among them, P 0 is the optimization target, st is subject to constraints, C1-C5 are constraints, Any quantifier.

[0079] Optionally, in one embodiment, based on the non-convex optimization problem model, the optimal resource allocation method in the NOMA-MEC network is obtained, including: processing the non-convex optimization problem by contradiction and auxiliary variable method to obtain a convex optimization problem, and then processing the convex optimization problem by a preset iterative algorithm to obtain the optimal resource optimization allocation method.

[0080] For example, a relaxation parameter is introduced into the optimization objective in the non-convex allocation optimization problem model. The optimization objective is a linear function And add constraints in, represents the total number of unloaded bits of the k-th IoT node;

[0081] Introduce Lemma 1: τ k The optimal value of is T, that is Where “*” indicates the optimal value;

[0082] Will Substitute into the optimization problem P 1 , then P 1 Transformed into optimization problem P 2 ,

[0083]

[0084] make and P k =p k t a , then x k and P k Bring in problem P 2 , we can get the problem P 3 :

[0085]

[0086] Due to the reference of NOMA technology, the optimization problem P 3 The constraint C6-1 is a non-convex constraint. To solve this problem, we introduce Lemma 2. Bring in the optimization problem P 3 The convex optimization problem model can be obtained from the model. Wherein, the preset iterative algorithm is the allocation algorithm based on Lemma 2.

[0087] Lemma 1: By observing the optimization problem P 1, when all variables except τ and f are fixed, τ and f are jointly optimized to maximize the objective. Next, we will prove by contradiction that the computational objective maximization will be Order and Represent the optimal computing frequency and time of the kth IoT node respectively. Assume Established Given other variables, P 1 All constraints. Then construct a feasible solution that satisfies and Assuming that the optimal value is not obtained at T, according to the constraints, From this we can get because and Then there is That is to say, the optimal value constructed does not calculate the bit rate under the condition of the same energy consumption. This is consistent with the assumption that is the optimal value. Obtained to be proven.

[0088] According to Lemma 1, the optimal value With optimization problem P 1 Transform it into a simpler optimization problem P 2 :

[0089]

[0090] Optimization Problem P 2 There is still a situation of multi-variable coupling in the problem, which can be solved by introducing auxiliary variable optimization problem: and P k =p k t a , x k and P k Bring in the problem P 2 The corresponding optimization problem model can obtain the problem P 3 The corresponding optimization problem model is:

[0091]

[0092]

[0093] Observe the optimization problem P 3 , C2, C3, C4-1, C5-1 and the objective function are linear constraints, problem P 3 Whether it is a convex problem depends mainly on constraints C1-1 and C6-1. It's about f k convex function, and the other functions are linear functions. Therefore, constraint C1-1 is a convex constraint. For constraint C6-1, It's about f k The linear function of constraint C6-1 is convex depending on and is a concave function, about It can be compared to The convexity of the two is consistent. 0 (x,y) is log 2 The perspective function of (1+y), F 0 (x,y) is a concave function of x and y, so About and x k is a concave function. Lemma 2 solves the non-convex problem caused by the constraints.

[0094] Lemma 2: Solution The non-convex problem caused by the constraints.

[0095] Given: For θ, Then there is in when When , the lower bound of the above formula holds.

[0096] make And into the above mathematical theorem we can get:

[0097] in At this time, introduce auxiliary variables Knowable P k Bring in problem P 3 Can get When k = K, Observe F(t a ,X k ), we only need to prove It is a convex function. The convexity and The convexity of is consistent, It has been proved that it is a convex function, so we get F(t a ,X k ) is a concave function.

[0098] Then F(t a ,X k ) into the optimization problem P 3By replacing the middle constraint C6-1 with the convex constraint C6-2, we can obtain the convex optimization model P. 4 :

[0099]

[0100]

[0101] In each algorithm iteration, solve P 4 The problem is solved and the corresponding optimal solution and target value are obtained. Then the iterative parameters are updated with the optimal solution until the algorithm converges, including:

[0102] Initialize system parameters: P t ,ξ,B,σ 2 etc.; define the maximum number of iterations L and the convergence threshold ε of the outermost layer;

[0103] Given a feasible initial value and μ k , and solve P using CVX 4 The target value λ;

[0104] Judgment|λ i+1 -λ i |≥ε is satisfied? If so, the result of the i-th calculation is used to update the i+1-th calculation result. and μ k ; If not satisfied, let λ=λ i+1 ,

[0105] Let λ * =λ, And output the optimal value λ * ,

[0106] Calculate the optimal number of bits of the worst IoT node according to the optimal value;

[0107] Among them, the CVX is a tool algorithm in the simulation software.

[0108] Through the above-mentioned backscatter-assisted wireless power supply network resource optimization allocation method, on the one hand, the fairness of the communication capacity of the Internet of Things nodes can be effectively guaranteed; on the other hand, it combines the advantages of backscattering and NOMA, and can utilize resources more effectively and flexibly.

[0109] It should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0110] In the embodiments of the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0111] In the embodiments of the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features not being in direct contact but being in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes the first feature being directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0112] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0113] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for optimizing resource allocation in a backscatter-assisted wireless power supply network, characterized in that: include: Establishing a wirelessly powered NOMA-MEC network, wherein the NOMA-MEC network includes an energy source, a MEC server, and a plurality of IoT nodes; The NOMA-MEC network sequentially performs energy collection phase, backscattering phase, active transmission phase, task calculation phase and data return phase; Obtaining variables in the energy collection phase, the backscattering phase, the active transmission phase, the task calculation phase, and the data return phase to obtain a variable group, and establishing a non-convex optimization problem model according to the variable group; The non-convex optimization problem model is established as follows: Taking the maximum and worst number of bits of IoT nodes as the goal, under the constraints of energy causality and computing power of IoT nodes, a joint non-convex optimization problem is established for the energy collection duration of the energy collection phase, the backscattering duration of the backscattering phase, the reflection coefficient, the NOMA transmission duration of the active transmission phase, the transmission power of the IoT node, the task calculation duration of the task calculation phase, the return duration of the data return phase, the local calculation duration of the local calculation module in the IoT node, and the local calculation frequency; Based on the non-convex optimization problem model, an optimal resource allocation method in the NOMA-MEC network is obtained; The non-convex optimization problem is processed by contradiction and auxiliary variable method to obtain a convex optimization problem, and then the convex optimization problem is processed by a preset iterative algorithm to obtain the optimal resource optimization allocation method; The convex optimization model for: in, T In order to complete the five stages, the entire time slot is divided into five parts. In the energy collection stage, Indicates the energy collection time in this stage, represents the channel gain of the link from the energy station to each IoT node, is the transmission power of the energy station, is the energy conversion efficiency in the linear energy harvesting model; in the backscattering stage, let Indicates k The time for IoT nodes to backscatter in this phase is represents the channel bandwidth, represents the thermal noise power spectral density, It represents the performance gap between backscattering and active communication. Indicates the channel gain of the link from the MEC server to each IoT node. represents the backscattered reflection coefficient, Indicates k The backscatter circuit power consumption of IoT nodes; in the active transmission stage, represents the transmission time of NOMA, Indicates k The transmission power of IoT nodes, Indicates k The circuit power consumption of each IoT node when performing NOMA transmission; In addition to implementing the above communication behaviors, each IoT node also performs local calculations in this time slot; For local calculations, let and Respectively represent k The computation frequency and time of each IoT node, To calculate the number of CPU cycles required for 1 bit, The processor chip is k The effective capacitance coefficient of an IoT node; , , , , , Indicates k The maximum CPU computing frequency of IoT nodes is used to construct the optimization problem ; Introduce , Indicates k The total number of computing bits of IoT nodes; then introduce , and Further simplifying the optimization problem, we can obtain and .

2. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 1, characterized in that: The energy collection stage includes: The energy source broadcasts an energy signal, and the IoT node collects energy; The energy collection time is a first preset value.

3. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 2, characterized in that: The backscattering stage includes: Using time division multiple access technology, the backscattering stage is divided into a number of time slots. In the time slots, the energy source sends the energy signal to the IoT node, and the IoT node sequentially backscatters the received energy signal to perform task offloading; Among them, the number of time slots corresponds to the number of IoT nodes, the backscattering duration is a second preset value, the reflection coefficient of the backscattering is a third preset value, and the IoT nodes collect energy when not performing backscattering.

4. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 3, characterized in that: The active transmission phase includes: The energy source stops broadcasting the energy signal, and the IoT node offloads the task to the MEC server based on NOMA technology; Among them, the NOMA transmission duration is the fourth preset value, and the transmission power of the Internet of Things node is the fifth preset value.

5. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 4, characterized in that: The task calculation phase includes: The MEC server decodes the offload task of the IoT node by using continuous interference cancellation technology to obtain decoded data, and performs task calculation on the decoded data, where the task calculation duration is a sixth preset value; Among them, the IoT node with the largest channel gain is decoded first, and the signals of other IoT nodes are regarded as interference. The signals of the IoT nodes are eliminated from the received signals, and then the IoT node with the largest channel gain among the IoT nodes is decoded to solve the task information of all the IoT nodes decoded by the MEC server.

6. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 5, characterized in that: The data return phase includes: The MEC server transmits the result of the task calculation back to each of the IoT nodes, and the transmission duration is a seventh preset value.

7. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 6, characterized in that: The Internet of Things node includes: An energy collection module, a backscattering module and a local computing module are used to enable the IoT node to perform task offloading and local computing simultaneously; Among them, the local calculation duration of the local calculation module is an eighth preset value, and the local calculation frequency of the local calculation module is a ninth preset value.

8. The method for optimizing resource allocation in a backscatter-assisted wireless power supply network according to claim 7, characterized in that: The variables in the variable group include: The energy collection duration, the backscattering duration, the reflection coefficient, the NOMA transmission duration, the transmission power, the task calculation duration, the return duration, the local calculation duration and the local calculation frequency.

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