A method and system for passive internet of things dynamic resource management, and an electronic device

Abstract

The present application belongs to the technical field of wireless communication network, and discloses a kind of passive Internet of Things dynamic resource management method and system, electronic equipment.The present application first constructs passive Internet of Things system model, passive Internet of Things system model includes a wireless radio base station, multiple passive users and a communication terminal;Then based on nonlinear radio frequency signal energy collection model, define the system throughput index for fairness;Afterwards, the optimization problem of maximizing the system throughput index for fairness is constructed, and the optimization problem is the collaborative optimization problem of wireless energy transmission strategy of wireless radio base station and wireless communication duration of multiple passive users;Finally, the optimization problem is solved, and the optimal configuration information is obtained.The present application can consider fairness and throughput, can realize the expected optimization performance, and can realize dynamic multi-domain resource collaborative optimization.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication network technology, and more specifically, relates to a method, system, and electronic device for passive Internet of Things dynamic resource management. Background Technology

[0002] With the rapid development of emerging fields such as the Internet of Things (IoT), smart cities, and the Industrial Internet, the number of user devices connected to wireless communication networks is growing exponentially, a large number of which are passive devices (such as passive sensors and RFID tags). Due to their small size, low cost, and lack of frequent battery replacements, these passive devices are widely used in environmental monitoring, asset tracking, and smart security scenarios, becoming a core component of ubiquitous communication networks. Passive communication technology, as a key technology supporting the networking of passive devices, operates on the principle of harvesting energy from radio frequency signals emitted by radio frequency powered base stations through radio frequency energy harvesting technology, thereby enabling information transmission and forming a typical communication architecture of "energy harvesting first, information transmission later." Compared to traditional wired or battery-powered power supply methods, radio frequency energy harvesting can provide a continuous energy supply for passive devices, effectively solving the bottleneck problem of insufficient battery life and greatly expanding the coverage and application scenarios of wireless communication networks.

[0003] However, in multi-user passive communication scenarios, existing technologies face challenges that urgently need to be addressed, including: (1) Insufficient adaptability of energy harvesting models and resource allocation strategies. In existing research, most studies use linear energy harvesting models for resource allocation optimization. This model ignores the nonlinear characteristics present in actual radio frequency energy harvesting circuits (such as the threshold effect of diodes, circuit saturation effect, etc.), resulting in a large deviation between the power and time allocation strategies designed based on linear models and the actual system, and failing to achieve the expected optimization performance. In addition, studies that fully consider nonlinear models, due to the non-convexity of the optimization problem, use approximate solution methods such as continuous convex approximation algorithms, which can only obtain local optimal solutions, making it difficult to balance system fairness and global throughput optimization. (2) Lack of dynamic multi-domain resource collaborative scheduling mechanism. Existing technologies usually separate power supply allocation, power supply time allocation and communication time allocation into independent optimization problems, lacking awareness of cross-domain resource collaborative scheduling. Summary of the Invention

[0004] This invention provides a method, system, and electronic device for passive IoT dynamic resource management, which solves the problems of insufficient adaptability and lack of collaborative scheduling mechanism in existing resource management schemes in multi-user passive communication scenarios.

[0005] In a first aspect, the present invention provides a method for passive Internet of Things (IoT) dynamic resource management, comprising the following steps:

[0006] Construct a passive Internet of Things (IoT) system model, which includes a wireless radio frequency base station, multiple passive users, and a communication terminal.

[0007] Based on a nonlinear radio frequency signal energy harvesting model, a fairness-oriented system throughput metric is defined.

[0008] An optimization problem is constructed to maximize the fairness-oriented system throughput metric. This optimization problem is a collaborative optimization problem that includes the wireless power transmission strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users. The optimal configuration information is obtained by solving the optimization problem.

[0009] Preferably, the passive Internet of Things system model includes a wireless power transmission stage and a wireless communication stage;

[0010] During the wireless power transmission phase, the wireless radio frequency base station performs wireless power transmission for multiple passive users, and the multiple passive users convert the received radio frequency signal power into DC power.

[0011] During the wireless communication phase, multiple passive users sequentially transmit wirelessly to the communication terminal in a time-division multiplexing manner.

[0012] Preferably, based on the nonlinear radio frequency signal energy harvesting model, the throughput of each passive user is calculated; and the throughput of the passive user with the lowest throughput among all passive users is defined as the fairness-oriented system throughput index.

[0013] Preferably, the throughput of the k-th passive user is represented as follows:

[0014]

[0015] In the formula, This represents the throughput of the k-th passive user. This represents the wireless communication duration of the k-th passive user. This represents the total charging energy of the k-th passive user. This represents the channel path gain from the k-th passive user to the communication terminal. Indicates noise power;

[0016] The total charging energy of the kth passive user is represented as follows:

[0017]

[0018] In the formula, P This indicates the transmit power of the wireless radio frequency base station. This indicates the maximum allowable transmit power of the wireless radio frequency base station. This represents the channel path gain from the wireless radio base station to the k-th passive user. This indicates that the k-th passive user receives a radio frequency signal with a power of 1. Charging power at that time, This represents the wireless power supply time density function of a wireless radio frequency base station.

[0019] Preferably, the optimization problem is expressed as follows:

[0020]

[0021] In the formula, The system throughput metric represents fairness, where K represents the total number of passive users, T represents the upper limit of total system latency, and E represents the total energy budget of the radio frequency base station.

[0022] Preferably, the optimization problem is a non-convex optimization problem. The non-convex optimization problem is converted into a convex optimization problem, and the convex optimization problem is solved to obtain the optimal configuration information.

[0023] Preferably, converting the non-convex optimization problem into a convex optimization problem includes: constructing a dual problem based on the Lagrange duality method, and obtaining sparse power points through subgradient analysis; the resulting convex optimization problem is represented as follows:

[0024]

[0025] In the formula, Represent the Lagrange function; Indicates the first i A sparse power point The corresponding wireless power transfer time, N represents satisfying The number of sparse power points; This represents the Lagrange multiplier of the throughput term for the k-th passive user in the Lagrange function. This represents the multiplier of the time constraint term in the Lagrange function. This represents the multiplier of the energy constraint term in the Lagrange function.

[0026] Preferably, the convex optimization problem is solved based on the ellipsoid method.

[0027] Secondly, the present invention provides a passive Internet of Things (IoT) dynamic resource management system, comprising:

[0028] The model building unit is used to build a passive Internet of Things (IoT) system model, which includes a wireless radio frequency base station, multiple passive users, and a communication terminal.

[0029] The index determination unit is used to define fairness-oriented system throughput indicators based on a nonlinear radio frequency signal energy harvesting model.

[0030] An optimization configuration unit is configured to construct an optimization problem that maximizes the fairness-oriented system throughput metric, wherein the optimization problem is a collaborative optimization problem involving the wireless power transmission strategy of a wireless radio frequency base station and the wireless communication duration of multiple passive users; and to solve the optimization problem to obtain optimal configuration information.

[0031] The passive IoT dynamic resource management system is used to perform the steps in the passive IoT dynamic resource management method provided in the first aspect of the present invention.

[0032] Thirdly, the present invention provides an electronic device including a passive Internet of Things dynamic resource management system as provided in the second aspect of the present invention.

[0033] One or more technical solutions provided in this invention have at least the following technical effects or advantages:

[0034] This invention first constructs a passive IoT system model (including a wireless radio frequency base station, multiple passive users, and a communication terminal). Then, based on a nonlinear radio frequency signal energy harvesting model, it defines a fairness-oriented system throughput index. Next, it constructs an optimization problem to maximize the fairness-oriented system throughput index (the optimization problem includes a collaborative optimization problem of the wireless energy transmission strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users). Finally, it solves the optimization problem to obtain the optimal configuration information. This invention targets multi-user passive communication scenarios, optimizing resource allocation based on a nonlinear radio frequency signal energy harvesting model. This reduces the deviation between the allocation strategy and the implemented system, achieving the expected optimized performance. Furthermore, this invention collaboratively schedules the wireless energy transmission strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users, providing a fair and throughput-optimized dynamic resource management scheme for passive IoT. It balances fairness and throughput, achieves dynamic multi-domain resource collaborative optimization, and meets the high performance requirements of emerging scenarios. Attached Figure Description

[0035] Figure 1 This is an overall flowchart of a passive Internet of Things (IoT) dynamic resource management method provided in Embodiment 1 of the present invention;

[0036] Figure 2 This is an architecture diagram of the passive Internet of Things (IoT) system model constructed in the passive IoT dynamic resource management method provided in Embodiment 1 of the present invention. Detailed Implementation

[0037] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0038] Example 1:

[0039] Example 1 provides a method for passive IoT dynamic resource management, see [link to example]. Figure 1 This includes the following steps:

[0040] S1. Construct a passive IoT system model, which includes a wireless radio frequency base station, multiple passive users (denoted as K passive users in Example 1), and a communication terminal. See [link to documentation]. Figure 2 .

[0041] S2. Based on the nonlinear radio frequency signal energy harvesting model, define a system throughput index oriented towards fairness.

[0042] S3. Construct an optimization problem that maximizes the fairness-oriented system throughput metric. The optimization problem is a collaborative optimization problem that includes the wireless power transfer (WPT) strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users. Solve the optimization problem to obtain the optimal configuration information.

[0043] The passive IoT system model constructed in step S1 is functionally divided into two stages: a wireless power transmission stage and a wireless communication stage. That is, the passive IoT system model includes both a wireless power transmission stage and a wireless communication stage, and the passive users adopt a "power harvesting first, information transmission later" architecture. Specifically, in the wireless power transmission stage, the wireless radio frequency base station broadcasts radio frequency signals to transmit power wirelessly to multiple passive users. These passive users then convert the received radio frequency signal power into DC power usable in the wireless communication stage using nonlinear energy harvesting technology. In the wireless communication stage, the multiple passive users consume the energy obtained in the wireless power transmission stage and sequentially transmit wirelessly to the communication terminal using time-division multiplexing.

[0044] Specifically, step S2 includes the following sub-steps:

[0045] S21. Based on the nonlinear radio frequency signal energy harvesting model, calculate the throughput of each passive user.

[0046] Specifically, the throughput of the k-th passive user is represented as follows:

[0047]

[0048] In the formula, This represents the throughput of the k-th passive user. This represents the wireless communication duration of the k-th passive user. This represents the total charging energy of the k-th passive user. This represents the channel path gain from the k-th passive user to the communication terminal. Indicates noise power.

[0049] In the wireless power transfer phase, the total charging energy of the k-th passive user is represented as follows:

[0050]

[0051] In the formula, P This indicates the transmit power of the wireless radio frequency base station. This indicates the maximum allowable transmit power of the wireless radio frequency base station. This represents the channel path gain from the wireless radio frequency base station to the k-th passive user; This indicates that the k-th passive user receives a radio frequency signal with a power of 1. Charging power at that time, Defined as a nonlinear relationship between charging power and received radio frequency signal power; The wireless power supply time density function of a wireless radio frequency base station can represent the wireless power transmission time at any power level.

[0052] exist In terms of design, this invention addresses multi-user scenarios. Considering the different charging channels for different users, the system's performance (throughput) is jointly affected by the energy harvesting performance of multiple users, i.e., by the combined influence of multiple charging quantity functions with uncertain concavity and convexity. Its concavity, monotonicity, and other characteristics cannot be directly determined through mathematical derivation, thus failing to obtain the optimal charging strategy. Therefore, optimization methods are needed to solve the problem. However, the complex formulas of the energy harvesting process lead to a non-convex problem, requiring the use of a continuous convex approximation method to approximate the energy harvesting expression and solve for a suboptimal solution through multiple iterations. However, we aim for the optimal solution, not a suboptimal one. To avoid the non-convex relationship between charging power and wireless energy transmission transmission power, this invention transforms the time-varying power domain into a time-domain optimization, i.e., the transmission power at different times is converted into the time corresponding to different transmission power levels. In this way, power P becomes a constant (integral) in the total charging quantity expression, rather than an optimization variable, thereby avoiding the non-convex relationship and potentially obtaining a global optimum. This invention performs the above equivalent transformation based on the disordered nature of charging.

[0053] At this time, the wireless power transmission strategy of the wireless radio frequency base station in this invention corresponds to The wireless communication duration of the multiple passive users corresponds to That is, the wireless power transmission strategy of the wireless radio frequency base station includes the dynamic power and power supply time of the wireless radio frequency base station.

[0054] S22. Define the throughput of the passive user with the lowest throughput among all the passive users as the fairness-oriented system throughput index.

[0055] That is, satisfying: , A system throughput metric indicating fairness.

[0056] Specifically, the optimization problem constructed in step S3 is represented as follows:

[0057]

[0058] In the formula, The system throughput metric represents fairness, where K represents the total number of passive users, T represents the upper limit of total system latency, and E represents the total energy budget of the radio frequency base station.

[0059] Since the optimization problem is non-convex and contains infinitely many variables (i.e., the optimization problem is a non-convex optimization problem), it is difficult to solve directly using traditional convex optimization methods. Therefore, this invention transforms the non-convex optimization problem into a convex optimization problem, solves the convex optimization problem, and obtains the optimal configuration information.

[0060] Specifically, transforming the non-convex optimization problem into a convex optimization problem and solving the convex optimization problem includes the following sub-steps:

[0061] (1) The Lagrangian function is constructed based on the Lagrangian duality method and is expressed as follows:

[0062]

[0063] In the formula, Represent the Lagrange function, This represents the Lagrange multiplier of the throughput term for the k-th passive user in the Lagrange function. This represents the multiplier of the time constraint term in the Lagrange function. This represents the multiplier of the energy constraint term in the Lagrange function.

[0064] (2) Construct the Lagrange duality problem, as follows:

[0065]

[0066] Among them, dual function Defined as:

[0067]

[0068] In order to make dual functions There exists supremum (i.e.) ), must satisfy Therefore, regarding The value of the term is always 0, that is... .

[0069] (3) Calculate the subgradient of the Lagrange function, as follows:

[0070]

[0071] Through subgradient analysis, let We can obtain:

[0072]

[0073] In the formula, This represents the Lamb function.

[0074] Substitute this conclusion A given multiplier can be obtained through numerical methods. Lower sparse power point The value of , where N represents satisfying The number of sparse power points.

[0075] (4) To solve the dual function, an optimization problem needs to be solved: .

[0076] Based on (2) regarding The analysis and (3) solution yielded sparse power points. The above problem can be transformed into:

[0077]

[0078] In the formula, Indicates the first i A sparse power point The corresponding wireless power transfer time, .

[0079] This problem is a convex problem, which can be solved using traditional optimization methods to obtain the dual function. Based on the obtained dual function, the value and subgradient of the dual problem under any multiplier can be obtained.

[0080] (5) Based on the values ​​of the dual problem under any multipliers and the subgradient obtained in (4), the globally optimal fairness throughput maximization can be obtained through the subgradient descent method (ellipsoid method), and the corresponding wireless power transmission strategy of the wireless radio frequency base station (including) can be obtained. and with One-to-one correspondence Wireless communication duration for multiple passive users .

[0081] In summary, Example 1 is designed for a more general multi-user scenario, closely resembling a real-world passive IoT scenario (including large-scale nodes), and focuses on maximizing throughput while ensuring fairness for multiple users. Due to constraints on total energy and total latency, resource competition is inevitable among multiple users. Therefore, Example 1 is designed around the following two aspects: (1) how to rationally design the power of wireless energy transmission to meet the diverse energy needs of multiple passive users; and (2) how to rationally allocate time to meet the diverse communication time needs of different passive users under different channel conditions and charging conditions. Example 1 can balance the performance of multiple users, reflecting the balance of resource allocation and meeting the fair communication requirements in multi-user scenarios. Overall, Example 1 improves upon the shortcomings of existing solutions in terms of fairness assurance, model adaptability, collaborative scheduling capabilities, and solution efficiency. Example 1 provides a resource management method that combines nonlinear energy harvesting characteristics to achieve dynamic multi-domain resource collaborative optimization, balancing fairness and throughput. It also presents a scheme for the collaborative allocation of instantaneous power and time in multi-user passive communication, meeting the high performance requirements of emerging scenarios.

[0082] Example 2:

[0083] Example 2 provides a passive IoT dynamic resource management system, comprising:

[0084] The model building unit is used to build a passive Internet of Things (IoT) system model, which includes a wireless radio frequency base station, multiple passive users, and a communication terminal.

[0085] The index determination unit is used to define fairness-oriented system throughput indicators based on a nonlinear radio frequency signal energy harvesting model.

[0086] An optimization configuration unit is configured to construct an optimization problem that maximizes the fairness-oriented system throughput metric, wherein the optimization problem is a collaborative optimization problem involving the wireless power transmission strategy of a wireless radio frequency base station and the wireless communication duration of multiple passive users; and to solve the optimization problem to obtain optimal configuration information.

[0087] The passive IoT dynamic resource management system is used to perform the steps in the passive IoT dynamic resource management method as described in Example 1.

[0088] Since the functions of each unit in the passive IoT dynamic resource management system provided in Embodiment 2 correspond to the steps in the passive IoT dynamic resource management method provided in Embodiment 1, Embodiment 2 can be understood by referring to the description of Embodiment 1, and will not be repeated here.

[0089] Example 3:

[0090] Example 3 provides an electronic device including a passive IoT dynamic resource management system as described in Example 2.

[0091] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for passive Internet of Things (IoT) dynamic resource management, characterized in that, Includes the following steps: Construct a passive Internet of Things (IoT) system model, which includes a wireless radio frequency base station, multiple passive users, and a communication terminal. Based on a nonlinear radio frequency signal energy harvesting model, the throughput of each passive user is calculated; the throughput of the passive user with the lowest throughput among all passive users is defined as the system throughput index for fairness. The throughput of the k-th passive user is represented as follows: In the formula, This represents the throughput of the k-th passive user. This represents the wireless communication duration of the k-th passive user. This represents the total charging energy of the k-th passive user. This represents the channel path gain from the k-th passive user to the communication terminal. Indicates noise power; The total charging energy of the kth passive user is represented as follows: In the formula, P This indicates the transmit power of the wireless radio frequency base station. This indicates the maximum allowable transmit power of the wireless radio frequency base station. This represents the channel path gain from the wireless radio base station to the k-th passive user. This indicates that the k-th passive user receives a radio frequency signal with a power of 1. Charging power at that time, The wireless power supply time density function represents the wireless radio frequency base station; An optimization problem is constructed to maximize the fairness-oriented system throughput metric. This optimization problem is a collaborative optimization problem that includes the wireless power transmission strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users. The optimal configuration information is obtained by solving the optimization problem.

2. The method for passive IoT dynamic resource management according to claim 1, characterized in that, The passive Internet of Things system model includes a wireless power transmission stage and a wireless communication stage; During the wireless power transmission phase, the wireless radio frequency base station performs wireless power transmission for multiple passive users, and the multiple passive users convert the received radio frequency signal power into DC power. During the wireless communication phase, multiple passive users sequentially transmit wirelessly to the communication terminal in a time-division multiplexing manner.

3. The method for passive IoT dynamic resource management according to claim 1, characterized in that, The optimization problem is expressed as follows: In the formula, The system throughput metric represents fairness, where K represents the total number of passive users, T represents the upper limit of total system latency, and E represents the total energy budget of the radio frequency base station.

4. The method for passive IoT dynamic resource management according to claim 3, characterized in that, The optimization problem is a non-convex optimization problem. The non-convex optimization problem is transformed into a convex optimization problem, and the convex optimization problem is solved to obtain the optimal configuration information.

5. The method for passive IoT dynamic resource management according to claim 4, characterized in that, Transforming the non-convex optimization problem into a convex optimization problem includes: constructing a dual problem based on the Lagrange duality method, and obtaining sparse power points through subgradient analysis; the resulting convex optimization problem is represented as follows: In the formula, Represent the Lagrange function; Indicates the first i A sparse power point The corresponding wireless power transfer time, N represents satisfying The number of sparse power points; This represents the Lagrange multiplier of the throughput term for the k-th passive user in the Lagrange function. This represents the multiplier of the time constraint term in the Lagrange function. This represents the multiplier of the energy constraint term in the Lagrange function.

6. The method for passive IoT dynamic resource management according to claim 5, characterized in that, The convex optimization problem is solved using the ellipsoid method.

7. A passive Internet of Things (IoT) dynamic resource management system, characterized in that, include: The model building unit is used to build a passive Internet of Things (IoT) system model, which includes a wireless radio frequency base station, multiple passive users, and a communication terminal. The index determination unit is used to define fairness-oriented system throughput indicators based on a nonlinear radio frequency signal energy harvesting model. An optimization configuration unit is used to construct an optimization problem that maximizes the fairness-oriented system throughput metric. The optimization problem is a collaborative optimization problem that includes the wireless power transmission strategy of the wireless radio frequency base station and the wireless communication duration of multiple passive users. And for solving the optimization problem to obtain the optimal configuration information; The passive IoT dynamic resource management system is used to perform the steps in the passive IoT dynamic resource management method as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, This includes the passive IoT dynamic resource management system as described in claim 7.

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