A method for dynamically allocating 5G network resources

Abstract

This invention relates to the field of 5G network resource management, and in particular to a dynamic resource allocation method for 5G networks. The method includes: acquiring the resource requirements and available resources of nodes in the 5G network to form the resource status of the nodes, and further constructing a resource status matrix; introducing a multi-dimensional harmonic mapping allocation algorithm to update the resource requirements of the nodes, further updating the resource status matrix; calculating the global resource pressure of the nodes based on the updated resource status matrix; performing nonlinear resource adjustment optimization on the updated resource requirements to obtain the final resource requirements; and allocating resources based on the final resource requirements. This method solves the problems that many scheduling algorithms fail to fully consider the resource interactions and mutual influences between nodes; existing technologies are slow to react in highly dynamic environments and struggle to adjust resource allocation according to real-time changes in network status; and existing resource allocation algorithms struggle to achieve rapid network resource balancing under peak load scenarios.

Description

Technical Field

[0001] This invention relates to the field of 5G network resource management, and in particular to a method for dynamic allocation of 5G network resources. Background Technology

[0002] With the rapid popularization of 5G networks, more and more devices are connecting to the network, including base stations, user equipment, Internet of Things (IoT) devices, and smart terminals. The network resource demands of these devices are highly dynamic and complex, especially in high-concurrency and high-data-rate application scenarios, such as high-definition video transmission, virtual reality, and remote control. This makes efficient allocation of network resources a core issue in 5G network management. Traditional network resource management methods often rely on static allocation models or simple time-slice rotation mechanisms. While these methods work well under low-load conditions, they cannot effectively adapt to the frequently fluctuating resource demands in high-load and dynamically changing 5G network environments. This can easily lead to low network resource utilization, significant resource waste, or unbalanced resource allocation.

[0003] Furthermore, existing resource allocation technologies have several limitations when facing the complexity of 5G networks. First, many scheduling algorithms fail to fully consider the resource interactions and mutual influences between nodes, treating the differences between node resource demands and available resources in a simplistic linear fashion. This makes efficient resource scheduling difficult when resource demands are complex or when there are significant differences between nodes. Second, existing technologies are slow to react in highly dynamic environments, struggling to adjust resource allocation according to real-time network conditions, easily leading to resource overload or underutilization of some nodes. Particularly under peak load scenarios, existing resource allocation algorithms struggle to achieve rapid network resource balancing, easily causing resource bottlenecks and impacting overall network performance and user experience. These shortcomings pose significant challenges to the stability and resource utilization of 5G network performance. Summary of the Invention

[0004] This invention provides a dynamic resource allocation method for 5G networks to address the problem that many scheduling algorithms fail to fully consider resource interactions and mutual influences between nodes, and that the differences between node resource requirements and available resources are simply treated linearly, making it difficult to achieve efficient resource scheduling when resource requirements are complex or the differences between nodes are large. Furthermore, existing technologies are slow to react in highly dynamic environments, making it difficult to adjust resource allocation according to real-time changes in network conditions, easily leading to resource overload or insufficient resources for some nodes. Additionally, under peak load scenarios, existing resource allocation algorithms struggle to achieve rapid network resource balancing, easily causing resource bottlenecks and consequently affecting the performance of the entire network and user experience.

[0005] A method for dynamic allocation of 5G network resources includes the following steps:

[0006] S1: Obtain the resource requirements and available resources of nodes in the 5G network to form the resource status of the nodes; construct a resource status matrix based on the resource status of all nodes; introduce a multidimensional harmonic mapping allocation algorithm to update the resource requirements of the nodes; update the resource status matrix according to the updated resource requirements of the nodes.

[0007] S2: Based on the updated resource state matrix, calculate the global resource pressure of the nodes; construct a global energy function, and combine it with the global resource pressure to perform nonlinear resource adjustment and optimization on the updated resource demand to obtain the final resource demand; allocate resources based on the final resource demand.

[0008] Preferably, S1 specifically includes:

[0009] The specific process of the multidimensional harmonic mapping assignment algorithm is as follows:

[0010] Calculate the interaction coefficients between nodes, and use the interaction coefficients and resource state matrix to calculate the harmonic mapping value for each node; update the resource requirements of the nodes based on the harmonic mapping value.

[0011] Preferably, S1 specifically includes:

[0012] The formula for the harmonic mapping value is as follows:

[0013]

[0014] in, It is the harmonic mapping value of node i; S i α represents the resource status of node i; i and j are index variables of nodes in the 5G network; N is the total number of nodes in the 5G network; α ij R represents the interaction coefficient between nodes. i It is the resource requirement of node i; R j This refers to the resource requirements of node j; C i C is the available resource of node i; j ε represents the available resources of node j; ε is a constant to prevent the denominator from being zero, and is set to 10. -9 exp is the exponential operation; It is an exponentially decaying term.

[0015] Preferably, S1 specifically includes:

[0016] The resource requirement update formula is as follows:

[0017]

[0018] Among them, R′ iλ is the resource requirement after node i is updated; λ is the adjustment coefficient.

[0019] Preferably, S2 specifically includes:

[0020] The formula for calculating global resource pressure is:

[0021]

[0022] Among them, P i It represents the global resource pressure of node i; R′ j This refers to the resource requirements of node j after the update.

[0023] Preferably, S2 specifically includes:

[0024] Based on the updated resource state matrix, a global energy function is constructed. The global energy function combines the interaction coefficients between nodes, the nonlinear differences between resource demand and available resources, and measures the balance of 5G network resource allocation.

[0025] Preferably, S2 specifically includes:

[0026] The nonlinear resource regulation optimization is based on the gradient descent method. It introduces fractional powers and hyperbolic sine functions, and combines them with global resource pressure to obtain the final resource requirements of nodes by minimizing the global energy function.

[0027] Preferably, S2 specifically includes:

[0028] The update formula for nonlinear resource adjustment optimization is:

[0029]

[0030] in, η is the resource requirement of node i after nonlinear resource adjustment optimization; η is the learning rate. The global energy function E(S) represents the resource requirement R′ of node i. i The gradient.

[0031] The beneficial effects of the technical solution of the present invention are:

[0032] 1. The multidimensional harmonic mapping allocation algorithm proposed in this invention achieves precise dynamic allocation based on node resource requirements and available resources by calculating the interaction coefficients between nodes and the harmonic mapping values ​​of nodes; by dynamically adjusting resource requirements according to network load, it maximizes network resource utilization and ensures the flexibility and accuracy of resource allocation; compared with the traditional static allocation method, this invention can better adapt to real-time changes in 5G networks and avoid resource waste and bottlenecks.

[0033] 2. By introducing a global energy function that combines the interaction coefficients between nodes, the nonlinear differences between resource requirements and available resources, not only are the resource requirements of each node considered, but also the global resource allocation is comprehensively optimized by calculating the interrelationships between all nodes in the network, ensuring a balanced distribution of resources in the network; reducing network congestion and latency issues, and improving the overall performance of the network, especially under high load scenarios, significantly reducing the probability of resource bottlenecks.

[0034] 3. Through nonlinear resource adjustment optimization, the resource requirements of nodes can be adaptively adjusted to ensure that the resource allocation in the network is always in the optimal state. By using the gradient descent method, the adjustment of node resource requirements is dynamically responded to, and the optimal resource allocation is finally provided through network-wide optimization and iteration. This not only improves the utilization efficiency of network resources, but also effectively balances the network load and ensures that all nodes in the network can obtain fair resource allocation, thereby improving the stability and reliability of the 5G network. Attached Figure Description

[0035] Figure 1 This is a flowchart of a 5G network resource dynamic allocation method according to the present invention. Detailed Implementation

[0036] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] The following description, in conjunction with the accompanying drawings, details a specific scheme for a dynamic allocation method for 5G network resources provided by the present invention.

[0039] See attached document Figure 1 The diagram illustrates a flowchart of a dynamic allocation method for 5G network resources according to an embodiment of the present invention. The method includes the following steps:

[0040] S1: Obtain the resource requirements and available resources of nodes in the 5G network to form the resource status of the nodes; construct a resource status matrix based on the resource status of all nodes; introduce a multidimensional harmonic mapping allocation algorithm to update the resource requirements of the nodes; update the resource status matrix according to the updated resource requirements of the nodes.

[0041] In a 5G network, there are N nodes. The resource requirements of a node are represented by Ri, and the available resources are represented by Ci. The resource requirements and available resources together constitute the resource status of a node, which is represented by (Ri, Ci), where i is the index variable of the node.

[0042] Specifically, the node is a network unit in the 5G network, such as a base station, user equipment (e.g., mobile phone, IoT device), or an entity that requires communication resources; the resource requirement is the amount of network resources currently needed by the node, which is related to communication bandwidth, computing resources, storage requirements, etc.; the available resources are the amount of network resources currently available to the node, representing the total amount of resources the node can obtain from the 5G network, such as the wireless spectrum bandwidth currently allocated to the base station, and the transmission rate and connection resources obtained by the user equipment from the 5G network; the resource status is the combination of the node's resource requirement and available resources, i.e., the resource allocation status of each node.

[0043] The resource status of all nodes in a 5G network constitutes a resource status matrix. This matrix contains the resource requirements and available resources of all nodes in the current 5G network, with each row corresponding to the resource status of a single node. Since the resource requirements and available resources of each node are dynamically changing, network resources must be dynamically allocated based on the real-time status of the 5G network.

[0044] This invention proposes a multidimensional harmonic mapping allocation algorithm, which updates the resource requirements of nodes by combining the interaction coefficients between nodes and the resource state matrix, reflecting the pressure on nodes in resource allocation. Furthermore, it performs nonlinear resource adjustment optimization on the updated node resource requirements, and allocates resources based on the final optimized resource requirements. This algorithm is suitable for dynamic network resource allocation scenarios, can effectively improve resource utilization, balance network load, and reduce bottlenecks and latency caused by uneven resource allocation.

[0045] The specific process of the multidimensional harmonic mapping assignment algorithm is as follows:

[0046] First, the interaction coefficients between nodes are calculated. Then, the harmonic mapping value of each node is further calculated using the interaction coefficients and the resource state matrix to adjust resource requirements.

[0047] The interaction coefficient between the nodes is denoted by α. ij Let α represent the interaction coefficient between node i and node j. The interaction coefficient is expressed as a scalar and has the range 0 ≤ α. ij The value is ≤1, and the closer the value is to 1, the stronger the correlation between nodes. The interaction coefficient is based on the physical distance between nodes or the network topology, and is obtained by calculating the Euclidean distance between nodes or the similarity of business needs. The specific calculation method of the interaction coefficient is selected according to the specific implementation scenario, and there are no restrictions here.

[0048] The harmonic mapping value is used to measure the pressure on a node in resource allocation. Specifically, the harmonic mapping value takes into account the resource status relationship between the node and other nodes, as well as the difference between the node's own resource demand and available resources, to comprehensively measure the pressure on the node in resource allocation. The larger the harmonic mapping value, the greater the imbalance between the node's resource demand and available resources. The smaller the harmonic mapping value or close to zero, the closer the node's resource demand is to available resources.

[0049] The formula for the harmonic mapping value is as follows:

[0050]

[0051] in, It is the harmonic mapping value of node i; S i R represents the resource status of node i; i and j are index variables of nodes in the 5G network; N is the total number of nodes in the 5G network, that is, the total number of all nodes participating in resource allocation in the 5G network; i It is the resource requirement of node i; R j This refers to the resource requirements of node j; C i C is the available resource of node i; j ε represents the available resources of node j; ε is a constant to prevent the denominator from being zero, and is set to 10. -9 exp is the exponential operation; It is an exponential decay term used to decay the difference between the resource demand and available resources of node i.

[0052] The harmonic mapping formula introduces fractional powers and exponential decay terms. When there is a large numerical difference between resource demand and available resources, the power operation is used to amplify the impact of the difference on resource demand. At the same time, the exponential decay term can reduce the impact of small differences when the difference between resource demand and available resources is small.

[0053] The resource requirements of nodes are updated based on the harmonic mapping values, ensuring that the updated resource requirements reflect the pressure on nodes in resource allocation. The resource requirement update formula is as follows:

[0054]

[0055] Among them, R′ i It represents the updated resource requirements of node i, i.e., the current resource requirements of node i; R i λ is the resource requirement before node i is updated; λ is the adjustment coefficient used to control the harmonic mapping value. The impact on resource demand is determined based on the network load in the specific implementation scenario; It is the average of the ratio of resource demand to available resources of all nodes in the network, which measures the relationship between the resource demand and available resources of network nodes.

[0056] The resource demand update formula introduces an exponential adjustment term for the harmonic mapping value and the average value of the ratio of total network resource demand to available resources, so that the updated resource demand can better reflect the pressure on nodes in resource allocation, providing a basis for network-wide resource optimization.

[0057] Finally, the resource status matrix is ​​updated based on the updated resource requirements, and the corresponding resource requirement values ​​in the resource status matrix are replaced to ensure that the resource status matrix always reflects the current resource allocation.

[0058] S2: Based on the updated resource state matrix, calculate the global resource pressure of the nodes; construct a global energy function, and combine it with the global resource pressure to perform nonlinear resource adjustment and optimization on the updated resource demand to obtain the final resource demand; allocate resources based on the final resource demand.

[0059] First, based on the updated resource state matrix, the global resource pressure of each node is calculated. This reflects the pressure of each node's resource demand relative to the overall network resource state and provides a basis for subsequent optimization. Specifically, the higher the global resource pressure value, the heavier the resource pressure on the node within the network, and the greater the pressure of its resource demand relative to available resources. The formula for calculating global resource pressure is:

[0060]

[0061] Among them, P i R′ represents the global resource pressure of node i, indicating that node i's resource demand is relative to the global resource pressure of other nodes. j This refers to the resource requirements of node j after the update.

[0062] Simultaneously, based on the updated resource state matrix, a global energy function is constructed to reflect the current balance of network resource allocation and provide a basis for subsequent optimization processes. This global energy function combines the interaction coefficients between nodes, the nonlinear differences between resource demand and available resources, and measures the overall balance of network resource allocation; a smaller global energy function value indicates a more balanced resource allocation.

[0063] The formula for the global energy function is:

[0064]

[0065] Where E(S) is the global energy function, used to measure the balance of resource allocation across the entire network; s is the resource state matrix; cosh is the hyperbolic cosine function, used to smooth the nonlinear effects of the difference between resource demand and available resources; R′ iThis represents the updated resource demand of node i. The global energy function smooths the difference between resource demand and available resources by introducing a hyperbolic cosine function, and enhances its sensitivity to resource differences by introducing a fractional power, enabling the global energy function to reflect the resource imbalance of the entire network.

[0066] Next, the updated resource requirements are optimized using nonlinear resource adjustment to obtain the final resource requirements. This nonlinear resource adjustment optimization is based on gradient descent, incorporating fractional powers and hyperbolic sine functions, and combined with global resource pressure. By minimizing the global energy function, the final resource requirements of each node are obtained. Based on these final resource requirements, network resources are dynamically allocated to achieve network resource balance, improve resource utilization efficiency, reduce bottlenecks and latency caused by uneven resource allocation, and optimize the performance and stability of the 5G network.

[0067] The update formula for nonlinear resource adjustment optimization is:

[0068]

[0069] in, It is the resource requirement of node i after nonlinear resource adjustment and optimization; R′ i η is the current resource requirement of node i; η is the learning rate, used to control the resource update step size, which is obtained through experimental optimization. The global energy function E(S) represents the resource requirement R′ of node i. i The gradient.

[0070] gradient The calculation formula enhances the dynamic response to node resource demand updates by introducing a hyperbolic sine function, allowing for adjustment of the update magnitude when resource demands change significantly. The specific calculation formula is as follows:

[0071]

[0072] Here, sinh is a hyperbolic sine function used to enhance the dynamic adjustment effect of resource demand changes.

[0073] Based on the specific implementation scenario, set the change threshold and the maximum number of iterations. When the change in the global energy function is less than the change threshold or the number of iterations reaches the maximum number of iterations, stop the nonlinear resource adjustment optimization. The resource requirements of the output node after the nonlinear resource adjustment optimization are used as the final resource requirements for resource allocation.

[0074] The system matches the final resource requirements of each node with available resources. If a node's final resource requirement is less than or equal to the available resources, the node is allocated the corresponding resources. If a node's final resource requirement exceeds the available resources, a fair allocation mechanism is selected based on the specific implementation scenario, such as proportionally reducing the resource requirements of each node to ensure that all nodes in the network receive a reasonable allocation of limited resources. After resource allocation is completed, the resource state matrix is ​​updated based on the allocation results to reflect the current resource allocation status of the network.

[0075] In summary, a method for dynamic allocation of 5G network resources has been developed.

[0076] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0077] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0078] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for dynamic allocation of 5G network resources, characterized in that, Includes the following steps: S1: Obtain the resource requirements and available resources of nodes in the 5G network to form the resource status of the nodes; construct a resource status matrix based on the resource status of all nodes; introduce a multi-dimensional harmonic mapping allocation algorithm to calculate the interaction coefficients between nodes, and use the interaction coefficients and the resource status matrix to calculate the harmonic mapping value of each node; the formula for the harmonic mapping value is as follows: in, It is the harmonic mapping value of node i; S i This represents the resource status of node i; i and j are index variables of nodes in the 5G network; N is the total number of nodes in the 5G network; a ij R represents the interaction coefficient between nodes. i It is the resource requirement of node i; R j This refers to the resource requirements of node j; C i C is the available resource of node i; j ε represents the available resources of node j; ε is a constant to prevent the denominator from being zero, and is set to 10. -9 exp is the exponential operation; It is an exponentially decaying term; Update the node's resource requirements based on the harmonic mapping value; update the resource state matrix based on the updated resource requirements of the node; S2: Based on the updated resource state matrix, calculate the global resource pressure of the nodes; construct a global energy function, and combine it with the global resource pressure to perform nonlinear resource adjustment and optimization on the updated resource demand to obtain the final resource demand; allocate resources based on the final resource demand.

2. The 5G network resource dynamic allocation method according to claim 1, characterized in that, S1 specifically includes: The resource requirement update formula is as follows: Among them, R′ i λ is the resource requirement after node i is updated; λ is the adjustment coefficient.

3. The 5G network resource dynamic allocation method according to claim 2, characterized in that, S2 specifically includes: The formula for calculating global resource pressure is: Among them, P i It represents the global resource pressure of node i; R′ j This refers to the resource requirements of node j after the update.

4. The 5G network resource dynamic allocation method according to claim 3, characterized in that, S2 specifically includes: Based on the updated resource state matrix, a global energy function is constructed. The global energy function combines the interaction coefficients between nodes, the nonlinear differences between resource demand and available resources, and measures the balance of 5G network resource allocation.

5. The 5G network resource dynamic allocation method according to claim 4, characterized in that, S2 specifically includes: The nonlinear resource regulation optimization is based on the gradient descent method. It introduces fractional powers and hyperbolic sine functions, and combines them with global resource pressure to obtain the final resource requirements of nodes by minimizing the global energy function.

6. The 5G network resource dynamic allocation method according to claim 5, characterized in that, S2 specifically includes: The update formula for nonlinear resource adjustment optimization is: in, η is the resource requirement of node i after nonlinear resource adjustment optimization; η is the learning rate. The global energy function E(S) represents the resource requirement R′ of node i. i The gradient.

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