Patent Publication Number: US-9893941-B1

Title: Unit sourcing

Description:
CLAIM OF PRIORITY UNDER 35 U.S.C. §119 
     The present application for patent claims priority to Provisional Patent Application No. 62/368,772, entitled “Unit Sourcing” filed Jul. 29, 2016, which is assigned to the assignee hereof and hereby expressly incorporated by reference herein for all purposes. 
    
    
     BACKGROUND 
     Field 
     The present disclosure relates generally to communication systems, and more particularly, to methods and apparatus for post deployment tuning of a user equipment (UE). 
     Background 
     Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems may employ multiple-access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple-access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems. 
     These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. An example telecommunication standard is Long Term Evolution (LTE). LTE is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by Third Generation Partnership Project (3GPP). LTE is designed to support mobile broadband access through improved spectral efficiency, lowered costs, and improved services using OFDMA on the downlink, SC-FDMA on the uplink, and multiple-input multiple-output (MIMO) antenna technology. 
     Prior to deployment of UEs, algorithms are built into the modems, however such algorithms are inherently rigid. The algorithms typically use only weighted linear averaging of parameters with the parameters and weights being fixed and not adaptable. The parameters and weights are determined based on data collected in labs and based on minimal field studies. The parameters and weights are conservatively tuned to support all devices and all markets. For example, the device specific configurations, capability and performance, such as antenna configuration, is not taken into consideration for tuning purposes. Performance variations of the UEs can vary based on the different regions of the network and the specific experience that the UE encounters traversing these networks. Typically, the modem is tuned once. The tuning does not consider the current active set of services in the choice of the algorithm behavior. As such, there is a demand for post deployment tuning of the modem algorithms. 
     SUMMARY 
     The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later. 
     According to an example, a method for wireless communications is provided. The method includes accessing a wireless network via a network access node; determining whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node; executing a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, the selected hypothesis being a unit sourced hypothesis if the network access node is determined to be a frequented node and the selected hypothesis being a crowd sourced hypothesis if the network access node is determined to be a non-frequented node, wherein the selected hypothesis is one of a set of hypotheses stored on the UE with each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point; and sending information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if the state information is available, in response to a trigger point being met when executing the modem function. 
     The method may also include sending, to the server, a modem algorithm identifier identifying a version of the set of hypotheses stored on the UE. The method may further include sending, to the server, a subset of hypotheses that the UE can use wherein the subset contains less hypotheses than the set of hypotheses stored on the UE. The method may further include sending, to the server, a time indicator that the server can store information received from the UE. The method may further include receiving, from the server, a selected unit sourced hypothesis and a selected crowd sourced hypothesis, with each selection corresponding to a modem function and including weights for each feature associated with the modem function, wherein the selected unit sourced hypothesis is based on information the UE sent to the server and the information being run through one or more learning algorithms and the selected crowd sourced hypothesis based on crowd sourced data. The UE may select the unit sourced hypothesis when the network access node is a frequented node based on an identifier for the network access node and the crowd sourced hypothesis when the network access node is a non-frequented node based on an identifier for the network access node. 
     In another aspect, a method for wireless communications is provided. The method includes receiving information, at a server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; storing, by the server, the received information; running, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; selecting, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the state information was provided by the UE; selecting, by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and sending, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function. 
     The method may also include receiving by the server from the UE, a modem algorithm identifier indicating a version of a set of hypotheses stored on the UE and the selected unit sourced hypothesis and the selected unit sourced hypothesis are selected from the set of hypotheses stored on the UE. The method may further include receiving by the server from the UE, a subset of hypotheses that the UE can use and the and the selected unit sourced hypothesis and the selected unit sourced hypothesis are selected from the subset of hypotheses stored on the UE, where the subset is less than a set of hypotheses stored on the UE. The method may further include employing neural networks and sending neural networks information for applying a selected hypothesis. The method may further include receiving, by the server from the UE, a time indicator that the server can store information received from the UE when the UE used a selected unit sourcing hypothesis. The method may further include sending, by the server to the UE, state information associated with a selected unit sourced hypothesis when the server ran one or more learning algorithms based on crowd sourced information when the state information was not provided by the UE. The method may further include sending, by the server to the UE, at least one of state information associated with a selected unit sourced hypothesis and state information associated with a selected crowd sourced hypothesis. 
     In another example, an apparatus for wireless communications is provided. The apparatus includes a transceiver, a memory configured to store instructions and one or more processors communicatively couple with the transceiver and the memory. The one or more processors are configure to execute instructions to: access a wireless network via a network access node; determine whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node; execute a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, the selected hypothesis being a unit sourced hypothesis if the network access node is determined to be a frequented node and the selected hypothesis being a crowd sourced hypothesis if the network access node is determined to be a non-frequented node, wherein the selected hypothesis is one of a set of hypotheses stored on the UE with each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point; and send information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if the state information is available, in response to a trigger point being met when executing the modem function. 
     In another example, a server for wireless communications is provided. The server includes a transceiver, a memory configured to store instructions and one or more processors communicatively couple with the transceiver and the memory. The one or more processors are configure to execute instructions to: receive information, at the server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; store, by the server, the received information; run, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; run, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; select, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; run, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the state information was provided by the UE; select by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and send, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function. 
     In another example, a user equipment (UE) for wireless communications is provided. The UE includes means for accessing a wireless network via a network access node; means for determining whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node; means for executing a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, the selected hypothesis being a unit sourced hypothesis if the network access node is determined to be a frequented node and the selected hypothesis being a crowd sourced hypothesis if the network access node is determined to be a non-frequented node, wherein the selected hypothesis is one of a set of hypotheses stored on the UE with each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point; and means for sending information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if the state information is available, in response to a trigger point being met when executing the modem function. 
     In another example, a server for wireless communications is provided. The server includes means for receiving information, at a server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; means for storing, by the server, the received information; means for running, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; means for running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; means for selecting, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; means for running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the state information was provided by the UE; means for selecting, by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and means for sending, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function. 
     In a further example, a non-transitory computer-readable medium storing executable code is provided. The code include code for accessing a wireless network via a network access node; code for determining whether the network access node is a frequented node or a non-frequented node based on an identifier for the network access node; code for executing a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function, the selected hypothesis being a unit sourced hypothesis if the network access node is determined to be a frequented node and the selected hypothesis being a crowd sourced hypothesis if the network access node is determined to be a non-frequented node, wherein the selected hypothesis is one of a set of hypotheses stored on the UE with each hypothesis corresponding to a modem function and including a plurality of features, state information and at least one trigger point; and code for sending information, to a server, the information comprising a device identifier identifying the UE, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if the state information is available, in response to a trigger point being met when executing the modem function. 
     In another example, a non-transitory computer-readable medium storing executable code is provided. The code include code for receiving information, at a server from a user equipment (UE), the information comprising a device identifier, a modem function executed by the UE, a selected hypothesis for the corresponding modem function, metrics for each feature associated with the corresponding modem function and state information, if the state information is available, in response to a trigger point associated with the modem function being met when executing the modem function; code for storing, by the server, the received information; code for running, by the server, one or more learning algorithms based on the stored information to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the UE provided the state information; code for running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a unit sourced hypothesis and associated weights for the corresponding modem function when the state information was not provided by the UE; code for selecting, by the server, the unit sourced hypothesis and associated weights based on the one or more learning algorithms; code for running, by the server, one or more learning algorithms based on crowd sourced information from other UEs to cluster the information and to select a crowd sourced hypothesis and associated weights for the corresponding modem function when the state information was provided by the UE; code for selecting, by the server, the crowd sourced hypothesis and associated weights based on the one or more learning algorithms; and code for sending, by the server to the UE, at least one of the selected unit sourced hypothesis and associated weights or the selected crowd sourced hypothesis and associated weights for the corresponding modem function. 
     To the accomplishment of the foregoing and related ends, the one or more aspects comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative features of the one or more aspects. These features are indicative, however, of but a few of the various ways in which the principles of various aspects may be employed, and this description is intended to include all such aspects and their equivalents. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram illustrating an example of a wireless communications system and an access network. 
         FIG. 2A  is a diagram illustrating an example of a DL frame structure in LTE. 
         FIG. 2B  is a diagram illustrating an example of DL channels within the DL frame structure in LTE. 
         FIG. 2C  is a diagram illustrating an example of an UL frame structure in LTE. 
         FIG. 2D  is a diagram illustrating an example of UL channels within the UL frame structure in LTE. 
         FIG. 3  is a diagram illustrating an example of an evolved Node B (eNB) and user equipment (UE) in an access network. 
         FIG. 4  is schematic diagram of a network architecture according to one or more described aspects. 
         FIG. 5  is a flowchart of an aspect of a method of wireless communication performed on a user equipment to tune a modem according to one or more described aspects. 
         FIG. 6  is a diagram illustrating an example of a hardware implementation for a user equipment employing a processing system according to one or more described aspects. 
         FIGS. 7A and 7B  are a flowchart of an aspect of a method of wireless communication performed on a unit sourcing server to tune a modem according to one or more described aspects. 
         FIG. 8  is a diagram illustrating an example of a hardware implementation for a unit sourcing server employing a processing system according to one or more described aspects. 
         FIG. 9  is a diagram illustrating a Neural Network based approach for LTE-WiFi handover management according to one or more described aspects. 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure generally relates to tuning a modem residing in a user equipment (UE) using a tuning component in the UE in conjunction with a unit sourcing server (USS), after deployment of the UE. 
     In one high-level aspect, a modem of a UE can be tuned, post deployment, by a USS in conjunction with a tuning component on the UE. By tuning the modem, the UE is able to improve the performance of the modem by improving or optimizing one or more algorithms that are stored on the UE. The algorithms are used to perform modem functions on the UE. For example, the algorithm for deciding when to switch from WiFi to LTE can be tuned to improve the timing of the switch from WiFi to LTE so that a user of the UE does not experience poor quality prior to the switch. To do so, a selected hypothesis having associated weights is employed when a modem algorithm is executing a corresponding modem function. The hypothesis is selected from a set of hypotheses that are stored on the UE. The hypothesis is an equation that has features and weights. The features are values associated with a modem function, such as WiFi Received Signal Strength Indicator (RSSI) or UpLink (UL) packet loss. The weights adjusts the values of the features. 
     To select a hypothesis and the associated weights, information is gathered from the UE. The UE is able to gather information and improve the “behavior” of the modem algorithms directly associated with the environment experienced by the UE. In response to a triggering point, the tuning component can send information associated with features for the executed modem function to the USS. The trigger point is used to gather metrics of features at a given time, e.g., a snapshot of the values of the features at the time. The trigger point can be when a determination is made to perform a modem function (e.g., switch from WiFi to LTE) or not to perform the modem function (e.g., not to switch from WiFi to LTE). By using the USS to store and process the data, the UE is able to reduce or minimize using the memory and processor of the UE. 
     The USS runs one or more learning algorithms using the information received from the UE and/or using crowd sourced information The USS runs the one or more learning algorithms to select a unit sourced hypothesis and associated weights and a crowd sourced hypothesis and associated weights. The selected unit sourced hypothesis can be employed when the UE connects to a wireless network via a network access point that the UE has used in a given time period. The selected crowd sourced hypothesis can be employed when the UE connects to the wireless network via a network access point that the UE has not used in the given time period. The UE employs the selected hypothesis and the associated weights when a corresponding modem function is executed. 
     Such an approach combines the use of cloud computing and machine learning to tune the behavior of a UE modem on an individual UE basis. Although the information/data sent from the UE to the USS can be used for crowd sourcing, the approach allows for unit sourcing so the modem algorithms can be fine-tuned based on the network experiences of the particular UE. Such an approach avoids the conservative modem configurations that are utilized in conventional approaches. Rather, the modem can be tuned based on data obtained by a given UE for frequented routes and can be tuned based on crowd sourced data when the UE travels outside of the frequented routes. In some implementations, the UE can employ or run both a selected unit sourced hypothesis and a selected crowd sourced hypothesis with the UE arbitrating across both to determine which hypothesis provides the better result. By using such an approach, the modem can be tuned where the relevant parameters and relative association in terms of determining trigger points is not well understood prior to deployment. Thus, the parameters can be tuned post deployment and can allow for more parameters to be included in making the hypotheses. 
     The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In some instances, well known structures and components are shown in block diagram form in order to avoid obscuring such concepts. 
     Several aspects of telecommunication systems will now be presented with reference to various apparatus and methods. These apparatus and methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “elements”). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. 
     By way of example, an element, or any portion of an element, or any combination of elements may be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system may execute software. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. 
     Accordingly, in one or more example aspects, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the aforementioned types of computer-readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer. 
       FIG. 1  is a diagram illustrating an example of a wireless communications system  100 , including one or more user equipments (UEs)  104  with each UE  104  having a tuning component  180 , a unit sourcing server  182  and a crowd sourcing server  184 . The tuning component  180  and unit sourcing server  182  are configured to tune a modem on a UE  104  after the UE  104  is deployed, e.g., post deployment. The crowd sourcing server  184  can be used to assist in the tuning of the UE  104 . The details of the operation and architecture associated with the tuning component  180  are discussed in more detail below with respect to  FIGS. 4-8 . The wireless communications system (also referred to as a wireless wide area network (WWAN)) includes base stations  102 , UEs  104 , and an Evolved Packet Core (EPC)  160 . The base stations  102  may include macro cells (high power cellular base station) and/or small cells (low power cellular base station). The macro cells include eNBs. The small cells include femtocells, picocells, and microcells. 
     The base stations  102  (collectively referred to as Evolved Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access Network (E-UTRAN)) interface with the EPC  160  through backhaul links  132  (e.g., S1 interface). In addition to other functions, the base stations  102  may perform one or more of the following functions: transfer of user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, radio access network (RAN) sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations  102  may communicate directly or indirectly (e.g., through the EPC  160 ) with each other over backhaul links  134  (e.g., X2 interface). The backhaul links  134  may be wired or wireless. 
     The base stations  102  may wirelessly communicate with the UEs  104 . Each of the base stations  102  may provide communication coverage for a respective geographic coverage area  110 . There may be overlapping geographic coverage areas  110 . For example, the small cell  102 ′ may have a coverage area  110 ′ that overlaps the coverage area  110  of one or more macro base stations  102 . A network that includes both small cell and macro cells may be known as a heterogeneous network. A heterogeneous network may also include Home Evolved Node Bs (eNBs) (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG). The communication links  120  between the base stations  102  and the UEs  104  may include uplink (UL) (also referred to as reverse link) transmissions from a UE  104  to a base station  102  and/or downlink (DL) (also referred to as forward link) transmissions from a base station  102  to a UE  104 . The communication links  120  may use MIMO antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links may be through one or more carriers. The base stations  102 /UEs  104  may use spectrum up to Y MHz (e.g., where Y=5, 10, 15, or 20 MHz) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x=number of component carriers) used for transmission in each direction. The carriers may or may not be adjacent to each other. Allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or less carriers may be allocated for DL than for UL). The component carriers may include a primary component carrier and one or more secondary component carriers. A primary component carrier may be referred to as a primary cell (PCell) and a secondary component carrier may be referred to as a secondary cell (SCell). 
     The wireless communications system may further include a Wi-Fi access point (AP)  150  in communication with Wi-Fi stations (STAs)  152  via communication links  154  in a 5 GHz unlicensed frequency spectrum. When communicating in an unlicensed frequency spectrum, the STAs  152 /AP  150  may perform a clear channel assessment (CCA) or Listen Before Talk (LBT) functionality prior to communicating in order to determine whether the channel is available (e.g., generally, to avoid transmitting on a channel where another transmission is occurring, which would cause interference). 
     The small cell  102 ′ may operate in a licensed and/or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell  102 ′ may employ LTE and use the same 5 GHz unlicensed frequency spectrum as used by the Wi-Fi AP  150 . The small cell  102 ′, employing LTE in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network. LTE in an unlicensed spectrum may be referred to as LTE-unlicensed (LTE-U), licensed assisted access (LAA), or MuLTEfire. 
     Base stations  102 , UEs  104 , APs  150 , and STAs  152  may also operate in one or more shared frequency bands, such as according to General Authorized Access (GAA) in the 3.5 GHz band. 
     The EPC  160  may include a Mobility Management Entity (MME)  162 , other MMES  164 , a Serving Gateway  166 , a Multimedia Broadcast Multicast Service (MBMS) Gateway  168 , a Broadcast Multicast Service Center (BM-SC)  170 , and a Packet Data Network (PDN) Gateway  172 . The MME  162  may be in communication with a Home Subscriber Server (HSS)  174 . The MME  162  is the control node that processes the signaling between the UEs  104  and the EPC  160 . Generally, the MME  162  provides bearer and connection management. All user Internet protocol (IP) packets are transferred through the Serving Gateway  166 , which itself is connected to the PDN Gateway  172 . The PDN Gateway  172  provides UE IP address allocation as well as other functions. The PDN Gateway  172  and the BM-SC  170  are connected to the IP Services  176 . The IP Services  176  may include the Internet, an intranet, an IP Multimedia Subsystem (IMS), a PS Streaming Service (PSS), and/or other IP services. The BM-SC  170  may provide functions for MBMS user service provisioning and delivery. The BM-SC  170  may serve as an entry point for content provider MBMS transmission, may be used to authorize and initiate MBMS Bearer Services within a public land mobile network (PLMN), and may be used to schedule MBMS transmissions. The MBMS Gateway  168  may be used to distribute MBMS traffic to the base stations  102  belonging to a Multicast Broadcast Single Frequency Network (MBSFN) area broadcasting a particular service, and may be responsible for session management (start/stop) and for collecting eMBMS related charging information. 
     The base station may also be referred to as a Node B, evolved Node B (eNB), an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), or some other suitable terminology. The base station  102  provides an access point to the EPC  160  for a UE  104 . Examples of UEs  104  include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a tablet, a smart device, a wearable device, or any other similar functioning device. The UE  104  may also be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communications device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. 
       FIG. 2A  is a diagram  200  illustrating an example of a DL frame structure in LTE, which may be utilized for communications between the wireless communication devices of  FIG. 1 , e.g., by one or more of base stations  102  or  102 ′, UEs  104 , APs  150 , and/or STAs  152 .  FIG. 2B  is a diagram  230  illustrating an example of channels within the DL frame structure in LTE, which may be utilized for communications between the wireless communication devices of  FIG. 1 .  FIG. 2C  is a diagram  250  illustrating an example of an UL frame structure in LTE, which may be utilized for communications between the wireless communication devices of  FIG. 1 .  FIG. 2D  is a diagram  280  illustrating an example of channels within the UL frame structure in LTE, which may be utilized for communications between the wireless communication devices of  FIG. 1 . Other wireless communication technologies may have a different frame structure and/or different channels. In LTE, a frame (10 ms) may be divided into 10 equally sized subframes. Each subframe may include two consecutive time slots. A resource grid may be used to represent the two time slots, each time slot including one or more time concurrent resource blocks (RBs) (also referred to as physical RBs (PRBs)). The resource grid is divided into multiple resource elements (REs). In LTE, for a normal cyclic prefix, an RB contains 12 consecutive subcarriers in the frequency domain and 7 consecutive symbols (for DL, OFDM symbols; for UL, SC-FDMA symbols) in the time domain, for a total of 84 REs. For an extended cyclic prefix, an RB contains 12 consecutive subcarriers in the frequency domain and 6 consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme. 
     As illustrated in  FIG. 2A , some of the REs carry DL reference (pilot) signals (DL-RS) for channel estimation at the UE. The DL-RS may include cell-specific reference signals (CRS) (also sometimes called common RS), UE-specific reference signals (UE-RS), and channel state information reference signals (CSI-RS).  FIG. 2A  illustrates CRS for antenna ports 0, 1, 2, and 3 (indicated as R 0 , R 1 , R 2 , and R 3 , respectively), UE-RS for antenna port 5 (indicated as R 5 ), and CSI-RS for antenna port 15 (indicated as R).  FIG. 2B  illustrates an example of various channels within a DL subframe of a frame. The physical control format indicator channel (PCFICH) is within symbol 0 of slot 0, and carries a control format indicator (CFI) that indicates whether the physical downlink control channel (PDCCH) occupies 1, 2, or 3 symbols ( FIG. 2B  illustrates a PDCCH that occupies 3 symbols). The PDCCH carries downlink control information (DCI) within one or more control channel elements (CCEs), each CCE including nine RE groups (REGs), each REG including four consecutive REs in an OFDM symbol. A UE may be configured with a UE-specific enhanced PDCCH (ePDCCH) that also carries DCI. The ePDCCH may have 2, 4, or 8 RB pairs ( FIG. 2B  shows two RB pairs, each subset including one RB pair). The physical hybrid automatic repeat request (ARQ) (HARQ) indicator channel (PHICH) is also within symbol 0 of slot 0 and carries the HARQ indicator (HI) that indicates HARQ acknowledgement (ACK)/negative ACK (NACK) feedback based on the physical uplink shared channel (PUSCH). The primary synchronization channel (PSCH) is within symbol 6 of slot 0 within subframes 0 and 5 of a frame, and carries a primary synchronization signal (PSS) that is used by a UE to determine subframe timing and a physical layer identity. The secondary synchronization channel (SSCH) is within symbol 5 of slot 0 within subframes 0 and 5 of a frame, and carries a secondary synchronization signal (SSS) that is used by a UE to determine a physical layer cell identity group number. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of the aforementioned DL-RS. The physical broadcast channel (PBCH) is within symbols 0, 1, 2, 3 of slot 1 of subframe 0 of a frame, and carries a master information block (MIB). The MIB provides a number of RBs in the DL system bandwidth, a PHICH configuration, and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted through the PBCH such as system information blocks (SIBs), and paging messages. 
     As illustrated in  FIG. 2C , some of the REs carry demodulation reference signals (DM-RS) for channel estimation at the eNB. The UE may additionally transmit sounding reference signals (SRS) in the last symbol of a subframe. The SRS may have a comb structure, and a UE may transmit SRS on one of the combs. The SRS may be used by an eNB for channel quality estimation to enable frequency-dependent scheduling on the UL.  FIG. 2D  illustrates an example of various channels within an UL subframe of a frame. A physical random access channel (PRACH) may be within one or more subframes within a frame based on the PRACH configuration. The PRACH may include six consecutive RB pairs within a subframe. The PRACH allows the UE to perform initial system access and achieve UL synchronization. A physical uplink control channel (PUCCH) may be located on edges of the UL system bandwidth. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and HARQ ACK/NACK feedback. The PUSCH carries data, and may additionally be used to carry a buffer status report (BSR), a power headroom report (PHR), and/or UCI. 
       FIG. 3  is a block diagram of an eNB  310  in communication with a UE  350  in an access network, where the eNB  310  may be an example of base stations  102  or  102 ′ and/or APs  150  of  FIG. 1 , and wherein the UE  350  may be an example of UEs  104  and/or STAs  152  of  FIG. 1 . In an aspect, the tuning component  180  may be part of the UE  350 , such as implemented within controller/processor  359  and/or memory  360 . In the DL, IP packets from the EPC  160  may be provided to a controller/processor  375 . The controller/processor  375  implements layer 3 and layer 2 functionality. Layer 3 includes a radio resource control (RRC) layer, and layer 2 includes a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The controller/processor  375  provides RRC layer functionality associated with broadcasting of system information (e.g., MIB, SIBs), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression/decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the transfer of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization. 
     The transmit (TX) processor  316  and the receive (RX) processor  370  implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding/decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation/demodulation of physical channels, and MIMO antenna processing. The TX processor  316  handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and/or frequency domain, and then combined together using an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator  374  may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and/or channel condition feedback transmitted by the UE  350 . Each spatial stream may then be provided to a different antenna  320  via a separate transmitter  318 TX. Each transmitter  318 TX may modulate an RF carrier with a respective spatial stream for transmission. 
     At the UE  350 , each receiver  354 RX receives a signal through its respective antenna  352 . Each receiver  354 RX recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor  356 . The TX processor  368  and the RX processor  356  implement layer 1 functionality associated with various signal processing functions. The RX processor  356  may perform spatial processing on the information to recover any spatial streams destined for the UE  350 . If multiple spatial streams are destined for the UE  350 , they may be combined by the RX processor  356  into a single OFDM symbol stream. The RX processor  356  then converts the OFDM symbol stream from the time-domain to the frequency domain using a Fast Fourier Transform (FFT). The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the eNB  310 . These soft decisions may be based on channel estimates computed by a channel estimator  358 . The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the eNB  310  on the physical channel. The data and control signals are then provided to the controller/processor  359 , which implements layer 3 and layer 2 functionality. 
     The controller/processor  359  can be associated with a memory  360  that stores program codes and data. The memory  360  may be referred to as a computer-readable medium. In the UL, the controller/processor  359  provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the EPC  160 . The controller/processor  359  is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations. 
     Similar to the functionality described in connection with the DL transmission by the eNB  310 , the controller/processor  359  provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression/decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization. 
     Channel estimates derived by the channel estimator  358  from a reference signal or feedback transmitted by the eNB  310  may be used by the TX processor  368  to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor  368  may be provided to different antenna  352  via separate transmitters  354 TX. Each transmitter  354 TX may modulate an RF carrier with a respective spatial stream for transmission. 
     The UL transmission is processed at the eNB  310  in a manner similar to that described in connection with the receiver function at the UE  350 . Each receiver  318 RX receives a signal through its respective antenna  320 . Each receiver  318 RX recovers information modulated onto an RF carrier and provides the information to a RX processor  370 . 
     The controller/processor  375  can be associated with a memory  376  that stores program codes and data. The memory  376  may be referred to as a computer-readable medium. In the UL, the controller/processor  375  provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets from the UE  350 . IP packets from the controller/processor  375  may be provided to the EPC  160 . The controller/processor  375  is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations. 
     Referring to  FIG. 4 , a wireless communications system  400 , which may be similar to wireless communications system  100  of  FIG. 1 , may include additional system components in one exemplary implementation for tuning a modem  402  of a UE  104  using the tuning component  180  in conjunction with the USS  182 . 
     In particular, wireless communications system  400  includes a UE  104  having a modem  402 , a tuning component  180  having a set of modem algorithms  404  and a set of hypotheses  406 . The modem  402  is configured to connect the UE  104  to a wireless network, e.g., IP services  176 , via an access point, e.g., a base station  102 . The modem  402  is configured to send and receive data, e.g., voice and/or data. The tuning component  180  is configured to tune the modem  402  by employing a selected hypothesis from the set of hypotheses  406  that are stored on the UE  104 . The selected hypothesis can be a selected unit sourced hypothesis or a selected crowd sourced hypothesis. The selected unit sourced hypothesis can be employed when the UE  104  is connected to a wireless network via a network access point that the UE  104  has used in a given time period. A user of UE  104  can frequently connect to the same network access points based on their routine for a given time period, e.g., weekly or monthly. For example, a user of UE  104  can connect to the same network access point when the user stops for morning coffee and can connect to a different network access point when the user arrives at work. This can be referred to as an UE frequented route. The selected crowd sourced hypothesis can be employed when the UE  104  is connected to the wireless network via a network access point that the UE  104  has not used in the given time period. For example, the selected crowd sourced hypothesis can be employed if a user takes a trip outside of his or her UE frequented route. The UE  104  can use the selected hypothesis having associated weights to execute a corresponding modem function, such as switch from WiFi to LTE. In response to a triggering point the tuning component  180  can send information associated with the features for the executed modem function to the USS  182 . For example, the UE  104  sends feature values (x 1 , x 2 , . . . , x n ) and state information associated with the corresponding modem function, if available, in response to a triggering point or event being met. The feature values and state information is explained below. The state information is an expected outcome. For example, state information can be switch from WiFi to LTE or stay on WiFi. 
     The USS  182  receives and can archive the received information/data. The USS  182  can run one or more learning algorithms  410  based on the information received from the tuning component  180 . As explained in more detail below, the USS  182  can use the received information from the UE  104  and/or use crowd sourced information to select a unit sourced hypothesis and associated weights and a crowd sourced hypothesis and associated weights. The crowd sourced data can be obtained from a crowd sourcing server  184 . The crowd sourcing server  184  can include information learned from multiple UEs. As a result, the USS  182  can tune the modem, post deployment, based on information from the UE and/or crowd sourced information. 
     The UE  104  can include a plurality of modem algorithms  404  with each executing a specific modem function. Each modem algorithm  404  can include a plurality of features (x i ) associated with a modem function. For example, the features can be Reference Signal Received Quality (RSRQ), Hybrid Automatic Repeat request (HARQ) 1 st  packet error, Residual HARQ Block Error Rate (BLER), Real-Time Transport Protocol (RTP) PER, RTP end to end (e2e) delay, etc. The features (X) can be combined. For example, a combination of features can be {x 1 , x 2 , x 3 , . . . , x n }. Each feature x i  can be a linear representation, a polynomial representation (x i   k ) and/or a product of multiple features (x i *x j ). The modem algorithm  404  can include linear and quadratic forms for all of the features. Each modem algorithm  404  can include one or more expected results (y i ) for the executed modem function. The expected results can be a set of output conditions that are considered to be met. The expected results can be referred to as state information which is the state when a modem function is executed. The state information can be optional. 
     For example, a modem algorithm  404  can be for LTE-WiFi handover (HO) for Voice over LTE (VOLTE). Table I shows the features (x i ) and expected results (Y). 
     
       
         
           
               
             
               
                 TABLE I 
               
             
            
               
                   
               
               
                 LTE - WiFi handover (HO) for Voice over LTE (VoLTE). 
               
            
           
           
               
               
               
            
               
                   
                 X 
                   
               
               
                   
               
               
                   
                 x 1   
                 WiFi RSS 
               
               
                   
                 x 2   
                 MAC retry count 
               
               
                   
                 x 3   
                 MAC PER 
               
               
                   
                 x 4   
                 MAC Uplink quality metric 
               
               
                   
                 x 5   
                 MAC Downlink quality metric 
               
               
                   
                 x 6   
                 Adaptive dejitter buffer depth 
               
               
                   
                 x 7   
                 95 th  Percentile DL Relative jitter 
               
               
                   
                 x 8   
                 DL Packet loss 
               
               
                   
                 x 9   
                 Average UL Relative jitter 
               
               
                   
                 x 10   
                 UL Packet loss 
               
               
                   
                 x 11   
                 LTE RSRP 
               
               
                   
                 x 12   
                 LTE RSRQ 
               
               
                   
                 x 13   
                 LTE SINR 
               
               
                   
                 X 14   
                 WiFi Access Point BSSID, corresponding cellular cluster 
               
               
                   
                 x 15   
                 (WiFi RSSI) 2   
               
               
                   
                 x 16   
                 (MAC retry count) 2   
               
               
                   
                 x 17   
                 (MAC PER) 2   
               
               
                   
                 . . . 
                   
               
               
                   
                 x n   
                   
               
               
                   
                 Y 
                   
               
               
                   
                 Y 1   
                 HO to LTE 
               
               
                   
                 Y 2   
                 Remain on WiFi 
               
               
                   
                 Y 3   
                 CS repoint 
               
               
                   
               
            
           
         
       
     
     In another example, a modem algorithm  404  can be for Modem Voltage Setting. Table II shows the features (x i ) and expected result (Y). 
     
       
         
           
               
             
               
                 TABLE II 
               
             
            
               
                   
               
               
                 Modem Voltage Setting 
               
            
           
           
               
               
               
            
               
                   
                 X 
                   
               
               
                   
               
               
                   
                 x 1   
                 PDSCH PER 
               
               
                   
                 x 2   
                 RLC error (HARQ residual error) 
               
               
                   
                 x 3   
                 RSRP/RSRQ 
               
               
                   
                 . . . 
                   
               
               
                   
                 x n . 
                   
               
               
                   
                 Y 
                   
               
               
                   
                 Y 1   
                 High 
               
               
                   
                 Y 2   
                 Low 
               
               
                   
               
            
           
         
       
     
     Tables I and II list the features x i  in linear and quadratic form. Table I shows three expected outcomes (Y) for LTE-WiFi HO for VOLTE: handover to LTE, remain on WiFi and association remains on WiFi but point to circuit switch (CS) for calling purposes. Table II shows two expected outcomes (Y) for Modem Voltage Settings: high and low. 
     When executing a modem function, the modem  402  and/or tuning component  180  employs a selected hypothesis having associated weights for each feature of the modem algorithm  404 . The selected hypothesis is selected from the set of hypothesis  406  stored on the UE  104 , e.g., in the memory of the UE  104 . Each hypothesis (h k (x)) includes a weighted combination of features x i . The UE  104  is provisioned with multiple possible hypothesis h k (x) for k=1 to m, where m may be a value up to any number. A hypothesis can be a linear regression, logistic regression or a support vector machine (SVM) with associated weights. For example, a linear regression or SVM hypothesis can be represented by h k (x)=Θ 0 +Θ 1 x 1 +Θ 2 x 2 +Θ n x n , where Θ j  are the weights applied to each feature. A logistic regression can be represented by h k (x)=1/(1 exp −Θ0+1x1+Θ2x2+ . . . Θnxm ), where Θ j  are the weights applied to each feature. A hypothesis can include neural network information associated with the hypothesis for handling hidden layers and applying different techniques for the representations for each layer or regression. 
     There are two types of hypotheses  406 : unit sourced hypotheses and crowd sourced hypotheses. A selected unit sourced hypothesis is used when the UE  104  accesses a frequented access node and a selected crowd sourced hypothesis is used when the UE  104  accesses a non-frequented access node. A frequented access node is an access node that the UE  104  has previously accessed within a given time period. For example, a frequented access node is an access node that the UE  104  accesses in a UE frequented route, such as when the UE user goes to work. A non-frequented access node is an access node the UE  104  has not previously accessed within a given time period. For example, a non-frequented access node is an access node that a UE  104  accesses when going outside the frequented route, such as when the UE user goes on travel. 
     In an aspect, other criteria can be used to arbitrate across the two selected hypotheses. For example, if the UE  104  has a dual modem, the unit sourced hypothesis can be employed on a first modem and the crowd sourced hypothesis can be employed on a second modem. Alternatively, the modem  402  can switch off between the two selected hypothesis to gather information to determine which selected hypothesis performs better. 
     At specific trigger points, the UE  104  can send information to the USS  182 . The information can include the metrics for the features associated with the executed modem algorithm. The metrics can be real values and/or can be Boolean values. For example, if a metric is for a voltage, the actual voltage can be sent or a Boolean value indicating whether the voltage is above a threshold or below a threshold can be sent. The threshold can be a predetermined value. If state information is available, the state information can be sent as well. The state information can be sent when the UE  104  is able to determine the expected outcome for a given input X. When state information is not provided to the USS  182 , the USS  182  treats this information as unclassified and employs unsupervised learning, which uses clustering of the data to classify the data. This mechanism can be combined with crowd sourced information to determine consistent versus anomalous behaviors. The anomalous behavior (e.g., anything outside the identified clusters) can be used as triggering functions as well. 
     For example, if the modem function is LTE-WiFi HO for VoLTE, the UE  104  would send the metrics for the features (x i ) listed in Table 1. In addition, if one of the expected outcomes was reached, e.g., HO to LTE, remain on WiFi or CS repoint, the state information can be sent to the USS  182 . The trigger points can be the expected outcomes and/or other trigger points. For example, if a call is dropped, the dropping of the call can be a trigger point. For some modem functions, there may not be an expected outcome. 
     The information that the UE  104  sends to the USS  182  can include the selected hypothesis, the selected unit sourced hypothesis or the selected crowd sourced hypothesis. The information can include the weights associated with the selected hypothesis. Since the USS  182  provided the selected hypothesis, the USS  182  can identify the weights associated with the selected hypothesis. 
     Also, the information that the UE  104  sends to the USS  182  can include a device identifier of the UE  104 . In an aspect, the device identifier may be a random identifier so that the provided information cannot be directly traced to the UE  104 . The random identifier can be generated by the UE  104  or can be selected from a group of device identifiers that are stored on the UE  104 . Additionally, the information that the UE  104  sends can include a time identifier which indicates a time period that the USS  182  can store or archive the information from the UE  104 . The USS  182  can use the received information for crowd sourcing. 
     Additionally, the information that the UE  104  sends to the USS  182  can include a modem algorithm identifier. The modem algorithm identifier can be used to identify the version of the set of hypotheses  406  that are stored on the UE  104 . Further, the information that the UE  104  sends can include information requesting specific approaches amongst the set of hypotheses  406  stored on the UE  104 . For example, if the modem  402  has limited processing capability, the UE  104  may want to only use linear regression hypotheses as opposed to a more processor-intensive hypothesis. For example, the UE  104  can send identification of a subset of hypotheses, e.g., linear regression hypotheses, that the UE  104  can use. The identification of the subset of hypotheses can limit the hypotheses that the USS  182  can select. The subset of hypotheses contains less hypotheses than the set of hypotheses  406 . 
     The USS  182  receives the information from the UE  104 . The USS  182  stores or archives the received information. The USS  182  runs one or more learning algorithms  410  to select hypotheses and weights for the UE  104 . The one or more learning algorithms  410  can be unsupervised learning algorithms. The unsupervised learning algorithms can be known algorithms, such as, liner regression, logistic regression, SVM, etc. For metrics and state information that is received from the UE  104  employing a selected unit sourced hypothesis, the USS  182  runs one or more learning algorithms  410  to cluster the information, e.g., known k-means unsupervised learning algorithm, and select a unit sourced hypothesis and associated weights for the corresponding modem function. The one or more learning algorithms  410  can use information from the UE that performed the same modem function using the same selected unit sourced hypothesis and/or one or more different selected unit sourced hypotheses. 
     If the received information includes metrics, but not state information, from a UE  104  that employed a selected unit sourced hypothesis, the USS  182  can run the one or more learning algorithms  410  using crowd sourced information to cluster the information and select a unit sourced hypothesis and associated weights. The crowd sourced information can be obtained from a crowd sourcing server  184 . The USS  182  can provide the output (e.g., state information) that is obtained from the crowd sourced information to the UE  104  which can serve as a trigger point. The crowd sourced information can be used to override information received from the UE  104 . 
     If the received information from the UE  104  employing a selected crowd sourced hypothesis, the USS  182  can run the one or more learning algorithms  410  using crowd sourced information to cluster the information and select a crowd sourced hypothesis and associated weights. The crowd sourced information can be obtained from a crowd sourcing server  184 . 
     The clustering can indicate good results and bad results. For example, a good result is closely correlated to a preferred cluster and a bad result is an anomaly that is not closely correlated to a preferred cluster. Classification can be applied on the clusters to employ anomaly detection based on falling out of the identified clusters. To prevent or minimize anomalies, the USS  182  can use an anomaly as a trigger point. For example, the USS  182  can send the classification information, e.g., y, to the UE  104 . The trigger point (e.g., y) can be sent as state information which can supplement or replace state information on the UE  104 . The sent state information can be used as a trigger point when the associated modem function is executed by the modem  402 . In one implementation, for example, a benefit can be to allow for the modem algorithms  404  to be deployed, particularly where the relevant parameters and the relative association in terms of determining trigger points is not well understood prior to deployment of the UE  104 . Thus, the parameters for the modem algorithms  404  can be tuned post deployment. This also allows for more parameters to be included making the hypotheses selection itself dynamic enabling a flexible algorithm definition. 
     After selecting a user sourced hypothesis and associated weights and selecting a crowd sourced hypothesis and associated weights, the USS  182  can send the selected hypotheses to the UE  104 . The UE  104  can receive the selected hypotheses and selected weights and can apply them for meeting specific trigger conditions on the device. As a result, the modem  402  of the UE  104  can be tuned using unit sourcing so that the algorithms can be fine-tuned to cater to the experiences specific to UE  104 . By fine-tuning the features or parameters of the modem functions, the behavior of the modem  402  is customized rather than using generalized parameters that are applicable to all UEs in all markets. 
     The USS  182  can use one or more of crowd sourcing, cloud computing, artificial neural networks and neural network processor unit (NPU) to process the received information to select the hypotheses and associated weights. Crowd sourcing in the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers. Crowd sourcing is typically employed in the cell phone industry to collect data from different devices in the market to detect patterns of failures and identifying optimizations both in the network and devices. Cloud computing is the practice of using a network of remote servers hosted on the Internet to store manage, and process data, rather than a local server or a personal computer. In machine learning and cognitive science, artificial neural networks (ANN) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain (which are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown with one or more hidden layers for training purposes. A NPU, typically consists of a graphics processing unit (GPU), central processing unit (CPU) and a digital signal processor (DSP) used for neural network computing on cellular devices. Neural networks can be applied to enable the hypotheses if relevant. If hidden layers are required with neural network processing, the layers and associated weights are provided to the UE  104 . The UE  104  can use this to apply a hypothesis for each layer. 
     Referring to  FIGS. 5 and 6 , an example aspect of a method  500  of wireless communication performed by a UE  104  to tune a modem  402  of the UE  104  and a hardware implementation for performing the method  500 . For example, method  500  relates to the above-discussed implementations, and may be performed by the UE  104 , such as the modem  402  and/or the tuning component  180 . 
     At block  502 , method  500  includes accessing a network via a network access node. For example, a modem  402  accesses a network, e.g. IP services  176 , via a network access node, e.g., via a base station  102 . 
     At block  504 , method  500  includes determining whether the network access node is a frequented node or a non-frequented node. For example, a node determiner component  604  determines whether the network access node is a frequented node or a non-frequented node. A frequented node is a network access node that the UE  104  has previously accessed in a given time period and a non-frequented node is a network access node that the UE  104  has not previously accessed in the given time period. The given time period can be a pre-determined value such as, one week, two weeks or a month. 
     At block  506 , method  500  includes executing a modem function using a corresponding selected hypothesis having associated weights for each feature associated with the modem function. For example, the modem  402 , or the modem  402  in conjunction with the tuning component  180 , executes a modem function using a selected hypothesis having associated weights for each feature associated with the modem function. The selected hypothesis is a unit sourced hypothesis if the network access node is determined to be a frequented node. The selected hypothesis is a crowd sourced hypothesis if the network node is determined to be a non-frequented access node. The selected hypothesis is one of a set of hypotheses  406  stored on the UE  104  with each hypothesis corresponding to a modem function and including a plurality of features and state information. Initially, when the UE  104  is deployed, the tuning component  180  can use a default hypothesis as the selected hypothesis. For example, the original equipment manufacturer can set the initial weights prior to deployment of the modem  402 /UE  104 . 
     At block  508 , method  500  includes sending information, to the USS  182 , the information comprising a device identifier, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if available, in response to a trigger point being met when executing the modem function. For example, a sending component  606  sends, to the USS  182 , a device identifier, the modem function, the selected hypothesis and associated weights, metrics for each feature and state information, if available, in response to a trigger point being met when executing the modem function. In one implementation, the sending component  606  sends a modem algorithm identifier which indicates a version of the set of hypotheses  406  stored on the UE  104 . In one implementation, the sending component  606  sends a subset identifier to the USS  182  identifying a subset of hypotheses that the UE  104  can use. 
     At block  510 , method  500  includes receiving, from the USS  182 , a selection of at least one of a unit sourced hypothesis or a crowd sourced hypothesis for a corresponding modem function, with the selected hypothesis including weights for each feature. For example, a receiving component  608  receives, from the USS  182 , a selection of at least one of a unit sourced hypothesis or a crowd sourced hypothesis for a corresponding modem function, with the selected hypothesis including weights for each feature. For example, the receiving component  608  can receive a selected unit sourced hypothesis, a selected crowd sourced hypothesis or both of a selected unit sourced hypothesis and a selected crowd sourced hypothesis. The selected unit sourced hypothesis can be based on information that the UE  104  sent to the USS  182 , with the information being run through one or more learning algorithms  410  and the selected crowd sourced hypothesis based on crowd sourced data that was run through one or more learning algorithms  410 . 
       FIG. 6  is a diagram  600  illustrating an example of a hardware implementation for an apparatus  600 , e.g., UE  104 , employing a processing system  602 . The processing system  602  may be implemented with a bus architecture, represented generally by the bus  610 . The bus  610  may include any number of interconnecting buses and bridges depending on the specific application of the processing system  602  and the overall design constraints. The bus  610  links together various circuits including one or more processors and/or hardware components, represented by the processor  612 , the components  180 ,  402 ,  602 ,  604 ,  606 ,  608  and  610  and the computer-readable medium/memory  614 . The bus  610  may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further. 
     The processing system  602  may be coupled to a transceiver  616 . The transceiver  616  is coupled to one or more antennas  618 . The transceiver  616  provides a means for communicating with various other apparatus, e.g., the USS  182  over a transmission medium. The transceiver  616  receives a signal from the one or more antennas  618 , extracts information from the received signal, and provides the extracted information to the processing system  602 , specifically the reception component  620  of modem  402 . In addition, the transceiver  616  receives information from the processing system  602 , specifically the transmission component  622 , and based on the received information, generates a signal to be applied to the one or more antennas  618 . The processing system  602  includes a processor  612  coupled to a computer-readable medium/memory  614 . The processor  612  is responsible for general processing, including the execution of software stored on the computer-readable medium/memory  614 . The software, when executed by the processor  612 , causes the processing system  602  to perform the various functions described supra for any particular apparatus. The computer-readable medium/memory  614  may also be used for storing data that is manipulated by the processor  612  when executing software. The processing system  602  further includes at least one of the components  180 ,  402 ,  602 ,  604 ,  606 ,  608  and  610 . The components may be software components running in the processor  612 , resident/stored in the computer readable medium/memory  614 , one or more hardware components coupled to the processor  612 , or some combination thereof. 
     The apparatus may include additional components that perform each of the actions described with respect to the aforementioned flowchart of  FIG. 5  and/or the aspects of  FIGS. 4-8 . As such, each action described with reference to the aforementioned flowchart of  FIG. 5  and/or the aspects of  FIGS. 4-8  may be performed by a component and the apparatus may include one or more of those components. The components may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by a processor configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by a processor, or some combination thereof. 
     Referring to  FIGS. 7A, 7B and 8 , an example aspect of a method  700  of wireless communication performed by the USS  182  to tune the modem  402  of the UE  104  and a hardware implementation for performing the method  700 . For example, method  700  relates to the above-discussed implementations, and may be performed by the USS  182 . 
     At block  702 , method  700  includes receiving information from a UE. For example, accessing a network via a network access node. For example, the USS  182  receives the information from the UE  104  via a reception component  806 . The information can include a device identifier, a modem function executed by the UE  104 , a selected hypothesis and associated weights for the corresponding modem functions, metrics for each feature of the corresponding modem function and state information, if the state information is available. The information is sent in response to a trigger point associated with the modem function being met when the modem function is executed. 
     At block  704 , method  700  includes storing the received information. For example, the USS  182  stores the received information on a UE basis using the device identifier. The information can be stored locally or remotely, e.g., on one or more servers communicatively coupled to the USS  182 . 
     At block  706 , method  700  includes running one or more learning algorithms based on the stored information to cluster the information and select a unit sources hypothesis and associated weights for the corresponding modem function when state information is provided by the UE. For example, the USS  182  runs the one or more learning algorithms  410  based on the stored information to cluster the information and select a unit sourced hypothesis and associated weights for the correspond modem function. 
     At block  708 , method  700  includes running one or more learning algorithms based on the crowd sourced information to cluster the information and select a unit sourced hypothesis and associated weights for the corresponding modem function when state information was not provided by the UE. For example, the USS  182  runs the one or more learning algorithms  410  based on crowd sourced information to cluster the information and select a unit sourced hypothesis and associated weights for the correspond modem function. The crowd sourced information can be obtained from one or more crowd sourcing servers  184 . 
     At block  710 , method  700  includes selecting the unit sourced hypothesis and associated weights based on the one or more learning algorithms. For example, a hypothesis selecting component  804  can select the unit sourced hypothesis and associated weights based on the one or more learning algorithms  410 . The received information can include a modem algorithm identifier which can identify the version of the set of hypotheses  406  stored on the UE  104 . The received information can include a request for specific approaches amongst the set of hypotheses  406  stored on the UE  104 . For example, if the modem  402  resides in a UE  104  that has limited processing capability, the UE  104  may want to only use linear regression hypotheses. The UE  104  can send a subset of hypotheses, e.g., linear regression hypotheses, that the UE  104  prefers to employ. Thus the received information can limit the hypotheses that the USS  182  can select. 
     At block  712 , method  700  includes running one or more learning algorithms based on the crowd sourced information to cluster the information and select a crowd sourced hypothesis and associated weights for the corresponding modem function when the state information was provided by the UE. For example, the USS  182  runs the one or more learning algorithms  410  to cluster the information and select a crowd sourced hypothesis and associated weights for the correspond modem function when the state information was provided by the UE  104 . The crowd sourced information can be obtained from one or more crowd sourcing servers  184 . 
     At block  714 , method  700  includes selecting the crowd sourced hypothesis and associated weights based on the one or more learning algorithms. For example, a hypothesis selecting component  804  can select the crowd sourced hypothesis and associated weights based on the one or more learning algorithms. 
     At block  716 , method  700  includes sending at least one of the selected unit sourced hypothesis and associated weights or selected crowd sourced hypothesis and associated weights for the corresponding modem function. For example, the USS  182  sends at least one of the selected hypotheses and associated weights using the transmission component  808 . In another example, the USS  182  sends the at least one selected unit source hypothesis and associated weights or the selected crowd source hypothesis and associated weights using the transmission component  808 . For example, the transmission component  808  can send a selected unit sourced hypothesis, a selected crowd sourced hypothesis or both of a selected unit sourced hypothesis and a selected crowd sourced hypothesis. 
     At block  718 , method  700  can optionally include sending state information associated with the selected unit sourced hypothesis when the USS  182  ran one or more learning algorithms  410  based on crowd source information when the state information was not provided by the UE  104 . For example, the USS  182  sends the state information using the transmission component  808 . The state information can be used to supplement or replace state information on the UE  104 . The sent state information can be used as a trigger point when the associated modem function is executed by the modem  402 . 
     At block  720 , method  700  can optionally include sending at least one of state information associated with the selected unit sourced hypothesis or state information associated with the selected crowd sourced hypothesis. For example, the USS  182  sends the state information using the transmission component  808 . The state information can be used to supplement or replace state information on the UE  104 . The sent state information can be used as a trigger point when the associated modem function is executed by the modem  402 . 
       FIG. 8  is a diagram  800  illustrating an example of a hardware implementation for an apparatus  800 , e.g., a USS  182 , employing a processing system  802 . The processing system  802  may be implemented with a bus architecture, represented generally by the bus  810 . The bus  810  may include any number of interconnecting buses and bridges depending on the specific application of the processing system  802  and the overall design constraints. The bus  810  links together various circuits including one or more processors and/or hardware components, represented by the processor  812 , the components  410 ,  804 ,  806  and  808  and the computer-readable medium/memory  814 . The bus  810  may also link various other circuits such as timing sources, peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further. 
     The processing system  802  may be coupled to a transceiver  816 . The transceiver  816  is coupled to one or more antennas  818 . The transceiver  816  provides a means for communicating with various other apparatus over a transmission medium. The transceiver  816  receives a signal from the one or more antennas  818 , extracts information from the received signal, and provides the extracted information to the processing system  802 , specifically the reception component  806 . In addition, the transceiver  816  receives information from the processing system  802 , specifically the transmission component  808 , and based on the received information, generates a signal to be applied to the one or more antennas  818 . The processing system  802  includes a processor  812  coupled to a computer-readable medium/memory  814 . The processor  812  is responsible for general processing, including the execution of software stored on the computer-readable medium/memory  814 . The software, when executed by the processor  812 , causes the processing system  802  to perform the various functions described supra for any particular apparatus. The computer-readable medium/memory  814  may also be used for storing data that is manipulated by the processor  812  when executing software. The processing system  802  further includes at least one of the components  410 ,  804 ,  806  and  808 . The components may be software components running in the processor  812 , resident/stored in the computer readable medium/memory  814 , one or more hardware components coupled to the processor  812 , or some combination thereof. 
     The apparatus may include additional components that perform each of the actions described with respect to the aforementioned flowchart of  FIGS. 7A and 7B  and/or the aspects of  FIGS. 4-8 . As such, each action described with reference to the aforementioned flowchart of  FIGS. 7A and 7B  and/or the aspects of  FIGS. 4-8  may be performed by a component and the apparatus may include one or more of those components. The components may be one or more hardware components specifically configured to carry out the stated processes/algorithm, implemented by a processor configured to perform the stated processes/algorithm, stored within a computer-readable medium for implementation by a processor, or some combination thereof. 
     As discussed earlier, neural networks can be used to assist a hypothesis to handle hidden layers and apply different techniques for the representations for each layer or regression. Neural networks allows for a computationally less intensive approach to implement a non-linear realization. The real time computations can be triggered based on the specific metrics changing beyond a specific delta value and the optimization of reducing or minimizing the computations can be tuned accordingly. The delta can be tuned on a per metric basis and can include a range of operations for that metric. For example, for a LTE-WiFi handover modem hypothesis, the RSSI delta value at a higher level, such as −50 dBM, will be different from the RSSI delta value used when the RSSI is at −85 dBm. The training of the neural networks can be done in the USS  182  and provide a means to ensure consistency. For example, one mechanism can apply a threshold level to determine the RTP error rate. In such a scenario, the RTP error rate can be a “1” when the RTP error rate is above a threshold level (e.g., 3%) and can be a “0” when the RTP error rate is below a threshold level (e.g., 3%). In another example, a second mechanism can apply an objective Means Opinion Score (MOS) prediction software to threshold the RTP error rate. In such a scenario, the MOS prediction software can generate a threshold of 3.2% with a “1” being outputted for an RTP error rate above the 3.2% threshold and a “0” being outputted for an TRP error rate below the 3.2% threshold. 
       FIG. 9  is a diagram  900  illustrating a Neural Network based approach for LTE-WiFi handover management. As shown, the input layer contains metrics for features for LTE-WiFi handover. The metrics can be paired to determine correlated behaviors, For example, as shown, the parameters from the UE  104  are shown as an input layer with the weights (Θ (1) ) as a first layer, the correlated parameters as hidden layer 1, the weights (Θ (2) ) as a second layer, the combination of correlated parameters as hidden layer 2, the (Θ (3) ) as a third layer and an output layer h k (x) having three state values: Y1: handover to LTE, Y2: remain on WiFi and Y3: circuit switch repoint. As shown, the WiFi RSSI and MAC retry count are correlated, WiFi RSSI and MAC PER are correlated, WiFi RSSI and adaptive dejitter buffer depth are correlated, adaptive dejitter buffer depth and 95th percentile DL relative jitter are correlated, adaptive dejitter buffer depth and LTE RSRP are correlated, and LTE RSRP and LTE RSRQ are correlated. The pairwise group in the hidden layer 1 allow for correlation across any two metrics to be captured. The hidden layer 2 allows for MAC layer specific metrics base computation to be executed and the same computations can be made available to other application. As shown, the pairwise combinations can be correlated by combining WiFi metrics and by combining media, WiFi and LTE metrics. Based on the combinations, one or more state information can be determined as expected outputs, e.g., ground truth, which allow for learning the behavior for a given device and also lean on patterns seen through the crowd sourced data. 
     It is understood that the specific order or hierarchy of blocks in the processes/flowcharts disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of blocks in the processes/flowcharts may be rearranged. Further, some blocks may be combined or omitted. The accompanying method claims present elements of the various blocks in a sample order, and are not meant to be limited to the specific order or hierarchy presented. 
     The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” include any combination of A, B, and/or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof” may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”