SYSTEM AND METHOD FOR USING MOBILITY INFORMATION IN HETEROGENEOUS NETWORKS - Patent application

Abstract

A system and method are provided for reducing or making more efficient handovers from one cell to another in a communication network. The method includes 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 handoff decision when handing off the device from the first cell to a second cell based at least in part on the classification. One example of mobility data is a speed at which the device is moving.

Description

[Technical field]

[0001] Priority This application claims priority to U.S. Patent Application No. 16 / 188,698, filed November 13, 2018, which is a continuation-in-part of U.S. Patent Application No. 14 / 978,751, filed December 22, 2015, and is incorporated by reference herein in its entirety.

[0002] 1.Technical Field The present disclosure relates to cell allocation in cellular networks, and more particularly to mobility data regarding the speed at which a device is moving in order to perform cell handoff allocation. The present invention relates to a system and method for using mobility data. [Background technology]

[0003] 2. Introduction A mix of large and small cells in Long Term Evolution (LTE) and other networks has the potential to make better use of limited wireless spectrum resources. However, when cellular devices connect to cells based on signal strength, this can lead to reduced efficiency due to variable connection quality and increased handover rates. As a result, heterogeneous cells may become a hindrance rather than an advantage. As efforts to make cellular and Wi-Fi networks interoperate lead to network convergence, Wi-Fi antennas increase the diversity of wireless access points that can be used. Summary of the Invention [Problem to be solved by the invention]

[0004] The need and potential benefits of having different types of cells to improve spectrum coverage and utilization has given rise to heterogeneous networks ("het nets"). There are challenges in properly performing handovers between different cells, both when the cells use the same protocol, such as LTE, or when the different cells use different protocols, such as an LTE cell adjacent to or overlapping a Wi-Fi cell. [Means for solving the problem]

[0005] overview The following description relates to a number of different examples of different approaches and systems for handling cell assignment and / or cell handoff in a cellular environment. The example method relates to a handoff 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 generate a classification, and making a handoff decision when handing off 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 may be estimated from the mobility data. In one example, the classification may be one of low speed, medium speed, and high speed. The classifications or classes may typically be separated by fixed or variable boundaries. For example, the classification may be a moving speed or rate of 10-30 MPH. The boundary between a low end of 10 MPH and a high end of 30 MPH may be fixed or variable based on one or more parameters. Boundaries can be reset.

[0006] When the system makes handoff decisions, each classification may include at least one of a preferred cell type and at least one allowable cell type. The preferred cell types for the slow classification may include micro and small cell types, and the allowable 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 allowable cell types for the medium classification include large cell types. The preferred cell types for the fast classification may include large cell types, and the allowable cell types for the fast classification include none.

[0007] When making a handoff decision, the device or system, in one embodiment, can only select a non-preferred cell type if the received signal is stronger than a higher threshold compared to the threshold required for the preferred cell type. Cell reselection can be triggered if the first cell no longer provides a sufficient signal and if the first cell is of a non-preferred type. Handoff can include handing off to a second cell if the second cell is of a preferred type and suitable.

[0008] The classification can include a possible location of the device within the first cell and a path through the first cell. Making the handoff decision can be further based at least in part on a first spectral efficiency of the first cell and a second spectral efficiency of the second cell. Making the handoff decision can be further based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0009] An example system includes a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform certain operations, including receiving mobility data for devices served by a first cell, classifying the devices based on the mobility data to generate a classification, and making a handoff decision when to handoff the device from the first cell to a second cell based at least in part on the classification.

[0010] Another example includes a computer-readable storage device storing instructions that, when executed by a computing device, cause the computing device to perform operations including 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 handoff decision when handing off the device from the first cell to a second cell based at least in part on the classification.

[0011] A second embodiment relates to performing an initial assignment of a device to a serving cell. In this embodiment, the method includes receiving mobility data for the 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 or estimated from the mobility data.

[0012] When allocating the device to a serving cell, a reselection can be triggered if the first cell no longer provides a sufficient signal and if the first cell is of a non-preferred type. Allocating the device to a serving cell can include allocating the device to a second cell if the second cell is of a preferred type and appropriate.

[0013] Mobility classes are used to determine the location of a device within a serving cell and across serving cells. The assigned device to the serving cell may include one of the paths passing through the serving cell. The assigned device to the serving cell may be further based at least in part on a spectral efficiency of the serving cell. The assigned device to the serving cell may be further based at least in part on a bandwidth used by the device and an available bandwidth of the serving cell. The assigned device to the serving cell may be further based at least in part on a plurality of prioritized lists of cells, each list including cells of the same type. In an aspect, cells in a higher prioritized list are measured more frequently than cells in a lower prioritized list.

[0014] In one possible implementation, the mobile device monitors its serving cell as well as neighbor cells from the list with the highest priority. If the size of this list (highest priority) is smaller than a specified parameter, the next list is also considered. In another possible implementation, higher priority cells are measured more frequently than lower priority cells. When determining the cell to handover to, cells from higher priority neighbor lists are always considered before cells from lower priority lists.

[0015] A system aspect of this second example includes a processor and a computer-readable storage device storing instructions that, when executed by the processor, cause the processor to perform operations including 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. A computer-readable storage device aspect of the second example includes a computer-readable storage device storing instructions that, when executed by a computing device, cause the computing device to perform operations including 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.

[0016] A third example embodiment relates to tracking load balancing across cells. Once a system with classification of devices and assignment to cell types is in place, the system can be monitored to track the distribution of users and load levels across cells. In addition to adjusting cell power levels if monitoring detects strong imbalances in cell loads, the system disclosed herein allows for dynamic adjustment of mobility class boundaries to optimize or improve the spectral efficiency and capacity of the network.

[0017] One example of such a condition is a temporary concentration of low-speed users in a particular area. In such an environment, reducing the class boundary value allows more users to be classified more dynamically and gain priority to be served by the larger cells. Another option is to reduce the power of microcells in the area, assigning fewer users to them.

[0018] According to this third embodiment, a method for balancing cell load in a network is disclosed in which 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 method includes monitoring a distribution of devices and load levels across a plurality of cells to generate an analysis. When the analysis indicates an imbalance in cell load, the method includes adjusting mobility class boundaries. When the analysis indicates an imbalance in cell load, the method includes adjusting a power level of at least one cell of the plurality of cells. When the analysis indicates an imbalance in cell load, the method includes performing both adjusting the power level of at least one cell of the plurality of cells and adjusting the mobility class boundaries. and. Adjusting the mobility class boundary includes one of increasing the upper rate associated with the upper boundary of the mobility class and decreasing the lower rate associated with the lower boundary of the mobility class. Performing one of adjusting the power level of at least one cell of the plurality of cells and adjusting the mobility class boundary improves the spectrum efficiency of the network with respect to a previous spectrum efficiency before adjusting one of the power level and the mobility class boundary. Note that in wireless transmission, data (bits) are composed of control bytes as well as content bytes. The closer the device is to the antenna or cell tower, the better the spectrum efficiency. When there is a handoff or during the handoff process, often more control bytes are transmitted than content bytes.

[0019] Performing one of adjusting a power level of at least one cell of the plurality of cells and adjusting a mobility class boundary improves the capacity of the network relative to a previous capacity before adjusting one of the power level and the mobility class boundary. When assigning devices to serving cells, each mobility class includes at least one of a preferred cell type and at least one allowable cell type. The preferred cell types for the low speed classification include a micro cell type and a small cell type, and the allowable cell types for the low speed classification include a large cell type. The preferred cell types for the medium speed classification include a small cell type, and the allowable cell types for the medium speed classification include a large cell type.

[0020] In one embodiment, the device selects only the non-preferred cell type only if the received signal is stronger than a higher threshold compared to a threshold required for the preferred cell type. Once the device is assigned to the serving cell, reselection is triggered based on one or more parameters or events. For example, reselection may be triggered if the first cell no longer provides a sufficient signal and / or if the first cell is of a non-preferred type. Assigning the device to the serving cell includes assigning the device to the second cell if the second cell is of the preferred type and suitable. The mobility class may include one of the device's location within the serving cell and a path through the serving cells. The device may be assigned to the serving cell based at least in part on a plurality of prioritized lists of cells, each list of the plurality of prioritized lists of cells including cells of the same type.

[0021] An example method includes receiving mobility data for a device served by a first cell, the mobility data including a vector indicative of a movement of the device, and classifying the device based on the mobility data to generate a classification. When making a reselection or handoff decision, the cell types of the classification can include at least one of a preferred cell type and at least one of an allowable cell type.

[0022] An example system according to a third embodiment is a system for distributing cell load in a network, where 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 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 a distribution and load levels of devices across a plurality of cells to generate an analysis, and adjusting mobility class boundaries when the analysis indicates an imbalance in cell load. The system may be a network infrastructure component or a mobile device, such as a smartphone or mobile phone.

[0023] An example computer-readable storage device 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 load in a network, classify devices into mobility classes based on mobility data, and assign devices 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 and load levels of devices across a plurality of cells to generate an analysis, and adjusting mobility class boundaries if the analysis indicates an imbalance in cell load. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 illustrates an exemplary system. [Figure 2A] FIG. 2A illustrates an exemplary heterogeneous cellular system. [Figure 2B] FIG. 2B illustrates one aspect of a control plane. [Figure 2C] FIG. 2C illustrates another control plane embodiment. [Diagram 3] FIG. 3 shows another example of a heterogeneous cellular system. [Figure 4] FIG. 4 shows a network in which devices move from cell to cell. [Diagram 5] FIG. 5 shows a macrocell overlay environment. [Figure 6] FIG. 6 illustrates an exemplary method. [Figure 7] FIG. 7 illustrates another exemplary method. [Figure 8] FIG. 8 illustrates another exemplary method. [Figure 9] FIG. 9 illustrates a method for updating mobility classes. [Figure 10] FIG. 10 illustrates a method for cell selection for a mobile device. [Figure 11] FIG. 11 is a diagram showing the RRC connection rules. [Figure 12] FIG. 12 shows how the neighbor list is updated. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] Various examples of the present disclosure are described in detail below. Although specific implementations are described, it should be understood that this is for illustrative purposes only. Other components and configurations can be used without departing from the spirit and scope of the present disclosure. Furthermore, it should be understood that a feature or configuration described herein with reference to one example can be implemented in or combined with other examples or examples of the present disclosure. That is, the terms "embodiment," "variant," "aspect," "example," "configuration," "implementation," "case," and any other terms that imply an embodiment, used herein to describe a particular feature or configuration, are not intended to limit any of the associated features or configurations to a particular or separate embodiment or embodiments, and should not be construed to suggest that such features or configurations cannot be combined with features or configurations described with reference to other embodiments, variants, aspects, examples, configurations, implementations, cases, etc. In other words, a feature described herein with reference to a particular example (e.g., an embodiment, variant, aspect, configuration, implementation, case, etc.) can be combined with a feature described with reference to another example. To be precise, one skilled in the art will readily recognize that the various embodiments or examples described herein, and their associated features, can be combined with each other.

[0026] This disclosure addresses concepts related to performing cell assignment and / or cell handoff in heterogeneous cellular systems. A brief introductory description of a basic generic system or computing device in Figure 1 that can be used to practice the disclosed concepts, methods, and techniques is given. This is followed by a more detailed description of various approaches to handoff and cell assignment.

[0027] These variations are described herein as various embodiments are described. The disclosure now turns to FIG. 1. Referring to FIG. 1, an exemplary system and / or computing device 100 includes a processing unit (CPU or processor) 120 and a system bus 110 connecting various system components, including a system memory 130, such as a read-only memory (ROM) 140 and a random access memory (RAM) 150, to the processor 120. The system 100 may include a cache 122 of high-speed memory that is directly connected to, in close proximity to, or integrated as part of the processor 120. The system 100 copies data from the memory 130 and / or storage device 160 to the cache 122 for quick access by the processor 120. In this manner, the cache provides a performance boost that avoids delays to the processor 120 while waiting for data. These and other modules may control or be configured to control the processor 120 to perform various operations or actions. Other system memories 130 may be available as well. The memory 130 may include multiple different types of memory having different performance characteristics. It should be understood that the present disclosure may operate on a computing device 100 having more than one processor 120, or on a group or cluster of computing devices networked together to provide greater processing power. Processor 120 may include any general-purpose processor configured to control processor 120, hardware or software modules such as module 1 162, module 2 164, and module 3 166 stored in storage device 160, as well as application-specific processors in which software instructions are embedded in the processor. Processor 120 may be a self-contained computing system including multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.Processor 120 may include multiple processors, such as a system having multiple physically separate processors in different sockets, or a system having multiple processor cores on a single physical chip. Similarly, processor 120 may include multiple distributed processors located in multiple separate computing devices but operating together over a communications network, etc. Multiple processors or processor cores may share resources, such as memory 130 or cache 122, or may operate using independent resources. Processor 120 may include one or more of a state machine, an application specific integrated circuit (ASIC), or a field programmable gate array (PGA), including a PGA.

[0028] 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, for example, in ROM 140, can provide basic routines that help transfer information between elements within the computing device 100, such as during start-up. The computing device 100 further includes a storage device 160 or computer-readable storage medium, such as a hard disk drive, a magnetic disk drive, an optical disk drive, a tape drive, a solid-state drive, a RAM drive, a removable storage device, a redundant array of inexpensive disks (RAID), a hybrid storage device, or the like. The storage device 160 can include software modules 162, 164, 166 for controlling the processor 120. The system 100 can include other hardware or software modules. The storage device 160 is connected to the system bus 110 by 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 modules that perform specific functions include the processor 120, The system includes software components stored in a tangible computer-readable storage device connected to necessary hardware components, such as the bus 110, display 170, etc., to perform specific functions. In another aspect, the system may 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 may 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 instructions to perform an "operation," the processor 120 may perform the operation directly and / or facilitate, direct, or cooperate with another device or component to perform the operation.

[0029] Although the exemplary embodiment described herein employs a hard disk 160, other types of computer-readable storage devices capable of storing computer-accessible data, such as magnetic cassettes, flash memory cards, digital versatile disks (DVDs), cartridges, random access memory (RAM) 150, read-only memory (ROM) 140, cables containing bit streams, etc., may also be used in the exemplary operating environment. Computer-readable storage medium, computer-readable storage device, or computer-readable memory device explicitly excludes ephemeral media, such as ephemeral waves, energy, carrier signals, electromagnetic waves, and the signals themselves.

[0030] To allow a user to interact with computing device 100, input device 190 represents any number of input mechanisms, such as a microphone for audio, a touch-sensitive screen for gesture or graphic input, a keyboard, a mouse, motion input, voice, etc. Output device 170 may also be one or more of a number of output mechanisms known to those skilled in the art. In some cases, a multi-mode system allows a user to provide multiple types of input to communicate with computing device 100. Communications interface 180 generally governs and manages user input and system output. There is no limitation to operation with any particular hardware configuration, and thus the basic hardware shown can be easily replaced with improvements as hardware or firmware configurations are developed.

[0031] For clarity of explanation, the exemplary system embodiment is presented as including individual functional blocks, including functional blocks labeled "processor" or processor 120. The functionality these blocks represent may be provided using either shared or dedicated hardware, including, but not limited to, hardware capable of executing software, and hardware such as processor 120 designed to perform operations equivalent to software executing on a general-purpose processor. For example, the functionality of one or more processors shown in FIG. 1 may be provided by a single shared processor or multiple processors. (The use of the term "processor" should not be construed as referring solely to hardware capable of executing software.) The described embodiment may include microprocessor and / or digital signal processor (DSP) hardware, read-only memory (ROM) 140 for storing software to perform the operations described below, and random access memory (RAM) 150 for storing results. Very large scale integrated circuit (VLSI) hardware embodiments may also be provided, as well as custom VLSI circuits in combination with general-purpose DSP circuits.

[0032] The logical operations of various embodiments may be represented as: (1) a series of computer-implemented steps, operations, or procedures performed on programmable circuitry within a general-purpose computer; (2) a series of computer-implemented steps, operations, or procedures performed on special-purpose programmable circuitry; and or (3) implemented as interconnected machine modules or program engines in programmable circuitry. The system 100 shown in FIG. 1 can perform all or a portion of the recited methods, can be part of the recited systems, and / or can operate according to instructions in the recited tangible computer-readable storage devices. Such logical operations can be implemented as modules configured to control the processor 120 to perform certain functions according to the programming of the modules. For example, FIG. 1 shows three modules Mod1 162, Mod2 164, and Mod3 166, which are modules configured to control the processor 120. These modules may be stored in the storage device 160, loaded into the RAM 150 or memory 130 at execution time, or stored in other computer-readable memory locations. The system 100 may have other modules (not shown in FIG. 1) configured to control the processor 120.

[0033] One or more portions of the exemplary computing device 100 may be virtualized, including up to the entire computing device 100. For example, a virtual processor may be a software object that executes according to a particular set of instructions, even if a physical processor of the same type as the virtual processor is not available. A virtualization layer or virtual "host" may enable virtualized components of one or more different computing devices or device types by translating the virtualized operations into real operations. However, ultimately, any type of virtualized hardware is implemented or executed by some underlying physical hardware. Thus, a virtualized computing layer may operate on top of a physical computing layer. A virtualized computing layer may include one or more of virtual machines, overlay networks, hypervisors, virtual switching, and other virtualized applications.

[0034] Processor 120 may include all types of processors disclosed herein, including virtual processors. However, when referring to a virtual processor, processor 120 includes software components associated with running the virtual processor in a virtualization layer, as well as the underlying hardware required to run the virtualization layer. System 100 may 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 on which virtual processor 120 runs.

[0035] Having disclosed some components of the general computing system of FIG. 1, the disclosure now turns to FIG. 2, which illustrates aspects of the disclosure. FIG. 2 depicts a heterogeneous network 200 that may be associated with a wireless protocol such as LTE or different wireless protocols. As shown in FIG. 2, a macrocell 202 is contained within a group of macrocells 224, 226, 228, 230, 232, and 234. Within each macrocell is a cell tower 204, as well as various other types of towers or cells. For example, microcells 206, 212, and 214 operate using eNodeB public antennas. A mobile device 218 is shown in communication with the microcell 206. The femtocell 208 may also communicate with another mobile device 220. Another example of a picocell 210 in a building is shown. Also shown in FIG. 2 are other mobile communication systems, such as vehicles 216 and 222, in communication with the macrocell 202. Thus, FIG. 2 illustrates an example of how a heterogeneous network can exist such that a mobile device 218, 220, 216, 222 can transition from one protocol to another and / or from a larger sized cell, such as a macrocell 202, to a smaller sized cell, such as a microcell 206, and vice versa, as the mobile device 218, 220, 216, 222 moves through a series of cells. to provide.

[0036] This disclosure provides methods and systems that leverage different types of cells in a heterogeneous wireless network with the goal of improving the service levels provided to users as well as the efficiency and capacity of the network. To achieve these goals, at least three new ideas are provided that can be integrated into one system. The first idea relates to classifying user devices into mobility classes based on their mobility state. The second idea relates to assigning user devices to serving cells based on their mobility classes. The third idea relates to dynamically adjusting cell powers and / or mobility class boundaries.

[0037] Most of the procedures described herein can be implemented in one or more user devices, 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 of the coordinated sequence of operations can also be performed in different devices. Device control is inherently distributed, but can also react more quickly to changing mobility conditions, and some algorithms fit this mode of operation. Using an RNC or NP provides a more centralized decision point, but can be relatively slow to react to changing conditions. Changing load conditions can also affect the mode of operation. For example, if an NP becomes overloaded, more processing can be performed at the device and / or RNC level.

[0038] We now expand 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. The mobility state can be obtained or estimated using several possible methods. Each device and / or processor in the network can determine the mobility state using one or more of these methods. For example, the mobility state may be 10 MPH or 70 MPH. The mobility state may also include vector components, where the state contains parameters related to the direction in which the user device is moving. Thus, the mobility state may include values ​​such as (65,10), which is the speed and compass heading (10 degrees in this case). The mobility state may also include other information such as acceleration, altitude, ascent / descent rate, etc.

[0039] The mobility state can be a mobility estimate based on device coordinates sampled every time unit. The speed of the device can be calculated based on the change in coordinates. Simple averaging or exponential smoothing over a time frame is an example of an approach that can be used to calculate the current mobility state. The system can additionally or alternatively use smart car technology to communicate speed. Devices in moving vehicles have their speed determined by the vehicle's speedometer and sent to a device or network processor, which can then be used to calculate the mobility state. For other users, wearable devices such as fitness monitors can be used.

[0040] Mobility estimates based on handover rate or time in a cell can be used to establish mobility state. The terms "handover", "handoff", and their variants usually refer to the process of transferring an ongoing call or data session of a device from one cell to another with minimal or no interruption in service as the device's geographic location changes. For example, assume that a device measures the time between handoffs. A network processor can estimate mobility state based on cell size and time in cell. This is a relatively inaccurate way to determine mobility state. However, it can be used as a feedback metric to evaluate the system. It can be used.

[0041] Mobility state can also be determined by a user device declaring itself as non-mobile. This approach requires a wireless protocol that allows a device to declare itself as either stationary or mobile. While the device is stationary, it does not need to perform mobility-related measurements.

[0042] Once the mobility state has 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 a variety of ways. One approach is to use an association between each mobility class and a range of mobility states. An example is shown in Table 1. [Table 1]

[0043] Speed ​​limits / thresholds are used to classify devices as defined by class boundaries. The class boundary in the above example is 10 meters per minute defining the boundary between class 1 (low speed) and class 2 (medium speed). Another class boundary in this example is 100 meters per minute defining the boundary between class 2 (medium speed) and class 3 (high speed). Of course, the structure can include more than three classes and the specific boundary can be any parameter or group of parameters. For example, a class can include a speed range as well as a direction range. If the direction the device is heading leads to a microcell within a certain time, it can be combined with the speed to be in a certain class. An example of this approach could be class 2 (medium speed, 12 degree movement leads to a microcell within 2 minutes). This classification may also be based on the relative speed at which the device approaches or leaves the microcell, regardless of the direction the device is moving.

[0044] As mentioned above, given a network having multiple types of cells of different sizes and power levels, and user devices classified based on mobility, the present disclosure now provides rules for assigning devices to cells based on their mobility classes.

[0045] For ease of explanation, the case of three types of cells are shown: large cell, small cell, and micro cell. Of course, the system can use more than three defined types of cells. Such definitions can include parameters based on one or more of the following: size, geography, altitude, buildings / infrastructure within the cell, shape, signal strength, signal range, device usage, number of devices within the cell, device mobility within the cell (i.e., does the cell cover a highway with an average device speed of 65 MPH), foliage, and other obstructions, etc.

[0046] This disclosure deals with the establishment and management of radio bearers and does not change the establishment and control of S-type bearers between cells and network gateways.

[0047] This document describes two sets of rules that are essential to the operation of a mobile network: Radio Resource Control (RRC) cell selection for idle devices and RRC cell selection for connected devices. This is an enhancement to the handover of mobile devices. By way of background, when a device is powered on, it needs to find a suitable serving cell and perform an authentication procedure and connection to the network. Then, the mobile device is communicating with the serving cell in RRC idle state without acquiring radio resources. In this state, the selection and reselection of the serving cell is under the control of the device. Once the RRC connection is established, the device periodically reports its measurements and is monitored by the network to ensure the quality of service. If the quality of service from the current serving cell is not acceptable and / or there is another cell that is better suited for the service, the network can request a handover.

[0048] A device in RRC idle state operates 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 to serve the device if the cell's RSRP is above a minimum threshold level and also meets certain service conditions, e.g., the service provider is on the device's prioritization list and is on a network where service is not barred, although these conditions are not really relevant to this disclosure.

[0049] Introducing cell types and mobility classes adds a new dimension to the cell selection process. This disclosure provides two possible rules.

[0050] The first rule is a simple priority rule that specifies the order of precedence as shown in Table 2. [Table 2]

[0051] Table 2 is merely illustrative and of course many different parameters could be included. For example, a slow mobile device would first look for a suitable micro or small cell capable of providing Wi-Fi communication and would only consider a large cell if none were available. A fast moving device would only consider a large cell and would not be able to consider a micro or small cell. This latter rule prevents excessive reselection.

[0052] The second rule specifies the additional parameter RSRP that is specified by the network. diff In this case, the cell is suitable for the device based on the result of the following formula: RSRP meas >RSRP min +I(MC,CT)×RSRP diff Here, RSRP min is the minimum acceptable received power, RSRP measis a measurement, I(MC,CT)=0 if cell type CT is preferred for user equipment (UE) mobility class MC, otherwise I(MC,CT)=1.

[0053] A more general version of this rule is the matrix of values ​​RSRP diff (MC,CT) and the cell is suitable if: RSRP meas >RSRP min +RSRP diff (MC,CT)

[0054] Under this rule, the device will select the non-preferred cell type only if the received signal is stronger than a higher threshold compared to the threshold required for the preferred cell type. Therefore, reselection is triggered when the serving cell no longer provides a sufficient signal. Furthermore, if the current serving cell is of a non-preferred type, reselection may occur if a preferred type cell is detected and suitable.

[0055] Another example process is for a device in the RRC connected state. Once the device is connected, if the rules of RRC-Idle are followed, the device is served by cells that fit its mobility class. At this point, in most LTE or other protocol networks, the device monitors its serving cell and listed neighbor cells.

[0056] The feature introduced here is that instead of a simple list of neighbor cells, there are multiple prioritized lists that can be analyzed. Each list may contain cells of the same type, or cells of different types, or have the property of sharing a certain number of parameters. In one possible implementation, the device monitors its serving cell as well as the neighbor cell with the highest priority from the list. If the size of this list (the one with the highest priority) is smaller than 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 the cell to handover to, cells from a higher priority neighbor list are always considered before cells from a lower priority list.

[0057] When a user (and therefore a device) changes its mobility class, the change is detected by the mobility measurement method(s) in use and is included in the measurement report to the serving cell (actually the base station within the cell, commonly called eNodeB). Initially, the device continues to be served by the same cell, but the rules for reselection (for RRC-Idle UEs) and handover (for RRC-Connected UEs) to a new serving cell are according to the new mobility class.

[0058] For example, if a walking user (medium speed class) currently served by a small cell gets into a vehicle, the device may move very quickly out of the current cell. In this case, the device is immediately classified as high speed and the optimal large cell is selected. Similarly, a high speed device in a large cell may enter a building and become stationary. The currently stationary device is still served by the large cell, but now seeks a micro or small cell to become eligible to be served by it.

[0059] Once a system with classification of devices and assignment 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 strong imbalances in cell loads, in addition to adjusting cell power levels, this disclosure enables dynamic adjustment of mobility class boundaries to optimize the spectral efficiency and capacity of the network.

[0060] One example of such a condition is a temporary concentration of low-speed users in a particular area. In such an environment, reducing the class boundary value allows more users to be classified more dynamically and gain priority to be served by the larger cells. Another option is to reduce the power of microcells in the area, assigning fewer users to them.

[0061] Boundary adjustments can also be based on historical data. Perhaps during rush hour, the boundaries are adjusted to account for the high volume of device movement through the cell area. However, certain times of the day or in certain areas may have bad traffic and therefore poor device behavior. will be slower. Thus, adjusting the boundaries can take into account actual device movement that occurs within the cell. Adjustments can be made for any number of reasons, including the addition of new cells or removal of cells, additional fees paid by users to allow adjustments or to give devices priority, etc.

[0062] 2A and 2B show control plane signaling between microcell 206, macrocell 204, and device 218. The signals are for (1) handover request, (2) admission control signaling, (3) handover acknowledgement, (4) radio resource control (RRC) reconfiguration signaling, (5) switch to new cell signaling, and (6) completion of RRC reconfirmation signaling.

[0063] Figure 3 illustrates an example of power levels of serving and neighboring cells. For example, feature 320 illustrates a group of cells that may be similar to the group of cells illustrated in Figure 2A. Feature 302 illustrates a macro overlay cell covering an area associated with cell tower 310 with overlapping microcells, such as microcell 304 served by tower 312, microcell 306 served by tower 316, and microcell 308 served by tower 318. The principles disclosed herein cover various methods and processes for cell assignment and inter-cell handoff in heterogeneous networks and in scenarios where there is overlap between cells of different types.

[0064] FIG. 4 illustrates an example of a handoff rate. As shown in a group of cells 400, various cells are shown, such as 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, and 436. Each is served by a cell tower or some type of antenna (not shown). Shown in FIG. 4 are different speeds of devices moving through the various cells. Three arrows are shown as feature 406, which may represent a fast moving device moving from cell 420 to 422. A single arrow is shown as feature 408, which may represent a slow moving device moving from cell 414 to 424. Arrow 412 may represent a device moving from cell 414 a short distance to cell 416 and then moving to cell 418. Arrow 412 illustrates an example of having to handle two quick handoffs as a device moves from cell 414 to 416 and then quickly again from cell 416 to 418. Similarly, feature 410 illustrates movement of a device transitioning from cell 424 to cell 428, as well as another transition from cell 428 to 426. Feature 404 illustrates another set of arrows representing movement of a device from cell 432 to 434, which may include some movement in cell 426. As will be appreciated, the principles disclosed herein improve cell assignment and handoff capabilities to minimize the processing required to perform the handoff.

[0065] 5 shows another example of cell allocation. System 500 shows several different cells including macro cell 502 with a capacity of 14 Mbps that is currently using 6 Mbps. Device 506 is a smartphone that is using 5 Mbps. Note that cell 504, a micro cell with a capacity of 5 Mbps, is currently communicating at 3 Mbps. The present disclosure enables cell allocation to improve spectral efficiency.

[0066] 6 illustrates an example method that may be executed on one or more of a mobile device and a system or network-based processor. The method includes receiving mobility data for a device served by a first cell (602), classifying the device based on a mobility state associated with the mobility data to generate a classification (604), and, based at least in part on the classification, classifying the device based on a mobility state associated with the mobility data (605). The method includes making a handoff decision when handing off the device from the first cell to the second cell (606). The mobility state may be calculated from the mobility data or may be inferred from the mobility data. The boundaries defining the mobility classes may be fixed or variable, and if the boundaries are variable, the boundaries may be reconfigurable.

[0067] When making a handoff 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 micro and small cell types, and the acceptable cell types for the slow classification may 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. The preferred cell types for the fast classification may include large cell types, and the acceptable cell types for the fast classification may include none. In one aspect, the device only selects non-preferred cell types if the received signal is stronger than a high threshold compared to the threshold required for the preferred cell type.

[0068] When making the handoff decision, a reselection can be triggered if the first cell no longer provides a sufficient signal and if the first cell is of a non-preferred type, and making the handoff decision includes handing off to the second cell if the second cell is of a preferred type and suitable. The classification can include a location of the device within the first cell and a path through the first cell. Making the handoff decision can further be based at least in part on a first spectral efficiency of the first cell and a second spectral efficiency of the second cell. Furthermore, making the handoff decision can be based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0069] FIG. 7 illustrates another example related to the cell allocation aspect of the disclosure. The 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 based at least in part on the mobility class (706). When assigning the 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 slow classification can include micro cell types and small cell types, and the acceptable cell types for slow classification can include large cell types. The preferred cell types for medium classification can include small cell types, and the acceptable cell types for medium classification can include large cell types. The preferred cell types for fast classification can include large cell types, and the acceptable cell types for fast classification can include none. In an aspect, the device selects only non-preferred cell types if the received signal is stronger than a high threshold compared to a threshold required for the preferred cell type.

[0070] When allocating a device to a serving cell, a reselection can be triggered if the first cell no longer provides a sufficient signal and if the first cell is of a non-preferred type, and allocating the device to the serving cell includes allocating the device to a second cell if the second cell is of a preferred type and suitable.

[0071] The mobility class may include the location of the device within the serving cell or a path through the serving cell. The allocation of the device to the serving cell may further be based at least in part on the spectral efficiency of the serving cell. The allocation 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 an aspect of the present invention, the allocation of the device to a serving cell can be further based at least in part on a plurality of prioritized lists of cells, each list including cells of the same type, with cells in higher prioritized lists being measured more frequently than cells in lower prioritized lists.

[0072] Once a system with classification of devices and assignment to cell types is in place, the system can be monitored to track the distribution of users and load levels across the cells. In addition to adjusting cell power levels if monitoring detects strong imbalances in cell load, the concepts disclosed herein allow for dynamic adjustment of mobility class boundaries to optimize or improve the spectral efficiency and capacity of the network.

[0073] One example of such a condition is a temporary concentration of low-speed users in a particular area. In such an environment, reducing the class boundary value allows more users to be classified more dynamically and gain priority to be served by the larger cells. Another option is to reduce the power of microcells in the area, assigning fewer users to them.

[0074] 8 illustrates an example method for balancing cell load in a network where 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 method includes monitoring a distribution and load levels of devices across a plurality of cells to generate an analysis (802) and adjusting mobility class boundaries if the analysis indicates an imbalance in cell load (804).

[0075] When the analysis indicates a cell load imbalance, the method may include adjusting a power level of at least one cell of the plurality of cells. When the analysis indicates a cell load imbalance, the method further includes performing both adjusting a power level of at least one cell of the plurality of cells and adjusting a mobility class boundary. Adjusting the mobility class boundary may include increasing an upper rate associated with an upper boundary of the mobility class and / or decreasing a lower rate associated with a lower boundary of the mobility class. Adjusting the power level or adjusting the mobility class boundary of at least one cell of the plurality of cells may improve or optimize a spectral efficiency of the network relative to a previous spectral efficiency before adjusting the power level or the mobility class boundary. Adjusting the power level or adjusting the mobility class boundary of at least one cell of the plurality of cells may improve or optimize a capacity of the network relative to a previous capacity before adjusting the power level or the mobility class boundary. The device may be assigned to a serving cell based at least in part on a plurality of prioritized lists of cells, each list of the plurality of prioritized lists of cells including cells of the same type or sharing at least a threshold number of characteristics.

[0076] FIG. 9 shows another method for setting mobility classes and updating mobility class settings. In a first step, the mobile device is in mobility class MC0 (902). The system performs mobility measurements (904) and updates the mobility state (MS) (906). This measurement can be done at a fixed or variable measurement interval (908). The system then determines (910) whether the mobility state is below threshold 1. If not, the system determines (912) whether the mobility state is below threshold 2. If the mobility state is equal to or greater than threshold 2, the system determines that the new mobility class is in a category such as high speed and sets the variable MCn=3 (918). If the mobility state is below threshold 2, the system determines that the mobility class is in another category such as low speed. The system determines that the new mobility class is in yet another category, such as very slow, and sets variable MCn=2 (916). If the mobility state is below threshold 1, the system determines that the new mobility class is in yet another category, such as very slow, and sets MCn=1 (914). The flow from each of steps (914), (916) and (918) is then to determine whether MCn=MC0 (920). If not, the system assigns MC0 to be MCn and reports the change in mobility class (922). If MCn equals MC0, the flow returns to step (904) of performing mobility measurements, and the process essentially begins again.

[0077] In another aspect, a method can include receiving mobility data for a device served by a first cell, the mobility data including a vector (or other data structure) indicative of device movement. The mobility data can be independent of one of a data usage pattern and a data rate associated with the device. The device movement can include any data related to the device's movement. For example, the device may be moving vertically and not moving in a particular north or south direction. The mobility data can include a simple yes / no value indicating that the device is moving. The mobility data can include a rate of movement of the device. The mobility data can also include data characterizing the speed as slow, medium, fast, or a particular value such as 10 mph, 60 mph, etc.

[0078] The method includes classifying the device based on the mobility data to generate a classification. When making a reselection or handoff decision, the cell types of the classification can include at least one of a preferred cell type and at least one of an allowable cell type. The at least one of the preferred cell types can include at least one cell from a prioritized list of cells. The mobility state can be calculated or estimated from the mobility data. In one aspect, the vector can include multiple dimensions. Optionally, one or more of the dimensions can include time. The mobility state associated with the mobility data can also be associated with a movement rate of the device. The classification can be one of slow, medium, and fast.

[0079] In another aspect, the boundary associated with the classification may be one of fixed or variable. For example, one or more cells in the network may have a boundary defined in terms of geographic size. The boundary may be fixed or variable, and its value may depend on many factors including the classification. In other words, if the device is classified with a first classification, it may report it, and thus the boundary associated with the cell may change when making a reselection or handoff decision. For example, if the mobility data results in a classification of the device being high velocity, the boundary associated with one or more cells involved in the reselection or classification may be defined as variable, so that the cell may expand to overlap with the device at either its current geographic location or an expected or predicted geographic location to improve the reselection or handoff process. Such variable use of the boundary may, for example, advance the initiation of communication between the device and a particular cell in time than when the cell would begin data exchange with the device under normal boundary size.

[0080] If the boundary is variable, it can also be used to balance the load between cells. For example, if a cell shrinks its boundary, some devices at the perimeter edge may be outside the boundary of the cell and be served by other cells.

[0081] The control plane and / or user plane may include at least one interface that identifies one or more elements of the device's mobility data or device classification to a control node. A signal indicator may be included.

[0082] In one aspect, preferred cell types for slow classification include micro and small cell types, and permissible cell types for slow classification include large cell types. Preferred cell types for medium classification can include small cell types, and permissible cell types for medium classification can include large cell types. In another aspect, preferred cell types for fast classification can include large cell types, and permissible cell types for fast classification can include none. Of course, these are merely examples of specific cell types associated with particular classifications of devices.

[0083] In one aspect, the device selects only the non-preferred cell type if the received signal is stronger than a higher threshold compared to the threshold required for the preferred cell type. In another aspect, when making a reselection or handoff decision, reselection is triggered if the first cell no longer provides a sufficient signal or if the first cell is of a non-preferred type. Making a handoff decision in this scenario can include handing off to a second cell if the second cell is one of the preferred cell types or is appropriate.

[0084] In another aspect, the classification can include the location of the first cell and a path through the first cell. The path through the first cell can include a predicted path based on the mobility data and / or an actual path traveled by the device that at least partially passes through the first cell. Thus, a vector indicative of the device's movement can include information that can be utilized to identify a path that the device has taken or will likely take. Additionally, mapping information, historical information, machine learning information, data associated with other devices, and the like can be utilized in conjunction with the mobility data to identify information regarding the device's previous movements as well as the device's predicted movements or paths. This information can be utilized when making one or more of the classification of the device and device reselection or handoff decisions.

[0085] In another aspect, making a handoff decision can be further 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 handoff decision can be further based at least in part on a bandwidth used by the device and an available bandwidth of the second cell.

[0086] It is also important to note that different aspects or embodiments of the present disclosure can be implemented on different devices. For example, in one aspect, steps performed by a mobile device can be claimed as an example. Thus, the mobile device can perform steps such as sending mobility data to a remote device or classifying the device based on the mobility data. In a reselection or handover process, the device also sends and / or receives communication information for handling the handoff. Thus, one aspect covers all of the various functions performed on the device.

[0087] In another aspect, the functions may be performed by a controller, or a server, a base station, or other device or combination of devices in the network. Thus, steps such as receiving mobility data, classifying devices, making or managing handoff decisions, etc., may be performed by one or more network-based components. An aspect of the disclosure is the steps performed by one or more of such devices. These devices may include one or more processors, storage devices, etc., as they may cover multiple devices managed by a single entity.

[0088] FIG. 10 illustrates the process of the radio resource control idle rule. First, the system determines that the mobile device is in a radio resource control idle state and / or mobility class I (1002). The method then determines whether there is a suitable cell that is preferred in mobility class I (1004). If YES (there is a suitable cell), the system selects the preferred cell C with the strongest signal (1008). Then, after a set delay (or a variable delay or a 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 is also below the judgment value. If NO (there is no suitable cell), the system determines whether cell C is still preferred for the mobile device (1002). If YES (preferred), the flow returns to the decision (1010) after a delay (fixed, variable, or no delay). If the determination in step (1010) is that the signal from cell C is less than the minimum value, then flow proceeds to location A and returns the process to step (1004) of determining whether there is a suitable preferred cell for mobility class I, as seen in Figure 10. Returning to step (1012), if cell C is still not preferred for the mobile device, the process again returns to the decision step (1004).

[0089] If the determination from step (1004) is that there is no preferred suitable cell in mobility class I, the system determines (1006) whether there is an acceptable suitable cell in mobility class I. If YES (there is a suitable cell), the system selects (1014) an acceptable cell C with the strongest signal. If there is no acceptable suitable cell from mobility class I, the system looks for cells on other carriers (1016) and the process proceeds to point A and finally returns to the determination step (1004).

[0090] FIG. 11 shows handover rules 1100 for radio resource control connected devices. At any given time, the connected device is served by cell C and is in mobility class I (1102). At each measurement interval (1110), the system checks whether the signal strength RSRP_meas exceeds a minimum level RSRP_min (1104). If so, there is no action until the next measurement time. If not, the system selects from the neighbor list the cell with the strongest signal, modified by a factor RSRP_diff that depends on the mobility class and cell type (1106). After the new cell is so determined, the system performs a handover to that cell (1108), which becomes the serving cell. Note that an example algorithm for determining what "suitable" means is RSRP_MEAS>RSRP_MIN+RSRP_DIFF(MC,CT).

[0091] FIG. 12 illustrates an example process 1200 for modifying the neighbor cell list taking into account mobility class. The system recognizes an RRC connected device with mobility class I (1202) and maintains a list of neighbor cells that are suitable and candidates to become a serving cell. These neighbor cells can be of any type, and using one of the rules described above, each one of these cells is classified as preferred or acceptable. The system also maintains an indicator IND that is set to 0 every time the device moves to a new serving cell. Every measurement interval 1110, the system checks whether the number of preferred cells in the neighbor list is greater than a threshold TH1 (1204). If so, and IND is equal to 1 (1210), all acceptable cells are removed from the neighbor list (1212). Returning to step 1204, if the number of preferred cells does not exceed the threshold, 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).

[0092] Another aspect of the present disclosure is a method for mobility-based cell allocation in a heterogeneous network. The present disclosure relates to a method and apparatus for performing handovers. Prior art has recognized the need and potential benefits of having different types of cells to improve spectrum coverage and use, giving rise to heterogeneous networks (het nets). Additionally, there are patents on estimating user mobility. However, the problem of how to combine the two and actually assign users to cells based on mobility information has not yet been solved. Additionally, there is prior art on how to improve handover execution, but the present disclosure addresses reducing the rate of handovers. Some aspects of the present disclosure include obtaining mobility estimation based on GPS data associated with the device, conveying speed using smart car technology, obtaining mobility estimation based on handover rate or time within a cell, and declaring non-mobile by the UE.

[0093] Next, the allocation of users to cells based on the above classification will be described. There may be a set of rules that combines mobility state, cell type, and current cell load to rank candidate cells for a new connection. Another rule may be implemented to determine when to perform handover and to which cell. An exemplary method of obtaining or estimating mobility state in a wireless communication network may be implemented using a control processor to optimize or take into account 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 the neighboring cells while calculating the estimates. The power levels may be the power levels of only the neighboring cells or of all cells, and may be displayed in aggregate or individually, taking into account different power levels for different types of neighboring cells. Distance estimates may include the geographical space of each neighboring cell (how big / small is the cell compared to other cells?) or the distance from the device's current location to the neighboring cell. The ability to handle cell changes may take into account that the potential neighboring cell is of the same type as the current cell or of a different type (i.e., WiFi vs. LTE).

[0094] The control processor can be present in at least one UE in a serving cell. The mobility estimation can be based on a GPS location of the UE. The speed estimation utilizes smart car technology. The mobility estimation can be based on a handover rate or time within the cell. The mobility estimation can be based on a non-mobile declaration by the UE. The mobility estimation can be based on an ability to determine signal suppression information from the serving cell to a neighboring cell. Any of these features can be combined with the systems and methods disclosed herein.

[0095] In one aspect, a system for obtaining or estimating mobility states in a wireless communication network may use a control processor to optimize or take into account 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 ability to handle cell changes from the serving cell to the neighboring cell while calculating the estimate.

[0096] In another aspect, a method is disclosed for classifying UEs in a wireless communications network using a control processor to optimize the following factors: power levels of the serving cell and neighboring cells, distance estimates of the serving cell and neighboring cells, and / or the ability to handle cell changes from the serving cell to the neighboring cell while calculating the estimates.

[0097] In yet another embodiment, a system for classifying user equipment (UE) in a wireless communications network using a control processor to optimize the following factors: power levels of serving cells and neighboring cells, distance estimates of serving cells and neighboring cells, and / or ability to handle cell changes from serving cells to neighboring cells while calculating the estimates. The system is disclosed.

[0098] Embodiments within the scope of the present disclosure may also include tangible and / or non-transitory computer-readable storage devices for carrying or retaining computer-executable instructions or data structures stored thereon. Such tangible computer-readable storage devices are any available devices accessible by a general purpose or special purpose computer, including the functional design of any special purpose processor as described above. For example, and without 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 devices that can be used to carry or store the necessary 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 communications connection (hardwired, wireless, or combination thereof), the computer properly regards the connection as a computer-readable medium. Accordingly, all such connections are properly referred to as computer-readable media. Combinations of the above should also be included within the scope of computer-readable storage devices.

[0099] Computer-executable instructions include, for example, instructions and data that cause a general purpose computer, special purpose computer, or special purpose processing device to perform a particular function or group of functions. Computer-executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, components, data structures, objects, and design-specific functions, such as special purpose processors, that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. A particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.

[0100] Other embodiments of the present disclosure may be practiced in networked computing environments with many 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 may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (by hardwired links, wireless links, or a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0101] In another example, a method may include receiving mobility data for a device served by a first cell, the mobility data including a vector indicative of movement of the device, and classifying the device based on the mobility data to generate a classification, where the cell types of the classification include at least one of a preferred cell type and at least one of an acceptable cell type when making a reselection or handoff decision.

[0102] In another example, a method includes receiving mobility data of a device served by a first cell in a heterogeneous network including at least a non-cellular node type and a cellular node type, and classifying the mobility data in a virtual layer of the device to generate a classification. and classifying the data, the classification including an access type and an indication of an access priority to the access type, the access priority including at least an allowable access type or a preferred access type, the access type being identified from a list of access types including at least one of a non-cellular node type and a cellular node type.

[0103] The method further includes measuring traffic characteristics from the prioritized list of traffic characteristics to generate measurements, the traffic characteristics including 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) distributing loads among nodes in the heterogeneous network based on communications with the plurality of devices; (5) distributing the plurality of devices in the heterogeneous network; and (6) distributing the plurality of devices in the heterogeneous network and load levels of each node, for a plurality of devices including the device operating in the heterogeneous network and at least one additional device; and making a handoff decision at the virtual layer based at least in part on the classification and the measurements when handing off the device from the first cell to the second cell.

[0104] The mobility state can be calculated or estimated from the mobility data. The vector can include multiple dimensions, where at least one of the multiple dimensions is time. The mobility state associated with the mobility data can be a movement rate of the device. The classification can also be one or more of slow, medium, and fast. The boundary associated with the classification can be one of fixed or variable. The boundary can be variable and can be used to balance the load between cells.

[0105] In another aspect, the control plane may include at least one signal indicator that identifies one or more elements of the device's mobility data or classification to the control node. Preferred cell types for slow classification may include micro and small cell types, and allowable cell types for slow classification include large cell types. Preferred cell types for medium classification may include small cell types, and allowable cell types for medium classification include large cell types. Preferred cell types for fast classification may include large cell types, and allowable cell types for fast classification include none.

[0106] The device may only select a non-preferred cell type if the received signal is stronger than a higher threshold compared to a threshold required for the preferred cell type. When making the handoff decision, a reselection may be triggered if the first cell no longer provides a sufficient signal or if the first cell is of a non-preferred type, and making the handoff decision includes handing off to a second cell when the second cell is of one of the preferred types or is suitable. In another aspect, the classification may include a location of the first cell and a path through the first cell. Making the handoff decision may further 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 the reselection or handoff decision may further be based at least in part on a bandwidth used by the device and an available bandwidth of the second cell. The mobility data may be independent of one of a data usage pattern and a data rate. At least one of the preferred cell types may include at least one cell from a prioritization list of cells. Any of the concepts disclosed above in any embodiment may be combined with any other concepts to generate example structures or processes that can be claimed.

[0107] An embodiment of the system includes one or more processors, and when executed by the processors, the one or more processors are configured to provide a mobility data table for a device served by a first cell. and a computer-readable storage device storing instructions causing the device to perform operations including receiving mobility data, the mobility data including a vector indicative of device movement, and classifying the device based on the mobility data to generate a classification, where when making a reselection or handoff decision, the cell types of the classification include at least one of a preferred cell type and at least one of an allowable cell type.

[0108] In another example, 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 including receiving mobility data of a device served by a first cell in a heterogeneous network including at least non-cellular and cellular node types; and classifying the mobility data in a virtual layer of the device to generate a classification, the classification including an access type and an indication of an access priority for the access type, the access priority including at least an allowable access type or a preferred access type, the access type identified from a list of access types including at least one of the non-cellular node types and the cellular node types. The operations further include measuring traffic characteristics at the virtual layer from the prioritized list of traffic characteristics to generate measurements, the traffic characteristics including 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) distributing loads between nodes in the heterogeneous network based on communications with the plurality of devices; (5) distributing the plurality of devices in the heterogeneous network; and (6) distributing the plurality of devices in the heterogeneous network and load levels of each node, for a plurality of devices including the device operating in the heterogeneous network and at least one additional device; and making a handoff decision at the virtual layer based at least in part on the classification and measurements when handing off the device from the first cell to the second cell.

[0109] Another 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.

[0110] The computer-readable storage device can store additional instructions that, when executed by the one or more processors, cause the one or more processors to perform further operations including selecting only non-preferred cell types if the received signal is stronger than a higher threshold compared to a threshold required for the preferred cell type. At least one of the preferred cell types can include at least one cell from a prioritization list of cells. The operations can include receiving mobility data of a device served by a first cell, the mobility data including a vector indicative of device movement, and classifying the device based on the mobility data to generate a classification, where the cell types of the classification include at least one of the preferred cell types and at least one of the allowable cell types when making a reselection or handoff decision.

[0111] The non-transitory computer-readable storage device can store additional instructions that, when executed by the one or more processors, cause the one or more processors to perform further operations including selecting only non-preferred cell types if the received signal is stronger than a higher threshold compared to a threshold required for the preferred cell type. At least one of the preferred cell types can include at least one cell from the prioritized list of cells.

[0112] The various examples described above are provided for illustrative purposes only and should not be construed as limiting the scope of the present disclosure. Without departing from the spirit and scope of the present disclosure without following the exemplary examples and applications illustrated and described herein, it is possible to Various modifications and variations can be made to the principles described. Claim language reciting "at least one" of a set indicates that one member of the set or more than one member of the set satisfies the scope of the claim.

Claims

1. 1. A method comprising: Receiving data, including mobility data or session data, at a control plane of 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 in the control plane of the device to generate categories, the categories including an indication of an access type and an access priority to the access type, the access priority including at least an allowable access type or a preferred access type, the access type being identified from a list of access types including at least one of a non-cellular node type and a cellular node type; adjusting a dynamic geographical boundary of at least the first node or the second node based on the classification of the data in at least the control plane of the device, wherein each of the first node and the second node is a base station or an access point of the heterogeneous network; making a handoff decision in the control plane when handing off the device from the first node to the second node based on the category and based on an adjustment of the dynamic geographic boundary; A method comprising:

2. the data includes a vector indicative of a velocity of the device; The method of claim 1 , wherein the vector is used to make the handoff decision in the control plane.

3. 3. The method of claim 2, wherein the vector includes a first dimension related to time and a second dimension related to one or more of a velocity of the device and a direction of motion of the device.

4. the preferred access types in a low speed category include micro cell types and small cell types, and the allowable access types in the low speed category include large cell types, the low speed category being associated with a relative speed of the mobile device; the preferred access type in a medium speed category includes a small cell type, and the allowable access type in the medium speed category includes a large cell type, the medium speed category being associated with a relative speed of the mobile device; the preferred access type in a high speed category includes a large cell type, and the allowable access types in the high speed category include no access types, the high speed category being associated with a relative speed of the mobile device; The method of claim 1 , wherein

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

6. 6. The method of claim 5, wherein when making a handoff decision, a reselection is triggered if the first node no longer provides a sufficient signal or if the first node is a non-preferred node type, and making the handoff decision includes handing off to the second node when the second node is one of the preferred node types or is appropriate.

7. The method of claim 1 , wherein the categories include a location of the mobile device relative to the first node and a path that passes through an area covered by the first node.

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

9. 2. The method of claim 1, wherein the dynamic geographic boundaries of at least the first node or the second node are adjusted by adjusting boundaries associated with an improvement in bit rate for communication with at least one of the first node or the second node.

10. data in a control plane for generating configurations of the device to optimize at least one of bit rate, quality of service, range associated with the category, preferred spectrum, preferred device characteristics, and efficiency; 2. The method of claim 1, wherein the control plane data of the device is used to evaluate bandwidth settings to improve bit rate or spectral efficiency in communications with the first node or the second node.

11. 1. A system comprising: A processor; A computer readable storage device that, when executed by the processor, causes the processor to perform the method of claim 1; The system comprises:

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