State updating scheduling method and system for multicast transmission scenario in wireless network

Abstract

The application belongs to the technical field of wireless communication, and discloses a state updating scheduling method and system for a multicast transmission scene in a wireless network, which collects and processes geometric environment information in a multicast scene, and determines a stopping set to represent a limited observation area around a transmitting node; according to local information of the transmitting node, a scheduling probability of the transmitting node is represented as a function of the stopping set, so as to ensure that the scheduling probability is a measurable function of a new information packet generation probability of accepting the local information; an optimization theory is used to solve an average network information age minimization optimization problem under the multicast scene, so as to obtain a state updating scheduling method at the transmitting node; an adaptive scheduling strategy is implemented for each transmitting node in the multicast scene, and whether the network environment changes is monitored in real time during network operation. According to the node state updating and dynamic adjustment of the transmission strategy of the network topology, the application can realize more efficient, real-time and low-delay multicast communication, and provide a more reliable data transmission basis for an intelligent system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, and in particular relates to a state update scheduling method and system for multicast transmission scenarios in wireless networks. Background Technology

[0002] Currently, multicast transmission plays a crucial role in numerous application scenarios within the Internet of Things (IoT) network, such as smart parking lots, connected autonomous vehicle networks, smart home systems, industrial automation, and smart grids. Multicast technology allows information to be transmitted simultaneously to multiple receiving nodes, effectively improving data transmission efficiency. In these scenarios, the accuracy and timeliness of real-time transmitted information are paramount to ensure the system can make accurate and timely decisions.

[0003] Traditional performance metrics, such as throughput, latency, and packet loss rate, while capable of measuring network communication quality, cannot adequately measure the freshness of real-time information. Therefore, Age of Information (AoI) has emerged as an effective performance metric, capturing the generation time and latency of state updates to better evaluate network transmission performance.

[0004] However, existing research primarily focuses on single-device transmission models and has not fully addressed the challenges of multicast transmission scenarios. In multicast scenarios, information needs to be transmitted to multiple receivers simultaneously, requiring more complex scheduling methods to ensure that all receivers receive up-to-date information. Current multicast networks lack scheduling strategies that consider information freshness, which can lead to information becoming outdated at the receiver, thus degrading system performance. Furthermore, existing technologies often employ simplified methods to handle interference and channel allocation issues during multicast transmission, potentially limiting system performance in complex network environments. For example, some research has explored a graph-based multicast network scheduling strategy. However, this method primarily focuses on network topology and bandwidth allocation, without adequately considering information age as a performance metric. Another study proposed a queuing-based scheduling algorithm aimed at minimizing network latency. While this method addresses information timeliness to some extent, it still does not fully consider the information age issue in multicast scenarios.

[0005] Based on the above analysis, the existing technologies suffer from the following problems and shortcomings: insufficient scheduling strategies for multicast scenarios, inadequate handling of interference and channel allocation in random multiple access wireless networks, insufficient utilization of geometric environment information in multicast scenarios, and a lack of adaptive scheduling strategies. These problems and shortcomings limit the performance and information timeliness of existing multicast networks in practical applications. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a state update scheduling method and system for multicast transmission scenarios in wireless networks, and particularly relates to a state update scheduling method, system, medium, device and terminal for multicast transmission scenarios based on information age optimization in wireless networks.

[0007] This invention is implemented as follows: a state update scheduling method for multicast transmission scenarios in a wireless network. The method includes: collecting and processing geometric environment information in the multicast scenario, including the spatial layout and location information of nodes in the network; determining a stopping set to represent the finite observation area around the transmitting node; expressing the scheduling probability of the transmitting node as a function of the stopping set based on the local information of the transmitting node, ensuring that the scheduling probability is a measurable function of the probability of generating new information packets by accepting local information; using optimization theory to solve the optimization problem of minimizing the average network information age in the multicast scenario, obtaining the state update scheduling method at the transmitting node; implementing an adaptive scheduling strategy for each transmitting node in the multicast scenario, and monitoring changes in the network environment in real time during network operation.

[0008] Furthermore, the state update scheduling method for multicast transmission scenarios in wireless networks includes the following steps:

[0009] Step 1: Collect and process geometric environment information in the multicast scene;

[0010] Step 2: Define the scheduling probability function for a typical transmitting node in a multicast scenario;

[0011] Step 3: Optimize the average age of information in the network based on the multicast scenario;

[0012] Step 4: Implement an adaptive scheduling strategy to achieve state update scheduling in the wireless network.

[0013] Furthermore, in step one, geometric environment information of each transmitting node in the multicast network scenario is collected to obtain the spatial layout and location information around each transmitting node in the network.

[0014] Using random geometric mathematical tools, the positions of the emission nodes are formed into independent densities of λ. t The Poisson Point Process (PPP) here Indicates node position The Dirac metric, that is, for any measurable set If X i ∈A, then Otherwise, it is 0. For each transmitting node X i The relevant receiving node yi The location forms a density of Point Process (PP) Where ν(·) is Lebesgue measure in λ r It is the density of nodes on this measure, B(X) i R) indicates that the center of the sphere is at X. i A sphere of radius R at location , therefore It doesn't have to be a Poisson distribution.

[0015] When the network topology is static, an arbitrary but fixed-point process is implemented at the beginning and remains invariant in the time domain. Each node in the network has the same statistical information as a typical node and the translation-invariant property of the state update strategy. Therefore, the typical transmitter node o and receiver node located at the origin are considered... The state update status between time slots is treated as an object. Time is divided into time slots, where the duration of each time slot is equal to the time it takes to transmit a single packet, and the transmitting node arbitrarily generates updates. At the beginning of each time slot, each transmitting node X... i With probability η i Generate a new message packet and send it to the receiving node y∈Φ. r,o The new update is encapsulated in a message packet. Focusing on the signal emitted by a typical transmitter X0 located at the origin, at time slot t, any receiver y∈Φ r,o Received signal Y at the location y (t) is considered to be the sum of signals sent from associated transmitting nodes and interference signals from all other non-associated transmitting nodes.

[0016] By using a stopping set W to represent a finite region within the geometric range of the transmitting node, including the location distribution information of both the transmitting and receiving nodes, local information is determined. A state update method is designed using the geometric environment information of each transmitter to further obtain a scheme that minimizes the network's average AoI. Therefore, the concept of a stopping set is first used to model the limited observation window, where the stopping set W = W(Φ) is a random variable. This represents the spatial implementation of network nodes in a multicast scenario, where each implementation of the stop set W is... One of the Borel sets.

[0017] Furthermore, in step two, based on the local information of a typical transmitting node, the scheduling probability of the transmitting node is expressed as a function of the stopping set, resulting in the scheduling probability η0 of the typical transmitting node:

[0018] η0 = η(W(Φ));

[0019] in, It is a measurable function that receives local information W(Φ) from a typical transmitting node and generates a new packet in each time slot, while the local information W(Φ) is the spatial distribution of network nodes in a multicast scenario. The function.

[0020] Choose appropriate input features to ensure that the scheduling probability is a measurable function of the probability of accepting local information and generating new packets in each time slot; preferably, provide a deterministic stopping set, for example, centered at X0 with a radius of R. W Fixed disk. The given stopping set W is regarded as an observation window in the network. The function η represents the basis for the transmitting node to spontaneously adjust its scheduling probability by obtaining local deterministic information and fuzzy statistical information outside the observation window through the distribution of other transmitting and receiving nodes within the window.

[0021] Furthermore, in step three, minimizing the average AoI of the network in the multicast scenario is taken as the optimization objective to solve the optimization problem. The optimization problem is solved using optimization theory and corresponding algorithms to obtain the optimal state update method for a typical transmitting node, specifically including:

[0022] The AoI increases linearly if no new update packets arrive at the receiving node; otherwise, it decreases to the time elapsed since the packet was generated when a new update was received. That is, the AoI of the transmitting node at time slot t is Δ(t) = tU(t); where U(t) represents the timestamp of the latest received update before time slot t. Packets sent from the transmitting node are successfully received only when the Signal-to-Interference-plus-Noise Ratio (SINR) exceeds the decoding threshold θ. Therefore, under the condition of spatial realization Φ in a multicast scenario, This represents the coverage of a typical transmitting node. Based on the properties of AoI, the conditional form of the average AoI in a multicast scenario, obtained using queuing theory, is as follows:

[0023]

[0024] Using the scheduling probability as the state update scheduling method for the transmitting node based on AoI optimization, the optimization problem is:

[0025]

[0026] st0≤η0=η(W(Φ))≤1;

[0027] The optimal scheduling probability of a typical launch node is obtained by solving the optimization problem using optimization theory.

[0028] Furthermore, the adaptive scheduling strategy in step four includes: introducing a shift operator and designing a translation-invariant strategy; applying the translation-invariant strategy to shift any transmitting node to a typical transmitting node within the network; solving the state update scheduling method for transmitting nodes, and obtaining the scheduling probability of any transmitting node based on the solution to the optimization problem and the translation-invariant strategy; and performing real-time monitoring and evaluation of network performance, and continuously adjusting and optimizing the adaptive scheduling method as needed, specifically including:

[0029] Introducing the shift operator S x , representing any set Transformed by vector -x, S x (A) = {ax: a∈A}; for the transmitting node X i This indicates the stop set of its local information. η i and μ i Let these represent the update probability and coverage rate of the node, respectively. The optimization problem is:

[0030]

[0031]

[0032] The optimization problem is represented using the shift operator S x Launch node X i By shifting the pointer to the typical transmitting node X0 within the network, all corresponding marker receiving nodes are shifted to achieve the goal of expanding the typical transmitting node state update scheduling method function η within the network.

[0033] Given a stopping set W = W(Φ), the solution is obtained by using a translation-invariant strategy and the optimization problem:

[0034] If the condition is satisfied The adaptive scheduling probability of any transmitting node is given by the following formula; otherwise, η i =1;

[0035]

[0036] in, This represents the transmitting node X. i and launch node X j The marked receiving node y kj The distance relative to the typical link spacing; f(r) represents the probability density function of the typical link spacing.

[0037] Any transmitting node can obtain the scheduling probability by applying the same state update scheduling function to the network topology observed from its perspective, and optimize the AoI of all associated receiving nodes. Thus, the average AoI across the network can be optimized through the state update scheduling strategy in the multicast scenario.

[0038] Another objective of this invention is to provide a state update scheduling system for multicast transmission scenarios in a wireless network, which applies the aforementioned state update scheduling method for multicast transmission scenarios in a wireless network. The state update scheduling system for multicast transmission scenarios in a wireless network includes:

[0039] The geometric environment information acquisition module is used to collect and process the spatial layout and location information of nodes in the network in the multicast scenario, and determine the stop set to represent the limited observation area around the transmitting node;

[0040] The scheduling probability function definition module is used to express the scheduling probability as a function of the stopping set based on the local information of the transmitting node, and to define the scheduling probability function of a typical transmitting node in a multicast scenario;

[0041] The average information age optimization module is used to solve the optimization problem of minimizing the average network information age in multicast scenarios using optimization theory, and obtains the state update scheduling method at the transmitting node.

[0042] The state update scheduling module is used to implement adaptive scheduling strategies for each transmitting node in a multicast scenario, thereby realizing state update scheduling for multicast transmission scenarios in a wireless network.

[0043] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, causing the processor to perform the steps of the state update scheduling method for multicast transmission scenarios in the wireless network.

[0044] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the state update scheduling method for multicast transmission scenarios in the wireless network.

[0045] Another objective of this invention is to provide an information data processing terminal for implementing a state update scheduling system for multicast transmission scenarios in a wireless network.

[0046] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0047] First, addressing the technical problems existing in the prior art and the difficulty of solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0048] In IoT multicast applications, the freshness of real-time information is crucial. To effectively transmit real-time information in such scenarios, this invention discloses a state update scheduling method for multicast transmission scenarios in wireless networks based on information age optimization. By collecting and processing geometric environment information and defining scheduling probability functions for typical transmitting nodes, the method aims to minimize the average information age in multicast scenarios and implements an adaptive scheduling strategy, thereby obtaining the optimal state update scheduling method for transmitting nodes. This state update scheduling method in wireless networks can effectively improve transmission efficiency and information timeliness in multicast networks and is applicable to various application scenarios such as smart parking lots and autonomous vehicle networks. By minimizing the average information age of the network, this invention helps ensure that information transmitted in multicast networks remains fresh, thereby improving the overall network performance.

[0049] By designing and optimizing a state update scheduling method for multicast scenarios, this invention effectively reduces the average information age of the entire network, thereby improving information timeliness. The transmitter node state update method proposed in this invention, based on stochastic geometry and probability theory, can achieve efficient scheduling in large-scale multicast transmission network scenarios, thus improving the overall information transmission efficiency of the network. This invention utilizes the geometric environment information of the transmitter node to design the state update method, thus maintaining high performance even when facing changes in node location or network topology. This invention adopts a distributed strategy, where each transmitter node only needs to adjust its media access probability based on a finite area around its geometric range, reducing implementation difficulty and computational complexity.

[0050] In summary, this invention proposes an effective state update scheduling method for multicast transmission scenarios, which can significantly improve the timeliness of information transmission, optimize system performance, and has strong robustness and ease of implementation. This will have a positive impact on multicast application fields such as the Internet of Things and intelligent transportation systems. Furthermore, this invention enables the scheduling probability of each transmitting node in the network to be adaptive, which minimizes the average information age of the network, thereby improving the performance of multicast network scenarios.

[0051] Second, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0052] This invention analyzes the AoI distribution in random multiple access wireless networks with multicast transmission and formulates a state update scheduling method for multicast transmission scenarios based on information age optimization. It fully considers the unique characteristics of multicast transmission and AoI metrics to reduce the overall AoI of the network. This invention dynamically adjusts the transmission strategy based on node state updates and network topology in multicast transmission, enabling more efficient, real-time, and low-latency multicast communication in IoT applications, thus providing a more reliable data transmission foundation for intelligent systems.

[0053] The technical solution protected by this invention addresses the information age optimization problem in multicast scenarios by providing a solution based on a state update scheduling strategy. This invention possesses the following technical effects and advantages:

[0054] Improving information timeliness: By optimizing the state update scheduling strategy, this invention effectively reduces the average information age (AoI) in multicast scenarios, thereby improving the real-time performance and timeliness of information.

[0055] Greater adaptability: This invention takes into account the geometric environment information in the network under multicast scenarios and uses the stopping set to model the limited observation range, making the scheduling strategy more adaptable and able to cope with different multicast scenarios and network environments.

[0056] Adaptive scheduling strategy: This invention provides an adaptive scheduling strategy that can dynamically adjust according to real-time network performance and environment, thereby achieving more efficient multicast transmission.

[0057] Reducing interference and channel allocation issues: By optimizing the scheduling strategy in multicast scenarios, this invention effectively reduces interference caused by concurrent transmission, improves channel utilization, and thus enhances overall network performance.

[0058] Easy to implement and expand: The technical solution provided by this invention adopts simple and easy-to-implement mathematical tools, such as stochastic geometry and probability theory, which are convenient for implementation and expansion in existing multicast networks.

[0059] In summary, the technical solution protected by this invention provides an effective and practically valuable solution for optimizing information age in multicast scenarios, and has significant technical effects and advantages.

[0060] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0061] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0062] The technical solution of this invention addresses the information age optimization problem in multicast scenarios by providing a solution based on a state update scheduling strategy. The expected benefits and commercial value of transforming this technical solution into a specific product are mainly reflected in the following aspects:

[0063] Improving network performance: This invention reduces the average information age in multicast scenarios by optimizing the state update scheduling strategy, thereby improving information timeliness. This will help improve the overall performance of the multicast network, including reducing network latency, increasing transmission rate, and improving channel utilization.

[0064] Cost reduction: By effectively reducing interference caused by concurrent transmission, this invention helps to reduce the complexity of channel allocation and resource scheduling in multicast scenarios, thereby reducing network deployment and operation costs.

[0065] Enhancing Competitiveness: The technical solution provided by this invention has significant advantages in the field of multicast networks. Applying it to actual products helps improve their market competitiveness and attract more customers.

[0066] Expanding application scenarios: The technical solution of this invention can be widely applied to various multicast scenarios, such as the Internet of Things, intelligent transportation systems, and smart factories, which can help promote the development of related industries and create more business value for enterprises.

[0067] Increased intellectual property value: The technical solution of this invention has high practicality and is expected to bring value to enterprises through patents and intellectual property rights, thereby enhancing their competitive advantage.

[0068] In summary, the technical solution of this invention has significant expected benefits and commercial value after transformation, which will help promote the development of multicast network technology and bring considerable economic benefits to related industries and enterprises.

[0069] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0070] The technical solution of this invention addresses the information age optimization problem in multicast scenarios by proposing a solution based on a state update scheduling strategy, effectively filling a technological gap in the industry both domestically and internationally. Specifically, this is reflected in the following aspects:

[0071] This invention optimizes for the characteristics of multicast scenarios: Unlike existing research methods that primarily focus on unicast, this invention delves into and optimizes multicast methods to address the unique features of multicast scenarios, such as the simultaneous transmission of information to multiple receiving nodes and the need for state updates among multiple receivers. This helps to better meet the timeliness requirements of information transmission in multicast scenarios.

[0072] Information age as a performance indicator: Compared with traditional performance indicators such as throughput and latency in multicast scenarios, this invention directly uses information age as a performance indicator. In response to the need for information timeliness in real-time applications, this method more intuitively and effectively describes the real-time nature of information and helps to more accurately evaluate network performance in multicast scenarios.

[0073] An adaptive state update strategy based on geometric environment information: This invention considers the geometric environment information of any transmitting node and designs an adaptive scheduling strategy based on state updates to minimize the average information age in multicast scenarios. This strategy has not been previously studied or applied in existing technologies for multicast scenarios, providing an innovative optimization method for information transmission in networks.

[0074] (3) The technical solution of the present invention solves a technical problem that people have long wanted to solve but have never been able to solve successfully:

[0075] The technical solution of this invention effectively overcomes a long-standing technical problem in optimizing real-time information in multicast scenarios. This innovative solution will strongly promote the advancement of multicast network technology, thereby improving the performance and operational efficiency of multicast networks. Specifically, this is reflected in the following aspects:

[0076] Optimization of real-time information in multicast scenarios: Multicast scenarios have wide application needs in various fields such as the Internet of Things and intelligent transportation. However, ensuring the real-time performance and timeliness of information in multicast scenarios has always been a technical challenge. This invention proposes a solution based on a state update scheduling strategy, which effectively solves this problem.

[0077] An Adaptive State Update Strategy Based on Geometric Environment Information: Existing research on multicast network performance rarely fully utilizes the geometric environment information of transmitting nodes to optimize performance metrics. This invention addresses this issue by proposing an adaptive state update strategy based on geometric environment information for multicast scenarios, effectively solving the information age optimization problem in multicast environments.

[0078] (4) The technical solution of the present invention overcomes technical bias:

[0079] The technical solution of this invention successfully overcomes several technical biases, providing an innovative solution for optimizing the real-time performance of information in multicast scenarios. This will help promote the development of multicast network technology and improve the performance and efficiency of multicast networks. Specifically, this is reflected in the following aspects:

[0080] Breaking through the limitations of traditional performance metrics: Traditional network performance metrics (such as throughput and latency) cannot fully reflect the real-time nature of information in multicast scenarios. This invention overcomes this technical bias by directly using information age as an optimization metric in multicast scenarios, providing a more intuitive and effective solution for optimizing the real-time nature of information in multicast environments.

[0081] Beyond the limitations of a single transmission model: Most existing research focuses on single-device transmission models, failing to adequately address the timeliness issues in multicast scenarios. This invention overcomes this bias, focusing on multicast transmission scenarios and proposing an effective solution.

[0082] Adaptive scheduling strategy for multicast scenarios: Existing technologies lack adaptive scheduling strategies for multicast scenarios. This invention overcomes this technical bias by dynamically adjusting the scheduling probability based on the real-time status of each transmitting node in the network, thereby optimizing information age in multicast scenarios. Attached Figure Description

[0083] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0084] Figure 1 This is a flowchart of a state update scheduling method for multicast transmission scenarios in a wireless network provided in an embodiment of the present invention;

[0085] Figure 2 This is a node implementation diagram of a typical multicast scenario for the state update scheduling method for multicast transmission scenarios in a wireless network provided in this embodiment of the invention.

[0086] Figure 3 This is a schematic diagram of the state update scheduling method for multicast transmission scenarios in a wireless network provided in this embodiment of the invention.

[0087] Figure 4 This is an interaction diagram of a state update scheduling system for multicast transmission scenarios in a wireless network provided in an embodiment of the present invention;

[0088] Figure 5A This is an experimental simulation result of the network average coverage of the state update scheduling method for multicast transmission scenarios in wireless networks provided in this embodiment of the invention;

[0089] Figure 5B This is an experimental simulation result of the network average AoI of the state update scheduling method for multicast transmission scenarios in wireless networks provided in this embodiment of the invention. Detailed Implementation

[0090] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0091] To address the problems existing in the prior art, this invention provides a state update scheduling method and system for multicast transmission scenarios in wireless networks. The invention will be described in detail below with reference to the accompanying drawings.

[0092] like Figure 1 As shown, the state update scheduling method for multicast transmission scenarios in a wireless network provided by this embodiment of the invention includes the following steps:

[0093] S101, collects and processes geometric environment information in the multicast scenario, including the spatial layout and location information of nodes in the network, and determines the stop set to represent the local information of the limited observation area around the transmitting node;

[0094] S102, Based on the local information of the transmitting node, the scheduling probability of the transmitting node is expressed as a function of the stopping set, ensuring that the scheduling probability in the state update scheduling method is a measurable function of the probability of generating new information packets by accepting local information.

[0095] S103 uses optimization theory to solve the optimization problem of minimizing the average network information age (AoI) in the multicast scenario, and obtains the state update scheduling method at the transmitting node;

[0096] S104 implements an adaptive scheduling strategy for each transmitting node in a multicast scenario and monitors the network environment in real time during network operation to determine if changes occur.

[0097] Timely delivery of new status updates to multiple IoT devices is crucial for many emerging IoT applications. Base stations or servers transmit information to multiple users via unidirectional transmission. The node implementation diagram of a typical multicast scenario in the status update scheduling method provided in this embodiment of the invention is shown below. Figure 2As shown. This service model in multicast scenarios can significantly reduce server energy consumption and better utilize valuable spectrum resources. Therefore, for one-to-many transmission services in multicast scenarios, this embodiment of the invention proposes a state update scheduling method for multicast transmission scenarios based on information age optimization in wireless networks. By observing the environmental information around the transmitting node through the stop set, the impact of concurrent transmissions in its geographical vicinity can be effectively measured. Based on the stop set of the transmitting node, the stop set is used as a function of its state update scheduling probability. Based on the optimization of the average information age of the network, the state update method of each transmitting node in the multicast scenario—adaptive scheduling probability—is obtained, which improves the timeliness of information in multicast scenarios while adapting to changes in network topology.

[0098] As a preferred embodiment, such as Figure 3 As shown, the state update scheduling method for multicast transmission scenarios in a wireless network provided in this embodiment of the invention specifically includes the following steps:

[0099] Step 1: Collect and process geometric environment information in the multicast scene;

[0100] Collect geometric environment information of each transmitting node in the multicast network scenario to obtain the spatial layout and location information around each transmitting node in the network.

[0101] To characterize the random distribution of network nodes, we first use stochastic geometric mathematical tools to form independent densities of λ for the positions of the transmitting nodes. t The Poisson Point Process (PPP) here Indicates node position The Dirac metric, that is, for any measurable set If X i ∈A, then Otherwise, it is 0. For each transmitting node X i The relevant receiving node y i The location forms a density of Point Process (PP) Where ν(·) is Lebesgue measure in λ r It is the density of nodes on this measure, B(X) i R) indicates that the center of the sphere is at X. i A sphere of radius R at location , therefore It doesn't have to be a Poisson distribution. Assume... It is independent, which in random geometry means Upper point process space implementation It is PPPΦ tIts independent identifier. Figure 2 A partial schematic diagram of the spatial distribution of network nodes in a multicast scenario under the above assumptions is shown.

[0102] Furthermore, since the timescales of fading and packet transmission are much smaller than the timescales of spatial dynamics, this embodiment of the invention assumes that the network topology is static, i.e., it implements arbitrary but fixed-point processes at the beginning and remains invariant in the time domain. Each node in the network has the same statistical information as a typical node and the translation-invariant property of the state update strategy. Therefore, the typical transmitting node o located at the origin and its receiving node are first... The state update status between time slots is taken as the object. For ease of understanding, this embodiment of the invention divides time into time slots, where the duration of each time slot is equal to the time for transmitting a single information packet, and the transmitting node can generate updates arbitrarily. That is, at the beginning of each time slot, each transmitting node X... i With probability η i A new update is generated, encapsulated in a packet, and sent to the intended receiving node y∈Φ. r,o Assume each transmitting node operates at a constant power P. t During transmission, the channel between any two nodes is affected by Rayleigh fading and path loss, with Rayleigh fading varying independently across different time slots. In this embodiment of the invention, the path loss function is expressed as... l(r) is a continuous and non-decreasing function, where r represents the distance. We first focus on the signal emitted by a typical transmitter X0 located at the origin, and then consider any receiver y∈Φ at time slot t. r,o Received signal Y at the location y (t) is considered to be the sum of signals transmitted from associated transmitting nodes and interference signals from all other non-associated transmitting nodes. This is determined by transmitting node X. i The transmitted signal is represented as x. i ,and This represents the additive noise at receiver y at time slot t and... Independent and identically distributed. y (t) represents the accumulated interference at receiver y at time slot t and is denoted as I. y (t)=Σ j≠0 l -1 (||yX j ||)P t CH y,j x j CH y,j (t) indicates that the link starts from X in time slot t. j The fading begins at y and follows the complex Gaussian distribution. Independent and identically distributed. This embodiment of the invention does not consider additional noise Z. y (t), i.e., σ 2=0. To obtain specific results, assume a commonly used path loss function l(r) = r α Where α > 2 is the path loss exponent, and r represents the distance. Therefore, when X0 sends a packet in time slot t, the signal-to-interference-plus-noise ratio (SINR) of any expected receiving node y (typical link) can be written as:

[0103]

[0104] Among them, H yj,t ~exp(1) represents the time slot t from the transmitting node X j The channel fading to the receiving node y, ||·|| represents the Euclidean norm, θ j,t ∈{0,1} represents the transmitting node X j Should a new information packet be launched?

[0105] According to the law of total probability, the coverage rate of a typical transmitting node, i.e., the average spatial coverage rate after deconditioning typical link length r and spatial location Φ, is:

[0106]

[0107] in, This indicates that, in the embodiments of the present invention, as shown below... Figure 2 The probability density function for a typical link spacing r is shown.

[0108] By using a stopping set W to represent a finite area within the geometric range of a transmitting node, including the location distribution information of both transmitting and receiving nodes, local information can be determined. More specifically, in practice, communication on wireless links is often affected by concurrent transmissions from geographically proximate locations, and understanding the actual location information of the transmitter has the potential to significantly improve overall network performance. Therefore, this embodiment of the invention utilizes the geometric environment information of each transmitter to design a state update method in a multicast scenario, aiming to obtain a state update scheduling method that minimizes the average AoI of the network in a multicast scenario. Due to limited sensing capabilities, each transmitting node can only observe a finite area within its geometric range. In this regard, this embodiment of the invention uses the concept of a stopping set to model the limited observation window, where the stopping set W = W(Φ) is a random variable. This represents the spatial implementation of network nodes in a multicast scenario, where each implementation of the stop set W is... One of the Borel sets.

[0109] Step 2: Define the scheduling probability function for a typical transmitting node in a multicast scenario;

[0110] Based on the local information of a typical transmitting node, the scheduling probability of the transmitting node is expressed as a function of the stopping set, and the scheduling probability η0 of the typical transmitting node is obtained as follows:

[0111] η0=η(W(Φ))

[0112] in, It is a measurable function that receives local information W(Φ) from a typical transmitting node and generates a new packet in each time slot, while the local information W(Φ) is the spatial distribution of network nodes in a multicast scenario. The function.

[0113] Choosing appropriate input features ensures that the scheduling probability is a measurable function of the probability of accepting local information and generating new packets in each time slot; preferably, a deterministic stopping set is provided, with X0 as the center and radius R in this embodiment of the invention. W A fixed disk is used as the stopping set. The given stopping set W is regarded as an observation window in the network. The function η represents the basis for the transmitting node to spontaneously adjust its scheduling probability by obtaining local deterministic information and fuzzy statistical information outside the observation window through the distribution of other transmitting and receiving nodes within the window.

[0114] Step 3: Use optimization theory to solve the optimization problem of minimizing the average AoI of the network in the multicast scenario, and obtain the state update scheduling method at the transmitting node;

[0115] First, an optimization objective needs to be established, namely, minimizing the average AoI of the network in the multicast scenario, and then solving this optimization problem. The optimization problem is solved using optimization theory and corresponding algorithms to obtain the optimal state update scheduling method for typical transmitting nodes. That is, when the conditional expression in this invention is satisfied, the scheduling probability of the transmitting node is determined according to the solution expression in this invention; otherwise, it is considered a transmitting node that frequently transmits.

[0116] Step 4: Implement an adaptive scheduling strategy for each transmitting node in the multicast scenario and monitor it in real time during network operation. This scheduling strategy mainly follows these steps:

[0117] First, a translation-invariant strategy is applied. To design a translation-invariant strategy, a shift operator is introduced to shift any transmitting node within the network to the location of a typical transmitting node.

[0118] Then, the state update scheduling method for the launch nodes is solved. The scheduling probability of any launch node is obtained based on the solution to the optimization problem and the translation-invariant policy.

[0119] Finally, the network environment is monitored and evaluated in real time to detect changes, and adaptive scheduling strategies are continuously adjusted and optimized as needed.

[0120] In step 3 of this embodiment, AoI is a powerful performance metric for characterizing the freshness of information from the receiver of updated information packets. The receiver's AoI is defined as the time difference between the current time and the time when the most recently received state update was generated. Considering low-cost implementation, this embodiment does not use any MAC protocol for retransmission or acknowledgment. Therefore, the state update sent in time slot t is generated before transmission. The AoI increases linearly if no new update packets arrive at the receiving node; otherwise, it decreases to the time elapsed since the generation of the packet sent when the new update was received. That is, the value of the transmitting node's AoI at time slot t is Δ(t) = tU(t); where U(t) represents the timestamp of the most recently received update generated before time slot t. Packets sent from the transmitting node are successfully received only when the Signal-to-Interference-plus-NoiseRatio (SINR) exceeds the decoding threshold θ. Therefore, under the condition of spatial implementation Φ in a multicast scenario, This represents the coverage of a typical transmitting node. Based on the properties of AoI, the conditional form of the average AoI in a multicast scenario, obtained using queuing theory, is as follows:

[0121]

[0122] From the above equation, it can be seen that for a typical transmitting node in a multicast scenario, there may exist a scheduling probability that minimizes the AoI of its receiving node, i.e., a state update scheduling method. Therefore, it is reasonable to use the scheduling probability as the state update scheduling method for transmitting nodes based on AoI optimization. This is because AoI grows linearly; it only decreases when the receiving node receives a new update. In other words, increasing the update rate is beneficial. However, increasing the transmission rate leads to increased interference between transmitting nodes, thus increasing the probability of reception failure, while AoI still increases linearly. At this point, the optimization problem becomes:

[0123]

[0124] st0≤η0=η(W(Φ))≤1

[0125] The optimal scheduling probability of a typical launch node is obtained by solving the optimization problem using optimization theory.

[0126] Step 4 of this embodiment of the invention, which implements adaptive scheduling probability for any transmitting node, specifically includes: introducing a shift operator S. x , representing any set Transformed by vector -x, S x(A) = {ax: a∈A}. Due to the spatial differences between wireless links, the observation window and scheduling probability of each node vary greatly. Therefore, designing a translation-invariant strategy is crucial. For a general transmitting node X... i Its observation window is η i and μ i Let represent the update probability and coverage rate of the node, respectively. The scheduling probability obtained for solving the optimization problem is:

[0127]

[0128]

[0129] The above formula indicates that, using the shift operator S x Launch node X i By shifting the pointer to the typical transmitting node X0 within the network, all corresponding marker receiving nodes are shifted to achieve the goal of expanding the typical transmitting node state update scheduling method function η within the network.

[0130] Given a stopping set W = W(Φ), the solution can be obtained by using a translation-invariant strategy and a step optimization problem:

[0131] If the condition is satisfied The adaptive scheduling probability of any transmitting node is given by the following formula; otherwise, η i =1.

[0132]

[0133] in, This represents the transmitting node X. i and launch node X j The marked receiving node y kj The distance relative to the typical link spacing; f(r) represents the probability density function of the typical link spacing. In this embodiment of the invention, when modeling the point process for the receiving node corresponding to the transmitting node in a multicast scenario, it is based on B(X) i In this embodiment of the invention, dots are evenly sprinkled within the circle R. This probability density function can be flexibly changed according to different multicast scenarios.

[0134] like Figure 4 As shown, the state update scheduling system for multicast transmission scenarios in a wireless network provided in this embodiment of the invention includes:

[0135] The geometric environment information acquisition module is used to collect and process the spatial layout and location information of nodes in the network in the multicast scenario, and determine the stop set to represent the limited observation area around the transmitting node;

[0136] The scheduling probability function definition module is used to express the scheduling probability as a function of the stopping set based on the local information of the transmitting node, and to define the scheduling probability function of a typical transmitting node in a multicast scenario;

[0137] The average information age optimization module is used to solve the optimization problem of minimizing the average network information age in multicast scenarios using optimization theory, and obtains the state update scheduling method at the transmitting node.

[0138] The state update scheduling module is used to implement adaptive scheduling strategies for each transmitting node in a multicast scenario, thereby realizing state update scheduling for multicast transmission scenarios in a wireless network.

[0139] To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides specific product or related technology application examples of the technical solution claimed.

[0140] Example 1: Intelligent Parking System

[0141] In intelligent parking systems, multicast transmission can be widely used for real-time transmission of parking space information. The technical solution of this invention can optimize the real-time performance of information in multicast scenarios, thereby improving the real-time update speed of parking space information, enabling users to find available parking spaces more quickly, and improving the overall operational efficiency of the parking system.

[0142] Example 2: Smart Factory Environment

[0143] In smart factory environments, multicast transmission is typically used to transmit real-time production data and control information. Applying the technical solution of this invention can optimize the real-time performance of information in multicast scenarios, ensuring the real-time transmission of data and control commands during production, thereby improving production efficiency and reducing production accidents.

[0144] Example 3: Intelligent Transportation System

[0145] In intelligent transportation systems, multicast transmission plays a crucial role in vehicle communication and road condition information dissemination. The technical solution of this invention optimizes information real-time performance in multicast scenarios, facilitating efficient communication between vehicles and rapid dissemination of road condition information, thereby improving road safety and traffic efficiency.

[0146] Example 4: Emergency Communication System

[0147] In emergency communication systems, multicast transmission is crucial for the real-time dissemination of alarms and emergency instructions. The technical solution of this invention optimizes the real-time performance of information in multicast scenarios, ensuring the rapid propagation of alarms and emergency instructions, improving emergency response speed, and reducing losses.

[0148] 1. Simulation conditions:

[0149] The simulation experiment of this invention is conducted on a Windows platform, with the following main configurations: CPU is Intel(R) i7-8550U, 1.80GHz; memory is 16G; operating system is Windows 10; and the simulation software environment is MATLAB R2022b.

[0150] by Figure 3 A partial schematic diagram of the spatial distribution of network nodes in a typical multicast scenario is used as a simulation scenario diagram in the simulation experiment of this embodiment of the invention. In this diagram, the positions of the transmitting nodes (star identifiers) form an independent density of λ. t Poisson point process here Indicates position The Dirac metric, that is, for any measurable set If X i ∈A, then Otherwise, it is 0. Similarly, for each transmitting node X i The relevant receiving node y i The location forms a density of Point process Where ν(·) is Lebesgue measure in λ r It is the density of nodes on this measure, B(X) i R) indicates that the center of the sphere is at X. i At point R, a circular plane with radius R. Each transmitting node transmits at a constant power P. t During transmission, the channel between any two nodes is affected by Rayleigh fading and path loss, with Rayleigh fading varying independently across different time slots. To obtain specific results, this embodiment of the invention expresses the path loss function as l(r) = r α , where α>2 is the path loss exponent, and r represents the distance.

[0151] 2. Simulation content and result analysis:

[0152] This simulation experiment uses the state update scheduling method of this invention to demonstrate its effectiveness in optimizing AoI in multicast scenarios. In each simulation run, the transmitting node and its marked receiving node are implemented independently via PP (Programmable Execution). Message packets are transmitted from the transmitting node to each receiving node according to an independent Bernoulli process. This simulation experiment collects statistical data for each communication link through an average of 1000 implementations, and finally calculates the average network coverage and AoI. The simulation experiment is conducted in an area of ​​4000×4000m². 2 The simulation will be conducted within the specified range. Unless otherwise specified, the process parameters are as follows: deployment density of the launch nodes λ t =10 -4 m2 Deployment density λ of receiving nodes r =500×(π×500) 2 ) -1 m 2 Path loss exponent α = 4, SINR decoding threshold θ = 0 dB, transmit power P t =200mW, the marking range R between the transmitting node and the receiving node is 100m, determine the radius R of the stop set. W =100m. The result was obtained through experimental simulation. Figures 5A-5B The results are shown.

[0153] Figures 5A-5B The average coverage and average information age were plotted for different transmit node densities in the network. In particular, this embodiment of the invention considers the deterministic stopping setting (in this example, it is a fixed disk radius R) under the condition of local adaptive scheduling probability. W And these are 100m and 200m respectively. This embodiment of the invention also describes the results when all transmitting nodes actively update in each time slot (unified transmitting node scheduling probability η = 0.9), that is... Figure 5A and Figure 5B The curve marked with a hollow dot.

[0154] first, Figure 5A This represents the relationship between average network coverage and the deployment density of transmitting nodes. From this, we can see that, given a deterministic stopping set W = B(0, R... W In this example, the stopping set is treated as a disk with a fixed radius, and all transmitting nodes use the adaptive scheduling probability η determined by the state update scheduling method in this invention. i The average coverage obtained by uniformly using a fixed scheduling probability η = 0.9 has a significant advantage: regardless of the density of network nodes, the former can always maintain a relatively high average coverage value (around 0.9); while when using a larger scheduling probability η = 0.9, as the density of nodes in the network increases (from 6 × 10⁻⁶), the average coverage decreases. -6 Up to 6×10 -5 As a result, the average network coverage decreased by 61.7% (from 0.8711 to 0.3328). Similarly, as long as a uniform scheduling probability is used, no matter how the scheduling probability value is chosen, it cannot satisfy the requirement for each transmitting node to adapt to arbitrary changes in the network topology, especially when the deployment density of network nodes is high.

[0155] also, Figure 5B This represents the relationship between the network's average AoI and the deployment density of transmitting nodes (with the same units on the left and right axes), given a deterministic stopping set W = B(0, R). WAll transmitting nodes use the state update scheduling method in this invention to determine the adaptive scheduling probability η. i The average AoI obtained by uniformly using a fixed scheduling probability η = 0.9 has a significant advantage: as the network node deployment density increases, the average AoI of the former can maintain a relatively gradual increase in growth rate, and the growth rate gradually decreases; while when using a larger uniform launch node scheduling probability η = 0.9, as the node density in the network increases (from 6 × 10⁻⁶), the average AoI of the former can increase gradually, and the growth rate gradually decreases; while when using a larger uniform launch node scheduling probability η = 0.9, the average AoI of the former increases gradually, and the average AoI of the latter increases gradually, and the average AoI of the former increases gradually, and the average AoI of the former decreases ... -6 Up to 6×10 -5 The network's average AoI increased significantly (by three orders of magnitude) and the growth rate also gradually increased. Further... Figure 5B The information contained herein is summarized as follows:

[0156] • The average AoI of a network always increases with increasing spatial density because densifying the infrastructure inevitably introduces additional interference, which reduces the probability of successful transmission and thus leads to a longer AoI.

[0157] Compared to methods that actively generate new updates in every time slot, the local adaptive method can significantly reduce the average network AoI (by two orders of magnitude for densely distributed networks). It's important to note that the average network AoI under the adaptive state update scheduling method can remain relatively low for different deployment densities. Therefore, it is highly resilient to various network topologies.

[0158] • When a deterministic stopping set is used, the scheduling method employed is more conducive to network densification. This is because by fixing the observation window, i.e., the size of the stopping set W, more information about neighbors can be considered as the spatial density increases, thus enabling the transmitting node to take better actions.

[0159] In conclusion, Figures 5A-5B The results demonstrate the effectiveness of the information age-based state update scheduling method for multicast transmission scenarios in wireless networks provided in this embodiment of the invention. This is because the adaptive state update scheduling method allows each transmitting node to determine the frequency of its state update generation based on its observations of its local environment. Furthermore, compared to the method of actively generating new updates in every time slot, the local adaptive method can significantly reduce the network information age. It is worth noting that the average network information age under the adaptive state update scheduling method can maintain a relatively low value for different deployment densities. This indicates that the information age-based state update scheduling method for multicast scenarios in wireless networks proposed in this invention helps reduce the average information age of the entire network. This method can be fully adjusted according to changes in the geographical environment, thus further expanding as the network scale increases.

[0160] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0161] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A state update scheduling method for multicast transmission scenarios in a wireless network, characterized in that, The state update scheduling method for multicast transmission scenarios in wireless networks includes: collecting and processing geometric environment information in the multicast scenario, including the spatial layout and location information of nodes in the network, and determining a stopping set to represent the finite observation area around the transmitting node; expressing the scheduling probability of the transmitting node as a function of the stopping set based on the local information of the transmitting node, ensuring that the scheduling probability is a measurable function of the probability of generating new information packets by accepting local information; using optimization theory to solve the optimization problem of minimizing the average network information age in the multicast scenario, and obtaining the state update scheduling method at the transmitting node; implementing an adaptive scheduling strategy for each transmitting node in the multicast scenario, and monitoring whether the network environment changes in real time during network operation.

2. The state update scheduling method for multicast transmission scenarios in a wireless network as described in claim 1, characterized in that, The state update scheduling method for multicast transmission scenarios in wireless networks includes the following steps: Step 1: Collect and process geometric environment information in the multicast scene; Step 2: Define the scheduling probability function for a typical transmitting node in a multicast scenario; Step 3: Optimize the average age of information in the network based on the multicast scenario; Step 4: Implement an adaptive scheduling strategy to achieve state update scheduling in the wireless network.

3. The state update scheduling method for multicast transmission scenarios in a wireless network as described in claim 2, characterized in that, In step one, geometric environment information of each transmitting node in the multicast network scenario is collected to obtain the spatial layout and location information around each transmitting node in the network; Using random geometric mathematical tools, the positions of the emission nodes are formed into independent densities of λ. t The Poisson Point Process (PPP) here Indicates node position The Dirac metric, that is, for any measurable set If X i ∈A, then Otherwise, it is 0; for each transmitting node X i The relevant receiving node y i The location forms a density of Point Process (PP) Where ν(·) is Lebesgue measure in λ r It is the density of nodes on this measure, B(X) i R) indicates that the center of the sphere is at X. i A sphere of radius R at location , therefore It doesn't have to be a Poisson distribution; When the network topology is static, an arbitrary but fixed-point process is implemented at the beginning and remains invariant in the time domain; each node in the network has the same statistical information as a typical node and the translation-invariant property of the state update strategy, therefore, the typical transmitting node o and the receiving node located at the origin are... The state update status between time intervals is treated as an object; time is divided into time slots, where the duration of each time slot is equal to the time of transmitting a single packet, and the transmitting node arbitrarily generates updates; at the beginning of each time slot, each transmitting node X... i With probability η i Generate a new message packet and send it to the intended receiving node y∈Φ. r,o The new sample is encapsulated in an information packet; the focus is on the signal emitted by a typical transmitter X0 located at the origin, and at time slot t, any receiver y∈Φ r,o Received signal Y at the location y (t) is considered to be the sum of signals sent from associated transmitting nodes and interference signals from all other non-associated transmitting nodes; By using a stopping set W to represent a finite region within the geometric range of the transmitting node, including the location distribution information of both the transmitting and receiving nodes, local information is determined. A state update method is designed using the geometric environment information of each transmitter to obtain a scheme that minimizes the network's average AoI. The concept of a stopping set is used to model the limited observation window; the stopping set W = W(Φ) is a random variable. This represents the spatial implementation of network nodes in a multicast scenario, where each implementation of the stop set W is... One of the Borel sets.

4. The state update scheduling method for multicast transmission scenarios in a wireless network as described in claim 2, characterized in that, In step two, based on the local information of a typical transmitting node, the scheduling probability of the transmitting node is expressed as a function of the stopping set, and the scheduling probability η0 of the typical transmitting node is obtained as follows: η0 = η(W(Φ)); in, It is a measurable function of the probability of receiving local information W(Φ) and generating new packets in each time slot, while the local information W(Φ) is the spatial distribution of network nodes in the multicast scenario. The function; Choose appropriate input features to ensure that the scheduling probability is a measurable function of the probability of accepting local information and generating new packets in each time slot; provide a deterministic stopping set centered at X0 with radius R. W Fixed disk; the given stopping set W is regarded as an observation window in the network. The function η represents the basis for the transmitting node to obtain local deterministic information and fuzzy statistical information outside the observation window by observing the distribution of the other transmitting and receiving nodes within the window, as well as the basis for spontaneously adjusting its scheduling probability.

5. The state update scheduling method for multicast transmission scenarios in a wireless network as described in claim 2, characterized in that, In step three, minimizing the average AoI of the network in the multicast scenario is taken as the optimization objective to solve the optimization problem. The optimization problem is solved using optimization theory and corresponding algorithms to obtain the optimal state update method for a typical transmitting node, specifically including: The state update transmitted in time slot t is generated before transmission. The AoI increases linearly if no new update packet arrives at the receiving node; otherwise, it decreases to the time elapsed since the generation of the packet when the new update was received. That is, the value of the transmitting node's AoI at time slot t is Δ(t) = tU(t), where U(t) represents the timestamp of the latest received update generated before time slot t. Packets transmitted from the transmitting node are successfully received only when the signal-to-interference-plus-noise ratio (SINR) exceeds the decoding threshold θ. Therefore, under the condition of spatial realization Φ in a multicast scenario, This represents the coverage of a typical transmitting node. Based on the properties of AoI, the conditional form of the average AoI in a multicast scenario, obtained using queuing theory, is as follows: Using the scheduling probability as the state update scheduling method for the transmitting node based on AoI optimization, the optimization problem is: st0≤η0=η(W(Φ))≤1; The optimal scheduling probability of a typical launch node is obtained by solving the optimization problem using optimization theory.

6. The state update scheduling method for multicast transmission scenarios in a wireless network as described in claim 2, characterized in that, The adaptive scheduling strategy in step four includes: introducing a shift operator and designing a translation-invariant strategy; applying the translation-invariant strategy to shift any transmitting node to a typical transmitting node within the network; solving the state update scheduling method for transmitting nodes, and obtaining the scheduling probability of any transmitting node based on the solution to the optimization problem and the translation-invariant strategy; and monitoring and evaluating network performance in real time, and continuously adjusting and optimizing the adaptive scheduling method as needed, specifically including: Introducing the shift operator S x , representing any set Transformed by vector -x, S x (A) = {ax: a∈A}; for the transmitting node X i This indicates the stop set of its local information. η i and μ i Let these represent the update probability and coverage rate of the node, respectively. The optimization problem is: The optimization problem is represented using the shift operator S x Launch node X i By shifting the typical transmitting node X0 within the network, all corresponding marker receiving nodes are shifted to achieve the goal of expanding the typical transmitting node state update scheduling method function η within the network. Given a stopping set W = W(Φ), the solution is obtained by using a translation-invariant strategy and the optimization problem: If the condition is satisfied The adaptive scheduling probability of any transmitting node is given by the following formula; otherwise, η i =1; in, This represents the transmitting node X. i and launch node X j The marked receiving node y kj The distance relative to the typical link spacing; f(r) represents the probability density function of the typical link spacing; Any transmitting node can obtain the scheduling probability by applying the same state update scheduling function to the network topology observed from its perspective, and optimize the AoI of all associated receiving nodes. Thus, the average AoI across the network can be optimized through the state update scheduling strategy in the multicast scenario.

7. A state update scheduling system for a multicast transmission scenario in a wireless network, applying the state update scheduling method for multicast transmission scenarios in a wireless network as described in any one of claims 1 to 6, characterized in that, State update scheduling systems for multicast transmission scenarios in wireless networks include: The geometric environment information acquisition module is used to collect and process the spatial layout and location information of nodes in the network in the multicast scenario, and determine the stop set to represent the limited observation area around the transmitting node; The scheduling probability function definition module is used to express the scheduling probability as a function of the stopping set based on the local information of the transmitting node, and to define the scheduling probability function of a typical transmitting node in a multicast scenario; The average information age optimization module is used to solve the optimization problem of minimizing the average network information age in multicast scenarios using optimization theory, and obtains the state update scheduling method at the transmitting node. The state update scheduling module is used to implement adaptive scheduling strategies for each transmitting node in a multicast scenario, thereby realizing state update scheduling for multicast transmission scenarios in a wireless network.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, it causes the processor to perform the steps of the state update scheduling method for multicast transmission scenarios in a wireless network as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the state update scheduling method for multicast transmission scenarios in a wireless network as described in any one of claims 1 to 6.

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