CN109600756B - Physical cell identification and distribution method based on maximum degree priority dyeing algorithm - Google Patents
Physical cell identification and distribution method based on maximum degree priority dyeing algorithm Download PDFInfo
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Abstract
The invention relates to a Physical Cell Identification and distribution method based on a maximum degree priority dyeing algorithm, which solves the problems that the Physical Cell Identification (PCI) for identifying a cellular network base station in a small cellular network technology is extremely limited, so that the small cellular network cannot be deployed smoothly, and the Quality of Service (QoS) of a user is greatly influenced. The method comprises the steps of firstly, carrying out clustering processing on massive network user mobile data by using a K-means clustering algorithm, and dividing and determining hot spot areas of users. And then PCI distribution and multiplexing are carried out on the cellular base stations in different hot spots based on a maximum degree priority dyeing algorithm. The method realizes the rapid and effective distribution and multiplexing of the PCI to the cellular base station, reduces the conflict confusion probability of the PCI and ensures the QoS of the users in the hot spot areas.
Description
Technical Field
The invention adopts the K-means clustering algorithm to perform clustering processing on massive network user mobile data, thereby realizing the determination and division of hot spots. Then, a PCI distribution method based on a maximum degree first dyeing algorithm is provided, the method can quickly and effectively carry out PCI distribution and multiplexing on the small cellular base station in the hot spot area, simultaneously reduces the conflict confusion probability of the PCI, and improves the network service quality of users in the hot spot area. A PCI distribution method based on a maximum degree priority dyeing algorithm belongs to the field of mobile communication networks.
Background
With the rapid development of wireless communication and mobile internet technologies, global mobile network data traffic has increased twenty-fold over the last five years. By the end of 2017, statistics show that global network data traffic reaches a staggering 7.2EB on average monthly, with 4G data traffic accounting for 69%. The explosive growth of data has made big data a concept of much interest in recent years, which refers to a technical system or a technical architecture for extracting its value by a method in pattern recognition or statistics by capturing, discovering and analyzing a large number of data sources with complex sources at high speed.
Big data was mainly applied to the business, finance and other fields in the early days, and gradually expanded to the traffic, medical and energy fields in the later days. Today, wireless networks are also seen as one of the important technical areas for large data applications. On one hand, with the updating development of intelligent terminal devices, network data service requests of users become more frequent, the generated network data traffic also increases, and effective transmission and processing of data with various structures and complex sources are required. On the other hand, as users have increased requests for network data, the construction and development of wireless networks have been hampered. The explosive growth of network data makes the capacity of traditional cellular networks far from the actual demand of users, especially for hot spots in cities, a large amount of network online services are generated in a short time, and simultaneously, the data traffic is also increased sharply, and the concentrated network requests can cause network blockage and even interruption, thereby causing immeasurable loss.
In order to deal with the problems caused by the rapid increase of the data traffic of the wireless network, the existing solutions generally adopt a technology of increasing the density of network cells to realize dense network deployment, so as to improve the transmission rate of the data traffic and increase the network capacity and the coverage area. The core of the scheme is that a large number of Small cell Network (SC) base stations are densely deployed in a traditional macro cell Network architecture to form a Heterogeneous Cell Network (HCN) so as to provide better Network service quality for users in hot spot areas. In heterogeneous cellular networks, large-scale randomly deployed small Cell network base stations require Physical Cell Identity (PCI) to distinguish one from another. However, the number of PCIs is extremely limited due to the limitations of physical spectrum resources. The number of PCIs available in Long Term Evolution (LTE) system is 504. Therefore, it is necessary to analyze and process a large amount of data related to network users and cellular base stations, design a reasonable and effective PCI allocation scheme, maximize the use of limited PCI resources, complete the construction of heterogeneous cellular networks in hot spots, and improve the Quality of network Service (QoS) of users.
Therefore, in order to solve the problems of extremely limited PCI resources and the like, the method adopts K-means clustering to cluster the network users in the cellular base station area with higher liveness, and determines the hot spot area according to the obtained clustering result. Then, graphical modeling is carried out on the complex cellular networks in different hot spot areas, and the PCI distribution problem of the cellular base station is innovatively modeled as a graphical dyeing process. And finally, a PCI distribution scheme based on a maximum degree priority dyeing algorithm is provided and adopted.
Disclosure of Invention
The coming of big data era causes the network data traffic in hot spot areas to show explosive growth, the capacity of the existing cellular network can not meet the requirements of users, and therefore small cellular technology is proposed and widely applied. However, limited Physical Cell Identification (PCI) is difficult to reasonably allocate to a large number of small Cell base stations, resulting in difficult cellular network deployment and affected Quality of network Service (QoS) of users. Therefore, the method adopts a K-means clustering algorithm to perform clustering processing on massive network user mobile data, and determines the hot spot area. Then, a PCI allocation method based on a maximum degree first dyeing algorithm is provided, the scheme can quickly and effectively allocate and multiplex the PCI to small Cellular base stations in a Heterogeneous Cellular Network (HCN) in the hotspot region, meanwhile, the conflict confusion probability of the PCI is reduced, and the QoS of users in the hotspot region is improved.
The method comprises the following specific steps:
The traditional cellular deployment model uses a Wrap-Around model, that is, the coverage area of a cell is modeled into a regular hexagon with a fixed length, and a plurality of countless regular hexagon cells are embedded into a circular plane with a limited size, so as to form a deployment structure of a cellular network, and a network topology diagram of the Wrap-Around model is shown in fig. 1.
With the introduction of small cell technology, the existing network service quality is greatly improved, and the capacity of a network cell is improved. However, the problem is that the number of small cells is large, random deployability is satisfied, and the traditional Wrap-Around model cannot be used for simulating the deployment situation of the small cells. The method uses a homogeneous Poisson point process in a random geometric theory to simulate the deployment of the small cellular base station.
The homogeneous poisson point process satisfies the following two conditions:
a) suppose that an Euclidean space region B is selected in space while the region satisfies the bounding property. The number of points n (B) within the area B follows a poisson distribution with a mean value λ vd(B) In that respect Wherein λ > 0, vd(B) Representing the area of region B. That is, with respect to the variable n (B), the calculation formula of the probability P (n (B) ═ s) of s points appearing in the region B is shown in formula (1):
b) randomly selecting a series of mutually disjoint bounded regions B in space1，B2，…，BnThen the number of points in these regions is independent of each other, i.e. the variable N (B)1)，N(B2)，…，N(Bn) Are independent of each other.
The properties basically meet the actual deployment needs of the small cell base station, so that a Poisson point process model is adopted to simulate the deployment of the small cell base station. The coverage area of the small cell base station adopts a common Voronoi model. The model is proposed by mathematician Georgy Voronoi and has wide application in architecture, physics and computer network communication. The model firstly assumes that some random points are distributed in a space region, the space is divided into a plurality of regions according to the positions of the points, and the boundary of the regions is a perpendicular bisector of a connecting line of adjacent points. Under such planning methods and strategies, each region belongs to the nearest point only. According to research and investigation, the division mode also conforms to the principle of near communication in a cellular network, so that the method is a small cell deployment model which is widely applied at present. A simulated deployment of a small cell base station is shown in figure 2.
And 2, after the deployment model of the cellular network base station is modeled in the step 1, establishing a PCI distribution model of the cellular network base station according to the obtained deployment model in the step 2.
Because the number of cellular base stations in the hot spot is large, in order to simplify the PCI distribution process of the cellular network base stations in the hot spot, the method abstracts the PCI distribution process in an HCN scene in a graphical mode. In the method, a network topology structure of the HCN is modeled into a connected graph and is represented by G ═ V, E, wherein a vertex set V represents a cellular base station needing PCI allocation, and an edge set E represents that PCI conflict or confusion relationship exists between two base stations.
If there is an edge set relationship between two vertices in the modeled graph, it indicates that two corresponding cellular base stations in the HCN need to be allocated different PCIs, thereby avoiding PCI collision and confusion. Therefore, the dynamic allocation problem of the PCI is converted into a given undirected connected graph and a limited number of color types, each vertex in the graph is dyed, and two vertices in the graph with edge set relations need to be dyed with different colors. Meanwhile, in order to further reduce PCI confusion and conflict existing in the PCI distribution process, the vertex in the graph is not only connected with the vertices of the adjacent regions thereof, but also connected with the adjacent regions of all the adjacent regions, so that the occurrence of PCI confusion and conflict can be further reduced.
After the graphical modeling is completed, the PCI allocation process is abstracted to the graph vertex shading problem. That is, given a directed connected graph G ═ V, E and c different colors, the vertices of the directed connected graph are colored with c colors, each vertex can only be colored one color, and the final goal of the process is to find a coloring method such that any two adjacent vertices in the graph G ═ V, E are colored differently.
And 3, after the PCI distribution model is established, determining and dividing hot spots in order to improve the efficiency of PCI distribution, so that the PCIs are specifically distributed to the cellular network base stations in different hot spots.
The number of network users in the hot spot area is large, and through analysis of users in the hot spot area, it is found that users in the hot spot area have mobile group and activity concentration, that is, users often initiate network data service requests in a large amount and in a concentrated manner in a certain place, which causes short-time network blocking and even interruption. Therefore, PCI needs to be preferentially allocated to the cellular network in the hot spot area, so that the construction and deployment of the heterogeneous cellular network in the area are quickly completed, and the QoS of network users in the hot spot area is guaranteed.
In order to quickly locate the hot spot area, the method adopts a K-means clustering algorithm to cluster the mobile data of the user, the mobile data points of the user are divided into different clusters, and each cluster represents one hot spot area. The K-means clustering algorithm is a simple and efficient clustering algorithm, and the core idea is to select an initial clustering center at random, calculate the Euclidean distance from each sample point to the initial clustering center, and assign the Euclidean distances to the class represented by the clustering center with the maximum similarity according to the closest criterion. And finally, calculating the mean value of all sample points of each cluster, and updating the clustering center until the target criterion function is converged.
And 4, after the establishment of the deployment model of the cellular network base station and the determination of the hot spot areas are completed, the method carries out graphical modeling on the topological structure of the heterogeneous cellular network according to the deployment models of the cellular network base stations in different hot spot areas in the step 4.
Giving an undirected connectivity graph G ═ V, E, where the set of vertices V ═ V (V, E)1，v2，…，vn) The edge set E ═ E1，e2，…，en). Let the degree of the vertex v be the number of edges associated with the vertex v in the graph G, and be denoted as Δ G. From the theory of the graph theory dyeing algorithm, it is known that when Δ G +1 ≦ c, there must be a dyeing algorithm that makes any two vertices of G ═ V, E different in color.
Before the graph coloring algorithm is performed, the undirected connectivity graph G ═ V, E, which represents the base station neighbor relation, needs to be acquired according to the base station deployment situation of the cellular network. Let the set of network base stations in the cellular network be BS ═ BS1，bs2，…，bsnWhere n denotes the number of base stations, let us assume base stations bsiIs the network user us in the sceneiThe user us can be calculated according to the signal-to-noise ratio formula (2)iTo other base stations bs in the scenejSignal to Interference plus Noise Ratio (SINR), thereby obtaining the SINRij. If the obtained SINRij≥SINRthresholdDenotes base station bsiAnd bsjThere is a neighbor relation between them, wherein SINRthresholdThe minimum SINR value allowed by the LTE system to access the base station is shown.
Where σ represents the background noise, M is the number of all cellular base stations in the cellular network scenario,
indicating the strength of the carrier signal received by user u from the kth subchannel from cellular base station m. Wherein the received signal strength
The calculation formula (2) is shown in formula (3):
wherein
Representing the carrier power on the mth subchannel of the cellular base station, AaddIndicating the antenna gain, delta, at the transmitting and receiving endsm，uRepresents the path loss, ζ, at a carrier frequency of 2.0GHzm，uIt represents a shadow fading between the cellular network base station m and the user u. Path loss deltam，uThe calculation formula (2) is shown in formula (4):
δm，u＝140.7+37.6-log10(dm，u) (4)，
wherein d ism，uWhich represents the distance between cellular base station m and user u in kilometers. Shadow fading ζ between cellular network base station m and user um，uObeying a logarithmic distribution, ζ, of mean 0 and standard deviation of the background noise value σm，uThe calculation formula (2) is shown in formula (5):
ζm，u＝log10(0，σ2) (5)
after the edge set relation exists between the vertexes of the undirected connected graph of the two base stations is obtained through calculation, the element e in the edge set is representedij1, it is stated that vertex i and vertex j need to be colored differently when they are dyed, thereby avoiding the base cellular station bsiAnd bsjPCI conflict and confusion occur. To further reduce PCI confusion, for elements in the edge set relationship set, if e existsij1 and ejkWhen 1, then there is e ik1, base station bsiAnd base station bskAnd a secondary adjacent region relation exists, and different colors are needed when vertex dyeing is carried out.
And 5, after a no-connection graph of the cellular network base station is constructed, coloring the modeled connection graph by using a maximum degree first coloring algorithm in the step 5.
And 5.1, firstly, calculating the degrees of all vertexes in the vertex set V, sequencing all vertexes from large to small, and marking different serial numbers for different vertexes to distinguish.
And 5.2, storing the usable colors in the set C, and marking different serial numbers for different colors to distinguish.
Step 5.3, the first undyed vertex V in the set of vertices V is colored with the first available color C in C1And (4) coloring.
Step 5.4, traverse other unstained vertices of the ordered set of vertices V and assign the same color to non-adjacent unstained vertices.
Step 5.5: steps 5.3 and 5.4 are applied iteratively until all vertices have been colored.
After the maximum degree first coloring algorithm in step 5 is completed, all the vertices of the connected graph complete the coloring process, and the two vertices having the edge set relationship are colored in different colors, which indicates that the cell base station having the neighboring cell relationship allocates different PCIs to avoid PCI collision and confusion.
Through a maximum degree first dyeing algorithm, the cellular base stations in the HCN are all distributed with unique PCIs, and the cellular base stations with the adjacent relation in the HCN are distributed with different PCIs, so that PCI conflict and confusion are avoided, and the PCIs are effectively multiplexed. The scheme not only saves precious PCI resources, but also guarantees the QoS of the user.
The invention is mainly characterized in that:
1) according to the invention, aiming at the mobile population and activity concentration of users in the hot spot area, a K-means clustering algorithm is adopted to perform clustering processing on massive user mobile data, so that the division and determination of the user hot spot area are realized;
2) the PCI distribution problem is innovatively modeled into the dyeing problem of the undirected connected graph, the PCI distribution process is greatly simplified, and the conflict and confusion of the PCI are reduced;
3) and the vertex dyeing is carried out on the modeling graph of the cellular base station by adopting a maximum degree priority dyeing algorithm, and compared with other existing schemes, the method has the advantages of shorter dyeing time, less color using quantity and higher PCI distribution time efficiency and PCI utilization rate.
Drawings
FIG. 1 is a diagram of a simulated deployment of a macrocell base station in accordance with the present invention
FIG. 2 is a diagram of a simulated deployment of a small cell network base station in the present invention
FIG. 3 is a schematic diagram of the mobile data visualization of a user of the present invention
FIG. 4 is a schematic diagram of hot spot area partitioning using K-means clustering according to the present invention
FIG. 5 is a graphical rendering of cellular network base station modeling of the present invention
Detailed Description
The invention adopts the K-means clustering algorithm to perform clustering processing on massive network user mobile data, thereby realizing the determination and division of hot spots. Then, a PCI distribution method based on a maximum degree first dyeing algorithm is provided, the method can quickly and effectively carry out PCI distribution and multiplexing on the small cellular base station in the hot spot area, simultaneously reduces the conflict confusion probability of the PCI, and improves the network service quality of users in the hot spot area.
The invention adopts the following technical scheme and implementation steps.
And 2, visualizing the mobile data of the user to find that the mobile data of the user has obvious cluster and concentration, wherein the visualized mobile data points of the user are shown in fig. 1. And then clustering the mobile data of the massive users by using a k-means clustering algorithm. The division and the determination of the user hot spot areas are realized. The user's moving data points are visualized as shown in FIG. 3.
The implementation steps of carrying out K-means clustering on the mobile data of the user are as follows:
and 2.1, inputting the number of clusters and a data point set of the user, wherein the specific number of the clusters needs to be specified by a K-means clustering algorithm before the clustering process starts, and a large amount of clusters can be generated in certain areas on a map by observing that the data point set of the user in the graph 3 has obvious hot spot area characteristics, and the areas are hot spot areas needing to be divided and determined in the text. The dividing result of the hot spot region can be adjusted by adjusting the clustering number of the K-means clustering algorithm, so that the hot spot region accords with the actual application effect.
And 2.2, randomly selecting K data points as the central points of the clusters, sequentially calculating Euclidean distances from other data points to the central points of the clusters, and attributing the data point with the minimum Euclidean distance to the cluster of the cluster center.
And 2.3, continuously adjusting the clustering center point, adjusting the clustering center point by adopting an average value method, after the clustering process is completed for the first time, calculating the average value of all data points of each cluster as a new clustering center point, and then repeating the step 1.2 until the target criterion function is converged.
As shown in fig. 4, a result diagram after K-means clustering is completed for the mobile data points of the user, and different color point sets represent different hot spot regions.
And 3, modeling the cellular network base station into a non-directional connected graph. According to the signal-to-noise ratio formula (2), the signal-to-interference-plus-noise ratio of each base station in the scene from the user can be calculated, and the SINR is obtainedij. If the obtained SINRij≥SINRthresholdDenotes base station bsiAnd bsjThere is a neighbor relation between them. And modeling the cellular network base station into a non-directional connected graph according to the calculated adjacent cell relation. Wherein each vertex can only be colored in one color, and two vertices with edge set relationships need to be colored in different colors. The coloring scheme of the connectivity graph is shown in FIG. 5.
And 4, coloring the modeled graph by using a maximum degree priority coloring algorithm, coloring all vertexes of the connected graph after the maximum degree priority coloring algorithm is completed, wherein the two vertexes with the edge set relation have different colors, and the different colors represent different PCIs, so that the two cell base stations are allocated with different PCIs, and the PCI conflict and confusion are avoided.
The simulation parameters in the method are all given in the simulation parameter table 1.
TABLE 1 simulation parameters Table
Claims (1)
1. A physical cell identification and distribution method based on a maximum degree priority dyeing algorithm is characterized by comprising the following steps:
step 1, constructing deployment models of a macro cellular network and a small cellular network;
the deployment of a macro cellular network is simulated by adopting a Wrap-Around model, namely, the coverage area of a cellular network cell is modeled into a regular hexagon with a fixed length, and a plurality of countless regular hexagons are embedded into a circular plane with a limited size;
simulating and simulating the deployment of the small cellular network base station by adopting a homogeneous Poisson point process model meeting the following two conditions, wherein the condition a is that an Euclidean space region B is supposed to be selected in the space, and the region meets the boundedness; the number of points n (B) within the area B follows a poisson distribution with a mean value λ νd(B) (ii) a Wherein λ>0，νd(B) Represents the area of region B; that is, the probability P (n (B) ═ s) that s points appear in the region B is calculated by the formula
Shown; condition B is that a series of mutually disjoint bounded regions B are randomly selected in space1,B2,…,BnThen the number of points in these regions is independent of each other, i.e. the variable N (B)1),N(B2),…,N(Bn) Are independent of each other;
the coverage area of the small cell base station adopts a Voronoi model;
step 2, after the deployment model of the cellular network base station is modeled in the step 1, establishing a PCI distribution model of the cellular network base station according to the deployment model obtained in the step 1;
abstracting a PCI (peripheral component interconnect) allocation process in a cellular network scene in a graphical mode, modeling a cellular network topology structure into a connected graph, and expressing the connected graph by using G (V, E), wherein a vertex set V expresses a cellular base station needing PCI allocation, and an edge set E expresses a neighboring cell relation existing between the base stations; if an edge set connection line exists between two vertexes in the connected graph, the adjacent cell relation exists between two corresponding cellular base stations in the cellular network, and different PCIs need to be distributed, so that the occurrence of PCI conflict and confusion is avoided;
therefore, the dynamic allocation problem of the PCI is converted into a given undirected connected graph and a limited number of color types, and each vertex in the connected graph is dyed, wherein two vertexes with adjacent area relations need to be dyed into different colors; connecting each vertex in the connected graph with not only the vertex of the adjacent region thereof, but also the vertex of the adjacent region thereof;
after the graphical modeling is completed, the distribution process of the PCI is abstracted to the vertex coloring problem of the connected graph; that is, given a non-directional connected graph G ═ V, E) and c different colors, the vertices of the non-directional connected graph are dyed with c colors, and each vertex can be only one color; the ultimate goal of this process is to find a coloring method such that any two adjacent vertices in graph G ═ V, E are colored differently;
step 3, after the establishment of the PCI distribution model is completed, in order to improve the efficiency of PCI distribution, hot spots need to be determined and divided so as to distribute the PCI to the cellular network base station in a targeted manner in different hot spots;
clustering mobile data of a user by adopting a K-means clustering algorithm, wherein the mobile data points of the user are divided into different clusters, and each cluster represents a hot spot area; the core idea of the K-means clustering algorithm is that an initial clustering center is selected randomly at first, then the Euclidean distance from each sample point to the initial clustering center is calculated, and the Euclidean distances are distributed to the class represented by the clustering center with the largest similarity according to the closest criterion; finally, calculating the mean value of all sample points of each cluster, and updating the clustering center until the target criterion function is converged;
step 4, after the establishment of the deployment model of the cellular network base station and the determination of the hot spot areas are completed, carrying out graphical modeling on the topological structure of the heterogeneous cellular network according to the deployment models of the cellular network base stations in different hot spot areas;
giving an undirected connectivity graph G ═ V, E, where the set of vertices V ═ V (V, E)1,v2,…,vn) The edge set E ═ E1,e2,…,en) (ii) a The degree of any vertex v is the number of edges associated with the vertex v in the graph G and is marked as delta G; according to the theory of graph theory dyeing algorithm, when Δ G +1 ≦ c, there must be a dyeing algorithm that makes any two adjacent vertexes G ═ (V, E) different in color;
before the graph coloring algorithm is performed, an undirected connectivity graph G ═ V, E indicating the relationship between base station neighbors needs to be acquired according to the base station deployment situation of the cellular network; let the set of network base stations in the cellular network be BS ═ BS1,bs2,…,bsnWhere n denotes the number of base stations, let us assume base stations bsiIs the network user us in the sceneiCalculating according to the signal-to-noise ratio formula (2) to obtain the user usiTo other base stations bs in the scenejTo the signal to interference plus noise ratio, to obtain the SINRij(ii) a If the obtained SINRij≥SINRthresholdDenotes base station bsiAnd bsjThere is a neighbor relation between them, wherein SINRthresholdThe minimum SINR value allowed by the LTE system to access the base station is shown;
where σ represents the background noise, M is the number of all cellular base stations in the cellular network scenario,
indicating that user u received data from the cellular baseThe carrier signal strength of the kth subchannel of station m; wherein the received signal strength
The calculation formula (2) is shown in formula (3):
wherein
Representing the carrier power on the mth subchannel of the cellular base station, AaddIndicating the antenna gain, delta, at the transmitting and receiving endsm,uRepresents the path loss, ζ, at a carrier frequency of 2.0GHzm,uIt represents a shadow fading between the cellular network base station m and the user u; path loss deltam,uThe calculation formula is shown as formula (4):
δm,u＝140.7+37.6·log10(dm,u) (4),
wherein d ism,uRepresents the distance between cellular base station m and user u in kilometers; shadow fading ζ between cellular network base station m and user um,uObeying a logarithmic distribution, ζ, of mean 0 and standard deviation of the background noise value σm,uThe calculation formula (2) is shown in formula (5):
ζm,u＝log10(0,σ2) (5)
after the edge set relation exists between the vertexes of the undirected connected graph of the two base stations is obtained through calculation, the element e in the edge set is representedij1, it is stated that vertex i and vertex j need to be colored differently when they are dyed, thereby avoiding the base cellular station bsiAnd bsjPCI conflicts and confusion occur; to further reduce PCI confusion, for elements in the edge set relationship set, if e existsij1 and ejkWhen 1, then there is eik1, base station bsiAnd base station bskThe secondary adjacent region relation exists, and different colors are needed to be dyed when the vertex dyeing is carried out；
And 5, after a no-connection graph of the cellular network base station is constructed, coloring the modeled connection graph by using a maximum degree first coloring algorithm, wherein the method specifically comprises the following steps:
step 5.1, firstly, calculating the degrees of each vertex in the vertex set V, sequencing all the vertices from large to small, and marking different serial numbers for different vertices to distinguish;
step 5.2, storing the available colors in the set C, and marking different serial numbers for different types of colors for distinguishing;
step 5.3, the first undyed vertex V in vertex set V is colored C with the first undyed vertex in C1Coloring;
step 5.4, traversing other undyed vertexes of the ordered vertex set V, and distributing the same color to the non-adjacent undyed vertexes;
step 5.5: steps 5.3 and 5.4 are applied iteratively until all vertices have been colored.
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|---|---|---|---|---|
| CN104025636A (en) * | 2011-12-02 | 2014-09-03 | 爱立信(中国)通信有限公司 | Physical cell identity allocation |
| CN106376018A (en) * | 2015-07-23 | 2017-02-01 | 南京新见微科技有限公司 | Multidimensional measurement data based frequency scrambling code PCI optimization apparatus and method thereof |
| CN108848509A (en) * | 2018-04-24 | 2018-11-20 | 浙江工业大学 | A kind of novel frequency spectrum distributing method of femtocell network based on improvement graph theory |
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- 2018-12-03 CN CN201811463217.0A patent/CN109600756B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104025636A (en) * | 2011-12-02 | 2014-09-03 | 爱立信(中国)通信有限公司 | Physical cell identity allocation |
| CN106376018A (en) * | 2015-07-23 | 2017-02-01 | 南京新见微科技有限公司 | Multidimensional measurement data based frequency scrambling code PCI optimization apparatus and method thereof |
| CN108848509A (en) * | 2018-04-24 | 2018-11-20 | 浙江工业大学 | A kind of novel frequency spectrum distributing method of femtocell network based on improvement graph theory |
Non-Patent Citations (3)
| Title |
|---|
| A physical cell identity self-organization algorithm in LTE-advanced systems;Jingjie Yu等;《7th International Conference on Communications and Networking in China》;20130124;全文 * |
| Femtocell辅助蜂窝系统关键技术研究;李宏佳;《中国博士学位论文全文数据库 信息科技辑》;20111215;全文 * |
| FHC-PCIA: A Physical Cell Identification Allocation Method Based on Fuzzy Hierarchical Clustering for Heterogeneous Cellular Network;Shanshan Tu等;《IEEE Access 》;20180824;全文 * |
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