Wireless resource management method based on personalized multi-granularity non-step graph coloring

Abstract

The present invention discloses a wireless resource management method based on personalized multi-granularity stepless graph coloring, comprising: establishing a multi-access fusion resource management model; obtaining potential interference relationships of wireless resources between user devices, and obtaining potential interference values ​​between user devices according to the potential interference relationships and the multi-access fusion resource management model, so as to establish an undirected interference graph with the user devices as vertices and the potential interference values ​​between user devices as edge weights; performing interference isolation on the vertices according to the undirected interference graph, so as to map the vertices to corresponding coloring axes, and coloring the vertices according to RGB color values ​​corresponding to the positions of the vertices on the coloring axes; clustering the isolated and colored vertices based on a multi-granularity clustering algorithm, and dividing the vertices on the coloring axis into multiple interference tolerance intervals with different tolerances, so that user devices in the same interference tolerance interval can reuse the same wireless resources; thereby reducing system interference and improving resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of communication resource management, and in particular to a wireless resource management method based on personalized multi-granularity stepless graph coloring. Background Art

[0002] With the development of wireless communication technology, the contradiction between limited wireless resources and increasing resource demands has become increasingly prominent. A massive number of user devices (UEs) are increasingly demanding spectrum, spatial, and temporal resources, yet the total resource supply is limited. Furthermore, different UEs have different service requirements and resource demands. For example, applications such as autonomous vehicles, industrial automation, and the tactile internet require ultra-reliable and low-latency communications (URLLC) services, which place high demands on the timeliness and stability of communication responses. Ultra-high-definition video, virtual reality, and augmented reality are enhanced mobile broadband (eMBB) services, which prioritize high communication rates delivered by sustained, high bandwidth over a period of time. Therefore, when multiple service demands coexist, coordinating the allocation of wireless resources for users of different service types while meeting the personalized quality of service (QoS) requirements of each user type is a pressing issue.

[0003] Resource reuse is an effective way to alleviate resource shortages; however, the simultaneous reuse of multiple resources will introduce interference, which will have a negative impact on the communication process. For example, when different users use the same spectrum resources, spatial resources and time resources, communication interference will occur between users, reducing the user's communication rate and communication reliability. At the same time, if the same resource allocation strategy is adopted for users with different service requirements, it will be impossible to take into account the special resource requirements of different users, making it difficult to achieve personalized resource allocation and meet the user's personalized service quality requirements. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent. To this end, one object of the present invention is to propose a wireless resource management method based on personalized multi-granularity stepless graph coloring, which can reduce system interference and thus improve resource utilization.

[0005] To achieve the above-mentioned objectives, an embodiment of the first aspect of the present invention proposes a wireless resource management method based on personalized multi-granularity stepless graph coloring, comprising the following steps: establishing a multi-access fusion resource management model, wherein the multi-access fusion resource management model includes multiple wireless resource reuse types; obtaining the potential interference relationship of wireless resources between user devices, and obtaining the potential interference value between the user devices based on the potential interference relationship and the multi-access fusion resource management model, so as to establish an undirected interference graph with the user devices as vertices and the potential interference value between the user devices as the weight of the edge; performing interference isolation on the vertices according to the undirected interference graph, so as to map the vertices to the corresponding coloring axis, and coloring the vertices according to the RGB color value corresponding to the position of the vertex on the coloring axis; clustering the isolated and colored vertices based on the multi-granularity clustering algorithm, and dividing the vertices on the coloring axis into multiple interference tolerance intervals with different tolerances, so that user devices in the same interference tolerance interval can reuse the same wireless resources.

[0006] According to an embodiment of the present invention, a wireless resource management method based on personalized multi-granularity non-step graph coloring is first established, wherein the multi-access fusion resource management model includes multiple wireless resource reuse types. Then, potential interference relationships of wireless resources between user devices are obtained, and potential interference values ​​between user devices are obtained based on the potential interference relationships and the multi-access fusion resource management model, so as to establish an undirected interference graph with the user devices as vertices and the potential interference values ​​between user devices as edge weights. Next, interference isolation is performed on the vertices according to the undirected interference graph, so as to map the vertices to corresponding coloring axes, and the vertices are colored according to the RGB color values ​​corresponding to the positions of the vertices on the coloring axes. Finally, the isolated and colored vertices are clustered based on a multi-granularity clustering algorithm, and the vertices on the coloring axes are divided into multiple interference tolerance intervals with different tolerances, so that user devices in the same interference tolerance interval can reuse the same wireless resources. In this way, system interference can be reduced, thereby improving resource utilization.

[0007] In addition, the wireless resource management method based on personalized multi-granularity non-step graph coloring proposed in the above embodiment of the present invention may also have the following additional technical features:

[0008] Optionally, the wireless resources include time resources, space resources and frequency resources, and the multiple wireless resource multiplexing types include no multi-type multiplexing, complete differentiated multiplexing, non-completely differentiated multiplexing and complete multiplexing, among which, the three wireless resources are non-intersecting for no multi-type multiplexing, there is any one wireless resource that intersects with each other for complete differentiated multiplexing, the three wireless resources are intersecting with each other but not completely overlapping for non-completely differentiated multiplexing, and the three wireless resources completely overlap for complete multiplexing.

[0009] Optionally, the potential interference relationship of wireless resources between user devices is obtained, and the potential interference value between the user devices is obtained based on the potential interference relationship and the multi-access fusion resource management model, including: obtaining non-human controllable and interference-related resources and human controllable and interference-related resources in the wireless resources between user devices, so as to allocate the same human controllable and interference-related resources to the user devices, so as to obtain the potential interference value corresponding to the non-human controllable and interference-related resources in the wireless resources between the user devices.

[0010] Optionally, the potential interference value is calculated according to the following formula:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016] in, is the roadside unit r k ∈R and user u j When downlink communication is performed via millimeter wave beams, the user equipment u i Received roadside unit r k Transmitted signal strength; is the roadside unit r k The transmission power; is the roadside unit r k On user device u i Transmitting antenna gain in direction; is the user device u i The receiving antenna gain, L ik Is the signal from the roadside unit r k To user device u i Power loss; G m is the roadside unit r k The main lobe center gain of the transmitting antenna; θ i,k,j is the user device u i With user equipment u j About Roadside Units k The angle θ -3dB is the half-power beamwidth; A m is the maximum loss; θ m is the roadside unit r k The main lobe width of the transmitting antenna; G0 is the roadside unit r kTransmitting antenna sidelobe gain; SINR i,k,j is the pseudo signal-to-interference-and-noise ratio, used to quantify the user equipment u j About Roadside Units k For user equipment u i The potential interference weight of R i Indicates user equipment u i Connectable roadside unit cluster, if u i Located within the coverage area of ​​the roadside unit, the roadside unit belongs to u i Roadside unit cluster; r k′ ∈R i |k′≠k represents u i Roadside unit cluster R i Middle roadside unit k Other roadside units other than σ 2 represents the intensity of ambient thermal noise; w′ j,i is the user device u j For user equipment u i Potential interference value caused; w′ i,j is the user device u i For user equipment u j Potential interference value; R i,j =R i ∩R j is the user device u i and user equipment u j The intersection of the roadside unit clusters.

[0017] Optionally, an undirected interference graph is established with the user devices as vertices and the potential interference values ​​between the user devices as edge weights, including: establishing a directed interference graph model with the first user device as the first vertex, the second user device as the second vertex, the potential interference value between the first user device and the second user device as the weight of the directed edge between the first vertex and the second vertex, and the potential interference value between the second user device and the first user device as the weight of the directed edge between the second vertex and the first vertex; selecting the larger of the weight of the directed edge between the first vertex and the second vertex and the weight of the directed edge between the second vertex and the first vertex, and normalizing the larger of the two to obtain the weight of the edge between the first vertex and the second vertex; summing the weights of the edges associated with each vertex to obtain the weight of the vertex, so as to convert the directed interference graph model into an undirected interference graph model.

[0018] Optionally, the undirected interference graph model is represented as G = (V, I, E, W, D), where W = {w 1,1 ,...,w 1,S ,w 22 ,...,w 2,S ,...,wS,S} is the weight set of all edges between vertices, w ii =0|1≤i≤S; D={d1,d2,...,d S} is the weight set of all vertices, Renumber the vertex subscripts in descending order of weight to obtain the vertex set V={v1,v2,...,v S}, and satisfy d i ≥d j ; is the set of service request identifiers of all user devices at time t, which is converted into the set of vertex type identifiers corresponding to the vertex set in descending order of interference weight in Represents vertex v s The service request identifier of the user equipment represented is URLLC service, Represents vertex v s The service request identifier of the user equipment represented is eMBB service.

[0019] Optionally, interference isolation is performed on the vertices according to the undirected interference graph so as to map the vertices to corresponding coloring axes, including: obtaining a coloring axis, the coloring axis including two coloring semi-axes; interference isolation is performed on the vertices according to a vertex set in descending order of interference weights in the undirected interference graph and a vertex type identifier set in the undirected interference graph so as to map the vertices to corresponding coloring axes; wherein, interference isolation is performed on the vertices according to the sorting of the vertex set in descending order of interference weights, and the greater the weight of the edge between vertices, the greater the distance between the corresponding vertices on the coloring axis, and at the same time, vertices of different service types are located on different coloring semi-axes.

[0020] Optionally, the interference tolerant intervals are distributed on the coloring axis, and user equipments in the same interference tolerant interval can reuse the same radio resources.

[0021] Optionally, the greater the number of interference-tolerant intervals on the dyeing axis, the lower the tolerance to interference, the more thorough the interference isolation, and the more human controllable and interference-related resources required; the fewer the number of interference-tolerant intervals, the higher the tolerance to interference, the less thorough the interference isolation, and the fewer human controllable and interference-related resources required.

[0022] Optionally, the multi-granularity clustering algorithm is a K-means clustering algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of a flow chart of a wireless resource management method based on personalized multi-granularity stepless graph coloring according to an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a multi-access convergence resource management model according to an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of multi-service coexistence communication according to an embodiment of the present invention;

[0026] Figure 4 2. A schematic diagram of a method for estimating user potential interference according to an embodiment of the present invention;

[0027] Figure 5 A schematic diagram of conversion from a directed interference graph to an undirected interference graph according to an embodiment of the present invention;

[0028] Figure 6 Schematic diagram of RGB dyeing strips and dyeing semi-axes according to an embodiment of the present invention;

[0029] Figure 7 Schematic diagram of the mapping of isolation intervals and coloring axes according to an embodiment of the present invention;

[0030] Figure 8 Schematic diagram of interference isolation and coloring process based on nonpolar graph coloring according to an embodiment of the present invention;

[0031] Figure 9 2 is a schematic diagram of the process of interference tolerance interval division and frequency resource allocation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0033] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0034] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] Figure 1 FIG is a flow chart of a wireless resource management method based on personalized multi-granularity non-step graph coloring according to an embodiment of the present invention, as shown in FIG. Figure 1As shown, the wireless resource management based on personalized multi-granularity stepless graph coloring includes the following steps:

[0036] S101: Establish a multi-access convergence resource management model, where the multi-access convergence resource management model includes multiple wireless resource reuse types.

[0037] It should be noted that wireless resources include time resources, space resources and frequency resources. The multiple wireless resource multiplexing types include no multi-type multiplexing, fully differentiated multiplexing, non-completely differentiated multiplexing and full multiplexing. Among them, the three wireless resources are non-intersecting for no multi-type multiplexing, there is an arbitrary two-way intersection among the three wireless resources for complete differentiated multiplexing, the three wireless resources are intersecting with each other but not completely overlapping for non-completely differentiated multiplexing, and the three wireless resources completely overlap for complete multiplexing.

[0038] As a specific embodiment, wireless resources related to interference are defined as interference-related resources; the resource types involved in interference-related parameters and that can be reused are called reused interference-related resources; in wireless communications, time, space, frequency, coding sequences, etc. can be regarded as reused interference-related resources; for the sake of simplicity, reused interference-related resources are referred to as wireless resources; when performing wireless resource allocation, the degree of interference between user devices depends on the reuse differences in type and quantity of wireless resources; if all wireless resource types are reused and the greater the intersection, the greater the interference; if all interference-related resource types are reused and the smaller the intersection, the smaller the interference; if not all interference-related resource types are reused, this is called differential interference-related resource reuse, and the interference at this time can be regarded as 0, that is, there is no interference; the resource allocation goal is to achieve the differential interference-related resource reuse strategy as much as possible; as an example, consider time resources, frequency resources and space resources in wireless communications, and assume that there are only these three types of reused interference-related resources.

[0039] like Figure 2 As shown, it is assumed that there are only these three types of reusable interference-related resources at this time; each circle in the figure represents a wireless resource, and the intersection between different wireless resources represents the common reuse rate between wireless resource types (here only the common reuse rate between different types is shown, and the reuse rate of each resource type is not reflected in the size of the intersection area); at the same time, as an example, only two reference values ​​of resource reuse rate and interference degree are considered here, and the optimization goal is to improve the resource reuse rate under the premise of suppressing interference (the multi-access fusion resource management model takes the ideal situation of no interference as the optimization goal, but at this time it is impossible to achieve the best performance of other communication performance, such as communication rate, etc. In the subsequent coloring and clustering process, in order to maximize the system throughput, interference will be appropriately introduced).

[0040] Figure 2In the embodiment of the present invention, a. When the three wireless resources are not intersected, multi-type wireless resource reuse does not occur, which is called the "no multi-type reuse" state. At this time, there is no interference between user devices. As an example, we can refer to the scenario where different users communicate at different times, at different frequencies, and in different directions. At this time, there is no interference between user devices. b. When there is an arbitrary intersection between the three wireless resources, multi-type wireless resource reuse occurs, but there are still unused resource types. This is called the "completely differentiated reuse" state. At this time, the resource reuse rate can be improved to a certain extent, and no interference will occur between user devices. As an example, when the time domain and the space domain intersect, we can refer to the scenario where different users communicate at the same time, at different frequencies, and in the same direction. At this time, the user devices can use the differences in frequency division multiple access (FDMA) to avoid interference. When the time domain and the frequency domain intersect, we can refer to the scenario where different users communicate at the same time, at the same frequency, and in different directions. At this time, the user devices can use space division multiple access (SDMA) to avoid interference. Time Division Multiple Access (TDMA) can be used to avoid interference; when the frequency domain and the space domain intersect, you can refer to the scenario where different users communicate at different times, at the same frequency, and in the same direction. At this time, the user devices can use the differences in Time Division Multiple Access (TDMA) to avoid interference; c. When the three wireless resources intersect with each other but do not completely overlap, full-type multi-type wireless resource multiplexing occurs, which is called "non-completely differentiated multiplexing". At this time, the resource reuse rate can be further improved, but the intersection area of ​​the three will cause a certain degree of interference between user devices; as an example, when the time domain, frequency domain and space domain intersect, you can refer to the scenario where different users communicate at part of the same time, at the same frequency, and in the same direction. At this time, there is no resource difference to avoid interference (without considering code division multiple access), so Interference will occur between user devices; in other non-intersecting areas, there are differences in non-intersecting resource types that can be used to avoid interference; D. When the three wireless resources completely overlap, full-type multi-type wireless resource reuse occurs, which is called the "full reuse" state. The resource reuse rate is the highest at this time, but the complete intersection of the three is equivalent to completely ignoring the impact of interference, and serious interference will occur between user devices; as an example, you can refer to the scenario where different users use the same time, the same frequency, and the same direction to communicate. At this time, there is no resource difference available, and serious interference will occur between user devices.

[0041] It should be noted that wireless resource management needs to find a balance between resource reuse and interference suppression; among the above four situations, the resource allocation method based on "completely differentiated reuse" (ie, type b) is the ideal resource allocation strategy.

[0042] S102, obtaining potential interference relationships of wireless resources between user equipments, and obtaining potential interference values ​​between user equipments based on the potential interference relationships and a multi-access fusion resource management model, so as to establish an undirected interference graph with the user equipments as vertices and the potential interference values ​​between user equipments as edge weights.

[0043] As an embodiment, the potential interference relationship of wireless resources between user devices is obtained, and the potential interference value between user devices is obtained based on the potential interference relationship and the multi-access fusion resource management model, including: obtaining non-human controllable and interference-related resources and human controllable and interference-related resources in the wireless resources between user devices, so as to allocate the same human controllable and interference-related resources to the user devices, so as to obtain the potential interference value corresponding to the non-human controllable and interference-related resources in the wireless resources between user devices.

[0044] It should be noted that when multiple wireless resources are all manually allocable, different combinations of "completely differentiated multiplexing" can be flexibly implemented according to actual service needs and resource inventory conditions, such as the three cases mentioned in type b. When one or more of the multiple wireless resources are not manually allocable, that is, the resources are in a locked state, it is necessary to reasonably allocate the remaining manually allocable resources to achieve "completely differentiated multiplexing". In actual application scenarios, many scenarios cannot manage resources completely according to the ideal allocation method.

[0045] One possible scenario is that the user position is determined, the position of the roadside unit is determined, and the connection relationship between the user and the roadside unit is determined. At this time, the positional relationship between users and between users and roadside units is determined. It can be considered that spatial resources such as wireless beams have been allocated, and the spatial resources are in a locked state; at the same time, since the connection relationship between the user and the roadside unit has been determined, that is, all users are in a communication state at this time, the time resources have also been allocated, and the time resources are in a locked state; at this time, interference suppression can only be performed through flexible allocation of the frequency resource domain. The principle is to increase the reuse rate of frequency resources as much as possible under the premise of suppressing interference; as an example, the above process is selected as the application scenario of the interference relationship modeling of the present invention.

[0046] The schematic diagram of coexistence communication of multiple service types is as follows Figure 3As shown in the figure, the dotted circle with the roadside unit as the center represents the coverage of the roadside unit. Users within the coverage area can access the roadside unit to obtain communication services. Assume that at time t, the set of roadside units in a certain area is R = {r1, r2, ..., r K}, where K is the number of roadside units in the area, and the set of all users with communication service needs is U = {u1,u2,...,u S}, the set U includes users with various service demand types; as an example, it is assumed that the user's service type includes ultra-reliable and low-latency communications (URLLC) service and enhanced mobile broadband (eMBB) service. For convenience, users with URLLC service are referred to as URLLC users, and users with eMBB service are referred to as eMBB users. It is assumed that the URLLC user set is C = {c1, c2, ..., c M}, where M is the total number of URLLC users; eMBB user set B = {b1, b2, ..., b N}, where N is the total number of eMBB user equipment; in this case, U = C∪B, S = M+N; through the user label u s To distinguish between two types of users in set U, this label can be obtained from the user service request identifier; represents time t at u s Are URLLC users? represents time t when r s is an eMBB user; the user service request identifiers of all users at time t constitute the user service request identifier set at time t It is worth noting that at different times, the service request of the same user may be different, which may be URLLC or eMBB. The user's service request identifier will change according to the actual service needs.

[0047]

[0048] In the above example, the user's location at time t is determined, and the roadside unit to which it is connected is determined (e.g. Figure 3 User u1 in the example connects to RSU r1, and u5 connects to RSUs r2 and r3). ​​The set of RSUs that can be connected is also determined (e.g. Figure 3 u1 in the example can access r1 and r2, and u4 can access r1, r2, and r3); therefore, the determined time t indicates that the time resource has been locked, and the reuse strategy of the user's spatial resource type has been determined (e.g. Figure 3The beam direction in the transmission has been determined). At this point, potential interference has already occurred between users due to the reuse of time and space resources. It is necessary to use the potential interference estimation method to reasonably allocate frequency resources and minimize the probability and degree of potential interference becoming actual interference. The definition of the potential interference estimation method is as follows:

[0049] Potential interference occurs when the spatial reuse strategy for a resource or resources has been determined and is locked, with no intervention or allocation planned. These resources are considered non-human-controllable and interference-related resources. In this case, we first assume that all human-controllable and interference-related resources other than these non-human-controllable and interference-related resources are reused by all users, meaning that all users use the same human-controllable and interference-related resources. This simulation simulates the interference between users and assesses the extent of the negative impact of resource reuse between different users. If the interference between two users is minimal, allocating the same human-controllable and interference-related resources to these two users will have a minimal negative impact. Therefore, during resource allocation, to improve other target parameters such as system throughput, it is advisable to allocate the same human-controllable and interference-related resources to these two users. If the interference between two users is significant, allocating the same human-controllable and interference-related resources to these two users will have a significant negative impact. Therefore, during resource allocation, to improve other target parameters such as system throughput, it is advisable to avoid allocating the same human-controllable and interference-related resources to these two users.

[0050] As an example, when time resources and space resources are locked, the potential interference between users can be calculated based on the potential interference estimation method, assuming that all users are allocated the same frequency resources; as an example, the potential interference between user u1 and user u2 is calculated, such as Figure 4 As shown, during the downlink communication between user equipment u1 and roadside unit r1 based on the communication beam, user equipment u1 receives the interference signal sent by roadside unit r2 to user u2, indicating that u2 has caused interference to u1 at this time; similarly, during the downlink communication between user equipment u2 and roadside unit r2 based on the communication beam, user equipment u2 receives the interference signal sent by roadside unit r1 to user u1, indicating that u1 has caused interference to u2 at this time.

[0051] Through the above process, we can get the roadside unit r k ∈R and user u j When downlink communication is performed via millimeter wave beams, user u i Can receive r k Transmitted signal strength One possible representation of is as follows:

[0052]

[0053] in, is the roadside unit r k The transmission power, Represents the roadside unit r k In user u i The transmitting antenna gain in the direction, is user u i The receiving antenna gain, L ik Is the signal from the roadside unit r k To user u i The power loss is mainly considered in the path loss and penetration loss The path loss It can be expressed as f k,j is the roadside unit r k With user u j Frequency during communication, d ik is the roadside unit r k With user u i The Euclidean distance; the penetration loss can be modeled as obeying the mean μ and variance σ p 2 The normal distribution of It can be expressed as:

[0054]

[0055] Among them, G m is the main lobe center gain of the roadside unit transmitting antenna, θ i,k,j is user u i With user u j About Roadside Units k The angle, θ -3dB is the half-power beamwidth, A m is the maximum loss, θ m is the main lobe width of the roadside unit transmitting antenna, and G0 is the side lobe gain of the roadside unit transmitting antenna.

[0056] To obtain UEr i and UEr j The potential interference degree between user u due to time resource and space resource locking is estimated by using the potential interference estimation method. i Received power give u i The negative impact caused by i with u j Assign the same frequency; further, define the interference weight factor as u i About U jReceive r k Power with u i The ratio of other received signal strengths, that is, u i Received Defined as an equivalent useful signal, u i The other signals received are defined as equivalent interference signals, and the pseudo signal-to-interference-and-noise ratio SINR is obtained. i,k,j , using pseudo signal-to-interference-and-noise ratio to quantify user u j About Roadside Units k For user u i Potential interference weights of:

[0057]

[0058] Among them, R i Represents user u i Connectable roadside unit cluster, if u i Located within the coverage area of ​​the roadside unit, the roadside unit belongs to u i Roadside unit cluster; r k′ ∈R i |k′≠k represents u i Roadside unit cluster R i Middle roadside unit k Other roadside units other than σ 2 Indicates the intensity of ambient thermal noise.

[0059] Using the potential interference estimation method and the definition of pseudo signal-to-interference-noise ratio, the user u is quantified j About Roadside Units k For user u i The potential interference weight of user u is calculated similarly. j About U i and u j Public roadside unit cluster R i,j To u i The potential interference weight caused by R i,j =R i ∩R j Indicates u i and u j The intersection of the roadside unit clusters; finally, u j To u i Sum up all potential interference weights caused by j To u i The potential interference value w′ caused j,i :

[0060]

[0061] Similarly, user u can be obtained i For user u j The potential interference value w′ i,j ,

[0062]

[0063] As an embodiment, an undirected interference graph is established with user devices as vertices and potential interference values ​​between user devices as edge weights, including: establishing a directed interference graph model with the first user device as the first vertex, the second user device as the second vertex, the potential interference value between the first user device and the second user device as the weight of the directed edge between the first vertex and the second vertex, and the potential interference value between the second user device and the first user device as the weight of the directed edge between the second vertex and the first vertex; selecting the larger one between the weight of the directed edge between the first vertex and the second vertex and the weight of the directed edge between the second vertex and the first vertex, and normalizing the larger one to obtain the weight of the edge between the first vertex and the second vertex; summing the weights of the edges associated with each vertex to obtain the weight of the vertex, so as to convert the directed interference graph model into an undirected interference graph model.

[0064] As an embodiment, the undirected interference graph model is represented as G=(V, I, E, W, D), where W={w 1,1 ,...,w 1,S ,w 22 ,...,w 2,S ,...,w S,S} is the weight set of all edges between vertices, w ii =0|1≤i≤S; D={d1,d2,...,d S} is the weight set of all vertices, Renumber the vertex subscripts in descending order of weight to obtain the vertex set V={v1,v2,...,v S}, and satisfy d i ≥d j ; is the set of service request identifiers of all user devices at time t, which is converted into the set of vertex type identifiers corresponding to the vertex set in descending order of interference weight in Represents vertex v s The service request identifier of the user equipment represented is URLLC service, Represents vertex v s The service request identifier of the user equipment represented is eMBB service.

[0065] That is to say, based on the graph model, the interference relationship between users is graphically described to establish an interference graph model; as a specific embodiment, the user u i As vertex v i , user u i with u j The potential interference value w′ between i,j As vertex v i to v j There is a directed edge e′ between i,j The weight of user u j with u i The potential interference value w′ between j,i As vertex v j to v i There is a directed edge e′ between j,i The weights of the directed interference graph model G′={V′,E′} are established, where V′={v1,v2,...,v S}, E′={w′ 1,1 ,...,w′ 1,S ,w′ 21 ,...,w′ 2,S ,...,w′ S,1 ,...,w′ S,S}, an example such as Figure 5 As shown in a, in order to reduce the complexity of the interference graph model and take into account the interference constraints between users, the directed interference graph model G′={V′,E′} is converted into an undirected interference graph model G={V′,E}, where V′={v1,v2,...,v S}, E={w 1,1 ,...,w 1,S ,w 22 ,...,w 2,S ,...,w S,S}, vertex v in G i to v j The weight of the edge between them is the normalized potential interference value w i,j , which is to first select w′ i,j and w′ j,i The larger of the two i,j , and w″ i,j The value obtained after normalization;

[0066] w″ i,j =max{w′ i,j ,w′ j,i}

[0067]

[0068] Based on the above process, we can obtain the weight set W={w 1,1 ,...,w 1,S ,w 22 ,...,w 2,S ,...,w S,S},w ii =0|1≤i≤S, the weight of each vertex can be obtained by summing the weights of the edges associated with each vertex. For any vertex v i , whose weight is

[0069] Using the above method, we can obtain the weight set D = {d1, d2, ..., d S}; Next, renumber the vertex subscripts in descending order of weight to obtain the interference weight descending vertex set V={v1,v2,...,v S}, and satisfy d i ≥d j ; is the set of service request identifiers of all users at time t, which is converted into the set of vertex type identifiers corresponding to the vertex set in descending order of interference weight in Represents vertex v s The service request identifier of the user represented is URLLC service, Represents vertex v s The service request identifier of the user represented is eMBB service.

[0070] Based on the above process, the interference relationship modeling based on multi-access fusion and graph model was completed, and an undirected interference graph model with users as vertices and the normalized potential interference value between users as weights was obtained: G = (V, I, E, W, D).

[0071] S103 , performing interference isolation on the vertices according to the undirected interference graph, so as to map the vertices to corresponding coloring axes, and coloring the vertices according to the RGB color values ​​corresponding to the positions of the vertices on the coloring axes.

[0072] As an embodiment, interference isolation is performed on vertices according to an undirected interference graph so as to map the vertices to corresponding coloring axes, including: obtaining a coloring axis, the coloring axis including two coloring half-axes; interference isolation is performed on vertices according to a vertex set in descending order of interference weights in the undirected interference graph and a vertex type identifier set in the undirected interference graph so as to map the vertices to corresponding coloring axes; wherein, interference isolation is performed on vertices according to the sorting of the vertex set in descending order of interference weights, and the greater the weight of the edge between vertices, the greater the distance between the corresponding vertices on the coloring axis, and at the same time, vertices of different service types are located on different coloring half-axes.

[0073] It should be noted that, like the positive and negative poles of a magnet, if the distance is very close, they will produce a strong attraction to each other, but if the distance is far enough, the effect of this attraction on both parties will be small enough to be ignored; therefore, interference isolation is then performed on users based on the potential interference relationship between users; if the potential interference between users is more serious, they need to be isolated farther, so as to reduce or avoid the actual interference between users caused by the allocation of the same spectrum resources; an interference isolation and coloring algorithm based on stepless graph coloring is proposed, the main guiding idea is: different types of vertices in the undirected interference graph G = (V, I, E, W, D) are isolated as a whole, that is, they are located in different coloring semi-axes, so as to facilitate resource allocation and interference management that better meet the personalized service needs of different users; at the same time, the greater the weight of the edge between vertices, the farther they need to be isolated on the coloring bar, that is, the greater the difference in the color value of the coloring; the coloring bar can be regarded as a mapping of artificially controllable and interference-related wireless resources, an optional coloring bar and coloring semi-axis model such as Figure 6 As shown; in order to maximize the color distinction, the coloring strip is obtained by taking half of the RGB full color strip. An optional method is to select the RGB half color strip on the left half axis as the coloring strip, where the value range of the RGB primary colors is (255,0,0)→(255,0,255)→(0,0,255)→(0,255,255). The values ​​of the RGB primary colors at a point on the coloring strip constitute the color of that point.

[0074] like Figure 6 As shown, the length of the dye strip is mapped to L0, and the dye strip is mapped to the origin 0 through the center point (127.5,0,255). Defined as URLLC staining semi-axis, used for isolation and staining of URLLC users, right semi-axis Defined as the eMBB staining semi-axis, it is used for the isolation and staining of eMBB users. At this time, the stepless staining interval on the staining bar is By A - and A + It consists of two half shafts on the left and right.

[0075] First, we need to find the vertex set V = {v1,v2,...,v S} in the range of (-∞, +∞), and the initial isolation axis with 0 as the origin is performed in the order of the vertex subscripts, specifically including: the vertex with the higher subscript order in the undirected interference graph G = (V, I, E, W, D) has a greater interference weight. To a certain extent, it can be understood that the overall interference impact of this user on other users is more serious, and it is necessary to isolate it first, thereby reducing the complexity of subsequent isolation of other vertices; while performing isolation sequentially, in order to provide personalized resource allocation for users of different service types, it is necessary to limit the area of ​​URLLC type vertices to the negative semi-axis A to the left of the origin. - The limited area of ​​the eMBB type vertex is the positive semi-axis A to the right of the origin + At the same time, in order to improve the efficiency of wireless resource allocation, it is hoped that the vertices can be more concentrated, so that more vertices can be assigned to the same cluster during the resource classification process. Since users in the same user cluster share the same resources, that is, more users share the same spectrum resources, it is necessary to formulate a unified vertex concentration principle. An optional vertex concentration strategy is to concentrate close to the origin, that is, under the premise of meeting the interference constraints with other vertices, all vertices can be closer to the origin; after preliminary isolation, the vertex V = {v1,v2,...,v S The coordinates of the initial isolation axis are A′={a′1,a′2,...,a′ S}, considering the constraints between vertices and the principle of being as close to the origin as possible, a′ s ∈A′ satisfies:

[0076]

[0077] Among them, A′ i,j Defines the vertex v i v j The safe isolation interval, that is, v j In consideration of the i Optional isolation region after interference constraints:

[0078]

[0079] After completing the preliminary isolation of the vertices, the results of the preliminary isolation are normalized by coloring, and all vertices on the preliminary isolation axis are mapped to the coloring interval [-L0 / 2, L0 / 2] to achieve final isolation and coloring. The interval between the minimum and maximum points on the preliminary isolation axis is defined as the isolation interval, such as Figure 7As shown, all vertex maps on the isolation interval are concentrated in the coloring interval [-L0 / 2, L0 / 2], and the RGB color values ​​corresponding to the coloring interval constitute the color of the vertex. The coloring process is completed, that is, the final isolation is achieved; A′={a′1,a′2,...,a′ S}Mapping A on the coloring axis = {a1, a2, ..., a S}, where a s ∈A satisfies:

[0080] Where a0=max{|a1|,|a2|,...,|a S |}.

[0081] At the same time, the vertices of URLLC type fall on the URLLC coloring semi-axis, and the vertices of eMBB type fall on the eMBB coloring semi-axis, that is, for satisfy:

[0082] As an example, the following process is proposed to illustrate the above interference isolation and coloring process based on the non-step graph coloring, as Figure 8 As shown, assuming that the vertex v1 in the preliminary isolation is located at the origin of the preliminary isolation axis, that is, a′1=0, for the vertex v2,

[0083]

[0084] A′ 12 =a′|(a′≤a′1-w 1,2 )||(a′≥a′1+w 1,2 )

[0085]

[0086] After the above operations, the vertex set V = {v1, v2, ..., v S} is finally isolated on the coloring axis, and the coloring process is completed, and the color value set A={a1,a2,...,a S}.

[0087] S104 , clustering the isolated and colored vertices based on a multi-granularity clustering algorithm, and dividing the vertices on the colored axis into multiple interference tolerance intervals with different tolerances, so that user equipments in the same interference tolerance interval can reuse the same wireless resources.

[0088] It should be noted that the interference tolerant intervals are distributed on the coloring axis, and user equipments in the same interference tolerant interval can reuse the same radio resources.

[0089] It should be noted that the more interference-tolerant intervals there are on the dyeing axis, the lower the tolerance to interference, the more thorough the interference isolation, and the more human controllable and interference-related resources are required; the fewer interference-tolerant intervals there are, the higher the tolerance to interference, the less thorough the interference isolation, and the less human controllable and interference-related resources are required.

[0090] As an embodiment, the multi-granularity clustering algorithm is a K-means clustering algorithm.

[0091] It should be noted that the potential interference between vertices is calculated using the potential interference estimation method, assuming that users use the same frequency band. Therefore, in order to avoid or reduce interference, different spectrum resources should be allocated to users with potential interference as much as possible to reduce the negative impact of co-channel interference on communications. However, due to limited spectrum resources, it is impossible to allocate different frequency bands to all user devices with potential interference. Therefore, it is necessary to find an effective compromise between interference elimination and spectrum utilization. At the same time, in order to simultaneously meet the personalized spectrum needs of users of URLLC service types and eMBB service types, the user service type dimension needs to be fully considered in the joint wireless resource allocation process of URLLC and eMBB.

[0092] Based on interference isolation and coloring, a multi-granular user clustering method based on user service type is proposed. Different user types have different tolerances to interference. For example, URLLC is more sensitive to interference than eMBB. Interference can significantly impact communication reliability, which URLLC cannot tolerate. Therefore, URLLC and eMBB vertices, after interference isolation and coloring, are divided into different numbers of interference tolerance intervals. An interference tolerance interval is an interval on the coloring axis where users within the area can be considered free of interference even if they are assigned the same frequency resources. In other words, users within an interference tolerance interval can be assigned the same frequency resources. A greater number of interference tolerance intervals on the coloring axis indicates lower tolerance to interference and more thorough interference isolation, but requires more frequency resources. A smaller number of interference tolerance intervals indicates higher tolerance to interference and less thorough interference isolation, but requires fewer frequency resources.

[0093] As an example, assume that the number of interference-isolating domains of a URLLC-stained semiaxis is n u , expressed as The number of interference-isolated domains of the eMBB-stained semi-axis is n e , expressed as Then, a clustering algorithm is used to divide vertices into different interference isolation domains with the goal of minimizing system interference. Users represented by vertices in the same interference isolation domain can be allocated the same frequency resources. An optional clustering algorithm is the K-means algorithm. It is worth noting that, according to the characteristics of the K-means algorithm, the lengths of different interference tolerance intervals are not necessarily the same, and the number of users divided by the interference tolerance interval is not necessarily the same. There are at most K granularities. An example of the interference tolerance interval division process based on K-means clustering is as follows: Figure 9 shown.

[0094] After users are divided into different interference tolerance intervals, frequency resource allocation can be performed to allocate the same frequency resources to users represented by the vertices of the same interference tolerance interval. As an example, assuming that the system bandwidth is B and the bandwidth of each radio resource block is B0, the total number of radio resource blocks that can be allocated is:

[0095]

[0096] The number of radio resource blocks allocated to URLLC users is N u , the total bandwidth is B u :

[0097] B u =N u B0

[0098] Similarly, the number of radio resource blocks allocated to eMBB users is N e , the total bandwidth is B e :

[0099] B e =BB u =N e B0

[0100] To ensure fairness among users, equal radio resource blocks are allocated to each interference tolerant interval regardless of its length and the number of vertices inside, so that each user will be allocated the same number of radio resource blocks.

[0101] Interference-tolerant interval ensembles for URLLC-stained semiaxes The average number of radio resource blocks allocated in each interference tolerance interval, that is, the number of radio resource blocks allocated to each URLLC user R u and bandwidth b u for:

[0102]

[0103]

[0104] Interference-tolerant interval set for eMBB staining semi-axes The average number of radio resource blocks allocated in each interference tolerance interval, that is, the number of radio resource blocks allocated to each eMBB user R e and bandwidth b e for:

[0105]

[0106] b e =R e B0

[0107] Define the same interference tolerance interval i z The sum of the weights of the edges between the inner vertices is used as the interference sum of the interference tolerance interval:

[0108]

[0109] From this, the system interference and I can be obtained as:

[0110]

[0111] Interference tolerance interval i z The allocated bandwidth is b z , then i z User device u in s Throughput rate T s :

[0112]

[0113] Furthermore, the interference tolerance interval i can be obtained z The sum of the throughput rates of all user devices in

[0114]

[0115] The sum of the throughput of all URLLC users T U :

[0116]

[0117] The sum of the throughput of all eMBB users is T E :

[0118]

[0119] Finally, the system throughput T can be obtained:

[0120] T=T U +T E

[0121] In summary, it should be noted that personalization means that different services can have different numbers of interference tolerance intervals and different resource allocation strategies; multi-granularity means that the lengths of interference tolerance intervals are diverse, and the number of users contained therein is also diverse. An optional method is clustering based on the k-means algorithm; stepless graph coloring means that coloring is no longer based on traditional discrete color values, but is performed on color bars in continuous color value intervals. Colors with similar color values ​​can be considered to be the same color and are allocated the same resources; wireless resources can be reusable resources in wireless communications, and optional resources include communication-related resources such as spectrum, space, and time.

[0122] In summary, according to the wireless resource management method based on personalized multi-granularity stepless graph coloring proposed in an embodiment of the present invention, first, based on the potential interference relationship between user devices, an undirected interference graph model is established with the user devices as vertices and the interference values ​​between the user devices as the weights of the edges; then, the weights of the vertices in the wireless interference graph are used as the basis for executing the interference isolation order, and the interference isolation of the vertices is performed on the coloring strip through the personalized stepless graph coloring method, and the RGB color value corresponding to the position of the vertex in the coloring strip constitutes the color of the vertex; wherein, the principle of interference isolation is that vertices of the same service type are scattered in the same area, and vertices of different service types are scattered in different areas. The results show that the vertices are scattered irregularly on the colored strips after interference isolation. Finally, the vertices are clustered based on the multi-granularity clustering algorithm, and the vertices on the colored strips are divided into multiple interference tolerance intervals with different tolerances. User equipment in the same interference tolerance interval can reuse the same wireless resources. In this way, differentiated wireless resource reuse can be achieved, reducing user interference. At the same time, personalized resource allocation strategies can be provided for users of different service types to meet the personalized service quality requirements of different users.

[0123] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0127] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third etc. does not indicate any order. These words may be interpreted as names.

[0128] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0129] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0130] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0131] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0132] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0133] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms should not be understood as necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0134] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A wireless resource management method based on personalized multi-granularity non-step graph coloring, characterized in that: The following steps are involved: Establishing a multi-access convergence resource management model, wherein the multi-access convergence resource management model includes multiple wireless resource reuse types; Obtaining potential interference relationships of wireless resources between user equipments, and obtaining potential interference values ​​between the user equipments based on the potential interference relationships and the multi-access convergence resource management model, so as to establish an undirected interference graph with the user equipments as vertices and the potential interference values ​​between the user equipments as edge weights; Performing interference isolation on the vertices according to the undirected interference graph so as to map the vertices to corresponding coloring axes, and coloring the vertices according to RGB color values ​​corresponding to the positions of the vertices on the coloring axes; Based on a multi-granularity clustering algorithm, the isolated colored vertices are clustered and the vertices on the colored axis are divided into multiple interference tolerance intervals with different tolerances, so that user equipment in the same interference tolerance interval can reuse the same wireless resources. The potential interference value is calculated according to the following formula: in, is the roadside unit r k ∈R and user u j When downlink communication is performed via millimeter wave beams, the user equipment u i Received roadside unit r k Transmitted signal strength; is the roadside unit r k The transmission power; is the roadside unit r k On user device u i Transmitting antenna gain in direction; is the user device u i The receiving antenna gain, L ik Is the signal from the roadside unit r k To user device u i Power loss; G m is the roadside unit r k The main lobe center gain of the transmitting antenna; θ i,k,j is the user device u i With user equipment u j About Roadside Units k The angle θ -3dB is the half-power beamwidth; A m is the maximum loss; θ m is the roadside unit r k The main lobe width of the transmitting antenna; G0 is the roadside unit r k Transmitting antenna sidelobe gain; SINR i,k,j is the pseudo signal-to-interference-and-noise ratio, used to quantify the user equipment u j About Roadside Units k For user equipment u i The potential interference weight of R i Indicates user equipment u i Connectable roadside unit cluster, if u i Located within the coverage area of ​​the roadside unit, the roadside unit belongs to u i Roadside unit cluster; r k′ ∈R i |k′≠k represents u i Roadside unit cluster R i Middle roadside unit k Other roadside units other than σ 2 represents the intensity of ambient thermal noise; w′ j,i is the user device u j For user equipment u i Potential interference value caused; w′ i,j is the user device u i For user equipment u j Potential interference value; R i,j =R i ∩R j is the user device u i and user equipment u j The intersection of the roadside unit clusters.

2. The wireless resource management method based on personalized multi-granularity non-step graph coloring according to claim 1, characterized in that: The wireless resources include time resources, space resources and frequency resources. The multiple wireless resource multiplexing types include no multi-type multiplexing, complete differentiated multiplexing, non-completely differentiated multiplexing and complete multiplexing. Among them, the three wireless resources are non-intersecting for no multi-type multiplexing, there is any two-way intersection among the three wireless resources for complete differentiated multiplexing, the three wireless resources are two-way intersection but not completely overlapping for non-completely differentiated multiplexing, and the three wireless resources completely overlap for complete multiplexing.

3. The wireless resource management method based on personalized multi-granularity non-step graph coloring according to claim 2, characterized in that: Obtaining a potential interference relationship of wireless resources between user equipments, and obtaining a potential interference value between the user equipments based on the potential interference relationship and the multi-access fusion resource management model, including: Obtain non-human controllable and interference-related resources and human controllable and interference-related resources in wireless resources between user equipments, so as to allocate the same human controllable and interference-related resources to the user equipments, and obtain potential interference values ​​corresponding to the non-human controllable and interference-related resources in the wireless resources between the user equipments.

4. The wireless resource management method based on personalized multi-granularity non-step graph coloring according to claim 1, characterized in that: An undirected interference graph is established with the user equipment as a vertex and the potential interference value between the user equipment as the weight of the edge, including: Establish a directed interference graph model with the first user device as the first vertex, the second user device as the second vertex, the potential interference value between the first user device and the second user device as the weight of the directed edge between the first vertex and the second vertex, and the potential interference value between the second user device and the first user device as the weight of the directed edge between the second vertex and the first vertex; Selecting the larger of the weight of the directed edge between the first vertex and the second vertex and the weight of the directed edge between the second vertex and the first vertex, and normalizing the larger of the weights to obtain the weight of the edge between the first vertex and the second vertex; The weights of the edges associated with each vertex are summed to obtain the weight of the vertex, so as to transform the directed interference graph model into an undirected interference graph model.

5. The wireless resource management method based on personalized multi-granularity non-step graph coloring according to claim 4, characterized in that: The undirected interference graph model is represented as G = (V, I, E, W, D), where W = {w 1,1 ,...,w 1,S ,w 22 ,...,w 2,S ,...,w S,S } is the weight set of all edges between vertices, w ii =0|1≤i≤S; D={d1,d2,…,d S } is the weight set of all vertices, Renumber the vertex subscripts in descending order of weight to obtain the vertex set V={v1,v2,…,v S }, and satisfy d i ≥d j ; is the set of service request identifiers of all user devices at time t, which is converted into the set of vertex type identifiers corresponding to the vertex set in descending order of interference weight in Represents vertex v s The service request identifier of the user equipment represented is URLLC service, Represents vertex v s The service request identifier of the user equipment represented is eMBB service.

6. The wireless resource management method based on personalized multi-granularity non-step graph coloring according to claim 5, characterized in that: Interference isolation is performed on the vertices according to the undirected interference graph so as to map the vertices to corresponding coloring axes, comprising: Acquire a chromatic axis, wherein the chromatic axis includes two chromatic semi-axes; According to the set of vertices in descending order of interference weight in the undirected interference graph and the set of vertex type identifiers in the undirected interference graph, the vertices are interference isolated so as to be mapped to the corresponding coloring axis; wherein, the vertices are interference isolated according to the sorting of the vertex set in descending order of interference weight, and the greater the weight of the edge between vertices, the greater the distance between the corresponding vertices on the coloring axis, and at the same time, vertices of different service types are located on different coloring semi-axes.

7. The wireless resource management method based on personalized multi-granularity stepless graph coloring according to claim 1, characterized in that: The interference tolerant intervals are distributed on the coloring axis, and user equipments in the same interference tolerant interval can reuse the same radio resources.

8. The wireless resource management method based on personalized multi-granularity stepless graph coloring according to claim 7, characterized in that: The more interference-tolerant intervals there are on the dyeing axis, the lower the tolerance to interference, the more thorough the interference isolation, and the more human controllable and interference-related resources are required; the fewer the number of interference-tolerant intervals, the higher the tolerance to interference, the less thorough the interference isolation, and the less human controllable and interference-related resources are required.

9. The wireless resource management method based on personalized multi-granularity stepless graph coloring according to claim 8, characterized in that: The multi-granularity clustering algorithm is a K-means clustering algorithm.

Images