System and method for using mobility information in heterogeneous networks

Abstract

Disclosed herein are systems and methods related to reducing or performing more efficient handovers from one cell to another cell in a communication network. The method includes: receiving mobility data of a device served by a first cell; classifying the device based on the mobility data to produce a classification; and making a handover decision when handing over the device from the first cell to a second cell based at least in part on the classification. An example of the mobility data is the speed at which the device is moving.

Description

[0001] This application is a divisional application of the patent application with application number 201980044444.6 and invention name “System and method for using mobility information in heterogeneous networks”.

[0002] priority

[0003] This application claims priority to U.S. patent application No. 16 / 188,698, filed on November 13, 2018, which is a continuation-in-part of U.S. patent application No. 14 / 978,751, filed on December 22, 2015, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0004] The present disclosure relates to cell allocation in a cellular network, and more particularly to systems and methods for using mobility data regarding the speed at which a device is moving for cell handover allocation. Background Art

[0005] The use of a mix of large and small cells in Long Term Evolution (LTE) and other networks has the potential to better utilize scarce wireless spectrum resources. However, as cellular devices connect to cells based on signal strength, variability in connection quality and increased handoff rates can lead to reduced efficiency. This can make heterogeneous cells a liability rather than an advantage. As efforts to interoperate cellular and Wi-Fi networks lead to merged networks, Wi-Fi antennas will increase the diversity of available wireless access points.

[0006] There is a need and potential advantage of having different types of cells to improve coverage and use of spectrum, resulting in heterogeneous networks ("het nets"). In both cases, it is difficult to properly perform handovers between different cells, either when the cells use the same protocol (e.g., LTE) or when different cells use different protocols (e.g., an LTE cell adjacent to or overlapping a Wi-Fi cell). SUMMARY OF THE INVENTION

[0007] The following description relates to a number of different embodiments of different methods and systems for handling cell allocation and / or cell handover in a cellular environment. The embodiment method involves making a handover decision. The method includes: receiving mobility data for a device served by a first cell; classifying the device based on a mobility state associated with the mobility data to produce a classification; and making a handover decision when handing the device from the first cell to the second cell based at least in part on the classification. The mobility state can be calculated from the mobility data or estimated from the mobility data. In one embodiment, the classification can be one of slow, medium, and fast. The classifications or classes are typically separated by boundaries that can be fixed or variable. For example, the classification can be a speed or mobility rate of 10-30 MPH. The boundaries of the low end 10 MPH and the high end 30 MPH can be fixed or variable based on one or more parameters. The boundaries can be reset.

[0008] When the system makes a handover decision, each classification may include at least one of a preferred cell type and at least one acceptable cell type. The preferred cell type for the slow classification may include a micro cell type and a small cell type, and the acceptable cell type for the slow classification may include a large cell type. The preferred cell type for the medium classification may include a small cell type, and the acceptable cell type for the medium classification may include a large cell type. The preferred cell type for the fast classification may include a large cell type, and the acceptable cell type for the fast classification may not include any cell type.

[0009] When making a handover decision, in one embodiment, the device or system may select a non-preferred cell type only when the received signal is stronger than a threshold higher than the threshold required for the preferred cell type. Cell reselection may be triggered when the first cell no longer provides an adequate signal and when the first cell is of a non-preferred type. Handover may include switching to a second cell when the second cell is of the preferred type and is suitable.

[0010] The classification may include a location in the first cell (possibly a location of the device) and a path traveled through the first cell. The handover decision may also be based at least in part on a first spectral efficiency of the first cell and a second spectral efficiency of the second cell. The handover decision may also be based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0011] A system embodiment includes a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform certain operations. The operations include receiving mobility data for a device served by a first cell; classifying the device based on the mobility data to generate a classification; and making a handover decision when handing the device from the first cell to a second cell based at least in part on the classification.

[0012] Another embodiment includes a computer-readable storage device storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising receiving mobility data for a device served by a first cell, classifying the device based on the mobility data to produce a classification, and making a handover decision when handing the device from the first cell to a second cell based at least in part on the classification.

[0013] A second embodiment relates to initially assigning a device to a serving cell. In this embodiment, a method includes receiving mobility data for a device; classifying the device based on a mobility state associated with the mobility data to generate a mobility class; and assigning the device to a serving cell based at least in part on the mobility class. The mobility state may be calculated from the mobility data or estimated from the mobility data.

[0014] When assigning the device to the serving cell, reselection may be triggered when the first cell no longer provides sufficient signal and is of a non-preferred type. Assigning the device to the serving cell may include assigning the device to the second cell when the second cell is of a preferred type and suitable.

[0015] The mobility category may include one of the location of the device in the serving cell and the path traveled across the serving cell. Assigning the device to the serving cell may further be based at least in part on the spectral efficiency of the serving cell. Assigning the device to the serving cell may also be based at least in part on the bandwidth used by the device and the available bandwidth of the serving cell. Assigning the device to the serving cell may also be based at least in part on multiple priority lists of cells, each of the multiple cell priority lists including cells of the same type. In one aspect, cells on a higher priority list are measured more frequently than cells on a lower priority list.

[0016] In one possible implementation, the mobile device monitors its serving cell and neighboring cells from the list with the highest priority. If the size of this list (with the highest priority) is less than the size of a given parameter, the next list is also considered. In another possible implementation, cells with higher priority are measured more frequently than cells with lower priority. When determining which cell to hand over to, cells from the neighbor list with higher priority are always preferred over cells from the list with lower priority.

[0017] A system aspect of this second embodiment includes a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform operations comprising: receiving mobility data for a device, classifying the device based on a mobility state associated with the mobility data to produce a mobility class, and assigning the device to a serving cell based at least in part on the mobility class. A computer-readable storage device aspect of the second embodiment includes a computer-readable storage device storing instructions that, when executed by a computing device, cause the computing device to perform operations comprising: receiving mobility data for a device, classifying the device based on a mobility state associated with the mobility data to produce a mobility class, and assigning the device to a serving cell based at least in part on the mobility class.

[0018] A third embodiment involves tracking load distribution across cells. Once a system with device classification and allocation by cell type is installed, it can be monitored to track the distribution of users and load levels across cells. If monitoring detects a significant imbalance in cell load, in addition to adjusting cell power levels, the disclosed system can dynamically adjust mobility class boundaries to optimize or improve spectrum efficiency and network capacity.

[0019] One example of this situation is a temporary concentration of slow users in a certain area. In this case, reducing the value of the class boundary will allow more users to be classified as more mobile and preferentially served by larger cells. Another option is to reduce the power of the microcells in the area so that fewer users are assigned to them.

[0020] According to this third embodiment, a method for balancing cell load in a network is disclosed, wherein devices are classified into mobility classes based on mobility data, and devices are assigned to serving cells based at least in part on the mobility classes. The method includes monitoring the distribution of devices and load levels across a plurality of cells to generate an analysis. When the analysis indicates a cell load imbalance, the method includes adjusting the boundaries of the mobility classes. When the analysis indicates a cell load imbalance, the method includes adjusting the power level of at least one of the plurality of cells. When the analysis indicates a cell load imbalance, the method further includes adjusting the power level of at least one of the plurality of cells and adjusting the boundaries of the mobility classes. Adjusting the boundaries of the mobility classes includes increasing an upper speed limit associated with an upper limit of the mobility class and decreasing a lower speed limit associated with a lower limit of the mobility class. Adjusting the power level of at least one of the plurality of cells and adjusting the boundaries of the mobility classes can improve the spectral efficiency of the network relative to the previous spectral efficiency before adjusting one of the power level and the mobility class boundaries. Note that in radio transmissions, data (bits) consist of control bytes and content bytes. The closer a device is to an antenna or base station, the better the spectral efficiency. When there is a handoff or during a handoff, usually more control bytes are sent than content bytes.

[0021] Adjusting the power level of at least one of the plurality of cells and adjusting one of the mobility class boundaries increases network capacity relative to a previous capacity before adjusting one of the power level and the mobility class boundary. When assigning a device to a serving cell, each mobility class includes at least one of a preferred cell type and at least one acceptable cell type. The preferred cell types for the slow speed classification include a micro cell type and a small cell type, and the acceptable cell types for the slow speed classification include a large cell type. The preferred cell types for the medium speed classification include a small cell type, and the acceptable cell types for the medium speed classification include a large cell type.

[0022] In one embodiment, the device selects a non-preferred cell type only when the received signal is stronger than a threshold that is higher than the threshold required for the preferred cell type. When the device is assigned to the serving cell, reselection is triggered based on one or more parameters or events. For example, reselection can be triggered when the first cell no longer provides sufficient signal and / or when the first cell is of a non-preferred type. Assigning the device to the serving cell includes assigning the device to the second cell when the second cell is of the preferred type and is suitable. The mobility category may include one of the location of the device in the serving cell and the path traveled across the serving cell. The device may be assigned to the serving cell based at least in part on a plurality of cell priority lists, each of the plurality of cell priority lists including cells of the same type.

[0023] An example method includes receiving mobility data for a device served by a first cell, wherein the mobility data includes a vector indicating movement of the device; and classifying the device based on the mobility data to generate a classification. When making a reselection or handover decision, the cell type used in the classification may include at least one of a preferred cell type and at least one of an acceptable cell type.

[0024] An embodiment of a system according to a third embodiment is a system for balancing cell loads in a network, wherein devices are classified into mobility classes based on mobility data and assigned to serving cells based at least on the mobility class. The system includes a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform operations including monitoring the distribution of devices and load levels across multiple cells to generate an analysis, and adjusting mobility class boundaries when the analysis indicates an imbalance in cell loads. The system can be a network infrastructure component or a mobile device such as a smartphone or cell phone.

[0025] A computer-readable storage device embodiment of this third embodiment includes a non-transitory computer-readable storage device or medium storing instructions that, when executed by a computing device, cause the computing device to balance cell loads in a network, wherein devices are classified into mobility classes based on mobility data and the devices are assigned to serving cells based at least in part on the mobility classes. The instructions cause the computing device to perform operations including monitoring a distribution of devices and load levels across a plurality of cells to generate an analysis, and adjusting boundaries of the mobility classes when the analysis indicates a cell load imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 An exemplary system is shown;

[0027] Figure 2A An exemplary heterogeneous cellular system is shown;

[0028] Figure 2B An aspect of the control plane is shown;

[0029] Figure 2C Another control plane aspect is shown;

[0030] Figure 3 Another embodiment of a heterogeneous cellular system is shown;

[0031] Figure 4 shows a network in which a device moves between cells; and

[0032] Figure 5 shows the macro cell coverage environment;

[0033] Figure 6 An exemplary method is shown;

[0034] Figure 7 Another exemplary method is shown;

[0035] Figure 8 Another exemplary method is shown;

[0036] Figure 9 Methods related to updating mobility classes are shown;

[0037] Figure 10 Methods relating to selection of a cell for a mobile device are shown;

[0038] Figure 11 RRC connection rules are shown; and

[0039] Figure 12 A method for updating a neighbor list is shown. Detailed Description of the Invention

[0040] Various embodiments of the present disclosure are described in detail below. Although specific implementation methods are described, it should be understood that this is for illustrative purposes only. Without departing from the spirit and scope of the present disclosure, other components and configurations may be used. In addition, it should be understood that the features or configurations of one embodiment herein may be implemented or combined with other examples or embodiments herein. That is, terms such as "embodiment," "variation," "aspect," "embodiment," "configuration," "implementation," "case," and any other terms that may imply an embodiment as used herein to describe a specific feature or configuration are not intended to limit any relevant feature or configuration to one or more specific or separate embodiments, and should not be interpreted as implying that such features or configurations cannot be combined with features or configurations described with reference to other embodiments, variations, aspects, embodiments, configurations, implementations, cases, etc. In other words, the features described herein with reference to a specific embodiment (e.g., embodiment, variation, aspect, configuration, implementation, case, etc.) may be combined with features described with reference to another embodiment. Accurately, one of ordinary skill in the art will readily recognize that the various embodiments or embodiments described herein and their associated features may be combined with each other.

[0041] The present disclosure addresses concepts related to performing cell allocation and / or cell switching in a heterogeneous cellular system. Figure 1 A brief introduction to the basic general system or computing device in FIG. 3 is provided, which can be used to practice the disclosed concepts, methods, and techniques. Various handover and cell allocation methods are then described in more detail.

[0042] These variations will be described herein as various embodiments are described. Figure 1 . refer to Figure 1 1. An exemplary system and / or computing device 100 includes a processing unit (CPU or processor) 120 and a system bus 110 that couples various system components to processor 120, including system memory 130, such as read-only memory (ROM) 140 and random access memory (RAM 150). System 100 may include a cache memory 122, a high-speed memory directly connected to, in close proximity to, or integrated as part of processor 120. System 100 copies data from memory 130 and / or storage device 160 to cache memory 122 for rapid access by processor 120. In this way, the cache memory provides a performance boost by avoiding delays while processor 120 waits for data. These and other modules may control or be configured to control processor 120 in performing various operations or actions. Other system memories 130 may also be used. Memory 130 may include a variety of different types of memory with varying performance characteristics. It will be appreciated that the present disclosure can be run on a computing device 100 having more than one processor 120, or on a group of computing devices or a cluster of computing devices networked together to provide greater processing power. Processor 120 can include any general-purpose processor and hardware or software modules, such as module 1 162, module 2 164, and module 3 166 stored in storage device 160, configured to control processor 120, as well as specialized processors into which software instructions have been incorporated. Processor 120 can be a standalone computing system comprising multiple cores or processors, a bus, a memory controller, a cache, and the like. Multi-core processors can be symmetric or asymmetric. Processor 120 can include multiple processors, such as a system with multiple physically separate processors in different sockets, or a system with multiple processor cores on a single physical chip. Similarly, processor 120 can include multiple distributed processors located in multiple separate computing devices but working together, for example, via a communication network. Multiple processors or processor cores can share resources, such as memory 130 or cache 122, or can operate using independent resources. Processor 120 may include one or more of a state machine, an application specific integrated circuit (ASIC), or a programmable gate array (PGA) including a field PGA.

[0043] The system bus 110 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. A basic input / output (BIOS) stored in ROM 140 or the like can provide basic routines that help transfer information between components within the computing device 100, such as during startup. The computing device 100 also includes a storage device 160 or computer-readable storage medium, such as a hard drive, magnetic disk drive, optical disk drive, tape drive, solid-state drive, RAM drive, removable storage device, redundant array of inexpensive disks (RAID), hybrid storage device, etc. The storage device 160 may include software modules 162, 164, 166 for controlling the processor 120. The system 100 may include other hardware or software modules. The storage device 160 is connected to the system bus 110 via a drive interface. The drives and associated computer-readable storage devices provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the computing device 100. In one aspect, the hardware module that performs a specific function includes a software component stored on a tangible computer-readable storage device in combination with the necessary hardware components for performing the specific function, such as the processor 120, the bus 110, the display 170, etc. In another aspect, the system can use a processor and a computer-readable storage device to store instructions that, when executed by the processor, cause the processor to perform an operation, method, or other specific action. The basic components and appropriate variations can be modified depending on the type of device, such as whether the device 100 is a small handheld computing device, a desktop computer, or a computer server. When the processor 120 executes an instruction to perform an "operation," the processor 120 can directly perform the operation and / or facilitate another device or component to perform the operation, direct another device or component to perform the operation, or collaborate with another device or component to perform the operation.

[0044] Although the exemplary embodiments described herein utilize a hard disk 160, other types of computer-readable storage devices may also store data accessible by the computer, such as magnetic tape cartridges, flash memory cards, digital versatile disks (DVDs), magnetic cassettes, random access memory (RAM) 150, read-only memory (ROM) 140, cables containing bit streams, etc., may also be used in the exemplary operating environment. The terms computer-readable storage medium, computer-readable storage device, or computer-readable memory device specifically exclude transient media, such as transient waves, energy, carrier signals, electromagnetic waves, and signals themselves.

[0045] To enable a user to interact with computing device 100, input device 190 represents any number of input mechanisms, such as a microphone for voice, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, speech, and the like. Output device 170 may also be one or more of many output mechanisms known to those skilled in the art. In some instances, multimodal systems enable a user to provide multiple types of input to communicate with computing device 100. Communication interface 180 generally governs and manages user input and system output. There is no limitation to operating on any particular hardware device; therefore, the basic hardware described can be easily substituted as improvements in hardware or firmware are developed.

[0046] For clarity of explanation, the illustrative system embodiments are presented as including separate functional blocks, including functional blocks labeled as "processors" or processor 120. The functions represented by these blocks may be provided through the use of shared or dedicated hardware, including but not limited to hardware capable of executing software, and hardware such as processor 120 that is purposefully constructed to be equivalent to software executing on a general-purpose processor. For example, Figure 1 The functionality of one or more processors presented in the can be provided by a single shared processor or by multiple processors. (The use of the term "processor" should not be construed as referring exclusively to hardware capable of executing software.) Illustrative embodiments may include microprocessor and / or digital signal processor (DSP) hardware, read-only memory (ROM) 140 for storing software that performs the operations described below, and random access memory (RAM) 150 for storing results. Very large scale integration (VLSI) hardware embodiments may also be provided, as well as custom VLSI circuits combined with general-purpose DSP circuits.

[0047] The logical operations of the various embodiments are implemented as: (1) a series of computer-implemented steps, operations or processes running on programmable circuits within a general-purpose computer, (2) a series of computer-implemented steps, operations or processes running on special-purpose programmable circuits; and / or (3) interconnected machine modules or program engines within programmable circuits. Figure 1 The illustrated system 100 can practice all or part of the referenced methods, can be part of the referenced systems, and / or can operate according to instructions in the referenced tangible computer-readable storage devices. Such logical operations can be implemented as modules configured to control the processor 120 to perform specific functions according to the programming of the modules. For example, Figure 1Three modules, Mod1 162, Mod2 164, and Mod3 166, are shown, which are modules configured to control the processor 120. These modules may be stored on the storage device 160 and loaded into the RAM 150 or the memory 130 at runtime, or may be stored in other computer-readable memory locations. The system 100 may have other modules ( Figure 1 not shown).

[0048] One or more portions of the example computing device 100, up to and including the entire computing device 100, can be virtualized. For example, a virtual processor can be a software object that executes according to a specific instruction set even if no physical processor of the same type as the virtual processor is available. A virtualization layer or virtual "host" can enable virtualized components of one or more different computing devices or device types by translating virtualized operations into actual operations. Ultimately, however, each type of virtualized hardware is implemented or executed by some underlying physical hardware. Therefore, a virtualized computing layer can run on top of a physical computing layer. The virtualized computing layer can include one or more virtual machines, overlay networks, hypervisors, virtual switches, and any other virtualization applications.

[0049] Processor 120 can include all types of processors disclosed herein, including virtual processors. However, when referring to a virtual processor, processor 120 includes the software components associated with executing the virtual processor in the virtualization layer and the underlying hardware required to execute the virtualization layer. System 100 can include a physical or virtual processor 120 that receives instructions stored in a computer-readable storage device, which instructions cause processor 120 to perform certain operations. When referring to a virtual processor 120, the system also includes the underlying physical hardware that executes the virtual processor 120.

[0050] Already in Figure 1 Having disclosed some components of a general purpose computing system, the present disclosure now turns to Figure 2A , which illustrates one aspect of the present disclosure. Figure 2A 2 represents a heterogeneous network 200 that may be associated with a wireless protocol such as LTE or a different wireless protocol. Figure 2A As shown, macrocell 202 is included within a group of macrocells 224, 226, 228, 230, 232, and 234. Within each macrocell is a cell tower 204 and various other types of towers or cells. Microcells 206, 212, and 214 operate using, for example, eNodeB common antennas. Mobile device 218 is shown communicating with microcell 206. Femtocell 208 can also communicate with another mobile device 220. Another embodiment of a building picocell 210 is shown. Figure 2AOther mobile communication systems are also shown, such as vehicles 216 and 222 communicating with macro cell 202. Figure 2A Examples are provided of how a heterogeneous network may exist such that mobile devices 218, 220, 216, 222 may transition from one protocol to another and / or from a cell having a larger size (e.g., macrocell 202) to a cell having a smaller size (e.g., microcell 206), and vice versa, as they traverse a series of cells.

[0051] This disclosure provides methods and systems for utilizing different cell types in heterogeneous wireless networks, with the goal of improving the level of service provided to users and the efficiency and capacity of the network. We provide at least three new concepts that can be integrated into a system to achieve these goals. The first concept involves classifying user equipment into mobility classes based on their mobility state. The next concept involves assigning user equipment to serving cells based on their mobility class. The third concept involves dynamically adjusting cell power and / or mobility class boundaries.

[0052] Most of the processes described in this article can be implemented on one or more user devices, in a radio network controller (RNC) that can coordinate a group of cells and / or a network processor (NP) that can perform more centralized optimization. Different steps in the coordinated series of operations can also be performed on different devices. Device control is inherently distributed, but can react more quickly to changes in mobility status, and certain algorithms are suitable for this operating mode. Using an RNC or NP provides a more centralized decision point, but can react relatively slowly to changes in conditions. Changes in load conditions can also affect the operating mode. For example, if the NP is overloaded, more processing can be performed at the device and / or RNC level.

[0053] We now provide more detail on the first concept of classifying user devices. The system can classify user devices based on the mobility state of the device. The mobility state is a value that represents the speed at which the user (and therefore the device) is moving. There are several potential methods that can be used to obtain or estimate the mobility state. Each device and / or processor anywhere in the network can employ one or more of these methods to determine the mobility state. For example, the mobility state can be 10MPH or 70MPH. The mobility state can also include a vector component, where the state includes parameters related to the direction the user device is moving. Thus, the mobility state can include a value such as (65,10), which can be the speed and the compass heading (10 degrees in this case). The mobility state can also include other information such as acceleration, altitude, ascent / descent rate, etc.

[0054] The mobility state can be a mobility estimate based on device coordinates sampled every time unit. The device speed can be calculated based on the change in coordinates. Simple averaging or exponential smoothing over a time window are examples of methods that can be used to calculate the current mobility state. The system can also or alternatively use smart car technology to communicate speed. The speed of a device in a moving vehicle can be determined by the vehicle's speedometer, transmitted to the device or a network processor, and then used to calculate the mobility state. For other users, wearable devices such as fitness monitors can be used.

[0055] Mobility estimates based on handover rate or time in cell can be used to establish mobility state. The terms "handover," "handover," and their variations refer to the process of transferring a device's ongoing call or data session from one cell to another with minimal or no service interruption, typically as the device changes geographic location. For example, suppose a device measures the time between handovers. The network processor can estimate mobility state based on cell size and time in cell. This is a relatively inaccurate method of determining mobility state. However, it can be used as a feedback metric for evaluating the system.

[0056] Mobility state can also be determined by the user equipment through non-mobile declaration. This method requires the wireless protocol to allow the device to declare itself as stationary or mobile. When the device is stationary, it does not need to perform mobility-related measurements.

[0057] Once the mobility states have been determined using one or more of the techniques disclosed above, the device or network processor then classifies each device into a mobility class. This can be accomplished in many different ways. One example approach is to associate each mobility class with a set of mobility states. One embodiment is shown in Table 1.

[0058]

[0059] Table 1: Mobility categories

[0060] Speed ​​limits / thresholds are used to classify devices defined by category boundaries. The category boundary in the example above is 10 meters per minute, which defines the boundary between category 1 (slow) and category 2 (medium speed). In this embodiment, another category boundary is 100 meters per minute, which defines the boundary between category 2 (medium speed) and category 3 (fast). Of course, the structure can include more than three categories, and the specific boundaries can be any parameter, or even a set of parameters. For example, the categories can include a speed range as well as a direction range. If the direction of movement of the device will cause it to reach a microcell within a certain time (perhaps related to speed), it may be a certain category. An embodiment of this method may be: Category 2 (moderate 12 degree movement, resulting in the formation of a microcell within 2 minutes). Classification can also be based on the relative speed of the device approaching or moving away from the microcell, regardless of the direction the device is moving.

[0061] As described above, given a network with multiple types of cells that differ in size and power level, and user equipment is classified based on mobility, the present disclosure now provides rules for assigning devices to cells based on their mobility class.

[0062] For illustrative purposes and simplicity of explanation, three types of cells are presented: large cells, small cells, and micro cells. Of course, the system may employ more than three defined cell types. Such definitions may include parameters based on one or more of the following: size, geographic location, altitude, buildings / infrastructure within the cell, shape, signal strength, signal range, device usage, number of devices within the cell, mobility of devices within the cell (i.e., does the cell cover a highway with an average device speed of 65 MPH), foliage and other obstructions, etc.

[0063] The present disclosure relates to the establishment and management of radio bearers and does not change the establishment and control of S-type bearers between cells and network gateways.

[0064] Described here are enhancements to two sets of rules that are critical to the operation of mobile networks: cell selection for Radio Resource Control (RRC) Idle devices and handover for RRC Connected devices. As background, when a device is turned on, it must find a qualified serving cell, then go through an authentication process and connect to the network. The mobile device is then communicating with a serving cell in the RRC Idle state without acquiring radio resources. In this state, serving cell selection and reselection are under the control of the device. Once an RRC connection is established, the device periodically reports its measurements and is monitored by the network to ensure quality of service. The network can request a handover when the quality of service from the current serving cell is unacceptable and / or another cell that is more suitable for service exists.

[0065] A device in the RRC Idle state will operate as follows: The device selects and reselects a serving cell based on a signal strength measurement called Reference Signal Received Power (RSRP). Specifically, a cell is suitable for serving a device if its RSRP exceeds a minimum threshold level and meets certain service conditions—for example, in a network where the service provider is on the device's priority list and its service is not prohibited—which are not relevant to the present disclosure.

[0066] The introduction of cell type and mobility class adds a new dimension to the cell selection process. This disclosure provides two possible rules:

[0067] The first rule is a simple precedence rule, which specifies the precedence order as shown in Table 2.

[0068] Device mobility category Preferred cell type Acceptable cell types Category 1 – Slow small big Category 2 – Medium Speed Small big Category 3 – Fast big none

[0069] Table 2: Preference rules for mobility categories

[0070] Table 2 is merely exemplary and, of course, could have many different parameters. For example, a slower mobile device would first look for a suitable microcell or small cell that can provide Wi-Fi communication, and only consider a large cell if no microcell or small cell is available. A fast-moving device might only consider a large cell, disallowing microcells or small cells. This latter rule prevents excessive reselection.

[0071] The second rule may require an additional parameter RSRP specified by the network diff In this case, the cell is suitable for the device based on the result of the following formula:

[0072] RSRP meas RSRP min +I(MC,CT)×RSRP diff ,

[0073] RSRP min is the minimum acceptable received power, RSRP meas is a measurement value, if the user equipment (UE) mobility category MC prefers the cell type CT, then I(MC, CT)=0, otherwise, I(MC, CT)=1.

[0074] A more general version of this rule is to have the value RSRP diff The cell is suitable if the following conditions are met:

[0075] RSRP meas RSRP min +RSRPdiff (MC,CT).

[0076] Under this rule, a device selects a non-preferred cell type only if the received signal is stronger than a threshold higher than that required for the preferred cell type. For both rules, reselection is triggered when the serving cell no longer provides a sufficiently good signal. Additionally, if the current serving cell is of a non-preferred type, reselection can occur if a preferred type is detected and is suitable.

[0077] Another example process involves a device in the RRC connected state. Once connected, if the RRC idle rules are followed, the device will be served by a cell that fits its mobility class. At this point, in most LTE or other protocol networks, the device monitors its serving cell and listed neighbor cells.

[0078] The feature introduced here is that instead of a simple list of neighboring cells, multiple priority lists can be analyzed. Each list includes cells of the same type, or can include cells of different types, or have characteristics that share a certain number of parameters. In one possible implementation, the device monitors its serving cell as well as neighbor cells from the highest priority list. If the size of this list (with the highest priority) is less than a given parameter, the next list will also be considered. In another possible implementation, cells with higher priority are measured more frequently than cells with lower priority. When determining which cell to hand over to, cells from the neighbor list with higher priority are always given priority over cells from the list with lower priority.

[0079] When a user (and therefore a device) changes its mobility class, the change is detected by the mobility measurement methods in use and included in the measurement reports sent to the serving cell (actually the base station in that cell, usually called an eNodeB). Initially, the device continues to be served by the same cell, but the rules for reselecting a new serving cell (for RRC Idle UEs) and handover (for RRC Connected UEs) will now be based on the new mobility class.

[0080] For example, if a walking user (medium category) currently served by a small cell enters a vehicle, the device may very quickly move out of its current cell. In this case, the device will quickly be classified as a fast device and will now select the best large cell. Similarly, a fast device in a large cell may enter a building and become stationary. The now stationary device will still be served by the large cell, but will now seek and be eligible to be served by a micro or small cell.

[0081] Once a system for device classification and assignment to cell types is in place, the system can be monitored to track the distribution of users and load levels across cells. If monitoring detects a strong imbalance in cell load, in addition to adjusting the power levels of the cells, the present disclosure can also dynamically adjust the mobility list boundaries to optimize spectrum efficiency and network capacity.

[0082] One example of this situation is a temporary concentration of slow users in a certain area. In this case, reducing the value of the class boundary will allow more users to be classified as more mobile and preferentially served by larger cells. Another option is to reduce the power of the microcells in the area so that fewer users are assigned to them.

[0083] Boundary adjustments can also be performed based on historical data. Perhaps during peak hours, the boundaries are adjusted to account for the high movement of devices throughout the cell area. However, at certain times and in certain areas, traffic is poor, resulting in slow device movement. Therefore, boundary adjustments can account for this actual device movement experienced within the cell. Adjustments can occur for a variety of reasons, including the addition or removal of new cells, additional fees paid by users to enable adjustments or prioritize devices, and so on.

[0084] Figure 2A and 2B Control plane signals are shown between microcell 206, macrocell 204, and device 218. These signals relate to: (1) handover request, (2) admission control signal, (3) handover confirmation, (4) radio resource control (RRC) reconfiguration signal, (5) handover to new cell signal, and (6) complete RRC reconfirmation signal.

[0085] Figure 3 Example power levels for the serving cell and neighboring cells are shown. For example, feature 320 shows a group of cells that may be similar to Figure 2A . Feature 302 shows a macro coverage cell covering an area associated with cell tower 310, with overlapping micro cells, such as micro cell 304 served by tower 312, micro cell 306 served by tower 316, and micro cell 308 served by tower 318. The present disclosure covers various methods and procedures for cell allocation and cell-to-cell handover in heterogeneous networks and in situations where there is overlap between different cell types.

[0086] Figure 4Example switching rates are shown. As shown in cell group 400, various cells are shown, such as 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, and 436. Each is served by some type of base station or antenna (not shown). Figure 4 4. The diagrams in FIG. 40 show the different speeds at which devices move through the various cells. For example, three arrows are shown as feature 406, which may represent a fast mobile device moving from cell 420 to cell 422. A single arrow is shown as feature 408, which may represent a slow mobile device moving from cell 414 to cell 424. Arrow 412 may represent a slow mobile device moving from cell 414 into cell 416, only briefly, and then into cell 418. Arrow 412 illustrates an embodiment in which two fast handoffs need to be processed when a device moves from cell 414 to cell 416 and then shortly thereafter from cell 416 to cell 418. Similarly, feature 410 shows the movement of a device transitioning from cell 424 to cell 428 and another transitioning from cell 428 to cell 426. Feature 404 shows another set of arrows representing the movement of a device from cell 432 to cell 434, which may include a portion of the movement within cell 426. It will be appreciated that the principles disclosed herein improve cell allocation and handoff capabilities to minimize the processing required to perform a handoff.

[0087] Figure 5 Another example cell allocation is shown. System 500 shows multiple different cells, including macro cell 502, which has a capacity of 14 Mbps and is currently using 6 Mbps. Device 506 is a smartphone using 5 Mbps. Note that cell 504 is a micro cell with a capacity of 5 Mbps but is currently communicating at 3 Mbps. The present disclosure enables the allocation of cells to improve spectral efficiency.

[0088] Figure 6 An example method is shown. The method may be executed on one or more of a mobile device and a system or network-based processor. The method includes receiving mobility data (602) for a device served by a first cell; classifying the device based on a mobility state associated with the mobility data to produce a classification (604); and making a handover decision (606) when handing over the device from the first cell to a second cell based at least in part on the classification. The mobility state may be calculated from the mobility data or estimated from the mobility data. Boundaries defining the mobility classes may be fixed or variable, and when the boundaries are variable, the boundaries may be reset.

[0089] When making a handover decision, each classification may include at least one of a preferred cell type and at least one acceptable cell type. The preferred cell types for the slow classification may include a micro cell type and a small cell type, and the acceptable cell types for the slow classification may include a large cell type. The preferred cell types for the medium classification may include a small cell type, and the acceptable cell types for the medium classification may include a large cell type. The preferred cell types for the fast classification may include a large cell type, and the acceptable cell types for the fast classification may be no cell type. In one aspect, the device only selects a non-preferred cell type when the received signal is stronger than a threshold higher than the threshold required for the preferred cell type.

[0090] When making a handover decision, reselection can be triggered when the first cell no longer provides an adequate signal and the first cell is of a non-preferred type, and wherein making the handover decision includes handing over to the second cell when the second cell is of a preferred type and is suitable. The classification may include a location of the device in the first cell and a path traveled through the first cell. Making the handover decision may also be based at least in part on a first spectral efficiency of the first cell and a second spectral efficiency of the second cell. Further, making the handover decision may be based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0091] Figure 7 Another example related to the cell allocation aspect of the present disclosure is shown. A method includes: receiving mobility data for a device (702); classifying the device based on a mobility state associated with the mobility data to generate a mobility class (704); and assigning the device to a serving cell (706) based at least in part on the mobility class. When assigning the device to the serving cell, each mobility class includes at least one of a preferred cell type and at least one acceptable cell type. The preferred cell type for the slow classification may include a micro cell type and a small cell type, and the acceptable cell type for the slow classification may include a large cell type. The preferred cell type for the medium classification may include a small cell type, and the acceptable cell type for the medium classification may include a large cell type. The preferred cell type for the fast classification may include a large cell type, and the acceptable cell type for the fast classification may be no cell type. In one aspect, the device only selects a non-preferred cell type when the received signal is stronger than a threshold higher than the threshold required for the preferred cell type.

[0092] When assigning the device to the serving cell, reselection may be triggered when the first cell no longer provides sufficient signal and the first cell is of a non-preferred type, and wherein assigning the device to the serving cell includes assigning the device to the second cell when the second cell is of the preferred type and suitable.

[0093] The mobility category may include the location of the device in the serving cell or the path traveled across the serving cell. The assignment of the device to the serving cell may further be based at least in part on the spectral efficiency of the serving cell. The assignment of the device to the serving cell may further be based at least in part on the bandwidth used by the device and the available bandwidth of the serving cell. In another aspect, the assignment of the device to the serving cell may further be based at least in part on multiple priority lists of cells, each of the multiple cell priority lists including cells of the same type. Cells in a high priority list are measured more frequently than cells in a low priority list.

[0094] Once a system for classifying devices and assigning them to cell types is in place, the system can be monitored to track the distribution of users and load levels across the cells. If monitoring detects a significant imbalance in cell load, in addition to adjusting the cell's power level, the concepts disclosed herein also allow for dynamic adjustment of mobility class boundaries to optimize or improve the network's spectral efficiency and capacity.

[0095] One example of this situation is a temporary concentration of slow users in a certain area. In this case, reducing the value of the class boundary will allow more users to be classified as more mobile and preferentially served by larger cells. Another option is to reduce the power of the microcells in the area so that fewer users are assigned to them.

[0096] Figure 8 An example method for balancing cell load in a network is shown in which devices are classified into mobility classes based on mobility data and assigned to serving cells based at least in part on the mobility classes. The method includes monitoring a distribution of devices and load levels across a plurality of cells to generate an analysis (802), and when the analysis indicates an imbalance in cell load, adjusting boundaries of the mobility classes (804).

[0097] When the analysis indicates an imbalance in cell load, the method may include adjusting a power level of at least one of the plurality of cells. When the analysis indicates an imbalance in cell load, the method may further include performing the steps of adjusting the power level of at least one of the plurality of cells and adjusting a boundary of a mobility class. Adjusting the boundary of a mobility class may include increasing a higher speed associated with an upper boundary of the mobility class and / or decreasing a lower speed associated with a lower boundary of the mobility class. Adjusting the power level of at least one of the plurality of cells or adjusting the boundary of the mobility class may improve or optimize the spectral efficiency of the network relative to a previous spectral efficiency before adjusting the power level or the boundary of the mobility class. Adjusting the power level of at least one of the plurality of cells or adjusting the boundary of the mobility class may improve or optimize the capacity of the network relative to a previous capacity before adjusting the power level or the boundary of the mobility class. The device may be assigned to a serving cell based at least in part on a plurality of cell priority lists, each of the plurality of cell priority lists including cells of the same type or sharing at least a threshold number of characteristics.

[0098] Figure 9 Another method of setting a mobility class and updating a mobility class setting is shown. In the first step, the mobile device is in mobility class MC0 (902). The system will perform mobility measurements (904) to update the mobility state (MS) (906). The measurement can be performed at a fixed or variable measurement interval (908). Next, the system determines whether the mobility state is less than a threshold 1 (910). If not, the system determines whether the mobility state is less than a threshold 2 (912). If the mobility state is not less than threshold 2, the system determines that the new mobility class is in a class such as fast and sets a variable MCn=3 (918). If the mobility state is less than threshold 2, the system determines that the mobility class is another class, such as slow, and sets a variable MCn=2 (916). If the mobility state is less than threshold 1, the system determines that the new mobility class is in yet another class, such as very slow, and sets MCn=1 (914). The flow from each of steps (914), (916), and (918) then determines if MCn = MC0 (920). If not, the system assigns MC0 as MCn and reports the change in mobility category (922). If MCn does equal MC0, the flow returns to the step of performing mobility measurements (904), and the process essentially begins again.

[0099] In another aspect, the method may include receiving mobility data for a device served by a first cell, wherein the mobility data includes a vector (or other data structure) indicating the movement of the device. The mobility data may be independent of one of a data usage pattern and a data rate associated with the device. The movement of the device may include any data associated with the movement of the device. For example, the device may be moving vertically without a specific north or south movement. The mobility data may include a simple yes / no value indicating that the device is in motion. The mobility data may include a rate of movement of the device. The mobility data may also include data characterizing the speed as slow, medium, fast, or a specific value (e.g., 10 mph, 60 mph, etc.).

[0100] The method includes classifying devices based on mobility data to generate a classification. When making a reselection or handover decision, the cell type used for the classification may include at least one preferred cell type and at least one acceptable cell type. At least one of the preferred cell types may include at least one cell in a cell priority list. A mobility state may be calculated or estimated from the mobility data. In one aspect, the vector may include multiple dimensions. Optionally, one or more dimensions may include time. The mobility state associated with the mobility data may also be associated with a movement rate of the device. The classification may be one of slow, medium, and fast.

[0101] In another aspect, the boundaries associated with the classification may be one of fixed or variable. For example, one or more cells in a network may have defined boundaries in terms of geographic size. The boundaries may be fixed or variable, and their values ​​may depend on a variety of factors, including the classification. In other words, if a device is classified as a first classification, it may be reported such that when a reselection or handover decision is made, the boundaries associated with the cell may be variable. For example, if mobility data concludes that a device has a fast classification, the boundaries associated with one or more cells involved in the reselection or classification may be defined as variables so that they may be extended to overlap with the device in a current geographic location or an expected geographic location or a predicted geographic location to improve the reselection or handover process. This variable use of boundaries may, for example, be moved upward in time as communication between the device and a particular cell begins during a time when the device would begin exchanging data with the device at a normal boundary size.

[0102] It can also be used to balance load between cells when boundaries are variable. For example, if a cell reduces its boundaries, some devices at the outer edge may find themselves outside the boundaries of the cell and be served by other cells.

[0103] The control plane and / or the user plane may include at least one signal indicator identifying one or more elements of the mobility data of the device or the classification of the device to the control node.

[0104] In one aspect, the preferred cell types for the slow classification include micro cell types and small cell types, and the acceptable cell types for the slow classification include large cell types. The preferred cell types for the medium classification may include small cell types, and the acceptable cell types for the medium classification may include large cell types. On the other hand, the preferred cell types for the fast classification may include large cell types, and the acceptable cell types for the fast classification may not include any cell types. These are, of course, merely examples of specific cell types associated with specific classifications of devices.

[0105] In one aspect, the device selects a non-preferred cell type only when the received signal is stronger than a threshold that is higher than the threshold required for the preferred cell type. In another aspect, when making a reselection or handover decision, reselection is triggered when the first cell no longer provides an adequate signal or when the first cell is of a non-preferred type. Making a handover decision in this case can include handing over to the second cell when the second cell is one of the preferred cell types or when appropriate.

[0106] On the other hand, the classification may include the location of the first cell and the path traveled through the first cell. The path traveled through the first cell may include a predicted path based on the mobility data and / or an actual path traveled by the device at least partially through the first cell. Thus, the vector indicating the movement of the device may include such information that can be used to identify the path that the device has taken or may take. In addition, mapping information, historical information, machine learning information, data associated with other devices, etc. can be used in conjunction with the mobility data to identify information about the previous movement of the device and the predicted movement or path of the device. This information can be utilized when making one or more of the device classification and the reselection or handover decision of the device.

[0107] In another aspect, making a handover decision may also be based at least in part on a first spectral efficiency of the first cell and a second spectral efficiency of the second cell.Making a reselection or handover decision may also be based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0108] It is also important to note that different aspects or embodiments of the present disclosure can be practiced on different devices. For example, in one aspect, steps performed by a mobile device can be claimed as embodiments. Thus, the mobile device can perform steps such as sending mobility data to a remote device or classifying the device based on mobility data. During a reselection or handover process, the device will also send and / or receive communication information used to handle the handover. Thus, one aspect encompasses all of the various functions that would be performed on the device.

[0109] In another aspect, the functionality may be performed at a controller, server, base station, or other device or combination of devices within the network. Thus, steps such as receiving mobility data, classifying devices, and making or managing handover decisions may be performed by one or more network-based components. One aspect of the present disclosure relates to steps performed by one or more such devices. These devices may encompass multiple devices managed by a single entity and thus include one or more processors, storage devices, and the like.

[0110] Figure 10 The radio resource control idle rule process is illustrated. First, the system determines that the mobile device is in a radio resource idle state and / or mobility class I (1002). Next, the method determines whether there is a suitable cell that is preferred for mobility class I (1004). If so, the system selects the preferred cell C with the strongest signal (1008). Next, after a set delay (or variable delay or delay based on some parameter), the system determines whether the signal from cell C is below a minimum value (1010). Of course, the signal from cell C will also be equal to or below the determined value. If not, the system determines whether cell C is still preferred for the mobile device (1002). If so, the process returns to the determination (1010) after a delay (fixed, variable or even no delay). Note that if the result of the determination in step (1010) is that the signal from cell C is below the minimum value, the process proceeds to position A, as shown in FIG. Figure 10 As shown, the process returns to step (1004) to determine whether there is a suitable cell suitable for mobility category I. Returning to step (1012), if cell C is still not preferred for the mobile device, the process again returns to the determination step (1004).

[0111] If the determination from step (1004) is that there are no suitable cells preferred for mobility class 1, the system determines if there are suitable cells acceptable for mobility class 1 (1006). If so, the system selects the acceptable cell C with the strongest signal (1014). If there are no suitable cells acceptable for mobility class 1, the system searches for cells on other carriers (1016), and the process continues toward A and eventually returns to the determination step (1004).

[0112] Figure 11Handover rules 1100 for a radio resource control connected device are shown. At any point in time, the connected device is served by cell C and is in mobility category I (1102). At each measurement interval (1110), the system checks whether the signal strength RSRP_meas is above a minimum level RSRP_min (1104). If so, no action is taken until the next measurement time. If not, the system selects the cell with the strongest signal strength from the neighbor list, which is modified by a factor RSRP_diff that depends on the mobility category and cell type (1106). After thus determining the new cell, the system performs a handover to that cell (1108), which now becomes the serving cell. Note that an example algorithm for determining what "suitable" means could be RSRP_MEAS>RSRP_MIN+RSRP_DIFF(MC,CT).

[0113] Figure 12 An example process 1200 for modifying a neighbor cell list to address a mobility class is described. The system identifies an RRC-connected device of mobility class 1 (1202) and maintains a list of suitable neighbor cells and candidates for becoming a serving cell. These neighbor cells can be of any type, and each of these cells is classified as "preferred" or "acceptable" using one of the rules described previously. The system also maintains an indicator IND that is set to 0 whenever the device moves to a new serving cell. At each measurement interval 1110, the system checks whether the number of preferred cells in the neighbor list is above a threshold TH1 (1204). If so, and IND is equal to 1 (1210), all acceptable cells are deleted from the neighbor list (1212). Returning to step 1204, if the number of preferred cells is not above the threshold, then if IND is equal to 0 (1206), all suitable acceptable cells are added to the neighbor list (1208). Otherwise, no action is taken, and the system waits for the next check (1202).

[0114] Another aspect of the present disclosure relates to a method and apparatus for mobility-based cell allocation in heterogeneous networks. The prior art recognizes the need and potential advantages of having different types of cells to improve coverage and use of spectrum, resulting in heterogeneous networks (HET-NET). Additionally, there are patents related to user mobility estimation. However, the question of how to combine the two to actually assign users to cells based on mobility information remains. Additionally, there is prior art on how to improve the execution of handovers, but the present disclosure is focused on reducing the rate of handovers. Several aspects of the present disclosure include obtaining mobility estimates based on GPS data associated with the device, using smart car technology to convey speed, obtaining mobility estimates based on handover rate or time in a cell, and non-mobility declaration of the UE.

[0115] Next, we discuss the assignment of users to cells based on the above classifications. A set of rules can exist that combine mobility state, cell type, and current cell load to rank candidate cells for new connections. Another set of rules can be implemented to determine when to perform a handover and which cell to move to. An example method for obtaining or estimating mobility state in a wireless communication network can be implemented using a control processor to optimize or account for one or more of the following factors: power levels of the serving cell and neighboring cells, distance estimates of the serving cell and neighboring cells, and the ability to handle cell changes from the serving cell to a neighboring cell when calculating the estimates. The power levels can be for only neighboring cells or for all cells, and can be viewed collectively or individually, with different power levels considered for different types of neighboring cells. The distance estimates can include the geographic location of each neighboring cell (how large / small is the cell relative to other cells?) or the distance from the device's current location to the neighboring cell. The ability to handle cell changes can take into account whether the potential neighboring cell is the same type as the current cell or a different type (i.e., Wi-Fi vs. LTE).

[0116] The control processor may be in at least one of the UEs in the serving cell. The mobility estimate may be based on the UE's GPS location. The speed estimate utilizes smart car technology. The mobility estimate may be based on the handover rate or time in the cell. The mobility estimate may be based on the UE's declaration of immobility. The mobility estimate may be based on the ability to determine signal suppression information from the serving cell to neighboring cells. Any of these features may be combined with the systems and methods disclosed herein.

[0117] In one aspect, a system for obtaining or estimating mobility states in a wireless communication network may use a control processor to optimize or consider one or more of the following factors: power levels of a serving cell and a neighboring cell, distance estimates of the serving cell and the neighboring cell, and the ability to handle cell changes from a serving cell to a neighboring cell when calculating the estimate.

[0118] In another aspect, a method for classifying UEs in a wireless communication network is disclosed for optimizing, using a control processor, the following factors: power levels of a serving cell and neighboring cells, distance estimates of the serving cell and neighboring cells, and / or the ability to handle cell changes from a serving cell to a neighboring cell when calculating the estimates.

[0119] In yet another embodiment, a system is disclosed for classifying user equipment (UE) in a wireless communication network using a control processor to optimize the following factors: power levels of a serving cell and neighboring cells, distance estimates of the serving cell and neighboring cells, and / or the ability to handle cell changes from a serving cell to a neighboring cell when calculating the estimates.

[0120] Embodiments within the scope of the present disclosure may also include tangible and / or non-transitory computer-readable storage devices for carrying or having stored thereon computer-executable instructions or data structures. Such tangible computer-readable storage devices can be any available device that can be accessed by a general-purpose or special-purpose computer, including the functional design of any special-purpose processor as described above. By way of example and not limitation, such tangible computer-readable devices may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage devices, magnetic disk storage devices or other magnetic storage devices, or any other device that can be used to carry or store the required program code in the form of computer-executable instructions, data structures or processor chip designs. When information or instructions are provided to a computer over a network or another communication connection (hardwired, wireless or a combination thereof), the computer correctly views the connection as a computer-readable medium. Therefore, any such connection is properly referred to as a computer-readable medium. The above combinations should also be included within the scope of computer-readable storage devices.

[0121] Computer-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a special-purpose processing device to perform a specific function or group of functions. Computer-executable instructions also include program modules that are executed by computers in a stand-alone or networked environment. Typically, program modules include routines, programs, components, data structures, objects, and functions inherent in the design of a special-purpose processor, etc., that performs a specific task or implements a specific abstract data type. Computer-executable instructions, associated data structures, and program modules represent examples of program code means for performing the steps of the methods disclosed herein. A particular sequence of such executable instructions or associated data structures represents an example of corresponding actions for implementing the functions described in those steps.

[0122] Other embodiments of the present disclosure can be practiced in network computing environments with various types of computer system configurations, including personal computers, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, etc. Embodiments can also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (by hardwire, wireless link, or a combination thereof) through a communications network. In a distributed computing environment, program modules can be located in local and remote memory storage devices.

[0123] In another embodiment, a method may include receiving mobility data for a device served by a first cell, wherein the mobility data includes a vector indicating movement of the device, and classifying the device based on the mobility data to produce a classification, wherein the cell type used for the classification includes at least one of a preferred cell type and at least one of an acceptable cell type when making a reselection or handover decision.

[0124] In another embodiment, a method may include receiving, in a virtual layer, mobility data of a device served by a first cell in a heterogeneous network, the heterogeneous network including at least a non-cellular node type and a cellular node type, and classifying the mobility data of the device in the virtual layer to produce a classification, wherein the classification includes an access type and an indication of an access priority for the access type, wherein the access priority includes at least an acceptable access type or a preferred access type, and wherein the access type is identified from a list of access types including at least one of the non-cellular node type and the cellular node type.

[0125] The method also includes measuring a service characteristic from a service characteristic priority list, for a plurality of devices operating within a heterogeneous network, the plurality of devices including the device and at least one additional device, the service characteristic including two or more of: (1) adjustment of node boundaries of nodes in the heterogeneous network; (2) adjustment of power levels of nodes in the heterogeneous network; (3) cell load balancing of nodes in the heterogeneous network, (4) load distribution among nodes in the heterogeneous network based on communications with the plurality of devices, (5) distribution of the plurality of devices in the heterogeneous network, and (6) distribution of the plurality of devices in the heterogeneous network and load levels of respective nodes to generate measurements and make a handover decision in a virtual layer when handing over the device from a first cell to a second cell based at least in part on the classification and the measurements.

[0126] A mobility state can be calculated or estimated from the mobility data. The vector can include multiple dimensions, at least one of which is time. The mobility state associated with the mobility data can be the device's movement rate. The classification can also be one or more of slow, medium, and fast. The boundaries associated with the classification can be fixed or variable. The boundaries can be variable and can be used to balance load between cells.

[0127] In another aspect, the control plane may include at least one signal indicator that identifies one or more elements of the mobility data of the device or the classification of the device to the control node. The preferred cell type for the slow classification may include a micro cell type and a small cell type, and the acceptable cell type for the slow classification may include a large cell type. The preferred cell type for the medium classification may include a small cell type, and the acceptable cell type for the medium classification may include a large cell type. The preferred cell type for the fast classification may include a large cell type, and the acceptable cell type for the fast classification may not include any cell type.

[0128] The device may only select a non-preferred cell type when the received signal is stronger than a threshold that is higher than the threshold required for the preferred cell type. When making a handover decision, reselection may be triggered when the first cell no longer provides sufficient signal or when the first cell is of a non-preferred type, and wherein making the handover decision includes handing over to the second cell when the second cell is one of the preferred cell types or is suitable. On the other hand, the classification may include the location of the first cell and the path traveled through the first cell. Making the handover decision is also based at least in part on the first spectral efficiency of the first cell and the second spectral efficiency of the second cell. Making the reselection or handover decision may also be based at least in part on the bandwidth used by the device and the available bandwidth of the second cell. The mobility data may also be independent of one of the data usage pattern and the data rate. At least one of the preferred cell types may include at least one cell in a cell priority list. Any concept disclosed in any embodiment above may be combined with any other concept to produce an example configuration or process that may be claimed.

[0129] A system embodiment may include one or more processors and a computer-readable storage device storing instructions that, when executed by the processors, cause the one or more processors to perform the following operations: receive mobility data for a device served by a first cell, wherein the mobility data includes a vector indicating movement of the device, and classify the device based on the mobility data to produce a classification, wherein, when making a reselection or handover decision, the cell type used for the classification includes: at least one preferred cell type and at least one acceptable cell type.

[0130] In another embodiment, the system may include a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform operations comprising: receiving mobility data of a device served by a first cell in a heterogeneous network comprising at least a non-cellular node type and a cellular node type, and classifying the mobility data of the device in a virtual layer to produce a classification, wherein the classification comprises an access type and an indication of an access priority for the access type, wherein the access priority comprises at least an acceptable access type or a preferred access type, and wherein the access type is identified from a list of access types comprising at least one of the non-cellular node type and the cellular node type. The operations also include measuring, in a virtual layer, a service characteristic from a service characteristic priority list, wherein, for a plurality of devices operating within a heterogeneous network, the plurality of devices including the device and at least one other device, the service characteristic includes two or more of: (1) adjusting node boundaries of nodes in the heterogeneous network; (2) adjusting power levels of nodes in the heterogeneous network; (3) balancing cell loads of nodes in the heterogeneous network; (4) load distribution among nodes in the heterogeneous network based on communications with the plurality of devices; (5) distribution of the plurality of devices in the heterogeneous network; and (6) distribution of the plurality of devices in the heterogeneous network and load levels of the respective nodes to generate measurement results, and making a switching decision when switching the device from a first cell to a second cell based at least in part on the classification and the measurements.

[0131] A separate embodiment may include a non-transitory computer-readable storage device storing instructions that, when executed by a computing device, cause the computing device to perform the operations described above.

[0132] The computer-readable storage device may store additional instructions that, when executed by one or more processors, cause the one or more processors to perform further operations, including selecting a non-preferred cell type only when a received signal is stronger than a threshold value that is higher than a threshold value required for a preferred cell type. At least one of the preferred cell types may include at least one cell in a cell priority list. The operations may include receiving mobility data for a device served by a first cell, wherein the mobility data includes a vector indicating movement of the device, and classifying the device based on the mobility data to produce a classification, wherein the cell type used for the classification when making a reselection or handover decision may include at least one of the preferred cell types and at least one of the acceptable cell types.

[0133] The non-transitory computer-readable storage device may store additional instructions that, when executed by the one or more processors, cause the one or more processors to perform further operations, including selecting a non-preferred cell type only when a received signal is stronger than a threshold value that is higher than a threshold value required for a preferred cell type. At least one of the preferred cell types may include at least one cell in the cell priority list.

[0134] The various embodiments described above are provided as examples only and should not be construed as limiting the scope of the present disclosure. Various modifications and variations may be made to the principles described herein without following the exemplary embodiments and applications shown and described herein, and without departing from the spirit and scope of the present disclosure. Claim language referring to "at least one" of a group indicates that one member of the group or multiple members of the group satisfy the claim.

Claims

1. A method for using mobility information in a heterogeneous network, comprising: In a heterogeneous network including at least a non-cellular node type and a cellular node type, receiving data including mobility data or session data in a control plane for a device served by a first node; classifying data of the device in a control plane to produce a category, wherein the category includes an access type and an indication of an access priority for the access type, wherein the access priority includes at least an acceptable access type or a preferred access type, and wherein the access type is identified from a list of access types including at least one of a non-cellular node type and a cellular node type; Measuring business service quality from a priority list of business service quality; adjusting the dynamic geographic boundary of the second node to generate a new geographic boundary of the second node based at least in part on the classification of the data of the device in the control plane; as well as A switching decision is made in a control plane when switching a device from the first node to the second node based at least in part on the dynamic geographic boundary, the predicted geographic location of the device to be covered by a new geographic boundary of the second node, the category, or the adjustment of the measurement.

2. The method of claim 1, wherein the data comprises a vector indicating a velocity of the device.

3. The method of claim 1 , wherein the boundaries defining the mobility classes are one of fixed or variable, and wherein: When the boundary is variable, the boundary is used to balance the load between nodes or optimize other indicators between nodes. 4 . The method of claim 2 , wherein the vector comprises a first dimension associated with time and a second dimension, wherein the second dimension relates to one or more of a speed of the device and a direction of motion of the device.

5. The method according to claim 1, wherein: Preferred access types for a slow category include a micro cell type and a small cell type, and acceptable access types for the slow category include a large cell type; The preferred access type for the medium speed category includes a small cell type, and the acceptable access type for the medium speed category includes a large cell type; as well as The preferred access type for the fast class includes the large area type, and the acceptable access types for the fast class do not include any type.

6. The method of claim 1, wherein the device only selects a non-preferred node type when a received signal is stronger than a higher threshold or meets an acceptable threshold compared to a threshold required for a preferred node type.

7. A method according to claim 6, wherein when making a switching decision, reselection is triggered when the first node no longer provides sufficient signal or when the first node is a non-preferred node type, and wherein making a switching decision includes switching to the second node when the second node is one of the preferred node types or is suitable. The method of claim 1 , wherein the category comprises a location of the first node and a path passing through the first node.

9. The method of claim 1, wherein making a handoff decision is further based at least in part on a bandwidth used by the device and an available bandwidth of the second node.

10. The method of claim 1, wherein the dynamic geographic boundary of the at least first node or second node is adjusted by adjusting the boundary, the boundary being associated with increasing a bit rate for communications by at least one of the first node or second node.

11. The method according to claim 1, wherein: data generation settings of the device in a control plane to optimize at least one of bit rate, quality of service, range, preferred spectrum, preferred device characteristics, or efficiency; and The data of the device in the control plane is used to evaluate bandwidth capacity settings for communicating with the first node or the second node to improve bit rate or spectrum efficiency.

12. A system for using mobility information in a heterogeneous network, comprising: processor; and A computer readable storage device storing instructions which, when executed by the processor, cause the processor to perform the method of claim 1.

13. A system for using mobility information in a heterogeneous network, comprising: processor; and A computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform operations comprising: transmitting data including mobility data or session data to a device served by a first node in a heterogeneous network, the first node including a cellular node type; wherein the heterogeneous network classifies data of the device in a control plane to generate a category, wherein the category includes an access type and / or an indication of an access priority for the access type, wherein the access priority includes at least an acceptable access type or a preferred access type, and wherein the access type is identified from a list of access types including at least one of a non-cellular node type and a cellular node type; and wherein the heterogeneous network measures a traffic quality of service in a control plane, and adjusts the dynamic geographic boundary of the second node based at least in part on data classification in the control plane of the device to generate an adjusted geographic boundary of the second node; identifying whether the system is within a signal range of the second node comprising a non-cellular node type; and The system is switched from the first node to the second node based on a switching decision in the control plane and at least in part based on one or more of signal range, traffic quality of service, an adjusted geographic boundary, a predicted location of the system that the adjusted geographic boundary of the second node will cover, and characteristics of the second node.

14. A system for using mobility information in a heterogeneous network, comprising: processor; and A computer readable storage device storing instructions that, when executed by a processor, cause the processor to perform further operations comprising: receiving data including mobility data or session data for a device served by a first node in a heterogeneous network including at least a non-cellular node type and a cellular node type; classifying the data of the device in a control plane to produce a category, wherein the category includes an access type and / or an indication of an access priority for the access type, wherein the access priority includes at least an acceptable access type or a preferred access type; and wherein the access type is identified from a list of access types including at least one of a non-cellular node type and a cellular node type; Measuring the business service quality in the control plane from a priority list of business service qualities; and A switching decision is made in the control plane when switching a device from a first node to a second node based at least in part on an adjustment of the dynamic geographic boundaries of the second node to generate new geographic boundaries for the second node, the category, characteristics of the second node, a predicted location of the system that the adjusted geographic boundaries of the second node will cover, or one or more of the measurements.

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