Method and apparatus for optimizing performance of user equipment in wireless network

Abstract

Embodiments of the disclosure describe a method 1000 for optimizing the performance by a user equipment (UE) in a wireless network. The method includes obtaining the IMS diagnosis information at the UE. The method includes transmitting the obtained IMS diagnosis information to a server. The method includes receiving a personalized feedback from the server in response to transmitting the obtained IMS diagnosis information. The method includes applying the received personalized feedback at the UE to optimize the performance of the UE.

Description

METHOD AND APPARATUS FOR OPTIMIZING PERFORMANCE OF USER EQUIPMENT IN WIRELESS NETWORK

[0001] The present invention generally relates to the field of telecommunication systems, and more specifically relates to a method and a system for optimizing performance of a User Equipment (UE) in the wireless network.

[0002] In telecommunication systems, voice service typically refers to the transmission of real-time audio communication, such as telephone calls, Voice-over IP (VoIP), and video conferencing. This allows users to have interactive, real-time conversations. Data service, on the other hand, refers to the transmission of non-voice digital information, such as text, images, videos, files, and internet access. Data services enable the exchange of information and access to online resources. Delivery of voice and data services is facilitated through a negotiation process between a User Equipment (UE) and a network entity (e.g., New Radio (NR), Long-Term Evolution (LTE), UMTS Terrestrial Radio Access Network (UTRAN)). The negotiation process is based on one or more available network conditions, such as the Radio Access Technology (RAT), Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ).

[0003] In the context of existing telecommunication systems, the UE is a mobile device which is typically in motion, and the one or more available network conditions can change during mobility. To ensure the continuity of the voice and data services, the network employs various migration mechanisms, such as an Enhanced Packet System Fallback (EPSFB) mechanism, a Circuit-Switched Fallback (CSFB) mechanism, and a Single Radio Voice Call Continuity (SRVCC) mechanism. For example, as illustrated in FIG. 1, in a network with Standalone (SA) and Voice over New Radio (VoNR) support, the preferred call option is to utilize an NR Radio Access Network (RAN). In a network with Non-Standalone (NSA) architecture, the data services are provided over a VoNR network, while the voice services are delivered over the LTE network using the EPSFB. During the mobility of the UE, if the coverage of NR and LTE is not available, the UE triggers the SRVCC to migrate the voice service to a Universal Terrestrial Radio Access Network (UTRAN) network. The existing telecommunication systems employ these migration techniques to improve a user's experience. However, several problems have been encountered in the existing telecommunication systems, as reported by Big Data analytics. These issues include call drops, call failures, audio muting, and reduced data connectivity during mobility. Additionally, there are delays in the network-driven migrations, resulting in reported failures.

[0004] For instance, consider a scenario where a user frequently travels between an office in a city center and client meetings in a suburb. The user relies on his / her 5G-enabled smartphone for seamless voice and data services connectivity throughout his commute. During a morning commute, the user's smartphone (i.e., the UE) is initially connected to the 5G NR network provided by a telecom operator. As the user travels out of the city center, the 5G coverage starts to degrade, and the UE detects a stronger LTE signal. Using the EPSFB mechanism, the UE seamlessly Hand Over (HO) an active voice service to the LTE network, ensuring uninterrupted voice service. As the user reaches the outskirts of a city, coverage of the LTE network also begins to weaken. Mobility management function of the UE anticipates an upcoming loss of LTE connectivity and proactively triggers the SRVCC mechanism. This migrates the user's voice service to the UTRAN network, maintaining the call's continuity despite the changing network conditions. However, during the SRVCC HO, the user experiences a brief audio glitch and reduced data throughput on the UE. This is due to the delay in the network-driven migration, which is a known issue in the existing telecommunication systems.

[0005] In addition, in the context of existing telecommunication systems, an initiation of a voice call through a call application on the User Equipment (UE) 100 involves a series of complex processes and interactions between various modules within the UE 100 and an operator network 300, as illustrated in FIG. 2. The UE first determines an appropriate call type, whether it is a Circuit-Switched (CS) call, a call over LTE, or a call over New Radio (NR). This determination is made based on factors such as network availability, user preferences, and device capabilities. Depending on the call type, the corresponding module within the UE is responsible for initiating a voice call procedure. For the CS call, a telephony module of the UE interacts with a 2G / 3G operator network, sending messages such as a CM Service Request. For the call over LTE or NR, an IP Multimedia Subsystem (IMS) module of the UE communicates with the LTE / NR operator network, typically using a Session Initiation Protocol(SIP) Invite requests.

[0006] However, in many scenarios, the communication between the UE and the operator network can face failures, leading to undesirable user experiences, such as call drops, call failures, and audio mute issues. Examples of these failure scenarios include:

[0007] a. Call drop and call fail due to a break in the link between a modem module and a 2G / 3G (CS) operator network.

[0008] b. Call drop / fail due to a break in the link between the modem module and an LTE operator network.

[0009] c. Call drop / fail and mute due to issues with an IMS server.

[0010] These failures can occur due to various factors, such as network coverage gaps, network congestion, or problems with a network infrastructure or the UE. To address these challenges and improve the user experience, there is a need for enhanced mechanisms and coordination between the different modules within the UE and the operator network.

[0011] Moreover, current network-based models are unable to effectively improve the UE performance at an application level, as one or more network quality parameters are controlled and communicated solely by the network (operator network). Furthermore, the network-based solutions are not designed to track the comprehensive state of the UE, as this information is under the control of an Original Equipment Manufacturer (OEM). Additionally, the existing telecommunication systems do not capture the following key information, for example:

[0012] a. Call state preference information;

[0013] b. Migration preference information; and

[0014] c. Network preference information.

[0015] This lack of user-specific information limits the ability of the network-based solutions to optimize the user experience effectively. Thus, it is desired to address the above-mentioned disadvantages or other shortcomings or at least provide a useful alternative for optimizing the performance of the UE in the wireless network.

[0016] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention nor is it intended for determining the scope of the invention.

[0017] According to an embodiment of the present disclosure, a method for optimizing a performance by a user equipment (UE) in a wireless network is disclosed herein. The method may comprise obtaining IP Multimedia Subsystem (IMS) diagnosis information at the UE. The method may comprise transmitting the obtained IMS diagnosis information to a server. The method may comprise receiving feedback from the server in response to transmitting the obtained IMS diagnosis information. The method may comprise applying the received feedback at the UE to optimize the performance of the UE.

[0018] The IMS diagnosis information may comprises at least one of call preference information, call state information, migration performance information, or network quality preference information.

[0019] The call preference information may comprise at least one of call setup time information and call termination time information.

[0020] The call state information may comprise at least one of ongoing call state information, incoming call state information during a success or a failure, call rate information, and call type count information.

[0021] The migration performance information may comprise at least one of Evolved Packet Data Gateway (ePDG) status information, cross Subscriber Identity Module (SIM) information, Voice over Wireless Fidelity (VoWIFI) preference information, Single Radio Voice Call Continuity (SRVCC) count information, Circuit-Switched Fallback (CSFB) count information, Evolved Packet System Fallback (EPSFB) count information and forward call count information.

[0022] The network quality preference information may comprise at least one of Reference Signal Received Power (RSRP) information, Reference Signal Received Quality (RSRQ) information, data roaming information, call failure count information, call drop count information, forward call count information and downgrade count information.

[0023] The IMS diagnosis information may comprise device information associated with the UE.

[0024] The IMS diagnosis information is determined at a regular time interval or a predetermined time interval.

[0025] The IMS diagnosis information may be transmitted for analysis by the server to identify at least one of one or more patterns, correlations, and potential issues affecting the performance of the UE.

[0026] Applying the received feedback at the UE to optimize the performance of the UE may comprises: dynamically configuring, based on the received feedback, one or more IMS services associated with at least one of a voice transmission or a data transmission associated with the UE by utilizing at least one Artificial Intelligence- Machine Learning (AI-ML) module, wherein the one or more IMS services comprise call priority information and codec selection information; and optimizing the performance of the UE based on the one or more dynamically configured IMS services

[0027] Applying the received feedback at the UE to optimize the performance of the UE may comprise: dynamically configuring, based on the received feedback, one or more application states associated with the UE by utilizing at least one Artificial Intelligence- Machine Learning (AI-ML) module, wherein the one or more application states comprise a foreground state, a background state, a suspended state, and a terminated state; and optimizing the performance of the UE based on the one or more dynamically configured application states.

[0028] Applying the received feedback at the UE to optimize the performance of the UE may comprises: selecting, based on the received feedback, an optimal Radio Access Technology (RAT) for at least one of voice transmission or data transmission by utilizing at least one Artificial Intelligence- Machine Learning (AI-ML) module, and optimizing the performance of the UE based on the selected optimal RAT.

[0029] Applying the received feedback at the UE to optimize the performance of the UE may comprise: determining, based on the received feedback, an optimal bandwidth for at least one of voice transmission or data transmission associated with the UE by utilizing at least one Artificial Intelligence-Machine Learning (AI-ML) module; and optimizing the performance of the UE based on the determined optimal bandwidth.

[0030] The method may comprise initiating a call. Initiating the call may comprise: obtaining a default Radio Access Technology (RAT) information; obtaining audio and video codec information supported by a current operator; determining whether the UE is in a roaming mode; obtaining a current cell ID, latitude, and longitude information; evaluating a pass probability for all available RATs based on the obtained audio and video codec information, the determined roaming mode, the obtained current cell ID, latitude, and longitude information; and evaluating a data throttling required probability based on the obtained audio and video codec information, the determined roaming mode, the obtained current cell ID, latitude, and longitude information.

[0031] The default RAT information, the audio and video codec information, the roaming mode, the current cell ID, the latitude, and the longitude may be input to an inference model prior to initiating the call.

[0032] According to an embodiment of the present disclosure, a method for optimizing the performance of a UE by a server in the wireless network is disclosed. The method may comprise receiving IMS diagnosis information from a plurality of UEs, including the UE. The method may comprise analyzing, by utilizing at least one AI- ML module, the received IMS diagnosis information to identify at least one of one or more patterns, correlations, and potential issues affecting the performance of the UE. The method may comprise sending, based on a result of the analysis, feedback to the UE to optimize the performance.

[0033] According to an embodiment of the present disclosure, the UE for optimizing the performance in the wireless network is disclosed. The UE may comprise memory storing instructions; and at least one processor. The instructions, when executed by the at least one processor, may cause the UE to perform operations. The operations may comprise obtaining the IMS diagnosis information at the UE. The operations may comprise transmitting the obtained IMS diagnosis information to the server. The operations may comprise receiving feedback from the server in response to transmitting the obtained IMS diagnosis information. The operations may comprise applying the received feedback at the UE to optimize the performance of the UE.

[0034] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.

[0035] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0036] FIGS. 1-2 illustrate various migration mechanisms of existing telecommunication systems and one or more problems associated with the various migration mechanisms, according to prior art;

[0037] FIG. 3 illustrates a block diagram of a system for optimizing performance of a User Equipment (UE) in a wireless network by utilizing IP Multimedia Subsystem (IMS) diagnosis information to control a behavior of the UE for voice and data transmission, according to an embodiment as disclosed herein;

[0038] FIG. 4 illustrates a block diagram of the UE for optimizing the performance in the wireless network, according to an embodiment as disclosed herein;

[0039] FIG. 5A and Fig. 5B illustrate one or more operations associated with a training module and an inference module of the UE to optimize the performance of the UE in the wireless network, according to an embodiment as disclosed herein;

[0040] FIG. 6 illustrates a block diagram of a server for optimizing the performance of the UE in the wireless network, according to an embodiment as disclosed herein;

[0041] FIG. 7A and Fig. 7B are sequential flow diagrams illustrating an example scenario for optimizing the performance of the UE in the wireless network, according to an embodiment as disclosed herein;

[0042] FIG. 8 is a flow diagram illustrating a call flow mechanism, according to an embodiment as disclosed herein;

[0043] FIG. 9A and Fig. 9B illustrate a comparison between a standard video call scenario and a video call using the disclosed method, according to an embodiment as disclosed herein;

[0044] FIG. 10 is a flow diagram illustrating a method for optimizing the performance of the UE in the wireless network, according to an embodiment as disclosed herein; and

[0045] FIG. 11 is a flow diagram illustrating a method for optimizing the performance of the UE in the wireless network, according to another embodiment as disclosed herein.

[0046] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

[0047] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

[0048] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.

[0049] Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase "in an embodiment", "in one embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0050] The terms "comprise", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises... a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

[0051] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term "or" as used herein, refers to a non-exclusive or unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0052] As is traditional in the field, embodiments may be described and illustrated in terms of blocks that carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, or by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the invention. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the invention.

[0053] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.

[0054] Referring now to the drawings, and more particularly to FIGs. 3 to 11, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0055] FIG. 3 illustrates a block diagram of a system 3000 for optimizing a performance of a User Equipment (UE) in a wireless network by utilizing IP Multimedia Subsystem (IMS) diagnosis information to control a behavior of the UE for voice and data services, according to an embodiment as disclosed herein. The system 3000 may include, but is not limited to, a plurality of UEs (e.g., 100A, 100B,...,100N), hereinafter referred as "the UE 100", and a server 200.

[0056] Examples of the UE 100 may include, but are not limited to, a smartphone, a tablet computer, a Personal Digital Assistance (PDA), an Internet of Things (IoT) device, a wearable device, etc. Examples of the server 100 may include, but are not limited to, a database server, a multimedia server, an application server, a Voice over Internet Protocol (VoIP) server, a web server, etc. Examples of the wireless network may include, but are not limited to, a cellular network, a Wi-Fi network, a satellite network, etc.

[0057] In one or more embodiments, the UE 100 and / or the server 200 may execute multiple operations to optimize the performance of the UE 100 for the voice and data services, which are given below.

[0058] The UE 100 is configured to obtain IP Multimedia Subsystem (IMS) diagnosis information at the UE 100 may include, but is not limited to, call preference information, call state information, migration performance information, and network quality preference information, as described in conjunction with FIG. 4. The UE 100 is further configured to transmit the obtained IMS diagnosis information to the server 200 associated with the wireless network. Upon receiving the obtained IMS diagnosis information, the server is configured to identify one or more patterns, correlations, and potential issues affecting the performance of the UE 100 by utilizing at least one Artificial Intelligence (AI) - Machine Learning (ML) module, as described in conjunction with FIG. 6. The server 200 is further configured to transmit a personalized feedback to the UE 100 to optimize the performance. Upon receiving personalized feedback from the server 200, the UE 100 is further configured to apply the received personalized feedback at the UE 100 to optimize the performance of the UE 100, as described in conjunction with FIG. 4, FIG. 5A, FIG. 5B, FIG. 7A, Fig. 7B, FIG. 8, FIG. 9A and Fig. 9B.

[0059] FIG. 4 illustrates a block diagram of the UE 100 for optimizing the performance in the wireless network, according to an embodiment as disclosed herein.

[0060] In one or more embodiments, the UE 100 may include a memory 110, a processor 120, a communicator 130, and an IMS framework module 140.

[0061] The memory 110 stores instructions to be executed by the processor 120 for optimizing the performance of the UE 100 in the wireless network, as discussed throughout the disclosure. The memory 110 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 110 may, in some examples, be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory 110 is non-movable. In some examples, the memory 110 can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 110 can be an internal storage unit, or it can be an external storage unit of the UE 100, a cloud storage, or any other type of external storage.

[0062] The processor 120 communicates with the memory 110, the communicator 130, and the IMS framework module 140. The processor 120 is configured to execute instructions stored in the memory 110 and to perform various for optimizing the performance of the UE 100 in the wireless network, as discussed throughout the disclosure. The processor 120 may include one or a plurality of processors, maybe a general-purpose processor, such as a Central Processing Unit (CPU), an Application Processor (AP), or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and / or an Artificial intelligence (AI) dedicated processor such as a Neural Processing Unit (NPU).

[0063] The communicator 130 is configured for communicating internally between internal hardware components and with external devices (e.g., server 200) via one or more networks (e.g., radio technology). The communicator 130 includes an electronic circuit specific to a standard that enables wired or wireless communication.

[0064] In one or more embodiments, the IMS framework module 140 is configured to deliver multimedia services over IP-based networks. The IMS framework module 140 encompasses several sub-modules that work together to provide a comprehensive solution for multimedia service management and delivery. In other words, the IMS framework module 140 exposes APIs to a Telephony Framework, enabling the management of Voice over LTE (Volte), Voice over NR (VoNR), and SMS over IP services. These APIs handle call control, session management, and quality of service to deliver seamless multimedia communication over IP networks. The IMS framework module 140 may include, for example, but is not limited to, a Voice over LTE (Volte) service module 141, a Volte handler module 142, a call manager module 143, an IMS diagnostic module 144, and an Artificial Intelligence (AI) - Machine Learning (ML) module 145. The IMS framework module 140 is implemented by processing circuitry such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.

[0065] In one or more embodiments, the Volte service module 141 is configured to manage and coordinate a Volte service within the IMS framework (IMS framework module 140). The Volte service module 141 is configured further to handle one or more registration, authentication, and authorization processes for VoLTE-enabled UEs. The Volte service module 141 is configured further to interact with one or more IMS core components, such as the Call Session Control Function (CSCF), to establish, maintain, and terminate VoLTE calls. The Volte service module 141 is configured further to manage one or more Quality of Service (QoS) parameters and ensures the optimal utilization of network resources for VoLTE service delivery.

[0066] In one or more embodiments, the Volte handler module 142 is configured to act as an interface between the UE 100 and the VoLTE service module 141. The Volte handler module 142 is configured further to manage one or more signaling and media sessions for VoLTE calls, including Session Initiation Protocol (SIP) registration, session establishment, modification, and termination. The Volte handler module 142 is configured further to handle a negotiation of codec and media parameters between the UE 100 and the network (e.g., server 200, operator network, etc.), ensuring seamless VoLTE call setup and maintenance. The Volte handler module 142 is configured further to provide a support for emergency calls, supplementary services (e.g., call hold, call transfer), and seamless handover between VoLTE and circuit-switched voice services.

[0067] In one or more embodiments, the call manager module 143 is configured to manage and coordinate an overall call control and signaling within the IMS framework. The call manager module 143 is further configured to provide an interface with various IMS components, such as the CSCF, to handle call setup, modification, and termination procedures. The call manager module 143 is further configured to manage session state information and ensures correct routing of signaling messages between the UE 100 and the network. The call manager module 143 is further configured to provide a support for advanced call features, such as call forwarding, call waiting, and conference calling.

[0068] In one or more embodiments, the IMS diagnostic module 144 is configured to obtain the IMS diagnosis information at the UE 100 may include, but is not limited to, the call preference information, the call state information, the migration performance information, and the network quality preference information.

[0069] In one or more embodiments, the IMS call preference information may include, but is not limited to, call setup time information and call termination time information.

[0070] In one or more embodiments, the IMS call state information may include, but is not limited to, ongoing call state information, incoming call state information during a success or a failure, call rate information, and call type count information.

[0071] In one or more embodiments, the IMS migration performance information may include, but is not limited to, Evolved Packet Data Gateway (ePDG) status information, cross Subscriber Identity Module (SIM) information, Voice over Wireless Fidelity (VoWIFI) preference information, Single Radio Voice Call Continuity (SRVCC) count information, Circuit-Switched Fallback (CSFB) count information, Evolved Packet System Fallback (EPSFB) count information and forward call count information.

[0072] In one or more embodiments, the IMS diagnosis information is determined at a regular time interval or a predetermined time interval (e.g., after 1 hour, daily once, etc.).

[0073] In one or more embodiments, the IMS diagnosis information may include, but is not limited to, device information associated (e.g., International Mobile Equipment Identity (IMEI) number, a type of device, a device model information, etc.) with the UE 100.

[0074] In one or more embodiments, the IMS diagnostic module 144 is further configured to transmit the obtained IMS diagnosis information to the server 200 associated with the wireless network, where the server 200 analyzes the transmitted IMS diagnosis information to identify one or more patterns, correlations, and potential issues affecting the performance of the UE, as described in conjunction with FIG. 6.

[0075] In one or more embodiments, the IMS diagnostic module 144 is further configured to receive personalized feedback from the server in response to transmitting the obtained IMS diagnosis information and apply the received personalized feedback at the UE to optimize the performance of the UE 100, as described in conjunction with FIGS. 5A-5B.

[0076] In one or more embodiments, the IMS diagnostic module 144 may execute one or more operations to apply the received personalized feedback at the UE 100 to optimize the performance of the UE 100, which are given below.

[0077] a. The IMS diagnostic module 144 may dynamically configure one or more IMS services associated with at least one of a voice transmission or a data transmission associated with the UE 100, based on the received personalized feedback, by utilizing the AI-ML module 145. The one or more IMS services may include, but are not limited to, call priority information and codec selection information.

[0078] b. The IMS diagnostic module 144 may optimize the performance of the UE 100 based on the one or more dynamically configured IMS services.

[0079] In one or more embodiments, the IMS diagnostic module 144 may execute one or more operations to apply the received personalized feedback at the UE 100 to optimize the performance of the UE 100, which are given below.

[0080] a. The IMS diagnostic module 144 may dynamically configure one or more application states associated with the UE 100, based on the received personalized feedback, by utilizing the AI-ML module 145. The one or more application states may include, but is not limited to, a foreground state, a background state, a suspended state, and a terminated state.

[0081] b. The IMS diagnostic module 144 may optimize the performance of the UE 100 based on the one or more dynamically configured application states.

[0082] In one or more embodiments, the IMS diagnostic module 144 may execute one or more operations to apply the received personalized feedback at the UE 100 to optimize the performance of the UE 100, which are given below.

[0083] a. The IMS diagnostic module 144 may select an optimal Radio Access Technology (RAT) for at least one of voice transmission or data transmission, based on the received personalized feedback, by utilizing the AI-ML module 145.

[0084] b. The IMS diagnostic module 144 may optimize the performance of the UE 100 based on the selected optimal RAT.

[0085] In one or more embodiments, the IMS diagnostic module 144 may execute one or more operations to apply the received personalized feedback at the UE 100 to optimize the performance of the UE 100, which are given below.

[0086] a. The IMS diagnostic module 144 may determine an optimal bandwidth for at least one of voice transmission or data transmission associated with the UE 100, based on the received personalized feedback, by utilizing the AI-ML module 145.

[0087] b. The IMS diagnostic module 144 may optimize the performance of the UE 100 based on the determined optimal bandwidth.

[0088] In one or more embodiments, the IMS diagnostic module may include a diagnosis constants, a diagnosis controller, a Packet-Switched (PS) daily info, and a PS call info (not shown in FIG.). The diagnosis constants may store one or more key-value pairs related to registration, RCS, VoLTE, and other relevant diagnostic data and may provide a centralized repository for diagnostic information parameters. The diagnosis controller may populate the diagnostic data into the big data platform and may collect and dump call-related information, including call type, bearer type, call duration, and call failure codes. The diagnosis controller may capture daily basis call statistics, such as CSFB count, SRVCC count, VoLTE end count, and EPS fallback count. The PS daily info may gather and store a daily PS network information. The data collection frequency may vary across different devices. The PS call info may capture the packet-switched call-specific information for each call and may record details such as call type, bearer type, call duration, and call failure codes. The PS call info may provide a granular call-level diagnostic data for troubleshooting and performance analysis.

[0089] In one or more embodiments, the AI-ML module 145 may include a training sub-module 145a and an inference sub-module 145b. The AI-ML module 145 may perform various operations to optimize the performance of the UE 100, as described in conjunction with FIGS. 5A-5B.

[0090] In one or more embodiments, a function associated with the various components of the UE 100 may be performed through the non-volatile memory, the volatile memory, and the processor 120. One or a plurality of processors controls the processing of the input data in accordance with a predefined operating rule or AI-ML module 145 stored in the non-volatile memory and the volatile memory. The predefined operating rule or AI-ML module 145 is provided through training or learning. Here, being provided through learning means that, by applying a learning algorithm to a plurality of learning data, a predefined operating rule or AI-ML module 145 of the desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and / or may be implemented through a separate server / system. The learning algorithm is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to decide or predict. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0091] The AI-ML module 145 may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through a calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), Generative Adversarial Networks (GAN), and Deep Q-networks.

[0092] Although FIG. 4 shows various hardware components of the UE 100, but it is to be understood that other embodiments are not limited thereon. In other embodiments, the UE 100 may include less or more number of components. Further, the labels or names of the components are used only for illustrative purposes and do not limit the scope of the invention. One or more components can be combined to perform the same or substantially similar functions to optimize the performance of the UE 100.

[0093] FIGS. 5A-5B illustrate one or more operations associated with the training sub-module 145a and the inference sub-module 145b of the UE 100 to optimize the performance of the UE 100 in the wireless network, according to an embodiment as disclosed herein.

[0094] The IMS diagnosis information collected by the UE 100 provides valuable insights into the quality of the IMS services between the UE 100 and an IMS network, as shown in Table-1. The UE 100 is configured to acquire this IMS diagnosis information and sharing it with the server 200 for further analysis. In Table-1, for example, the "call type" category includes two options: a voice call (011) and a video call (012). In one embodiment, 011 and 012 have been used only as an example to indicate the call type, i.e., the voice call and the video call. However, any other combination of binary digits. The "ePDG status" category also has two options: Wi-Fi (0) and 3G / 4G / 5G / 6G (1).

[0095] Call performanceCall stateMigration preferenceNetwork quality preference informationSetup / end timeCall TypeePDG statusCross SIMSRVCC----RSRPRSRQData RoamingFail CountCall DropRAT3620011101----95100Pass1LTE6047012011----100201Fail0NR7356011010----80181Fail0GSM4469012111----90140Pass1LTE5037000100----100251Pass1NR

[0096] In addition, the server 200 is configured to analyze the received IMS diagnosis information and identify any potential issues that may be affecting the UE's application performance or network / location / service quality. Based on this analysis, the server 200 can recommend potential adjustments to optimize the UE's performance, as shown in Table-2, as described in conjunction with FIG. 6. In Table-2, for example, the "Codec" category includes two options: Narrow-Band Adaptive Multi-Rate (NB-AMR) (101) and Wide-Band AMR (WB-AMR) (102), the "call priority" category has four options: voice (01), video (02), download (03), and stream (04).

[0097] CodecCall priorityApp stateRATNB-AMRWB-AMRVoiceStreamDownloadNetworkdelayBitrate--------101000010000High100----NR000102000403Medium180----GSM000102010000Low240----LTE101000000400Very low120----NR101000000003Medium260----LTE

[0098] The server 200 generates the personalized feedback for the UE 100, which is then used to perform model inference (inference sub-module 145b) using the AI-ML module 145. This AI-ML module 145 incorporates, for example, a random forest algorithm to predict the most suitable Radio Access Technology (RAT) for the current scenario, taking into account factors such as the preferred RAT, preferred codec, and data throttling settings. The goal is to optimize the UE's performance by providing recommendations for the best RAT to use in the current network conditions and service requirements. This process of leveraging IMS diagnosis information, analyzing it at the server 200, and then providing personalized feedback to the UE 100 for model inference (inference sub-module 145b) and performance optimization is a crucial component of the training sub-module 145a and inference sub-module 145b within the UE 100. By continuously monitoring the IMS service quality and adapting the UE's configuration accordingly, the UE 100 may ensure optimal performance and user experience for the voice and data services.

[0099] FIG. 6 illustrates a block diagram of the server 200 for optimizing the performance of the UE in the wireless network, according to an embodiment as disclosed herein.

[0100] In one or more embodiments, the server 200 may include a memory 210, a processor 220, a communicator 230, and an AI-ML module 240.

[0101] The memory 210 stores 210 stores instructions to be executed by the processor 220 for optimizing the performance of the server 200 in the wireless network, as discussed throughout the disclosure. The memory 210 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory 210 may, in some examples, be considered a non-transitory storage medium. The term "non-transitory" may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted that the memory 210 is non-movable. In some examples, the memory 210 can be configured to store larger amounts of information than the memory. In certain examples, a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache). The memory 210 can be an internal storage unit, or it can be an external storage unit of the server 200, a cloud storage, or any other type of external storage.

[0102] The processor 220 communicates with the memory 210, the communicator 230, and the AI-ML module 240. The processor 220 is configured to execute instructions stored in the memory 210 and to perform various for optimizing the performance of the server 200in the wireless network, as discussed throughout the disclosure. The processor 220 may include one or a plurality of processors, maybe a general-purpose processor, such as a Central Processing Unit (CPU), an Application Processor (AP), or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a Visual Processing Unit (VPU), and / or an Artificial intelligence (AI) dedicated processor such as a Neural Processing Unit (NPU).

[0103] The communicator 230 is configured for communicating internally between internal hardware components and with external devices (e.g., UE 100) via one or more networks (e.g., radio technology). The communicator 230 includes an electronic circuit specific to a standard that enables wired or wireless communication.

[0104] In one or more embodiments, the AI-ML module 240 may include an IMS diagnostic receiver sub-module 241, a correlation sub-module 242, and a personalized feedback generator sub-module 243. The AI-ML module 240 is implemented by processing circuitry such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like.

[0105] In one or more embodiments, the IMS diagnostic receiver sub-module 241 is configured to receive the IMS diagnosis information from a plurality of UEs (e.g., 100A, 100b,..., 100N), including the UE 100, may include, for example, but is not limited to, the call preference information, the call state information, the migration performance information, and the network quality preference information. The IMS diagnostic receiver sub-module 241 is further configured to pass the received IMS diagnosis information to the correlation sub-module 242 for further processing, for example, as shown in Table-3. In Table-3, for example, the "call direction" category has two options: Mobile Originated (MO) call (1) and Mobile Terminated (MT) call (0). The "network type" category includes three options: NR (3), LTE (1), and Wi-Fi (0). The "roaming" category indicates whether the UE 100 is in roaming (1) or not in roaming (0).

[0106] MO / MTNW TYPEROAMINGERROR code1314801101401031" "1101803110" "

[0107] In one or more embodiments, the correlation sub-module 242 is configured to analyze the received IMS diagnosis information to identify one or more patterns, correlations, and potential issues affecting the performance of the UE 100. The correlation sub-module 242 is further configured to generate a correlation matrix and share it with the personalized feedback generator sub-module 243 for further processing.

[0108] The correlation sub-module 242 is configured to implement a dedicated function or method to perform the error code to IsCallPass conversion logic. The function may take the error code as an input parameter and return the corresponding IsCallPass value, where 0 indicates a failed call and 1 indicates a successful call, as shown in Table-4.

[0109] If the error code is not null or an empty string, then the call may be considered as failed, and the IsCallPass flag may be set to 0, indicating a failed call.

[0110] If the error code is null or an empty string, then the call may be considered as passed, and the IsCallPass flag may be set to 1, indicating a successful call.

[0111] This function or method may be implemented within the server's codebase to encapsulate the error code to IsCallPass conversion logic, ensuring consistent and reliable handling of call status determination.

[0112] MO / MTNW TYPEROAMINGISCallPassSAMPLE NUMBER1310111002031131100411015

[0113] In one or more embodiments, the personalized feedback generator sub-module 243 is configured to generate the personalized feedback based on the received correlation matrix by utilizing, for example, the random forest algorithm. The personalized feedback generator sub-module 243 is further configured to send the personalized feedback to the UE 100 to optimize the performance.In one or more embodiments, a function associated with the various components of the server 200 may be performed through the non-volatile memory, the volatile memory, and the processor 220. One or a plurality of processors controls the processing of the input data in accordance with a predefined operating rule or the AI-ML module 240 stored in the non-volatile memory and the volatile memory. The predefined operating rule or the AI-ML module 240 is provided through training or learning. Here, being provided through learning means that, by applying a learning algorithm to a plurality of learning data, a predefined operating rule or the AI-ML module 240 of the desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and / or may be implemented through a separate server / system. The learning algorithm is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to decide or predict. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0114] The AI-ML module 240 may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through a calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include, but are not limited to, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), Generative Adversarial Networks (GAN), and Deep Q-networks.

[0115] Although FIG. 4 shows various hardware components of the server 200, but it is to be understood that other embodiments are not limited thereon. In other embodiments, server 200 may include less or more number of components. Further, the labels or names of the components are used only for illustrative purposes and do not limit the scope of the invention. One or more components can be combined to perform the same or substantially similar functions to optimize the performance of the server 200.

[0116] FIG. 7A and Fig. 7B are sequential flow diagrams illustrating an example scenario for optimizing the performance of the UE 100 in the wireless network, according to an embodiment as disclosed herein. The UE 100 may execute multiple operations to optimize the performance, which are given below.

[0117] At operation 700, in the provided example scenario, when the user of the UE (100) dials a phone number (+91xxxx) using a dialer application, a call application forwards a request to a telephony layer. The telephony layer has two input parameters such as phone number and call type. The call type parameter can have two values: 1 for a voice call and 2 for a video call. Before initiating the call, the UE 100 performs various internal operations and checks to determine the appropriate call setup procedure, which may relate to operations 701, 702a, 702b, 702c, and 703.

[0118] At operations 701 to 703, the training sub-module 145a is configured to collect and process the IMS diagnosis information obtained by the UE 100. The IMS diagnosis information provides insights into the quality of the IMS services between the UE 100 and the IMS network. The UE 100 shares this IMS diagnosis information with the server 200, at the regular time interval or the predetermined time interval, which then analyzes it to identify potential issues affecting the UE's application performance or network / location / service quality. Based on this analysis, the server 200 generates personalized feedback for the UE 100. The inference sub-module 145b takes this personalized feedback and uses it to perform model inference. This involves leveraging the AI-ML module 145, which incorporates the random forest algorithm, to predict the best RAT for the current scenario or said preferred RAT / codec / data throttling. The AI-ML module 145 considers factors like preferred RAT, preferred codec, and preferred data throttling settings to optimize the UE's performance and user experience. This integrated approach of training and inference enables the UE 100 to adapt to changing network conditions and service requirements, ensuring optimal performance and reliability.

[0119] In one or more embodiments, inference sub-module 145b may execute various operations to determine preferred RAT, preferred codec, and preferred data throttling settings, which are given below. The inference sub-module 145b is configured to receive a default RAT information, audio, and video codec information supported by a current operator. The inference sub-module 145b is further configured to determine whether the UE 100 is in a roaming mode. The inference sub-module 145b is further configured to receive current cell ID, latitude, and longitude information associated with the UE 100. The inference sub-module 145b is further configured to evaluate a pass probability for all available RATs based on the received audio and video codec information, the determined roaming mode, the received current cell ID, latitude, and longitude information. The inference sub-module 145b is further configured to evaluate the data throttling (preferred data throttling settings) required probability based on the received audio and video codec information, the determined roaming mode, the received current cell ID, latitude, and longitude information. After that, the UE 100 performs various internal operations and checks to determine the appropriate call setup procedure, which may relate to operations 704, 705, 706, 707, and 708.

[0120] At operations 704 to 707, in the example scenario, based on the results of model interference, the UE 100 selects a 2G / 3G network as the preferred RAT. Additionally, the UE 100 chooses Adaptive Multi-Rate (AMR) and Enhanced Voice Services (EVS) as the preferred codecs, and implements preferred data throttling settings by pausing data usage in one or more applications on the UE 100. The IMS framework module 140 (IMS) then sends a Session Initiation Protocol (SIP) invite message to a modem of the UE 100, transmits the IMS diagnostic information to the server 200, and sends a CM service request to the 2G / 3G network. Upon sending the CM service request, the modem of the UE 100 receives a connect acknowledgement message from the 2G / 3G network. Subsequently, the telephony layer establishes a Real-Time Transport Protocol (RTP) streaming session with the 2G / 3G network.

[0121] At operations 704 and 708, in the example scenario, based on the results of model interference, the UE 100 selects an LTE / NR network as the preferred RAT. Additionally, the UE 100 chooses the AMR and the EVS as the preferred codecs, and implements preferred data throttling settings by pausing data usage in one or more applications on the UE 100. The modem then sends an invite message to the LTE / NR network. Upon sending the invite request, the modem of the UE 100 receives a 200 OK message from the LTE / NR network. Subsequently, the telephony layer establishes the RTP streaming session with the LTE / NR network. This integrated workflow demonstrates the UE's ability to dynamically adapt to network conditions and user preferences to ensure a seamless and optimized call experience.

[0122] FIG. 8 is a flow diagram illustrating a call flow mechanism 800, according to an embodiment as disclosed herein. At operation 801, the user of the UE 100 dials a number to establish a call with another user. At operation 802, the user makes a telephony request. In an embodiment, the telephony request may include a voice call and a video call. At operation 803, it is determined if the telephony request is the CS request. If yes, then at operation 811, a Session Initiation Protocol (SIP) packet associated with the call is sent over a modem, i.e., modem associated with a current RAT. If not, then at operation 804, an IMS invite request is created. Thereafter, at operation 805, the best RAT for the current scenario is predicted. Then, at operation 806, the model i.e., AI module is trained based on a feedback after the prediction. Simultaneously, at operation 807, it is determined if a modification is required in the IMS invite created at operation 804. If yes, the at operation 808, IMS invite request is changed. If not, then at operation 809, it is determined if the Wi-Fi bearer is selected. When the Wi-Fi bearer is selected, then the call flow reaches an operator through Wi-Fi network using the Wi-Fi calling and ePDG server of the operator. Accordingly, at operation 810, the SIP packet is transmitted over the Wi-Fi network. Otherwise, at operation 811, the SIP packet is transmitted using the modem. Further, in an embodiment, the operations 805, 807,and 808 are performed by the AI-ML module 145. Further, it should be noted that the call flow described with respect to Fig. 8 is performed by the UE 100.

[0123] FIG. 9 and Fig. 9B illustrate a comparison between a standard video call scenario 901 and a video call using the disclosed method 902, according to an embodiment as disclosed herein.

[0124] In the standard video call scenario 901, the UE 10A of a first user attempts to establish a video call with the UE 10B of a second user. As per the existing telecommunication systems, the UE 10A is capable of supporting both New Radio (NR) and Long-Term Evolution (LTE) RATs. However, due to a default modem protocol configuration, the UE 10A is initially latched or connected to the NR RAT. Given this initial state, the video call is established between the UE 10A and the UE 10B. Nevertheless, the video call requires a significant amount of network bandwidth, which is currently not available to the UE 10A in the NR RAT. As a result of the bandwidth limitation, the UE 10A is only able to transmit low-quality video frames to the UE 10B. Consequently, the video call is not set up properly, and the call quality is degraded. The inability to dynamically select the optimal RAT based on the current network conditions limits the call's reliability and responsiveness, potentially impacting overall communication quality and user satisfaction.

[0125] In the video call using the disclosed method 902, the disclosed method dynamically adjusts resource allocation for voice and data streams based on UE behavior and network conditions. For instance, the UE 100A of a first user determines that the optimal RAT is LTE and data throttling is required by utilizing the IMS framework module 140. Based on this determination, the UE 100A pauses data usage by other applications, ensuring the total available bandwidth is dedicated to the video call. As a result, a high-quality video call is established between the UE 100A and the UE 100B.

[0126] In one or more embodiments, the disclosed method performs a proactive network optimization to ensure optimal user experience by dynamically selecting the most appropriate RAT based on the user's location and network conditions, for example:

[0127] a. In a high-density area, where the available RATs include VoWiFi, LTE, and 5G, with the default network registration on 5G, the disclosed method selects VoWiFi to provide better quality of service to the user.

[0128] b. As the user moves to an open area, where the available RATs change to LTE and 5G, with the default network registration remaining on 5G, the disclosed method seamlessly transitions the user to the 5G network to maintain the quality of service.

[0129] c. As the user moves to an area with significant signal attenuation, such as a basement parking, where the available RATs are 2G and LTE, with the default network registration on LTE, the disclosed method intelligently selects the 2G network to ensure the user maintains connectivity and optimal service quality.

[0130] This proactive network optimization technique enables the user to experience uninterrupted, high-quality connectivity by dynamically adapting to the changing network conditions and selecting the most suitable RAT to meet the user's requirements.

[0131] FIG. 10 is a flow diagram illustrating a method 1000 for optimizing the performance of the UE 100 in the wireless network, according to an embodiment as disclosed herein. The flow diagram includes several operations outlined as follows.

[0132] At operation 1001, the method 1000 includes obtaining the IMS diagnosis information at the UE 100 comprising at least one of the call preference information, the call state information, the migration performance information, and the network quality preference information. At operation 1002, the method 1000 includes transmitting the obtained IMS diagnosis information to the server 200 associated with the wireless network. At operation 1003, the method 1000 includes receiving the personalized feedback from the server 200 in response to transmitting the obtained IMS diagnosis information. At operation 1004, the method 1000 includes applying the received personalized feedback at the UE 100 to optimize the performance of the UE 100. Further, a detailed description related to the various steps of FIG. 10 is covered in the description related to FIG. 4, FIG. 5A, FIG. 5B, FIG. 7A, Fig. 7B, FIG. 8, FIG. 9A and Fig. 9B, and is omitted herein for the sake of brevity.

[0133] FIG. 11 is a flow diagram illustrating a method 1100 for optimizing the performance of the UE 100 in the wireless network, according to another embodiment as disclosed herein. The flow diagram includes several operations outlined as follows.

[0134] At operation 1101, the method 1100 includes receiving, at the server 200, the IMS diagnosis information from the plurality of UEs, including the UE 100, comprising at least one of the call preference information, the call state information, the migration performance information, and the network quality preference information. At operation 1102, the method 1100 includes analyzing, by utilizing at least one AI-ML module 240, the received IMS diagnosis information to identify the one or more patterns, correlations, and potential issues affecting the performance of the UE 100. At operation 1103, the method 1100 includes sending, based on a result of the analysis, personalized feedback to the UE 100 to optimize the performance. Further, a detailed description related to the various steps of FIG. 11 is covered in the description related to FIG. 6, FIG. 7A, Fig. 7B, FIG. 8, FIG. 9A and Fig. 9B, and is omitted herein for the sake of brevity.

[0135] The disclosed method(s) has several advantages over the existing telecommunication systems, for example, which are stated below.

[0136] a. Adjusting resource allocation for voice and data streams:

[0137] i. Prioritizing bandwidth for critical communication;

[0138] ii. Throttle background data in low-signal areas; and

[0139] iii. Optimizing codec selection for improved call quality.

[0140] b. Predictive maintenance and anomaly detection:

[0141] i. Optimizing network configuration of the UE 100 or UE's before experiencing any problem.

[0142] c. Application-specific:

[0143] i. IMS configuration / adjustment (using pattern / network condition);

[0144] ii. Prioritize bandwidth for streaming applications;

[0145] iii. Latency control for game applications; and

[0146] iv. Optimize battery consumption for background processes.

[0147] The various actions, acts, blocks, steps, or the like in the sequence / flow diagrams may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some of the actions, acts, blocks, steps, or the like may be omitted, added, modified, skipped, or the like without departing from the scope of the invention.

[0148] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one ordinary skilled in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0149] While specific language has been used to describe the present subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method to implement the inventive concept as taught herein. The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.

[0150] The embodiments disclosed herein can be implemented using at least one hardware device and performing network management functions to control the elements.

[0151] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

Claims

1.A method for optimizing a performance by a user equipment, UE, (100) in a wireless network, the method (1000) comprising:obtaining (1001) IP Multimedia Subsystem, IMS, diagnosis information at the UE (100);transmitting (1002) the obtained IMS diagnosis information to a server (200);receiving (1003) feedback from the server (200) in response to transmitting the obtained IMS diagnosis information; andapplying (1004) the received feedback at the UE (100) to optimize the performance of the UE (100).2.The method of claim 1, wherein the IMS diagnosis information comprises at least one of call preference information, call state information, migration performance information, or network quality preference information.3.The method of claim 2, wherein the call preference information comprises at least one of call setup time information and call termination time information,wherein the call state information comprises at least one of ongoing call state information, incoming call state information during a success or a failure, call rate information, and call type count information,wherein the migration performance information comprises at least one of Evolved Packet Data Gateway, ePDG, status information, cross Subscriber Identity Module, SIM, information, Voice over Wireless Fidelity, VoWIFI, preference information, Single Radio Voice Call Continuity, SRVCC, count information, Circuit-Switched Fallback, CSFB, count information, Evolved Packet System Fallback, EPSFB, count information and forward call count information,wherein the network quality preference information comprises at least one of Reference Signal Received Power, RSRP, information, Reference Signal Received Quality, RSRQ, information, data roaming information, call failure count information, call drop count information, forward call count information and downgrade count information, andwherein the IMS diagnosis information comprises device information associated with the UE (100).4.The method of claim 2, wherein the IMS diagnosis information is determined at a regular time interval or a predetermined time interval.5.The method of claim 1, wherein the IMS diagnosis information is transmitted for analysis by the server (200) to identify at least one of one or more patterns, correlations, and potential issues affecting the performance of the UE (100).6.The method of claim 1, wherein applying the received feedback at the UE (100) to optimize the performance of the UE (100) comprises:dynamically configuring, based on the received feedback, one or more IMS services associated with at least one of a voice transmission or a data transmission associated with the UE (100) by utilizing at least one Artificial Intelligence- Machine Learning, AI-ML, module (145), wherein the one or more IMS services comprise call priority information and codec selection information; andoptimizing the performance of the UE (100) based on the one or more dynamically configured IMS services.7.The method of claim 1, wherein applying the received feedback at the UE (100) to optimize the performance of the UE (100) comprises:dynamically configuring, based on the received feedback, one or more application states associated with the UE (100) by utilizing at least one Artificial Intelligence- Machine Learning, AI-ML, module (145), wherein the one or more application states comprise a foreground state, a background state, a suspended state, and a terminated state; andoptimizing the performance of the UE (100) based on the one or more dynamically configured application states.8.The method of claim 1, wherein applying the received feedback at the UE (100) to optimize the performance of the UE (100) comprises:selecting, based on the received feedback, an optimal Radio Access Technology (RAT) for at least one of voice transmission or data transmission by utilizing at least one Artificial Intelligence- Machine Learning, AI-ML, module (145), andoptimizing the performance of the UE (100) based on the selected optimal RAT.9.The method of claim 1, wherein applying the received feedback at the UE (100) to optimize the performance of the UE (100) comprises:determining, based on the received feedback, an optimal bandwidth for at least one of voice transmission or data transmission associated with the UE (100) by utilizing at least one Artificial Intelligence- Machine Learning, AI-ML, module (145); andoptimizing the performance of the UE (100) based on the determined optimal bandwidth.10.The method of claim 1, further comprising initiating a call, comprising:obtaining a default Radio Access Technology, RAT, information;obtaining audio and video codec information supported by a current operator;determining whether the UE (100) is in a roaming mode;obtaining a current cell ID, latitude, and longitude information;evaluating a pass probability for all available RATs based on the obtained audio and video codec information, the determined roaming mode, the obtained current cell ID, latitude, and longitude information; andevaluating a data throttling required probability based on the obtained audio and video codec information, the determined roaming mode, the obtained current cell ID, latitude, and longitude information.11.The method (1000) as claimed in claim 10, wherein the default RAT information, the audio and video codec information, the roaming mode, the current cell ID, the latitude, and the longitude are input to an inference model prior to initiating the call.12.A method for optimizing a performance of a user equipment, UE, (100) by a server (200) in a wireless network, the method comprising:receiving (1101) IP Multimedia Subsystem, IMS, diagnosis information from a plurality of UEs, including the UE (100);analyzing (1102), by utilizing at least one Artificial Intelligence - Machine Learning, AI-ML, module, the received IMS diagnosis information to identify at least one of one or more patterns, correlations, or potential issues affecting the performance of the UE (100); andsending (1103), based on a result of the analysis, feedback to the UE (100) to optimize the performance.13.The method of claim 12, wherein analyzing the received IMS diagnosis information comprises:preprocessing the received IMS diagnosis information; andtraining the at least one AI module on the preprocessed IMS diagnosis information to learn one or more patterns, correlations, and potential issues within the preprocessed IMS diagnosis information.14.A user equipment, UE, (100) for optimizing a performance of the UE (100) in a wireless network, the UE (100) comprising:memory (110) storing instructions; andat least one processor (120),wherein the instructions, when executed by the at least one processor (120), cause the UE (100) to perform operations comprising:obtaining IP Multimedia Subsystem, IMS, diagnosis information at the UE (100);transmitting the obtained IMS diagnosis information to a server (200);receiving feedback from the server (200) in response to transmitting the obtained IMS diagnosis information; andapplying the received feedback at the UE (100) to optimize the performance of the UE (100).15.The UE of claim 14, wherein the operations comprises at least one operation according to a method in one of claims 2 to 11.

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