WO2015038097A1

WO2015038097A1 - Mobile and base station grouping in heterogeneous networks

Info

Publication number: WO2015038097A1
Application number: PCT/US2013/058989
Authority: WO
Inventors: Amitav Mukherjee; Salam Akoum; Joydeep Acharya
Original assignee: Hitachi, Ltd.
Priority date: 2013-09-10
Filing date: 2013-09-10
Publication date: 2015-03-19

Abstract

Example implementations described herein are directed to methods for low-complexity grouping of UEs and base stations that also take into account traffic loads of candidate serving cells, base station and UE capabilities, and wireless link quality. In example implementations, a base station receives measurement information from associated user equipment and reporting information from neighboring base stations to calculate grouping assignments for the associated user equipment and/or the neighboring base stations.

Description

MOBILE AND BASE STATION GROUPING IN HETEROGENEOUS NETWORKS

BACKGROUND

Field

[0001] The present application is related to wireless systems, and more specifically, towards user equipment (UE) and base station (BS) grouping in heterogeneous networks.

Related Art

[0002] In related art Long Term Evolution (LTE) systems, emphasis has been placed on deployment of dense heterogeneous networks and new carrier types (NCT), as well as coverage enhancements via vertical beamforming. Dynamic reconfigurations of transmission point activity states (such as small cell on off switching), carrier switching, and coverage may occur at the radio subframe level. To avoid degradations in Quality of Service (QoS), multiple UEs that are affected by these changes may need to be simultaneously re-associated with accessible adjacent base stations. Macro and small cell base stations may also need to form cooperation clusters over non-ideal backhaul links in a dynamic manner.

[0003] Related art methods for base station association in LTE include cell reselection (RRC IDLE mode) and conventional handover (HO). In conventional HO, a UE is handed over from a source base station to a target base station based mainly on measured downlink received power (RSRP). Furthermore, conventional HO is carried out individually on a UE-by-UE basis. In Release 12 (Rel-12) LTE, very dense small cell deployment scenarios are possible, with up to 40 small cells within the coverage of a macro base station. Furthermore, due to dynamic reconfigurations of the small cell tier, multiple UEs may need to be simultaneously re-associated with new base stations. Conventional HO may not be dynamic enough to handle such scenarios, which raises the need for more efficient grouping algorithms that take into account traffic loads of candidate serving cells, UE and eNB capabilities, QoS requirements, and the impact of admitting additional UEs.

[0004] One proposed solution in the related art involves a two-step process based on stable matching. The stable matching is used to first match femtocells with UEs, followed by matching femtocells to wireless operators. The related art solution utilizes pricing in the utility function and utilizes known algorithms for matching. However, the related art solution also assumes complete instantaneous channel knowledge at base stations, and does not take into account traffic loads of candidate serving cells, UE and enhanced NodeB (eNB) capabilities, or QoS. The related art solution may not be suitable for implementation in Rel-12 LTE and beyond.

Summary

[0005] Example implementations involve a base station which can include a memory configured to store measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and a processor, configured to assign the associated UEs to the one or more neighboring base stations from a calculation based on the measurement information and the reporting information. The base station may be in the form of an eNB that manages a small cell, a macro base station, and so on, depending on the desired implementation. [0006] Example implementations involve a method, which can include storing, at a base station, measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and assigning the associated UEs to the one or more neighboring base stations from a calculation based on the measurement information and the reporting information.

[0007] Example implementations involve a computer program, which can include code for storing, at a base station, measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and code for assigning the associated UEs to the one or more neighboring base stations from a calculation based on the measurement information and the reporting information. The computer program may also be stored as instructions in a computer readable storage medium or a computer readable signal medium as described below.

[0008] Example implementations involve a base station which can include a memory configured to store measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and a processor, configured to assign the one or more neighboring base stations to one or more groups from a calculation based on the measurement information and the reporting information. The base station may be in the form of an eNB that manages a small cell, a macro base station, and so on, depending on the desired implementation.

[0009] Example implementations involve a method, which can include storing, at a base station, measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and assigning the neighboring base stations to one or more groups from a calculation based on the measurement information and the reporting information.

[0010] Example implementations involve a computer program, which can include code for storing, at a base station, measurement information of User Equipment (UEs) associated with the base station and reporting information from one or more neighboring base stations; and code for assigning the one or more neighboring base stations to one or more groups from a calculation based on the measurement information and the reporting information. The computer program may also be stored as instructions in a computer readable storage medium or a computer readable signal medium as described below.

Brief Description of the Drawings

[0011] FIG. 1 illustrates a small cell deployment scenario utilizing discontinuous transmission (DTX) or on/off switching, in accordance with an example implementation.

[0012] FIG. 2 illustrates an example hardware configuration of logical small cell macro base station modules and the communication during reassociation, in accordance with an example implementation.

[0013] FIG. 3(a) illustrates a computation module located at the base station performing the process initialization, in accordance with an example implementation. FIG. 3(b) illustrates a first application example of the computation module in a system diagram, in accordance with an example implementation. FIG. 3(c) illustrates a second application example of the computation module in a system diagram, in accordance with an example implementation. [0014] FIG. 4 illustrates an example flowchart for the reassociation process for assigning UEs, in accordance with an example implementation.

[0015] FIG. 5 illustrates a flowchart for the confirmation and grouping procedure, in accordance with an example implementation.

[0016] FIG. 6 illustrates an example grouping outcome, in accordance with an example implementation.

[0017] FIG. 7 illustrates a flowchart of a reassociation process for grouping BSs, in accordance with an example implementation.

[0018] FIG. 8 illustrates an example grouping outcome, in accordance with an example implementation.

[0019] FIG. 9 illustrates a flowchart of a grouping algorithm in accordance with an example implementation.

Detailed Description

[0020] The following detailed description provides further details of the figures and example implementations of the present application. Reference numerals and descriptions of redundant elements between figures are omitted for clarity. Terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term "automatic" may involve fully automatic or semi-automatic implementations involving user or administrator control over certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing implementations of the present application. The terms eNB, SC, BS and pico cells may be utilized interchangeably throughout the example implementations. The implementations described herein are also not intended to be limiting, and can be implemented in various ways, depending on the desired implementation.

[0021] Example implementations are directed to methods for low-complexity grouping of UEs and base stations that also take into account traffic loads of candidate serving cells and wireless link quality, which may hold advantages over existing macro-oriented handover methods.

[0022] Example implementations involve UE and base station grouping algorithms that are based on the principles of stable matching theory, though other matching algorithms may also be used depending on the desired implementation. The example implementations may involve three different scenarios: Associating a group of UEs with a set of available base stations, forming clusters of cooperating base stations from a large candidate set, and forming pairs or larger sets of cooperating UEs such as for direct device-to-device (D2D) communications.

[0023] To form stable matches, utility functions are computed for the participating UEs and base stations to determine their most preferred partners. Preferences may be determined based on the quality of the wired link or wireless channel between candidates, current traffic loads and interference conditions, and related factors. Groups are then formed such that all UEs and base stations achieve a certain minimum satisfaction with their assignments. The grouping algorithms are intended to be low-complexity and suitable for implementation in a distributed manner without excessive overhead. They also take into account at least one of the following: (i) traffic loads of candidate serving cells, (ii) UE and eNB capabilities (multiple-input multiple- output (MIMO) capabilities, carrier aggregation (CA) configuration, legacy device/not legacy device, etc.) (iii) UE QoS requirements, and (iv) the impact of admitting additional UEs, thereby achieving load balancing and other desirable attributes. For convenience, the proposed methods are referred to as "reassociation" requirements.

[0024] In a typical heterogeneous network, dynamic reconfigurations of transmitter activity states, power levels, beamforming coverage, and frequency carriers (carrier aggregation) are expected to occur to handle time-varying traffic and for purposes of interference mitigation. As non-limiting examples, on off switching of small cells and New Carrier Type (NCT) are several features that are supported in LTE Rel- 12, while Full Dimension MIMO (FD-MIMO) for vertical beamforming may also be supported. For each of these features, a non-limiting example scenario of an example implementation is provided below.

[0025] FIG. 1 illustrates a small cell (SC) deployment scenario utilizing discontinuous transmission (DTX), in accordance with an example implementation. On off switching or discontinuous transmission (DTX) of small cells may be utilized for energy saving and interference mitigation, as illustrated in FIG. 1. When the 'off duration is longer than a few subframes, the UEs originally associated with the deactivated small cell should be quickly re-associated to an adjacent small cell/macro eNB. It may be possible in an example small cell environment (SCE) scenario to have no macro coverage available, none of the small cells share a reliable backhaul, and adjacent small cells with highly variable traffic loads. Thus, UE reassociation methods may be needed for DTX scenarios. [0026] In the case of NCT, the new carrier may be deployed in non-standalone mode along with a Backward Compatible Carrier Type (BCCT), or as a standalone carrier that cannot support legacy UEs. In a small cell deploying NCT with a small fraction of legacy UEs, the small cell can switch all carriers to standalone NCT (S-NCT) mode to maximize throughput for the Rel-12 UEs. In this case, legacy UEs need to be reassociated with a suitable adjacent BCCT taking into account at least RSRP and existing BCCT load.

[0027] Dynamic clustering of small cells or a combination of macro eNBs and SCs can be utilized for the purposes of joint transmission (CoMP), coordinated scheduling/beamforming (CS/CB), or other interference mitigation methods. Thus, a need arises for dynamic formation of groups of base stations, where the size of the group is at least two.

[0028] An eNB deploying FD-MIMO via a 2D array can dynamically pinpoint downlink beams in both azimuthal and elevation dimensions. Depending on UE clustering, UEs that were in coverage can find themselves out of coverage when the eNB redirects its FD- MIMO beams to a denser UE cluster. Thus, a new use case of inter-beam handover will arise in this situation where UEs need to be grouped with adjacent base stations based on their beamforming coverage and current traffic loads.

[0029] Other example scenarios include grouping in Control-plane/User-plane split scenarios, where a UE receives control channels from a macro base station and data channels from a small cell. Here, a grouping of size 3 can be constructed of the form {macro, SC, UE} . An example of UE-to-UE grouping arises in device-to-device (D2D) communications, where UEs communicate directly without routing signals through a base station.

[0030] The grouping computations are generally carried out at a single macro eNB or small cell, after acquiring relevant information from UEs and neighbor base stations. The hardware modules involved in this process at a set of neighboring base stations are shown in FIG. 2. FIG. 2 illustrates an example hardware configuration of logical small cell/macro base station modules and the communication during reassociation, in accordance with an example implementation. In the example configuration of FIG. 2, eNBl and eNB2 each have a controller 201 , a signal processor 202, and a memory 203. The controller 201 can include a processor utilized to manage the respective eNB and can also manage the signal processor 202 to communicate to other eNBs by a backhaul through an X2 interface. The signal processor 202 can process signals received at the respective eNB. During the reassociation process, the eNBs may perform a process initialization, an information exchange and a confirmation procedure, as further described below. The memory 203 can be used to store the algorithms described herein for execution by the processor 201 , and can also be configured to store measurement information from associated UEs as well as reporting information from neighboring BSs. Memory may take the form of a computer readable signal medium or a computer readable storage medium as described below.

[0031] FIG. 3(a) illustrates a computation module located at the base station performing the process initialization, in accordance with an example implementation. The grouping algorithm can be executed in a computation module 300 located at the base station that initiates the reassociation process, as illustrated in FIG. 3(a). It has the following general structure. [0032] Process Initialization and Information Exchange 301 : During the process initialization and information exchange procedure, example implementations of the computation module 300 define preference functions for the transceivers or devices to be grouped. For the example of eNB-UE grouping, preference functions can be defined as FE(.) at eNBs, and FU(.) at UEs. Preference functions may be different for members of the same category in general (e.g., different base stations may have different preference functions).

[0033] Preference functions of adjacent base stations may take as inputs at least one or more of the following factors: their current traffic loads, UE signal strengths based on RSRPs or RSRQs, UE and eNB capabilities (MIMO capabilities, CA configuration, legacy device or not, etc.), backhaul quality to neighbor eNBs, UE QoS requirements, and the impact of admitting additional UEs. In FIG. 3(a), BS A and BS B report their preference functions and associated information needed to evaluate the preferences to the BS that initiates the reassociation process at 301 -1.

[0034] Preference functions of UEs take as inputs at least one or more of the following factors: UE signal strengths to adjacent eNBs or UEs based on RSRPs or Reference Signal Received Quality (RSRQs), UE and eNB capabilities (MIMO capabilities, CA configuration, legacy device/not legacy device, etc.), UE QoS requirements, UE battery life or other energy-efficiency metrics.

[0035] Confirmation and grouping procedure 302: For each device, the associated preference function is used to compute a list of rankings of the potential eNBs and/or UEs that it may be grouped with. Higher rankings indicate a more preferred group partner. Given the rankings of potential partners computed at 301 , a numerical algorithm finds best pairs or groups to satisfy some pre-defined network performance metric. The results are communicated to adjacent eNBs, wherein the adjacent eNBs commence service to the newly associated UEs at 302-1. The numerical algorithm may be based on stable matching or similar methods. Examples of performance metrics may include minimal delay/latency, maximum throughput, max-min cell edge throughput, and other related measures.

[0036] Described below are two of the example application scenarios. The first application example demonstrates eNB-UE grouping, while the second application example demonstrates eNB-eNB grouping. However, the present application is not limited to these two application examples. Similar concepts described herein can be used to conduct UE-UE grouping, depending on the desired implementation.

[0037] FIG. 3(b) illustrates a first application example for a system in accordance with an example implementation. Suppose BS x receives a DTX command or initiates load balancing and thereby handing over the UEs to the neighboring BS. At 301 , BS x initiates reassociation and requests reporting information from the eNBs via a backhaul, as shown in the dashed lines. BS x also collects measurement information from the UEs associated with BS x, which can include, for example, SINR between the respective UE and the adjacent BSs A and B. Based on the received measurement information and reporting information, BS x calculates the assignment of the associated UEs, which can be based on rankings of associated UEs to neighboring BSs and neighboring BSs to UEs. BS x then reassigns the associated UEs before shutting down or to complete the load balancing. In this example, BS x reassigns the associated UEs to BS B as illustrated by the solid line arrows. [0038] FIG. 3(c) illustrates a second application example for a system in accordance with an example implementation. Suppose BS x receives a DTX command or initiates load balancing and is part of a BS cluster with BS B. At 301 , BS x initiates reassociation and requests reporting information from the eNBs via a backhaul, as shown in the dashed lines. BS x also collects measurement information from the UEs associated with BS x, which can include, for example, SINR between the respective UE and the adjacent BSs A and B. Based on the received measurement information and reporting information, BS x calculates new groupings for neighboring BSs that are presently grouped with BS x, which can be based on rankings of associated UEs to neighboring BSs and neighboring BSs to UEs. BS x then reassigns the neighboring BSs to new groups before shutting down or to complete the load balancing. In this example, BS x reassigns BS B to be part of the group of BS A and the Macro BS as illustrated by the solid line arrows. More detailed examples of the first application example and the second application example are provided below.

[0039] First application example: Consider a small cell (denoted as SC x) that is either commanded to initiate DTX after T radio frames by a macro eNB/clusterhead, or decides to do so autonomously, depending on the desired implementation. The DTX SC is assumed to operate two component carriers, one BCCT, and one NCT, thus supporting a mix of legacy and Rel-12 UEs. The DTX SC initiates the reassociation process for the UEs it is currently serving, by exchanging information with active adjacent base stations (corresponding to 301 in FIG. 3(a)). A high-level overview of the reassociation process is shown in the flowchart of FIG. 4. [0040] FIG. 4 illustrates an example flowchart for the reassociation process for assigning UEs, in accordance with an example implementation. The flowchart beings at 400, when SC x receives/initiates the DTX command to commence after T radio frames. At 401 , SC x instructs the UEs associated with SC x to conduct and report measurement information (e.g. signal strength measurements such as intra/inter-frequency measurements, etc.). At 402, SC x contacts neighboring active eNBs, including both SC and macro BS. At 403, each of the neighbor eNBs provides reporting information, such as traffic loads, carrier configuration information, and preference function categories associated with the neighbor eNB, depending on the desired implementation. At 404, SC x computes eNB-UE and UE-eNB rankings based on the report from the eNBs and UEs, At 405, SC x calculates a grouping of the UEs with the neighbor eNBs from the rankings and informs the neighbor eNBs of the proposed grouping.

[0041] At 406 if the neighbor eNBs accept the proposed grouping (YES), then the UEs are associated with the neighbor eNBs from the proposed grouping and the process ends. If not (NO), then the grouping is recomputed for the remaining UEs at 407, based on which of the neighbor eNBs are rejecting the proposed grouping. The neighbor eNBs may accept or reject the proposed grouping for various reasons, depending on the desired implementation. For example, if the neighbor eNB also received a DTX command, then that neighbor eNB may refuse all proposed groupings and inform SC x that it is shutting down. If the neighbor eNB is active and is presently undergoing heavy traffic load, then the neighbor eNB may also reject the proposed grouping. [0042] As a non-limiting example, the preference functions for the adjacent active eNBs may take into account UE RSRQs, their own traffic loads on the BCCT/NCT, and UE capabilities:

F_E = RSRQ - a (BCCTload)\_{legacy ( )}

[0043] RSRQ is the signal to interference/noise ratio (SINR) of a particular UE from that eNB, a is a constant coefficient, BCCTload is a metric between 0 and 1 , where values close to 1 indicate that the BCCT of an eNB is highly overloaded with legacy UEs already being served, and lie_gaCy is an indicator function that is equal to 1 if a candidate UE is a legacy UE, 0 otherwise. The UE preference functions F_v can be based purely on RSRQs, i.e., they have the highest preference for eNBs that can provide the highest SINR:

F_u = RSRQ ₍₂₎

[0044] The example performance metric can be assumed to be to minimize service delay of the UEs. This is achieved by reassociating legacy UEs to lightly-loaded BCCTs which will have a lower scheduling delay and more frequency resources available for serving the reassociated UEs.

[0045] After evaluating these preference functions for different UEs and eNBs, the DTX SC obtains the list of rankings of the adjacent eNBs for their preferred UEs, and can also compute the corresponding rankings for the UEs to be reassociated (i.e. no computations are performed by UEs).

[0046] Assume that UEs have the highest RSRQ from the base station located nearest to them. Therefore, Legacy UE-1 in FIG. 6 will have the highest RSRQ and subsequently highest preference rank for eNB 3, for example. An example set of rankings is then shown below in Table 1 for the case where each eNB is willing to admit at most one new UE. Here, rankings are in descending order of preferred partners.

Table 1: Example of rankings computed based on preference functions.

[0047] The example description of the grouping procedure (302 in FIG. 3) is shown in the flowchart of FIG. 5. The flow being at 500 wherein the DTX SC initializes all of the associated UEs and all of the neighboring eNBs as unattached. At 501 , the DTX SC repeats the flow from 502 to 504 while there is an unattached UE that has an eNB that it can potentially attach to. At 502, the DTX SC calculates the highest ranked eNB for which the unattached UE has not yet approached. At 503, if the calculated eNB is willing to accept new UEs, then the unattached UE becomes provisionally paired to the calculated eNB. If not, then the eNB is already attached to another UE, and a calculation is made as to whether the calculated eNB prefers the unattached UE to the already attached UE at 504. If it does, then the already attached UE is detached, and the unattached UE becomes associated with the calculated eNB, otherwise, the association remains the same.

[0048] The grouping algorithm then yields an outcome as shown in FIG. 6, where legacy UEs are reassociated with active eNBs that have lightly-loaded BCCTs even if they may have slightly higher SINRs from more loaded eNBs. Similarly, Rel-12 UEs are assigned to lightly-loaded NCTs even though they are capable of operating on a BCCT, which improves their service rate since NCT offers greater spectral efficiency compared to BCCT. Thus, a load-balancing outcome that takes into account UE capabilities is achieved without excessively degrading UE SINR.

[0049] Second Application Example: The second example examines a problem of grouping base stations for the purpose of coordinated multipoint (CoMP) transmission to cell-edge UEs. CoMP transmission may include any scheme based on joint transmission or coordinated scheduling/beamforming.

[0050] Consider five eNBs (2 macro, 3 SCs), with wireless backhaul links between some of them, and a fiber backhaul between eNB 1 and eNB 2, as illustrated in FIG. 8. The base stations intend to form a CoMP cluster of size 3 eNBs.

[0051] The example eNB preference function takes into account at least three factors:

(i) potential partner eNB should be an immediate neighbor (baseline), such that there exists at least one cell-edge UE that can benefit from CoMP transmission.

(ii) quality of the backhaul link (fiber has the least latency - which allows higher rate of cooperation).

(iii) resources that the partner can allocate for CoMP UEs. If the eNB is already serving multiple cell-center UEs, then it will have fewer time-frequency resources to allocate for CoMP transmission.

[0052] The overall reassociation process for this example is described in the flowchart of FIG. 7, which illustrates a flowchart of a reassociation process for grouping BSs, in accordance with an example implementation. The flow begins at 700 for a given eNB x, when eNB x initiates a clustering process. At 701 , eNB x contacts neighboring active eNBs, including small cell and macro BS. At 702, the neighbor eNBs provide reporting information, such as traffic loads, backhaul, quality measurements and preference function category. At 703, eNB x computes eNB-eNB rankings, and calculates a stable grouping assignment from the rankings for clustering eNBs. At 704, the eNB x informs the neighbor eNBs of the proposed clustering. At 705, if the neighbor eNBs accept the proposed clustering (YES), then the eNBs form clusters according to the proposed clustering and the process ends. Otherwise (NO), the grouping is recomputed for remaining eNBs based on which of the neighbor eNBs are rejecting the proposed grouping. The neighbor eNBs may accept or reject the proposed grouping for various reasons, depending on the desired implementation. For example, if the neighbor eNB also received a DTX command, then that neighbor eNB may refuse all proposed groupings and inform eNB x that it is shutting down.

[0053] An example network performance metric in this case can be to maximize the minimum QoS of cell-edge UEs. This performance metric is clearly impacted by all three factors considered by the eNB preference function. An example eNB preference function for a potential partner eNB at a known distance can be constructed as

- Cellload - al,

Distance

[0054] Here, a is a constant factor, and the indicator function is defined as fo if fiber backhaul to this eNB

1 1 if copper backhaul to this eNB

2 if microwave backhaul to this eNB

[0055] Each eNB knows the locations of its neighboring eNBs and can request their current cell load conditions via existing mechanisms for intra-LTE load exchange. The preference functions are then evaluated to construct a list of rankings of preferred partners for each eNB, followed by a numerical algorithm (e.g., based on stable matching or stable roommates) to generate the best possible clusters. An example outcome of this process is shown in FIG. 8, where eNB 1 , eNB 2, and eNB 5 form a cluster, even though eNBs 5, 3, 2 have the lowest mutual distances.

[0056] FIG. 9 illustrates a flowchart of a grouping algorithm in accordance with an example implementation. The flow begins at 900, wherein the eNBs are defined as being unattached. At 901 , the flow reiterates the flow at 902-905 while all of the eNB groups are less than size 3, though this can vary depending on the desired implementation. At 902, if there is an unattached eNB b from the set of eNBs that has a preferred eNB to potentially group with, then let c be the highest ranked eNB whom b has not yet approached. At 903, if eNB c allows for a partner, then eNBs b and c become provisionally grouped, otherwise there is some group out there (bl ', b2', ....,c) that already exists a priori. At 904, if c prefers b to the lowest ranked member of the existing group (e.g., bl ' from the above example), then (b, b2',....,c) become provisionally grouped and bl ' becomes unattached, otherwise at 905, the a priori group (bl ', b2', ....,c) remains grouped.

[0057] Finally, some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to most effectively convey the essence of their innovations to others skilled in the art. An algorithm is a series of defined steps leading to a desired end state or result. In example implementations, the steps carried out require physical manipulations of tangible quantities for achieving a tangible result.

[0058] Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout the description, discussions utilizing terms such as "processing," "computing," "calculating," "determining," "displaying," or the like, can include the actions and processes of a computer system or other information processing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system's memories or registers or other information storage, transmission or display devices.

[0059] Example implementations may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer readable medium, such as a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation.

[0060] Various general-purpose systems may be used with programs and modules in accordance with the examples herein, or it may prove convenient to construct a more specialized apparatus to perform desired method steps. In addition, the example implementations are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the example implementations as described herein. The instructions of the programming language(s) may be executed by one or more processing devices, e.g., central processing units (CPUs), processors, or controllers.

[0061] As is known in the art, the operations described above can be performed by hardware, software, or some combination of software and hardware. Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application. Further, some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software. Moreover, the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format. 2] Moreover, other implementations of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings of the present application. Various aspects and/or components of the described example implementations may be used singly or in any combination. It is intended that the specification and example implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims.

Claims

CLAIMS What is claimed is:

1. A base station, comprising: a memory configured to store measurement information of one or more User Equipment (UE) associated with the base station and reporting information from one or more neighboring base stations; and a processor, configured to: assign the associated one or more UE to the one or more neighboring base stations from a calculation based on the measurement information and the reporting information.

2. The base station of claim 1 , wherein the calculation is based on rankings of the associated one or more UE derived from the measurement information and the reporting information.

3. The base station of claim 2, wherein the calculation comprises one or more grouping assignments of the associated one or more UE from the rankings.

4. The base station of claim 1, wherein the reporting information comprises at least one of: traffic load information, carrier configuration information, and preference function information.

5. The base station of claim 1, wherein the measurement information comprises signal strength information of each of the associated one or more UE for each of the one or more neighboring base stations.

6. The base station of claim 1 , wherein the processor is further configured to: define one or more preference functions for one or more grouping assignments; transmit the one or more preference functions to the one or more neighboring base stations receive the reporting information from the one or more neighboring base station based on the one or more preference functions; and collect measurement information from the associated one or more UE.

7. A method, comprising: storing, at a base station, measurement information of one or more User Equipment (UE) associated with the base station and reporting information from one or more neighboring base stations; and assigning the associated one or more UE to the one or more neighboring base stations from a calculation based on the measurement information and the reporting information.

8. The method of claim 7, wherein the calculation is based on rankings of the associated one or more UE derived from the measurement information and the reporting information.

9. The method of claim 8, wherein the calculation comprises one or more grouping assignments of the associated one or more UE from the rankings.

10. The method of claim 7, wherein the reporting information comprises at least one of: traffic load information, carrier configuration information, and preference function information.

1 1. The method of claim 7, wherein the measurement information comprises signal strength information of each of the associated one or more UE for each of the one or more neighboring base stations.

12. The method of claim 7, further comprising: defining one or more preference functions for one or more grouping assignments;

transmitting the one or more preference functions to the one or more neighboring base stations

receiving the reporting information from the one or more neighboring base station based on the one or more preference functions; and

collecting measurement information from the associated one or more UE.