WO2022150039A1

WO2022150039A1 - Data analytics for non-conflicting and non-confusing physical cell identifier allocation

Info

Publication number: WO2022150039A1
Application number: PCT/US2021/012463
Authority: WO
Inventors: Sivaramakrishnan Swaminathan; Rakshesh P BHATT; Konstantinos Samdanis; Anatoly ANDRIANOV
Original assignee: Nokia Solutions And Networks Oy; Nokia Of America Corporation
Priority date: 2021-01-07
Filing date: 2021-01-07
Publication date: 2022-07-14

Abstract

A method may include receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number (NR-ARFCN), and a unique cell identifier of the one or more nodes in a given network. The method may further include determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the NR-ARFCN. In addition, prime numbers may be reserved within a predetermined range for moving network nodes, and the physical cell identifier within the predetermined range may be allocated to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un-reserved list for static network nodes.

Description

DATA ANALYTICS FOR NON-CONFLICTING AND NON-CONFUSING PHYSICAL

CELL IDENTIFIER ALLOCATION

FIELD: [0001] Some example embodiments may generally relate to mobile or wireless telecommunication systems, such as Long Term Evolution (LTE) or fifth generation (5G) radio access technology or new radio (NR) access technology, or other communications systems. Lor example, certain embodiments may relate to apparatuses, systems, and/or methods for data analytics for non-conflicting and non-confusing physical cell identifier (PCI) allocation for static and moving nodes for 5G NR cells.

BACKGROUND:

[0002] Examples of mobile or wireless telecommunication systems may include the Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (UTRAN), Long Term Evolution (LTE) Evolved UTRAN (E-UTRAN), LTE- Advanced (LTE- A), MuIteFire, LTE-A Pro, and/or fifth generation (5G) radio access technology or new radio (NR) access technology. Fifth generation (5G) wireless systems refer to the next generation (NG) of radio systems and network architecture. 5G is mostly built on a new radio (NR), but the 5G (or NG) network can also build on E-UTRAN radio. It is estimated that NR will provide bitrates on the order of 10-20 Gbit/s or higher, and will support at least enhanced mobile broadband (eMBB) and ultra-reliable low-latency-communication (URLLC) as well as massive machine type communication (mMTC). NR is expected to deliver extreme broadband and ultra-robust, low latency connectivity and massive networking to support the Internet of Things (IoT). With IoT and machine-to-machine (M2M) communication becoming more widespread, there will be a growing need for networks that meet the needs of lower power, low data rate, and long battery life. It is noted that, in 5G, the nodes that can provide radio access functionality to a user equipment (i.e., similar to Node B in UTRAN or eNB in LTE) are named gNB when built on NR radio and named NG-eNB when built on E-UTRAN radio. SUMMARY:

[0003] Some example embodiments are directed to a method. The method may include receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The method may further include determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number. In addition, the method may include reserving prime numbers within a predetermined range for moving network nodes. Further, the method may include allocating the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

[0004] Other example embodiments are directed to an apparatus that may include at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured, with the at least one processor to cause the apparatus at least to receive location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The apparatus may further be caused to determine a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number. In addition, the apparatus may be caused to reserve prime numbers within a predetermined range for moving network nodes. Further, the apparatus may be caused to allocate the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

[0005] Other example embodiments are directed to an apparatus. The apparatus may include means for receiving location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The apparatus may further include means for determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number. In addition, the apparatus may include means for reserving prime numbers within a predetermined range for moving network nodes. Further, the apparatus may include means for allocating the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes. [0006] In accordance with other example embodiments, a non-transitory computer readable medium may be encoded with instructions that may, when executed in hardware, perform a method. The method may include receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The method may further include determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio frequency channel number. In addition, the method may include reserving prime numbers within a predetermined range for moving network nodes. Further, the method may include allocating the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes. [0007] Other example embodiments may be directed to a computer program product that performs a method. The method may include receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The method may further include determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number. In addition, the method may include reserving prime numbers within a predetermined range for moving network nodes. Further, the method may include allocating the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

[0008] Other example embodiments may be directed to an apparatus that may include circuitry configured to receive location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network. The apparatus may further include circuitry configured to determine a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number. In addition, the apparatus may include circuitry configured to reserve prime numbers within a predetermined range for moving network nodes. Further, the apparatus may include circuitry configured to allocate the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with a reserved prime number is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

[0009] Certain example embodiments may be directed to a method. The method may include receiving, at a network entity, a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The method may also include reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The method may further include segregating cells based on the cluster identifier for static network nodes. In addition, the method may include. In addition, the method may include arranging the cells in descending order of probability for the cluster. Further, the method may include selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. The method may also include allocating a physical cell identifier associated with the non prime number to a static network node.

[0010] Other example embodiments may be directed to an apparatus. The apparatus may include at least one processor and at least one memory including computer program code. The at least one memory and computer program code may be configured to, with the at least one processor, cause the apparatus at least to receive a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The apparatus may also be configured to reserve prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The apparatus may further be configured to segregate cells based on the cluster identifier for static network nodes. In addition, the apparatus may be configured to arrange the cells in descending order of probability for the cluster. Further, the apparatus may be configured to select a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. The apparatus may also be configured to allocate a physical cell identifier associated with the non prime number to a static network node.

[0011] Other example embodiments may be directed to an apparatus. The apparatus may include means for receiving a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The apparatus may also include means for reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The apparatus may further include means for segregating cells based on the cluster identifier for static network nodes. In addition, the apparatus may include means for arranging the cells in descending order of probability for the cluster. Further, the apparatus may include means for selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. The apparatus may also include means for allocating a physical cell identifier associated with the non prime number to a static network node. [0012] In accordance with other example embodiments, a non-transitory computer readable medium may be encoded with instructions that may, when executed in hardware, perform a method. The method may include receiving, at a network entity, a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The method may also include reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The method may further include segregating cells based on the cluster identifier for static network nodes. In addition, the method may include. In addition, the method may include arranging the cells in descending order of probability for the cluster. Further, the method may include selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. The method may also include allocating a physical cell identifier associated with the non-prime number to a static network node.

[0013] Other example embodiments may be directed to a computer program product that performs a method. The method may include receiving, at a network entity, a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The method may also include reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The method may further include segregating cells based on the cluster identifier for static network nodes. In addition, the method may include. In addition, the method may include arranging the cells in descending order of probability for the cluster. Further, the method may include selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. The method may also include allocating a physical cell identifier associated with the non-prime number to a static network node.

[0014] Other example embodiments may be directed to an apparatus that may include circuitry configured to receive a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The apparatus may also include circuitry configured to reserve prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The apparatus may further include circuitry configured to segregate static cells based on the cluster identifier for static network nodes. In addition, the apparatus may include circuitry configured to arrange the cells in descending order of probability for the cluster. Further, the apparatus may include circuitry configured to select a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved cell identifiers for static network nodes. The apparatus may also include circuitry configured to allocate a physical cell identifier associated with the non-prime number to a static network node.

BRIEF DESCRIPTION OF THE DRAWINGS:

[0015] For proper understanding of example embodiments, reference should be made to the accompanying drawings, wherein:

[0016] FIG. 1(a) illustrates an overview of an example system architecture. [0017] FIG. 1(b) illustrates an overview of a system architecture, according to certain example embodiments. [0018] FIG. 2(a) illustrates a data analytics procedure in management data analytics services (MDAS) for static nodes, according to certain example embodiments.

[0019] FIG. 2(b) illustrates physical cell identifier (PCI) allocation by a centralized self organizing network) (C-SON) for static nodes, according to certain example embodiments. [0020] FIG. 3 illustrates PCI allocation by the C-SON for moving nodes, according to certain example embodiments.

[0021] FIG. 4 illustrates a probabilistic clustering using Gaussian Mixed Model (GMM), according to certain example embodiments.

[0022] FIG. 5 illustrates cluster label and associated probabilities, according to certain example embodiments.

[0023] FIG. 6 illustrates an example of a process of PCI allocation in C-SON for static nodes, utilizing the probabilities and clustering information, according to certain example embodiments. [0024] FIG. 7 illustrates a table of data for PCI allocation analysis, according to certain example embodiments. [0025] FIG. 8 illustrates a PCI planning analytics report, according to certain example embodiments.

[0026] FIG. 9 illustrates an observe-orient-decide-act (OODA) control loop and a legend, according to certain example embodiments.

[0027] FIG. 10 illustrates a flow diagram of a method, according to certain example embodiments.

[0028] FIG. 11 illustrates a flow diagram of another method, according to certain example embodiments.

[0029] FIG. 12(a) illustrates an apparatus, according to certain example embodiments.

[0030] FIG. 12(b) illustrates another apparatus, according to certain example embodiments.

DETAILED DESCRIPTION:

[0031] It will be readily understood that the components of certain example embodiments, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. The following is a detailed description of some example embodiments of systems, methods, apparatuses, and computer program products for analytics for non-conflicting and non-confusing physical cell identifier (PCI) allocation for static and moving nodes for 5G NR cells.

[0032] The features, structures, or characteristics of example embodiments described throughout this specification may be combined in any suitable manner in one or more example embodiments. For example, the usage of the phrases “certain embodiments,” “an example embodiment,” “some embodiments,” or other similar language, throughout this specification refers to the fact that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment. Thus, appearances of the phrases “in certain embodiments,” “an example embodiment,” “in some embodiments,” “in other embodiments,” or other similar language, throughout this specification do not necessarily all refer to the same group of embodiments, and the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments.

[0033] Additionally, if desired, the different functions or steps discussed below may be performed in a different order and/or concurrently with each other. Furthermore, if desired, one or more of the described functions or steps may be optional or may be combined. As such, the following description should be considered as merely illustrative of the principles and teachings of certain example embodiments, and not in limitation thereof.

[0034] Each 5G New Radio (NR) cell may correspond to a physical cell ID (PCI), and the PCI may be used to distinguish cells on the radio side. The PCI planning for 5G NR may be similar to PCI planning for LTE and scrambling code for 3G universal mobile telecommunications system (UMTS). However, incorrect or incomplete planning may affect the synchronization procedure, demodulation, and handover signaling and thereby degrade network performance. Thus, 5G PCI planning may be done while considering several factors. These factors may include avoiding PCI collision, avoiding PCI confusion, and minimizing impact on network performance. [0035] To avoid PCI collision, it is preferable that neighboring cells are not allocated the same PCI. If neighboring cells are allocated the same PCI, then just one of the neighboring cells can be synchronized during the initial cell searching in the overlapping area. However, the cell may not be the most appropriate one. Such a phenomenon is known as “collision”. PCI collision may result in downlink (DL) synchronization in the overlapping zone, high block error ratio (BLER) and decoding failure of physical channels scrambled using PCI, and handover failures. The physical separation between cells using the same PCI may be sufficient to ensure that the UE does not receive the same PCI from more than one cell.

[0036] In order to avoid PCI confusion, under this principle of network planning, two neighboring cells of one cell cannot be allocated with the same PCI. If they are allocated the same PCI, the base station may not know which is the target cell upon a handover request of a UE. The approach while allocating PCI may be such that a cell may not have multiple neighbors using the same PCI, and the physical separation between the cells using the same PCI should be sufficiently large enough to avoid neighbor confusions.

[0037] To minimize impact on network performance, based on the design of different physical layer signals (e.g., packet-switched streaming (PSS), demodulation reference signal (DMRS), and sounding reference signal (SRS)), channels (physical uplink shared channel (PUSCH) and physical uplink control channel (PUCCH)) and time-frequency allocation, PCI planning may consider the following Mod to reduce interference. According to the Mod principle, the UE may not be able to simultaneously receive multiple PCI with the following modes: PCI Mod 3; PCI Mod 4; and PCI Mod 30. In an example of “Mod 3 PCI”, the neighboring cell may not be allocated PCI 25 and 28 because both have Mod 3 as value 1.

[0038] The PCI Mod 3 rule may be based on a relationship between PCI and a sequence generated by PSS. For example, there may be 3 PSS (0, 1, 2) that are reused across the network. The cells having the same “PCI Mod 3” result may use the same PSS. If the UE receives the same PSS from multiple cells, delays in cell acquisition and misleading channel estimation may result. As such, this may impact synchronization delay and user experience.

[0039] The PCI Mod 4 rule may be based on sub-carrier positions of DMRS for physical broadcast channel (PBCH). The sub-carriers may be allocated to DMRS using “Mode 4” computation. If a neighboring cell uses PCI having the same Mod 4 value, DMRS to DMRS interference may result. In PCI Mod 30, DMRS for PUCCH/PUSCH and SRS based on the ZC sequence may result in 30 groups of roots. The roots may be associated with the PCI and, thus, the neighbor cell may not have PCIs having the same Mod 30 value to ensure the uplink (UL) inter cell interference.

[0040] Conventional 5G PCI planning may exhibit certain drawbacks. For example, there is no method available to allocate PCIs for moving nodes in 5G NR. There is also no standardized mechanism for allocating non-conflicting and non-confusing PCIs for static as well as moving nodes. In addition, individual self-organizing network (SON) vendors may use their propriety methods that aim at reducing the PCI conflicts and confusions, and frequently perform re allocations. Moreover, existing methods are based on iterative allocation and re-allocations, which is similar to a trial-error basis. Further, multiple allocations and reallocations of PCI may occur with the current approach, and provisioning of PCI are occurring without consideration of the statistics of already provisioned PCI. As such, it may be advantageous to provide a robust method that performs PCI allocation with absolute zero instances of PCI collision and PCI confusion.

[0041] Certain example embodiments may provide a method that can allocate PCIs with zero instances of PCI collision and PCI confusion using probabilistic clustering with prime number of clusters, and reserving prime numbers for allocating PCI for moving nodes. That is, certain example embodiments may use probabilistic clustering for deciding PCI values. In other example embodiments, a prime number of clusters may be used in deciding the number of clusters based on the total number of cells in a unique new radio absolute radio-frequency channel number (NR- ARFCN). In further example embodiments, non-conflicting and non-confusing PCI may be provisioned for moving cells in 5G networks by reserving prime numbers. As such, according to certain example embodiments, it may be possible to ensure that there is zero collision and confusion at the boundaries of two clusters of cells even if the cells are physically located far apart. However, there may be a chance that these cells are neighbors due to the larger coverage areas.

[0042] According to certain example embodiments, it may be possible to receive the location coordinates of all the nodes and their unique cell IDs in a given network. Certain example embodiments may also perform data processing and cleanup as required. In addition, for unique NR-ARFCN, probabilistic clustering may be performed having a prime number of clusters. In some example embodiments, the larger the number of cells in the NR-ARFCNs, the larger the prime number may be used as number of clusters. Certain example embodiments may also reserve the prime numbers between 1 and 1008 for moving nodes. In addition, other example embodiments may use the cluster ID and the probabilities of a cell belonging to any cluster, and non-conflicting and non-confusing PCI may be allocated from the unreserved list of PCIs.

[0043] Certain example embodiments may introduce new interfaces from management data analytics services (MDAS). For example, inputs to the MDAS may include location coordinates, unique cell IDs, and NR-ARFCN for a group of cells. Further, outputs from the MDAS may include, for one or more cell IDs, a cluster ID and probabilities for the cell belonging to a certain cluster, and a list of PCI IDs that can be used for moving cells.

[0044] FIG. 1(a) illustrates an overview of an example system architecture, and FIG. 1(b) illustrates an overview of a system architecture, according to certain example embodiments. As illustrated in FIGs. 1(a) and 1(b), a centralized SON may be provided. However, as illustrated in FIG. 1(b), a MDAS entity may also be provided. According to certain example embodiments, the centralized-SON (C-SON) may receive cell identifiers, location coordinates, and unique NR- ARFCN from the network management system (NMS). The C-SON may transmit the cell identities, location coordinates, and unique NR-ARFCN to the MDAS. With the obtained information from the C-SON, the MDAS may implement the analytics services for PCI allocation, and provide the cluster ID and probabilities of one or more cells belonging to a particular cluster as input to the C-SON. In some example embodiments, the C-SON may utilize these insights to arrive at non-conflicting and non-confusing PCI IDs, and may provide the PCI IDs to the 5G radio access network (RAN) and moving nodes.

[0045] FIG. 2(a) illustrates a data analytics procedure in MDAS for static nodes, according to certain example embodiments. In particular, the example of FIG. 2 illustrates a data analytics procedure for PCI allocation in the MDAS for static nodes. As illustrated in FIG. 2, at 200, the

MDAS may receive location coordinates and unique cell IDs from the C-SON. According to certain example embodiments, the MDAS may apply probabilistic clustering on the unique cell

IDs and location coordinates it received from the C-SON. An example of probabilistic cluster may include Gaussian Mixed Model (GMM) clustering. This form of clustering may provide insights on which cell belongs to which cluster, and the probability of the cell belonging to a certain cluster. According to certain example embodiments, a prime number of clusters may have a minimum number of clusters equal to 5. Using a prime number of clusters may ensure that all the neighboring cells will have at least a prime offset. In certain example embodiments, this may help ensure that the PCI Mod 3, PCI Mod 4 and PCI Mod 30 rules are met.

[0046] According to certain example embodiments, the exact prime number may be decided based on the total number of cells in a given NR-ARFCN. In certain example embodiments, neighboring cells may reuse PCIs if the NR-ARFCNs are different. In such cases, the PCI conflicts and confusions may not apply because the cells use a different frequency. As illustrated in FIG. 2, at 205, the MDAS may perform data processing and cleanup after receiving the location coordinates and unique cell IDs. At 210, for each NR-ARFCN, if the number of cells with the same NR-ARFCN is less than 1008, the number of clusters may be set to 5, for example. Fiowever, if the number of cells with the same NR-ARFCN is between 1008 and 2016, the number of clusters may be set to 7, for example. In addition, if the number of cells with the same NR-ARFCN is between 2016 and 3024, the number of clusters may be set to 11, for example. Further, at 210, for each NR-ARFCN, probabilistic cluster may be applied on the cells. At 215, an output may be prepared. For example, for one or more of the cells, a cluster ID along with the probabilities of the cell belonging to each cluster may be provided. In addition, a list of prime numbers as PCIs may be provided for moving nodes. According to certain example embodiments, non-prime numbers between 1-1008 may be used as PCI for static nodes.

[0047] FIG. 2(b) illustrates PCI allocation by the C-SON for static nodes, according to certain example embodiments. In particular, FIG. 2(b) illustrates steps that may be taken by the C-SON to make a decision for allocating PCIs. As illustrated in FIG. 2(b), at 220, prime numbers between 1 and 1008 may be used as PCIs for moving nodes. In certain example embodiments, this may ensure that when a moving node needs a PCI allocation, a non-conflicting and non-confusing PCI value may be allocated from this list of prime numbers. According to certain example embodiments, utilizing prime numbers as PCIs for moving nodes may ensure that no conflicts and confusions occur due to PCI Mod 3, PCI Mod 4, and PCI Mod 30 rules. At 225, for each NR- ARFCN, the cells may be segregated based on the cluster ID (e.g., GMM label), and arranged in descending order of probability for that cluster. In addition, according to certain example embodiments, a cluster may further be sub-divided into “n” number of parts, where “n” is equal to the number of clusters. Further, the PCI may be allocated from the unreserved list. At 230, the PCIs may be output/allocated for the cell IDs, and passed on to a network management system (NMS).

[0048] FIG. 3 illustrates PCI allocation by the C-SON for moving nodes, according to certain example embodiments. In the example of FIG. 3, at 300, the C-SON may receive location coordinates and unique cell IDs from the NMS. At 305, for each NR-ARFCN, the C-SON may use Delaunay triangulation and a Voronoi diagram to identify the nearest neighbors for the moving node. The C-SON may also select a prime number for the reserved PCI with a maximum difference with reference to the highest PCI of the neighbors. For instance, in certain example embodiments, when a moving node approaches a location, and among its nearest neighbors, the highest PCI value may be “x”. Thus, a prime number larger than “x” may be selected. In addition, the C-SON may use the prime number as the PCI for the moving node. At 310, the C-SON may assign the PCI value to the moving node.

[0049] FIG. 4 illustrates a probabilistic clustering using GMM, according to certain example embodiments. In some example embodiments, when this form of clustering is applied on location coordinates (e.g., latitude and longitude), it may provide a clear demarcation between clusters based on the density of cells. The example output from GMM clustering may also provide probabilities for each cell belonging to a certain cluster, and assign a label which identifies the most probable cluster that the cell belongs to.

[0050] FIG. 5 illustrates cluster label and associated probabilities, according to certain example embodiments. As illustrated in the example of FIG. 5, P0 indicates probability of the cell belonging to cluster 0, PI indicates probability of the cell belonging to cluster 1, P2 indicates probability of the cell belonging to cluster 2, P3 indicates probability of the cell belonging to cluster 3, and P4 indicates probability of the cell belonging to cluster 4. FIG. 5 also illustrates the cells labelled for cluster 0.

[0051] FIG. 6 illustrates an example of a process of PCI allocation in C-SON for static nodes, utilizing the probabilities and clustering information, according to certain example embodiments. As illustrated in FIG. 6, where a is marked, prime numbers are skipped while allocating PCIs for static network nodes. For example, PCI-18* has skipped PCI-17. Further, prime numbers ma be reserved for moving network nodes, whereas non-prime numbers may be used for static network nodes, as demonstrated in this example. According to some example embodiments, the order of allocation is such that in cluster 1, the first cell with the highest probability may be allocated PCI-1. In cluster 2, the first cell in the second sub-group may be allocated PCI-6 (1 +

“n”), where “n” represents the number of clusters. In cluster 3, the first cell in the third sub-group may be allocated PCI-12 (6 + “n” + 1), where the prime number 11 may be reserved for moving nodes. In cluster 4, the first cell in the fourth sub-group may be allocated PCI-18 (12 + “n” + 1), where the prime number 17 may be reserved for moving nodes. In cluster 5, the first cell in the fifth sub-group may be allocated PCI-24 (18 + “n” + 1), where the prime number 23 may be reserved for moving nodes. In certain example embodiments, in cluster 1, the cell with the second-highest probability may be allocated PCI-30 (24 + “n” + 1). This may ensure that the PCI conflicts and confusion rules are taken care of with its nearest neighbor that was allocated PCI-

1. In some example embodiments, when the PCI number wraps around and reaches 1005, the next set of allocations may start with PCI-4 (1 + wrap-offset, where “wrap-offset” maybe 1, and excluding 2 and 3 as prime numbers, the next value may be 4. A different offset maybe selected).

According to certain example embodiments, further allocations may continue with offsets of “n”, and in a manner that, if the number of cells in a cluster reaches the least probability (where the probability may be set from 0-1; 0 being the least probability and 1 being the highest), PCIs starting from the highest priority of that cluster may be allocated until all cells are allocated a PCI value. [0052] In certain example embodiments, arranging the cells in descending order of their priorities may imply that the cells with the lowest priorities in the clusters may be the cells at the boundaries of a cluster and possible neighbors of another cluster. According to certain example embodiments, allocating the PCIs in the manner described above may ensure that cells in neighboring clusters do not end up in any PCI conflict or confusion. According to some example embodiments, during the PCI allocation, if the allocation is under PCI Mod 3, PCI Mod 4 or PCI Mod 30 may be taken care of with the offset “n” as described herein. However, in other example embodiments, implementations may vary in terms of usage of the unused PCIs, if any.

[0053] According to certain example embodiments, the PCI may be configured in various ways. For example, an authorized consumer of provisioning management services (MnS) may have the capability of utilizing analytics services provided by management data analytics service (MDAS) that can be used to configure/re-configure PCI values in an efficient manner for NR cells, requiring zero reconfigurations in the live network. According to other example embodiments, the authorized consumer of provisioning MnS may have the capability of utilizing analytics services provided by MDAS that can be used to re-allocate PCI values when a collision is detected, and notify the consumer about the detection or resolution of PCI collision. According to further example embodiments, the authorized consumer of provisioning MnS may have the capability of allocating non-conflicting and non-confusing PCI values for moving NR base transceiver station (BTS) that comes up at any location that is being managed by the service. [0054] As noted above, a 5G NR cell may correspond to a PCI, which may be used to distinguish cells on the radio side. The PCI planning for 5G NR may be similar to PCI planning for LTE and scrambling code planning for 3G UMTS. However, incorrect or incomplete planning may affect the synchronization procedure, demodulation, and handover signaling, and therefore degrade the network performance. As such, 5G PCI planning may be performed keeping in mind that there are no collisions or confusions. As per the Mod Principle, the UE may not be able to simultaneously receive multiple PCI with PCI Mod 3, PCI Mod 4 or PCI Mod 30. In an example of Mod 3 PCI, the neighboring cell may not be allocated PCI 25 and 28 since both may have Mod 3 as value 1.

[0055] In 5G, there may be moving NR cells, which may be added to the network at any point in time, and at any physical location. This may add an additional variable that can interfere with existing PCI allocation in the live network. As such, analytics services from MDAS may assist C-SON, which may be the MDAS consumer, in an example embodiment, to ensure that PCIs can be allocated in a non-conflicting and non-confusing manner, while also taking care of the moving NR cells.

[0056] In certain example embodiments, the PCI planning may include potential requirements. For example, the MDAS consumer may have the capability of providing location coordinates, NR-ARFCN, and unique cell IDs for a group of cells, as input to the MDAS producer. In some example embodiments, the MDAS producer may have the capability of providing analytics reports describing the location-coordinates-based clusters, cluster labels for each cell, and the probabilities of each cell belonging to a certain cluster. In other example embodiments, the MDAS producer may have the capability of providing a list of PCI IDs that can be used for moving NR cells.

[0057] FIG. 7 illustrates a table of data for PCI allocation analysis, according to certain example embodiments. Further, FIG. 8 illustrates a PCI planning analytics report, according to certain example embodiments. According to certain example embodiments, the MDAS producer may apply probabilistic clustering on the data (see FIG. 7) for PCI allocation analysis to provide cluster labels and probabilities for each cell belonging to a certain cluster, as illustrated in FIG. 8. In addition, the MDAS producer may provide a list of PCI IDs recommended to be reserved for moving NR cells, and the clustering may be performed separately on sub-groups of cells having the same NR-ARFCN.

[0058] FIG. 9 illustrates an observe-orient-decide-act (OODA) control loop and a legend, according to certain example embodiments. According to certain example embodiments, the OODA control loop may be applicable for static nodes. For example, in the observe step, the new parameters may be collected for a group of cells that can be selected by the operator. The new parameters may include location coordinates and NR-ARFCN. In the orient step, an analysis based on location coordinates may be performed by the MDAS. For instance, in certain example embodiments, the MDAS may determine the probability of each cell belonging to a certain cluster, and the cluster ID for each cell. In the decide step, a new decision may be taken. According to certain example embodiments, the C-SON may allocate non-conflicting and non confusing PCIs to cells based on the probabilities of each cell belonging to each cluster. In other example embodiments, in the act step, the derived PCI IDs may be applied to the base stations from the C-SON to the NetAct.

[0059] According to certain example embodiments, the OODA control loop may be applicable for moving nodes. For example, in the observe step, the new parameters may be collected for a moving node that has come up. The new parameters may include location coordinates and NR-

ARFCN. In the orient step, a method of PCI allocation for moving nodes may be provided. For example, the moving node may be allocated a prime number as PCI in a manner that the prime number has a maximum difference with the PCIs of the neighboring cells. Alternatively, a randomly generated prime number, for example between 1 and 1008 may also be used. In the decide step, a decision may be taken to allocate the PCI provided in the orient step to the moving node. In the act step, the PCI IDs may be applied to the moving node.

[0060] FIG. 10 illustrates a flow diagram of a method, according to certain example embodiments. In some example embodiments, the flow diagram of FIG. 10 may be performed by a network entity such as a MDAS, for instance similar to apparatus 10 illustrated in FIG. 12(a). According to one example embodiment, the method of FIG. 10 may include, at 400, receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number (NR-ARFCN), and a unique cell identifier of the one or more nodes in a given network. In addition, the method may include, at 405, determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the NR-ARFCN. Further, at 410, the method may include reserving prime numbers within a predetermined range for moving network nodes. At 415, the method may includeallocating the physical cell identifier within the predetermined range to a network node, in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un reserved list are used for static network nodes.

[0061] According to certain example embodiments, the method may also include communicating the cluster IDs, probabilities, and a list of reserved prime numbers for moving nodes, to another network entity such as C-SON. According to further example embodiments, the probabilistic clustering may be performed using a prime number of clusters. In some example embodiments, the method may also include performing data preprocessing and clean up of the location coordinates and the unique cell identifier. According to further example embodiments, the method may include allocating, for the unique cell identifier, the cluster identifier, probabilities for one of the one or more cells belonging to a certain cluster, and a list of prime numbers to be used for allocating physical cell identifiers for moving cells. According to other example embodiments, the probabilistic clustering determines a cluster to which each cell belongs, and a probability of each cell belonging to a certain cluster. In certain example embodiments, a number of clusters may be a prime number, and may be determined based on a total number of cells with a larger prime number being selected for a larger total number of cells. In some example embodiments, the total number of cells may be between 1 and 1008, and the number of clusters used may be 5, the total number of cells may be between 1008 and 2016, and the number of clusters used may be 7, or, the total number of cells may be between 2016 and

3024, and the number of clusters used may be 11. According to certain example embodiments, the method may also include setting a number of clusters as a prime number, based on the total number of cells considered for physical cell identifier allocation.

[0062] FIG. 11 illustrates a flow diagram of another method, according to certain example embodiments. In an example embodiment, the method of FIG. 11 may be performed by a network entity or network node such as, for example, a C-SON similar to apparatus 20 illustrated in FIG. 12(b). According to an example embodiment, the method of FIG. 11 may include, at 500, receiving, at a network entity, a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The method may also include, at 505, reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The method may also include, at 510, segregating cells based on the cluster identifier for static network nodes. At 515, the method may include arranging the cells in descending order of probability for the cluster. Further, at 520, the method may include selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. In addition, at 525, the method may include allocating a physical cell identifier associated with the non-prime number to a static network node.

[0063] According to certain example embodiments, the method may also include receiving, at the network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number (NR-ARFCN), and a unique cell identifier of the one or more nodes in a given network. According to other example embodiments, the method may further include forwarding the location coordinates and the unique cell identifier to another network entity. In some example embodiments, the method may also include sub-dividing the cluster into n number of parts, n being a number of clusters. In certain example embodiments, the physical cell identifier may be allocated according to probabilities of each cell belonging to a certain cluster.

[0064] FIG. 12(a) illustrates an apparatus 10 according to certain example embodiments. In an embodiment, apparatus 10 may be a node or network element in a communications network or associated with such a network, such as a MDAS, or other similar device. It should be noted that one of ordinary skill in the art would understand that apparatus 20 may include components or features not shown in FIG. 12(a).

[0065] In some example embodiments, apparatus 10 may include one or more processors, one or more computer-readable storage medium (for example, memory, storage, or the like), one or more radio access components (for example, a modem, a transceiver, or the like), and/or a user interface. In some embodiments, apparatus 10 may be configured to operate using one or more radio access technologies, such as GSM, LTE, LTE-A, NR, 5G, WLAN, WiFi, NB-IoT, Bluetooth, NFC, MulteFire, and/or any other radio access technologies. It should be noted that one of ordinary skill in the art would understand that apparatus 10 may include components or features not shown in FIG. 12(a).

[0066] As illustrated in the example of FIG. 12(a), apparatus 10 may include or be coupled to a processor 12 for processing information and executing instructions or operations. Processor 12 may be any type of general or specific purpose processor. In fact, processor 12 may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples. While a single processor 12 is shown in FIG. 12(a), multiple processors may be utilized according to other embodiments. For example, it should be understood that, in certain example embodiments, apparatus 10 may include two or more processors that may form a multiprocessor system (e.g., in this case processor 12 may represent a multiprocessor) that may support multiprocessing. According to certain example embodiments, the multiprocessor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster).

[0067] Processor 12 may perform functions associated with the operation of apparatus 10 including, as some examples, precoding of antenna gain/phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the apparatus 10, including processes illustrated in FIGs. 1-10. [0068] Apparatus 10 may further include or be coupled to a memory 14 (internal or external), which may be coupled to processor 12, for storing information and instructions that may be executed by processor 12. Memory 14 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and/or removable memory. For example, memory 14 can be comprised of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (F1DD), or any other type of non-transitory machine or computer readable media. The instructions stored in memory 14 may include program instructions or computer program code that, when executed by processor 12, enable the apparatus 10 to perform tasks as described herein.

[0069] In an embodiment, apparatus 10 may further include or be coupled to (internal or external) a drive or port that is configured to accept and read an external computer readable storage medium, such as an optical disc, USB drive, flash drive, or any other storage medium. For example, the external computer readable storage medium may store a computer program or software for execution by processor 12 and/or apparatus 10 to perform any of the methods illustrated in FIGs. 1-10.

[0070] In some embodiments, apparatus 10 may also include or be coupled to one or more antennas 15 for receiving a downlink signal and for transmitting via an uplink from apparatus 10. Apparatus 10 may further include a transceiver 18 configured to transmit and receive information.

The transceiver 18 may also include a radio interface (e.g., a modem) coupled to the antenna 15.

The radio interface may correspond to a plurality of radio access technologies including one or more of GSM, LTE, LTE-A, 5G, NR, WLAN, NB-IoT, Bluetooth, BT-LE, NFC, RFID, UWB, and the like. The radio interface may include other components, such as filters, converters (for example, digital-to-analog converters and the like), symbol demappers, signal shaping components, an Inverse Fast Fourier Transform (IFFT) module, and the like, to process symbols, such as OFDMA symbols, carried by a downlink or an uplink.

[0071] For instance, transceiver 18 may be configured to modulate information on to a carrier waveform for transmission by the antenna(s) 15 and demodulate information received via the antenna(s) 15 for further processing by other elements of apparatus 10. In other embodiments, transceiver 18 may be capable of transmitting and receiving signals or data directly. Additionally or alternatively, in some embodiments, apparatus 10 may include an input and/or output device (I/O device). In certain embodiments, apparatus 10 may further include a user interface, such as a graphical user interface or touchscreen.

[0072] In an embodiment, memory 14 stores software modules that provide functionality when executed by processor 12. The modules may include, for example, an operating system that provides operating system functionality for apparatus 10. The memory may also store one or more functional modules, such as an application or program, to provide additional functionality for apparatus 10. The components of apparatus 10 may be implemented in hardware, or as any suitable combination of hardware and software. According to an example embodiment, apparatus 10 may optionally be configured to communicate with apparatus 20 via a wireless or wired communications link 70 according to any radio access technology, such as NR.

[0073] According to certain example embodiments, processor 12 and memory 14 may be included in or may form a part of processing circuitry or control circuitry. In addition, in some embodiments, transceiver 18 may be included in or may form a part of transceiving circuitry. [0074] As discussed above, according to certain example embodiments, apparatus 10 may be a UE for example. According to certain embodiments, apparatus 10 may be controlled by memory 14 and processor 12 to perform the functions associated with example embodiments described herein. For instance, in one embodiment, apparatus 10 may be controlled by memory 14 and processor 12 to receive location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number (NR-ARFCN), and a unique cell identifier of the one or more nodes in a given network. Apparatus 10 may further be controlled by memory 14 and processor 12 to determine a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the NR- ARFCN. In addition, apparatus 10 may be controlled by memory 14 and processor 12 to reserve prime numbers within a predetermined range for moving network nodes. Further, apparatus 10 may be controlled by memory 14 and processor 12 to allocate the physical cell identifier within the predetermined range to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non prime numbers from an un-reserved list are used for static network nodes.

[0075] FIG. 12(b) illustrates an apparatus 20 according to certain example embodiments. In an example embodiment, the apparatus 20 may be a network element, a node, host, or server in a communication network or serving such a network. For example, apparatus 20 may be a C-SON, SON, or other similar device, associated with a radio access network (RAN), such as an LTE network, 5G or NR. It should be noted that one of ordinary skill in the art would understand that apparatus 20 may include components or features not shown in FIG. 12(b).

[0076] As illustrated in the example of FIG. 12(b), apparatus 20 may include a processor 22 for processing information and executing instructions or operations. Processor 22 may be any type of general or specific purpose processor. For example, processor 22 may include one or more of general-purpose computers, special purpose computers, microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and processors based on a multi-core processor architecture, as examples. While a single processor 22 is shown in FIG. 12(b), multiple processors may be utilized according to other embodiments. For example, it should be understood that, in certain embodiments, apparatus 20 may include two or more processors that may form a multiprocessor system (e.g., in this case processor 22 may represent a multiprocessor) that may support multiprocessing. In certain embodiments, the multiprocessor system may be tightly coupled or loosely coupled (e.g., to form a computer cluster.

[0077] According to certain example embodiments, processor 22 may perform functions associated with the operation of apparatus 20, which may include, for example, precoding of antenna gain/phase parameters, encoding and decoding of individual bits forming a communication message, formatting of information, and overall control of the apparatus 20, including processes illustrated in FIGs. 1-9 and 11.

[0078] Apparatus 20 may further include or be coupled to a memory 24 (internal or external), which may be coupled to processor 22, for storing information and instructions that may be executed by processor 22. Memory 24 may be one or more memories and of any type suitable to the local application environment, and may be implemented using any suitable volatile or nonvolatile data storage technology such as a semiconductor-based memory device, a magnetic memory device and system, an optical memory device and system, fixed memory, and/or removable memory. For example, memory 24 can be comprised of any combination of random access memory (RAM), read only memory (ROM), static storage such as a magnetic or optical disk, hard disk drive (HDD), or any other type of non-transitory machine or computer readable media. The instructions stored in memory 24 may include program instructions or computer program code that, when executed by processor 22, enable the apparatus 20 to perform tasks as described herein. [0079] In an embodiment, apparatus 20 may further include or be coupled to (internal or external) a drive or port that is configured to accept and read an external computer readable storage medium, such as an optical disc, USB drive, flash drive, or any other storage medium. For example, the external computer readable storage medium may store a computer program or software for execution by processor 22 and/or apparatus 20 to perform the methods illustrated in FIGs. 1-9 and 11.

[0080] In certain example embodiments, apparatus 20 may also include or be coupled to one or more antennas 25 for transmitting and receiving signals and/or data to and from apparatus 20. Apparatus 20 may further include or be coupled to a transceiver 28 configured to transmit and receive information. The transceiver 28 may include, for example, a plurality of radio interfaces that may be coupled to the antenna(s) 25. The radio interfaces may correspond to a plurality of radio access technologies including one or more of GSM, NB-IoT, LTE, 5G, WLAN, Bluetooth, BT-LE, NFC, radio frequency identifier (RFID), ultrawideband (UWB), MulteFire, and the like. The radio interface may include components, such as filters, converters (for example, digital-to- analog converters and the like), mappers, a Fast Fourier Transform (FFT) module, and the like, to generate symbols for a transmission via one or more downlinks and to receive symbols (for example, via an uplink).

[0081] As such, transceiver 28 may be configured to modulate information on to a carrier waveform for transmission by the antenna(s) 25 and demodulate information received via the antenna(s) 25 for further processing by other elements of apparatus 20. In other embodiments, transceiver 18 may be capable of transmitting and receiving signals or data directly. Additionally or alternatively, in some embodiments, apparatus 20 may include an input and/or output device (I/O device).

[0082] In an embodiment, memory 24 may store software modules that provide functionality when executed by processor 22. The modules may include, for example, an operating system that provides operating system functionality for apparatus 20. The memory may also store one or more functional modules, such as an application or program, to provide additional functionality for apparatus 20. The components of apparatus 20 may be implemented in hardware, or as any suitable combination of hardware and software. [0083] According to some embodiments, processor 22 and memory 24 may be included in or may form a part of processing circuitry or control circuitry. In addition, in some embodiments, transceiver 28 may be included in or may form a part of transceiving circuitry.

[0084] As used herein, the term “circuitry” may refer to hardware-only circuitry implementations (e.g., analog and/or digital circuitry), combinations of hardware circuits and software, combinations of analog and/or digital hardware circuits with software/firmware, any portions of hardware processor(s) with software (including digital signal processors) that work together to cause an apparatus (e.g., apparatus 10 and 20) to perform various functions, and/or hardware circuit(s) and/or processor(s), or portions thereof, that use software for operation but where the software may not be present when it is not needed for operation. As a further example, as used herein, the term “circuitry” may also cover an implementation of merely a hardware circuit or processor (or multiple processors), or portion of a hardware circuit or processor, and its accompanying software and/or firmware. The term circuitry may also cover, for example, a baseband integrated circuit in a server, cellular network node or device, or other computing or network device.

[0085] As introduced above, in certain embodiments, apparatus 20 may be a be a network element, a node, host, or server in a communication network or serving such a network. For example, apparatus 20 may be a C-SON, SON, or other similar device associated with a radio access network (RAN), such as an LTE network, 5G or NR. According to certain embodiments, apparatus 20 may be controlled by memory 24 and processor 22 to perform the functions associated with any of the embodiments described herein.

[0086] For instance, in one embodiment, apparatus 20 may be controlled by memory 24 and processor 22 to receive a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical identifiers for moving network nodes. Apparatus 20 may also be controlled by memory 24 and processor 22 to reserve prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. Apparatus 20 may further be controlled by memory 24 and processor 22 to segregate cells based on the cluster identifier for static network nodes. In addition, apparatus 20 may be controlled by memory 24 and processor 22 to arrange the cells in descending order of probability for the cluster. Further, apparatus 20 may be controlled by memory 24 and processor 22 to select a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes. Apparatus 20 may also be controlled by memory 24 and processor 22 to allocate a physical cell identifier associated with the non-prime number to a static network node.

[0087] Further example embodiments may provide means for performing any of the functions or procedures described herein. For example, certain example embodiments may be directed to an apparatus that includes means for receiving location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number (NR-ARFCN), and a unique cell identifier of the one or more nodes in a given network. The apparatus may further include means for determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the NR-

ARFCN. In addition, the apparatus may include means for reserving prime numbers within a predetermined range for moving network modes. Further, the apparatus may include means for allocating the physical cell identifier within the predetermined range to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

[0088] Other example embodiments may be directed to a further apparatus that includes means for receiving a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes. The apparatus may also include means for reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list. The apparatus may further include means for segregating cells based on the cluster identifier for static network nodes. Further, the apparatus may include means for arranging the cells in descending order of probability for the cluster. In addition, the apparatus may include means for selecting a prime number from reserved physical cell identifiers for moving network nodes, and, a non-prime number from the un-reserved physical cell identifiers for static network nodes. The apparatus may also include means for allocating a physical cell identifier associated with the non-prime number to a static network node. [0089] Certain example embodiments described herein provide several technical improvements, enhancements, and /or advantages. In some example embodiments, it may be possible to allocate PCIs in a manner that ensures that even the cells in neighboring clusters do not end up in any PCI conflict or confusion even if the cells are physically far apart. It may also be possible to allocate PCIs with zero instances of PCI collision and PCI confusion using probabilistic clustering with prime numbers of clusters, and reserving prime numbers for allocating PCI for moving nodes. In other example embodiments, it may be possible to use prime number of clusters to ensure that the minimum distance between two neighboring cells is a prime number.

[0090] A computer program product may comprise one or more computer-executable components which, when the program is run, are configured to carry out some example embodiments. The one or more computer-executable components may be at least one software code or portions of it. Modifications and configurations required for implementing functionality of an example embodiment may be performed as routine(s), which may be implemented as added or updated software routine(s). Software routine(s) may be downloaded into the apparatus. [0091] As an example, software or a computer program code or portions of it may be in a source code form, object code form, or in some intermediate form, and it may be stored in some sort of carrier, distribution medium, or computer readable medium, which may be any entity or device capable of carrying the program. Such carriers may include a record medium, computer memory, read-only memory, photoelectrical and/or electrical carrier signal, telecommunications signal, and software distribution package, for example. Depending on the processing power needed, the computer program may be executed in a single electronic digital computer or it may be distributed amongst a number of computers. The computer readable medium or computer readable storage medium may be a non-transitory medium. [0092] In other example embodiments, the functionality may be performed by hardware or circuitry included in an apparatus (e.g., apparatus 10 or apparatus 20), for example through the use of an application specific integrated circuit (ASIC), a programmable gate array (PGA), a field programmable gate array (FPGA), or any other combination of hardware and software. In yet another example embodiment, the functionality may be implemented as a signal, a non-tangible means that can be carried by an electromagnetic signal downloaded from the Internet or other network.

[0093] According to an example embodiment, an apparatus, such as a node, device, or a corresponding component, may be configured as circuitry, a computer or a microprocessor, such as single-chip computer element, or as a chipset, including at least a memory for providing storage capacity used for arithmetic operation and an operation processor for executing the arithmetic operation.

[0094] One having ordinary skill in the art will readily understand that the invention as discussed above may be practiced with steps in a different order, and/or with hardware elements in configurations which are different than those which are disclosed. Therefore, although the invention has been described based upon these example embodiments, it would be apparent to those of skill in the art that certain modifications, variations, and alternative constructions would be apparent, while remaining within the spirit and scope of example embodiments. Although the above embodiments refer to 5G NR and LTE technology, the above embodiments may also apply to any other present or future 3GPP technology, such as LTE-advanced, and/or fourth generation (4G) technology.

[0095] Partial Glossary:

[0096] 3GPP 3^rd Generation Partnership Project

[0097] 4G 4^th Generation Wireless Technology [0098] 5G 5^th Generation Wireless Technology

[0099] AI Artificial Intelligence

[0100] C-SON Centralized SON [0101] eNB Enhanced Node B

[0102] gNB 5G or NR Base Station [0103] LTE Long Term Evolution

[0104] MDAS Management Data Analytics Services [0105] ML Machine Learning

[0106] NR New Radio

[0107] NR-ARFCN New Radio Absolute Radio-Frequency Channel Number [0108] OODA Observe, Orient, Decide, Act

[0109] PCI Physical Cell ID

[0110] PSS Packet-switched Streaming Service [0111] SON Self-Organizing Network [0112] UE User Equipment

Claims

1. A method, comprising: receiving, at a network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network; determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio- frequency channel number; reserving prime numbers within a predetermined range for moving network nodes; and allocating the physical cell identifier within the predetermined range to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

2. The method according to claim 1 , wherein the probabilistic clustering is performed using a prime number of clusters.

3. The method according to claims 1 or 2, further comprising: performing data preprocessing and clean up of the location coordinates and the unique cell identifier.

4. The method according to any of claims 1-3, further comprising allocating, for the unique cell identifier, the cluster identifier, probabilities for one of the one or more cells belonging to a certain cluster, and a list of prime numbers to be used for allocating physical cell identifiers for moving cells.

5. The method according to any of claims 1-4, wherein the probabilistic clustering determines a cluster to which each cell belongs, and a probability of each cell belonging to a certain cluster.

6. The method according to any of claims 1-5, wherein a number of clusters is a prime number, and is determined based on a total number of cells with a larger prime number being selected for a larger total number of cells.

7. The method according to claim 6, wherein the total number of cells is between 1 and 1008, and the number of clusters used is 5, the total number of cells is between 1008 and 2016, and the number of clusters used is 7, or the total number of cells is between 2016 and 3024, and the number of clusters used is

11.

8. The method according to claim 6, further comprising setting a number of clusters as a prime number, based on the total number of cells considered for physical cell identifier allocation.

9. A method, comprising: receiving, at a network entity, a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes; reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list; segregating cells based on the cluster identifier for static network nodes; arranging the cells in descending order of probability for the cluster; selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes; and allocating a physical cell identifier associated with the non-prime number to a static network node.

10. The method according to claim 9, further comprising receiving, at the network entity, location coordinates of one or more nodes of one or more cells in a new radio absolute radio- frequency channel number, and a unique cell identifier of the one or more nodes in a given network.

11. The method according to claims 9 or 10, further comprising forwarding the location coordinates and the unique cell identifier to another network entity.

12. The method according to any of claims 9-11, further comprising sub-dividing the cluster into n number of parts, n being a number of clusters.

13. The method according to any of claims 9-12, wherein the physical cell identifier is allocated according to probabilities of each cell belonging to a certain cluster.

14. An apparatus, comprising: at least one processor; and at least one memory comprising computer program code, the at least one memory and the computer program code are configured, with the at least one processor to cause the apparatus at least to receive location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network; determine a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio- frequency channel number; reserve prime numbers within a predetermined range for moving network nodes; and allocate the physical cell identifier within the predetermined range to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un-reserved list are used for static network nodes.

15. The apparatus according to claim 14, wherein the probabilistic clustering is performed using a prime number of clusters.

16. The apparatus according to claims 14 or 15, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to perform data preprocessing and clean up of the location coordinates and the unique cell identifier.

17. The apparatus according to any of claims 14-16, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to allocate, for the unique cell identifier, the cluster identifier, probabilities for one of the one or more cells belonging to a certain cluster, and a list of prime numbers to be used for allocating physical cell identifiers for moving cells.

18. The apparatus according to any of claims 14-17, wherein the probabilistic clustering determines a cluster to which the each cell belongs, and a probability of each cell belonging to a certain cluster.

19. The apparatus according to any of claims 14-18, wherein a number of clusters is a prime number, and is determined based on a total number of cells with a larger prime number being selected for a larger total number of cells.

20. The apparatus according to claim 19, wherein the total number of cells is between 1 and 1008, and the number of clusters used is 5, the total number of cells is between 1008 and 2016, and the number of clusters used is 7, or the total number of cells is between 2016 and 3024, and the number of clusters used is

11.

21. The apparatus according to claim 19, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to set a number of clusters as a prime number, based on the total number of cells considered for physical cell identifier allocation.

22. An apparatus, comprising: at least one processor; and at least one memory comprising computer program code, the at least one memory and the computer program code are configured, with the at least one processor to cause the apparatus at least to receive a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes; reserve prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list; segregate cells based on the cluster identifier for static network nodes; arrange the cells in descending order of probability for the cluster; select a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes; and allocate a physical cell identifier associated with the non-prime number to a static network node.

23. The apparatus according to claim 22, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to receive location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network.

24. The apparatus according to claims 22 or 23, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to forward the location coordinates and the unique cell identifier to another network entity.

25. The apparatus according to any of claims 22-24, wherein the at least one memory and the computer program code are further configured, with the at least one processor to cause the apparatus at least to sub-divide the cluster into n number of parts, n being a number of clusters.

26. The apparatus according to any of claims 22-25, wherein the physical cell identifier is allocated according to probabilities of each cell belonging to a certain cluster.

27. An apparatus, comprising: means for receiving location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network; means for determining a physical cell identifier and cluster identifier by performing probabilistic clustering on the location coordinates, the unique cell identifier, and the new radio absolute radio-frequency channel number; means for reserving prime numbers within a predetermined range for moving network nodes; and means for allocating the physical cell identifier within the predetermined range to a network node in a manner that the physical cell identifier being associated with one of the reserved prime numbers is used for a moving network node, and non-prime numbers from an un reserved list are used for static network nodes.

28. The apparatus according to claim 27, wherein the probabilistic clustering is performed using a prime number of clusters.

29. The apparatus according to claims 27 or 28, further comprising means for performing data preprocessing and clean up of the location coordinates and the unique cell identifier.

30. The apparatus according to any of claims 27-29, further comprising means for allocating, for the unique cell identifier, the cluster identifier, probabilities for one of the one or more cells belonging to a certain cluster, and a list of prime numbers to be used for allocating physical cell identifiers for moving cells.

31. The apparatus according to any of claims 27-30, wherein the probabilistic clustering determines a cluster to which each cell belongs, and a probability of each cell belonging to a certain cluster.

32. The apparatus according to any of claims 27-31, wherein a number of clusters is a prime number, and is determined based on a total number of cells with a larger prime number being selected for a larger total number of cells.

33. The apparatus according to claim 32, wherein the total number of cells is between 1 and 1008, and the number of clusters used is 5, the total number of cells is between 1008 and 2016, and the number of clusters used is 7, or the total number of cells is between 2016 and 3024, and the number of clusters used is

11.

34. The apparatus according to claim 32, further comprising means for setting a number of clusters based on the predetermined range of prime numbers.

35. An apparatus, comprising: means for receiving a cluster identifier, probabilities for a cell belonging to a cluster, and a list of prime numbers reserved as physical cell identifiers for moving network nodes; means for reserving prime numbers for moving network nodes within a predetermined range for the physical cell identifiers to be used for moving network nodes in the list; means for segregating cells based on the cluster identifier for static network nodes; means for arranging the cells in descending order of probability for the cluster; means for selecting a prime number from reserved physical cell identifiers for moving network nodes, and a non-prime number from un-reserved physical cell identifiers for static network nodes; and means for allocating a physical cell identifier associated with the non-prime number to a static network node.

36. The apparatus according to claim 35, further comprising means for receiving location coordinates of one or more nodes of one or more cells in a new radio absolute radio-frequency channel number, and a unique cell identifier of the one or more nodes in a given network.

37. The apparatus according to claims 35 or 36, further comprising means for forwarding the location coordinates and the unique cell identifier to another network entity.

38. The apparatus according to any of claims 35-37, further comprising means for sub- dividing the cluster into n number of parts, n being a number of clusters.

39. The apparatus according to any of claims 35-38, wherein the physical cell identifier is allocated according to a priority of the physical cell identifier compared to other physical cell identifiers.

40. A non-transitory computer readable medium comprising program instructions stored thereon for performing the method according to any of claims 1-13.

41. An apparatus comprising circuitry configured to cause the apparatus to perform a process according to any of claims 1-13.