DESC:FORM 2
THE PATENTS ACT, 1970 (39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)

SYSTEM AND METHOD FOR SELECTING AN OPTIMAL COMMUNICATION NETWORK

Jio Platforms Limited, an Indian company, having registered address at Office -101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India

The following complete specification particularly describes the disclosure and the manner in which it is performed.

TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field of wireless communication networks. More particularly, the present disclosure relates to a system and a method for selecting an optimal communication network.
BACKGROUND OF THE INVENTION
[0002] The subject matter disclosed in the background section should not be assumed or construed to be prior art merely due to its mention in the background section. Similarly, any problem statement mentioned in the background section or its association with the subject matter of the background section should not be assumed or construed to have been previously recognized in the prior art.
[0003] The rapid expansion of telecommunication systems using different technologies has transformed the always-best connected phenomenon into a reality. In order to ensure optimal connectivity at an affordable bandwidth cost, network operators collaborate with each other to provide efficient and flexible network capacities for users, offering a diverse data-transmission rates and costs. In order to ensure optimal connectivity for users, it is essential that the users are always connected to the best available network and access technology. The best available network and the access technology are assessed based on user preferences, size and capabilities of the network, application requirements, security, operator or corporate policies, available network resources, and network coverage. Based on the applications and user preferences, the users are connected over one access point at a time or over multiple accesses points in parallel.
[0004] Due to recent technological advancements, the number of users connecting to the network are massively increased. The increase in the number of users results in increased network traffic causing an overload at the network side. consequently, the users experience poor signal quality and signal strength. When the user is in a location where the signal strength is very weak, then the user faces challenges in finding an available network and connecting to the available network. This issue is more prominent in rural areas where the network coverage is very poor. Additionally, in urban areas comprising a greater number of users, the user faces difficulty in selecting the network due to increased network traffic and improper connectivity. Therefore, the users need to choose the best network based on their location, signal strength, network connectivity, bandwidth, coverage, cost, reliability, latency, and security.
[0005] In conventional systems, determining the optimal network involves several factors. As there are different types of communications services are available over a network, connectivity is provided by various physical channels, such as coaxial cable, fiber optics, phone lines, satellite networks, or cellular networks. Further, even if at least if one type of communication service is available, the upload and download speeds vary from one location to another. Hence, determining the optimal network at a location requires coordination among many different systems or network providers, which results in significant delays in the selection and implementation of suitable communication service.
[0006] In light of the aforementioned challenges and considerations, there is a need for an improved system and a method for selecting an optimal communication network that provides better signal strength, network connectivity, bandwidth, and reliability.
SUMMARY
[0007] The following embodiments present a simplified summary in order to provide a basic understanding of some aspects of the disclosed invention. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
[0008] In an embodiment, a method for selecting an optimal communication network is disclosed. The method includes transmitting, by a transmission module, via one of an online mode or an offline mode, a network service request to an application server. The method further includes receiving, by a reception module, network information of each of a plurality of available networks from the application server based on the network service request. Further, the method includes selecting, by a selection module, an optimal communication network among the plurality of available networks based on the network information of each of the plurality of available networks.
[0009] In some aspects of the present disclosure, in the online mode, the network service request is transmitted via an application installed on a User Equipment (UE).
[0010] In some aspects of the present disclosure, in the offline mode, the network service request is transmitted as a text message.
[0011] In some aspects of the present disclosure, in the online mode, the network service request comprises information of one or more locations to identify the optimal communication network at the one or more locations.
[0012] In some aspects of the present disclosure, in the offline mode, the network service request comprises information of one or more locations to receive recommendations for available networks at the one more or locations.
[0013] In another embodiment, disclosed is a system for selecting an optimal communication network. The system includes a transmission module configured to transmit, via one of an online mode or an offline mode, a network service request to an application server. The system further includes a reception module configured to receive network information of each of a plurality of available networks from the application server based on the network service request. Further, the system includes a selection module configured to select an optimal communication network among the plurality of available networks based on the network information of each of the plurality of available networks.
[0014] In another embodiment, a method for transmitting network information of a plurality of available networks is disclosed. The method includes receiving, by a reception module of an application server, a network service request from a User Equipment (UE). The method further includes analyzing, by an analyzing module using a Machine Learning (ML) model based on the network service request, user preferences and network usage patterns associated with the UE to determine the plurality of available networks. Further, the method includes acquiring, by an acquiring module from a database, network information of each of the determined plurality of the available networks. Furthermore, the method includes transmitting, by a transmission module to the UE, the network information of each of the determined plurality of available networks.
[0015] In some aspects of the present disclosure, in an online mode, the network service request is received via an application installed on the UE.
[0016] In some aspects of the present disclosure, in an offline mode, the network service request is received as a text message.
[0017] In some aspects of the present disclosure, in the online mode, the network service request comprises information of one or more locations to identify the optimal communication network at the one or more locations.
[0018] In some aspects of the present disclosure, in the offline mode, the network service request comprises information of the one or more locations to receive recommendations for available networks at the one or more locations.
[0019] In another embodiment, disclosed is a system for transmitting network information of a plurality of available networks. The system includes a reception module configured to receive, by an application server, a network service request from a User Equipment (UE). The system further includes an analysis module configured to analyze, using a Machine Learning (ML) model upon receiving the network service request, user preferences and network usage patterns associated with the UE to determine the plurality of available networks. Further, the system includes an acquiring module configured to acquire, from a database, network information of each of the plurality of the available networks. Furthermore, the system includes a transmission module configured to transmit, to the UE, the network information of each of the plurality of available networks.
BRIEF DESCRIPTION OF DRAWINGS
[0020] Various embodiments disclosed herein will become better understood from the following detailed description when read with the accompanying drawings. The accompanying drawings constitute a part of the present disclosure and illustrate certain non-limiting embodiments of inventive concepts disclosed herein. Further, components and elements shown in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. For the purpose of consistency and ease of understanding, similar components and elements are annotated by reference numerals in the exemplary drawings.
[0021] FIG. 1 illustrates a diagram depicting an environment of a communication network, in accordance with an embodiment of the present disclosure.
[0022] FIG. 2 illustrates a block diagram of a system for selecting an optimal communication network, in accordance with an embodiment of the present disclosure.
[0023] FIG. 3 illustrates a block diagram depicting communication between a database, a server, and user equipment, in accordance with an embodiment of the present disclosure.
[0024] FIG. 4 illustrates a process flow diagram depicting a method for selecting the optimal communication network, in accordance with an embodiment of the present disclosure.
[0025] FIG. 5 illustrates a process flow diagram depicting a method for transmitting network information of available networks, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE INVENTION
[0026] Inventive concepts of the present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which examples of one or more embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Further, the one or more embodiments disclosed herein are provided to describe the inventive concept thoroughly and completely, and to fully convey the scope of each of the present inventive concepts to those skilled in the art. Furthermore, it should be noted that the embodiments disclosed herein are not mutually exclusive concepts. Accordingly, one or more components from one embodiment may be tacitly assumed to be present or used in any other embodiment.
[0027] The following description presents various embodiments of the present disclosure. The embodiments disclosed herein are presented as teaching examples and are not to be construed as limiting the scope of the present disclosure. The present disclosure should in no way be limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified, omitted, or expanded upon without departing from the scope of the present disclosure.
[0028] The following description contains specific information pertaining to embodiments in the present disclosure. The detailed description uses the phrases “in some embodiments” which may each refer to one or more or all of the same or different embodiments. The term “some” as used herein is defined as “one, or more than one, or all.” Accordingly, the terms “one,” “more than one,” “more than one, but not all” or “all” would all fall under the definition of “some.” In view of the same, the terms, for example, “in an embodiment” refers to one embodiment and the term, for example, “in one or more embodiments” refers to “at least one embodiment, or more than one embodiment, or all embodiments.”
[0029] The term “comprising,” when utilized, means “including, but not necessarily limited to;” it specifically indicates open-ended inclusion in the so-described one or more listed features, elements in a combination, unless otherwise stated with limiting language. Furthermore, to the extent that the terms “includes,” “has,” “have,” “contains,” and other similar words are used in either the detailed description, such terms are intended to be inclusive in a manner similar to the term “comprising.”
[0030] In the following description, for the purposes of explanation, various specific details are set forth to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features.
[0031] The description provided herein discloses exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the foregoing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing any of the exemplary embodiments. Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it may be understood by one of the ordinary skilled in the art that the embodiments disclosed herein may be practiced without these specific details.
[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein the description, the singular forms "a", "an", and "the" include plural forms unless the context of the disclosure indicates otherwise.
[0033] The terminology and structure employed herein are for describing, teaching, and illuminating some embodiments and their specific features and elements and do not limit, restrict, or reduce the scope of the present disclosure. Accordingly, unless otherwise defined, all terms, and especially any technical and/or scientific terms, used herein may be taken to have the same meaning as commonly understood by one having ordinary skill in the art.
[0034] The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
[0035] Various aspects of the present disclosure provide a system and a method for selecting an optimal communication network.
[0036] In another aspect of the present disclosure, the system and the method provide network information about available networks.
[0037] In another aspect of the present disclosure, the system and the method enable a user to find the optimal network among the available networks in the location when a user equipment is in an online mode or an offline mode.
[0038] In another aspect of the present disclosure, the system and the method utilize machine learning models to analyze user preferences and network usage patterns to recommend the best available networks in a location.
[0039] The present disclosure relates to a system and a method for selecting an optimal communication network. A User Equipment (UE) may latch on to a network with optimal connectivity and signal strength. The recent developments in the telecommunication networks have increased the usage of data services provided by the network than text and voice services provided by the network. Hence, users may install applications in the UE to find an optimal network service provider using the data service of the network. This may facilitate users in the online mode to find a suitable network for getting best signal strength and connectivity. However, in conventional systems, the user in the offline mode may not be able to use the data services provided by the network. Hence, the user in the offline mode often faces challenges in finding a suitable network. Therefore, there is a need for a solution that enables the user to find an optimal network service provider both in the online mode and the offline mode.
[0040] FIG. 1 illustrates a diagram depicting an environment of a communication network 100, in accordance with an embodiment of the present disclosure.
[0041] The wireless communication network 100 includes coverage regions 106-1 to 106-N (hereinafter cumulatively referred to as the coverage region 106). The coverage region 106 is served by one or more Base Stations (BSs) 102-1 to 102-N. Each base station among the BSs 102-1 to 102-N may have same or similar configuration and may also be referred to as “BS 102” or “network node 102”. The BSs 102-1 to 102-N serves one or more User Equipment (UEs) 104-1 to 104-N in the coverage region 106. Each user equipment among the UEs 104-1 to 104-N may have same or similar configuration and may also be referred to as “UE 104”. The BSs 102-1 to 102-N are connected to a network 108 to provide one or more services to the UEs 104-1 to 104-N. The wireless communication network 100 further includes a server 110 connected to the network 108. The server 110 is configured to execute data processing and data storing operations to acquire network information of the available networks and transmit the acquired network information to the UE 104.
[0042] The BS 102 may be at least one relay, and at least one Distributed Unit (DU). Typically, the BS 102 may be a network infrastructure that provides wireless access to one or more terminals. The BS 102 has coverage defined to be a predetermined geographic area based on the distance over which a signal may be transmitted. The BS 102 may be referred to as, in addition to “base station”, “network node” “access point (AP)”, “evolved NodeB (eNodeB or eNB)”, “5G node (5th generation node)”, “next generation NodeB (gNB)”, “wireless point”, “transmission/reception point (TRP)”, “Radio Access Network (RAN)” or other terms having equivalent technical meanings.
[0043] The UE 104 may be, at least one DU, at least one Mobile Termination (MT) unit, and at least one relay. Typically, the term “user equipment” or “UE” can refer to any component such as “mobile station”, “subscriber station”, “remote terminal”, “wireless terminal”, “receive point”, or “end user device”. The at least one second node may access the at least one first node.
[0044] The network 108 may include suitable logic, circuitry, and interfaces that may be configured to provide several network ports and several communication channels for transmission and reception of data related to operations of various entities of the wireless communication network 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The network 108 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the various entities of the wireless communication network 100.
[0045] The communication data may be transmitted or received via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. In some aspects of the present disclosure, the communication data may be transmitted or received via at least one communication channel of several communication channels in the network 108. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, an optical fiber network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.
[0046] The server 110 may be a network of computers, a software framework, or a combination thereof, that may provide a generalized approach to create a server implementation. Examples of the server 110 may include, but are not limited to, personal computers, laptops, mini-computers, mainframe computers, any non-transient and tangible machine that can execute a machine-readable code, cloud-based servers, distributed server networks, or a network of computer systems. The server 110 may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a personal home page (PHP) framework, or any web-application framework. In other aspects of the present disclosure, the server 110 may be configured to execute one or more data processing and/or storage operations to acquire network information of the available networks and transmit the acquired network information to the UE 104.
[0047] FIG. 2 illustrates a block diagram of a system 200 for selecting an optimal communication network 100, in accordance with an embodiment of the present disclosure. The system 200 may include a network 108, a group of wireless network nodes or BS 102 (collectively referred to as BS 102) connected to the network 108, a group of user equipment (UEs) 104-1 to 104-N (collectively referred to as UE 104) connected to the network 108 via the group of wireless network nodes 102, the server 110, a Network Management System (NMS) 230, and an external database 228 (hereinafter referred to as the database 228). FIG. 2 shows a group of wireless network nodes or BS 102 and a group of UE 104 to simplify the illustration as each BS among the BSs 102-1 to 102-N have same or similar configuration and each user equipment among the UEs 104-1 to 104-N have same or similar configuration.
[0048] The server 110 includes a communication interface 202, a processor 204, a memory 206 coupled to the processor 204, and a server database 208. The processor 204 may control the operation of the server 110. The processor 204 may also be referred to as a Central Processing Unit (CPU). The memory 206 may provide instructions and data to the processor 204 for performing functions of the server 110. The memory 206 may include a Random Access Memory (RAM), a Read-Only Memory (ROM) and a portion of the memory 206 may also include Non-Volatile Random Access Memory (NVRAM). The processor 204 may perform logical and arithmetic operations based on instructions stored within the memory 206. The communication interface 202 may allow transmission and reception of data between the server 110 and the network 108. The communication interface 202 may include a transmitter, a receiver, and a single or multiple transmit antennas electrically coupled to the transmitter and the receiver of the communication interface 202.
[0049] The communication interface 202 may be configured to enable the server 110 to communicate with various entities of the system 200 via the network 108. Examples of the communication interface 202 may include, but are not limited to, a modem, a network interface such as an Ethernet card, a communication port, and/or a Personal Computer Memory Card International Association (PCMCIA) slot and card, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and a local buffer circuit. It will be apparent to a person of ordinary skill in the art that the communication interface 202 may include any device and/or apparatus capable of providing wireless or wired communications between the server 110 and various other entities of the system 200.
[0050] In some aspects of the present disclosure, the server 110 may be coupled to the database 228 that provides data storage space to the server 110. The database 228 may store information related to configuration parameters, details related to the BS 102 and other relevant information needed for the operation of the server 110. The database 228 may correspond to a centralized database system configured to store and manage structured data, such as network-related data and configurations. The database 228 may be a relational database organizing related data such as in a table, or a non-relational database organizing graphical and time series data.
[0051] The UE 104 may include a user interface 210, a processor 212, a communication interface 214, and a memory 216 coupled to the processor 212. The processor 212 may control the operation of the UE 104. The processor 212 may also be referred to as the CPU. The memory 216 may provide instructions and data to the processor 212 for performing several functions. The processor 212 may perform logical and arithmetic operations based on instructions stored within the memory 216.
[0052] The memory 216 may include a Random Access Memory (RAM) 218, a Read-Only Memory (ROM) 220, and a portion of the memory 216 may also include non-volatile random-access memory (NVRAM). The memory 216 may include an Operating System (OS) 222 for serving as an interface between the hardware components and the UE 104. The memory 216 may comprise one or more applications 224 (may also be referred to as “application 224”) and a database 226 to store data based on the one or more applications 224. The OS 222 may provide functions to and support communication standards for the UE 104. The OS 222 may also provide a common programming platform or executing environment for the one or more applications 224.
[0053] The communication interface 214 may allow transmission and reception of data between the UE 104 and the network 108. The communication interface 214 may include a transmitter, a receiver, and a single or multiple transmit antennas electrically coupled to the transmitter and the receiver of the communication interface 214. The one or more computer executable applications may be stored on the UE 104.
[0054] The user interface 210 may include suitable logic, circuitry, interfaces, and/or codes that may be configured to receive input(s) and present (or display) output(s) on the UE 104. For example, the user interface 210 may have an input interface and an output interface. The input interface may be configured to enable a user to provide input(s) to trigger (or configure) the UE 104 to perform various operations. Examples of the input interface may include, but are not limited to, a touch interface, a mouse, a keyboard, a motion recognition unit, a gesture recognition unit, a voice recognition unit, or the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the input interface including known, related art, and/or later developed technologies without deviating from the scope of the present disclosure. The output interface may provide the output(s) based on an instruction provided via the user interface 210. Examples of the output interface of the user interface 210 may include, but are not limited to, a digital display, an analog display, a touch screen display, an appearance of a desktop, and/or illuminated characters.
[0055] The processors 204 and 212 may include one or more general purpose processors and/or one or more special purpose processors, a microprocessor, a digital signal processor, an application specific integrated circuit, a microcontroller, a state machine, or ay any type of programmable logic array. The processors 204 and 212 may include may include an intelligent hardware device including a general-purpose processor, such as, for example, and without limitation, a Central Processing Unit (CPU), an Application Processor (AP), a dedicated processor, or the like, a graphics-only processing unit such as a Graphics Processing Unit (GPU), a microcontroller, a Field-Programmable Gate Array (FPGA), a programmable logic device, a discrete hardware component, or any combination thereof. The processors 204 and 212 may be configured to execute computer-readable instructions stored in the memories 206 and 216 to cause the server 110 to perform various functions.
[0056] The memories 206 and 216 may further include, but not limited to, non-transitory machine-readable storage devices such as hard drives, magnetic tape, floppy diskettes, optical disks, compact disc read-Only Memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, RAMS, programmable read-only memories PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions.
[0057] In addition, the memory may, in some examples, be considered a non-transitory storage medium. The "non-transitory" storage medium is not embodied in a carrier wave or a propagated signal. However, the term "non-transitory" should not be interpreted as the memory is non-movable. In some examples, the memory may be configured to store larger amounts of information. 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 may be an internal storage unit or an external storage unit of the server, cloud storage, or any other type of external storage.
[0058] Embodiments of the present technology may be described herein with reference to flowchart illustrations of methods and systems according to embodiments of the technology, and/or procedures, algorithms, steps, operations, formulae, or other computational depictions, which may also be implemented as computer program products. In this regard, each block or step of the flowchart, and combinations of blocks (and/or steps) in the flowchart, as well as any procedure, algorithm, step, operation, formula, or computational depiction can be implemented by various means, such as hardware, firmware, and/or software including one or more computer program instructions embodied in computer-readable program code. As will be appreciated, any such computer program instructions may be executed by one or more computer processors, including without limitation a general-purpose computer or special purpose computer, or other programmable processing apparatus to perform a group of operations comprising the operations or blocks described in connection with the disclosed methods.
[0059] Further, these computer program instructions, such as embodied in computer-readable program code, may also be stored in one or more computer-readable memory or memory devices (for example, the memories 206 and 216) that can direct a computer processor or other programmable processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory or memory devices produce an article of manufacture including instruction means which implement the function specified in the block(s) of the flowchart(s).
[0060] It will further be appreciated that the term “computer program instructions” as used herein refer to one or more instructions that can be executed by the one or more processors (for example, the processors 204 and 212) to perform one or more functions as described herein. The instructions may also be stored remotely such as on a server, or all or a portion of the instructions can be stored locally and remotely.
[0061] Although FIG. 1 and FIG. 2 illustrate one example of the environment of the wireless communication network 100 and the system 200, various changes may be made to FIG. 1 and FIG. 2. For example, the system 200 may include any number of user devices in any suitable arrangement. Further, in another example, the server 110 may include any number of components in addition to the components shown in FIG. 2. Further, various components in FIG. 1 and FIG. 2 may be combined, further subdivided, or omitted and additional components may be added according to particular needs.
[0062] In one embodiment of the present disclosure, when the UE 104 is in the online mode, the user may utilize a network finding application to find a suitable network. A Software Development Kit (SDK) for finding the suitable network may be developed and integrated into the application 224 installed in the UE 104 for selecting the optimal network among the available networks. The network finding application may be installed in the application 224 of the UE 104 via the SDK. Further, when the UE 104 is in the offline mode, the UE 104 may send specific locations as text messages to a server to find the suitable network. This further enables the user to easily find the best network in any location. The application 224 may enable the UE 104 to find internet access in specific locations when the UE 104 is in the online mode. The application 224 may be helpful to the UE to find the best network available to the UE.
[0063] The network finding application may be designed to run in the background so that the operation of the UE 104 is not impacted. The network finding application may offer several advantages to the UE 104 by enhancing the connectivity experience and addressing specific needs. The network finding application may be integrated with location-based services to allow the UE 104 to discover networks available in their vicinity.
[0064] In some aspects of the present disclosure, the processor 212 of the UE 104 may comprise one or more modules such as, a transmission module 240, a reception module 242, and a selection module 244. When the UE 104 is in the online mode, the transmission module 240 of the UE 104 may be configured to send a network service request as location information to the server 110. The reception module 242 may be configured to receive network information of the available networks in an ascending order from the server 110 based on the network service request. The network information may be a list of names of the available networks sorted in the ascending order. The selection module 244 may be configured to select the optimal network among the available networks.
[0065] In some embodiments, when the UE 104 is in the online mode, the network service request comprises information of geographical locations and service requirements to identify the optimal communication network at the geographical locations. The service requirements may include one or more service providers and/or wireless technology preferred. For example, the wireless technology may be a Fourth Generation (4G), a Fifth Generation (5G), or a Sixth Generation (6G) technology. Based on the requirement of the user, the application 224 installed in the UE 104 may send the preferred geographical locations and the service requirements to the server 110. When the UE 104 is in the offline mode, the network service request may comprise information of geographical locations to receive recommendations for the available networks at the preferred geographical locations. Since the UE 104 is in the offline mode, the service requirements may not be included in the network service request. The UE 104 may send the request as a text message to the server 110.
[0066] In some aspects of the present disclosure, the processor 204 of the server 110 may comprise one or modules such as, a reception module 232, an analysis module 234, an acquiring module 236, and a transmission module 238. The reception module 232 may be configured to receive the network service request from the UE 104. When the UE 104 sends the request in the online mode, the reception module 232 may be configured to receive the request comprising the geographical locations and the service requirements from the UE 104. When the UE 104 sends the request in the offline mode, the reception module 232 may be configured to receive the request comprising the geographical locations.
[0067] The analysis module 234 may be configured to utilize machine learning models deployed in the server 110 to analyze user preferences and network usage patterns to provide intelligent recommendations about the best network. The analysis module 234 may be configured to determine the network information in the form of a list of the available networks based on the user preferences and network usage patterns to respond back to the network service request from the UE 104. The acquiring module 236 may acquire/retrieve the network information from the database 228 for the list of the available networks.
[0068] In some aspects of the present disclosure, the analysis module 234 may utilize the machine learning models as Machine Learning as a Service (MLaaS) deployed in cloud-based servers. The MLaaS is group of services that provide machine learning tools as a constituent of cloud computing services. Service provider in MLaaS provide tools such as deep learning, data visualization, predictive analysis, recognitions for training, evaluation, and pre-processing.
[0069] The transmission module 238 may be configured to transmit a network service response comprising the network information comprising the list of the available networks in the ascending order. The UE 104 may choose the optimal network based on the network information comprising the list of the available networks provided by the server 110.
[0070] FIG. 3 illustrates a block diagram 300 depicting communication between the database 228, the server 110, and the user equipment 104-1 to 104-N, in accordance with an embodiment of the present disclosure.
[0071] In one embodiment, the UE 104 may be installed with the application 224 for performing finding an optimal network. The application 224 may be integrated in the OS 222 of the UE 104. The application 224 may be configured to run in the background so that the operations of the UE 104 may not be impacted. When the UE 104 is in the online mode, the application 224 installed in the UE 104-1 to 104-N may send the network service request to the server 110 via a load balancer 302. The load balancer 302 may be configured to balance and evenly distribute network service requests from the UE 104-1 to 104-N to the server 110. In particular, the load balancer 302 may be configured to act as a reverse proxy and may distribute network or application traffic across one or more servers 110. Further, the load balancer 302 may create balance between the load from the UE 104-1 to 104-N and may ensure that each server 110 receives an appropriate amount of data from the UE 104-1 to 104-N. More specifically, the load balancer 302 may prevent the server 110 from bearing too much load, thereby preventing failing of, or slowing down of the application.
[0072] In some embodiments, when the UE 104 is in the offline mode, the UE 104 may send specific locations as a text message to the server 110 to find the best network nearest to the UE 104. For example, the UE 104 may send the text message to the server 110 as a plain text with one or more Attention Commands (AT). The UE 104 may also send an AT+COPS command as the plain text message to the server 110. The UE 104 may send the network service request as the text message comprising one or more locations and the service requirements to the server 110.
[0073] The server 110 may be configured to receive the network service requests from the UE 104 both in the online mode and the offline mode. The server 110 may retrieve network information of the available networks in the location of the UE 104 from the external database 228. The server 110 may utilize machine learning models to provide intelligent recommendations about the best available networks among the available networks in the location of the UE 104. Upon receiving the recommendations from the server 110, the UE 104 may select the optimal network among the best available networks.
[0074] The UE 104 may choose the optimal network based on the network information comprising one or more signal parameters associated with signal quality and signal power. The one or more signal parameters may comprise Carrier-to-Interference ratio (C/I), Signal-to-Interference-plus-Noise Ratio (SINR), Received Signal Strength Indication (RSSI), Received Signal Code Power (RSCP), Reference Signal Received Power (RSRP), and Reference Signal Received Quality (RSRQ).
[0075] The C/I ratio is the ratio between a desired carrier (C) and an interfering carrier (I) received by the UE 104. The SINR is defined as a ratio of the signal power to a sum of interference and noise power, determining a minimum required value for successful packet reception in the wireless communication network. The RSRP denotes a linear average of reference signal power in resource elements that carry cell-specific reference signals within considered measurement frequency bandwidth. The RSSI is a measurement of total received power observed by the UE 104 over a specific bandwidth. The RSSI is used as an indicator of the signal strength in conjunction with performance metrics like the RSRP and the RSRQ. The RSCP denotes the power measured by UE 104 on a particular physical communication channel and reported to the BS 102. The RSRQ is a quality metric represented as a ratio of the RSRP to the total RSSI in a measured bandwidth. In particular, the RSRQ indicates a quality of the signal relative to interference and noise. The server 110 may receive the values of the one or more signal parameters of each network node 102 in the network 108. The server 110 may store the values of the one or more signal parameters as system information in the database 228.
[0076] The values of the one or more signal parameters of each network node 102 in the network 108 may be stored in the database 228 based on each geographical location. The UE 104 may transmit the network service request comprising the geographical location and the service requirements to the server 110 for obtaining the list of available networks in the geographical location. The server 110 may retrieve the values of the one or more signal parameters from the database 228 for the requested geographical location. The server 110 may determine whether the values of the one or more signal parameters are within a prescribed range. The server 110 may sort the available networks in the requested geographical location based on the values of the one or more signal parameters with a higher range of the prescribed range. The server 110 may transmit the list of available networks to the UE 104. The UE 104 may determine the best network among the available networks and select the best network as the optimal network.
[0077] In a non-limiting example, the prescribed range for the C/I may be -9 dB to -1 dB, the prescribed range for the SINR may be 0 dB to 20 dB, the prescribed range for the RSRQ may be -20 dBm to -10 dBm, the prescribed range for the RSRP may be -115 dBm to -80 dBm, the prescribed range for the RSCP may be -124 dBm to -10 dBm, and the prescribed range for the RSSI may be -110 dBm to -70 dBm. A sample values of the one or more signal parameters measured for four networks in a geographical location is stored in the database 228 is illustrated below in Table 1:
Table 1: Values of one or more signal parameters stored in the database 228
Network C/I
(dB) SINR
(dB) RSRQ
(dBm) RSRP
(dBm) RSCP
(dBm) RSSI
(dBm)
First -2 12.5 -5 -84 -20 -73
Second -5 10 -9 -95 -40 -85
Third -8 7 -12 -110 -60 -95
Fourth -11 3 -15 -115 -85 -110

[0078] The server 110 may sort the networks based on the values of the one or more signal parameters and prepare the list of the available networks. The server 110 may forward the list of the available networks to the UE 104. The UE 104 may choose the optimal network from the list of the available networks. In a non-limiting example, the UE 104 may choose the first network from the list of the available networks based on the values of the one or more signal parameters, as the first network comprise the values near the higher range of the prescribed range.
[0079] FIG. 4 illustrates a process flow diagram depicting a method 400 for selecting the optimal communication network, in accordance with an embodiment of the present disclosure. The method 400 comprises a series of operation steps indicated by blocks 402 through 406.
[0080] At block 402, the processor 212 may be configured to transmit, via one of the online mode or the offline mode, the network service request to the server 110.
[0081] In some aspects of the present disclosure, the network service request may be transmitted in the online mode via the application 224 installed on the UE 104. Further, the network service request may be transmitted as the text message in the offline mode.
[0082] At block 404, the processor 212 may be configured to receive network information of each of a plurality of available networks from the server 110 based on the network service request.
[0083] In some aspects of the present disclosure, the network service request in the online mode may comprise information of one or more locations to identify the optimal communication network at the one or more locations. The application 224 may be configured to extract the location of the UE 104. The location of the UE 104 may be at least one of a latitude, a longitude, or a zip code of the geographical location of the UE 104.
[0084] In some aspects of the present disclosure, the network service request in the offline mode may comprise information of one or more locations to receive recommendations for available networks at the one or more locations.
[0085] At block 406, the processor 212 may be configured to select the optimal communication network among the plurality of available networks based on the network information of each of the plurality of available networks.
[0086] In some embodiments, the server 110 may receive the network service request from the UE 104. The server 110 may analyze the user preferences, the network usage patterns and other factors using machine learning models based on the request. The server 110 may provide intelligent recommendations about the best available networks for the preferred geographical locations of the UE 104. The machine learning models may be used as a platform for remote users to obtain network availability predictions for a specific location using models that have already been trained on historical data. The UE 104 may send the network service request and receive predictions through an Application Programming Interface (API), a web page, the application 224, text message (Short Message Service (SMS)), or other interface on an on-demand basis.
[0087] In some aspects of the present disclosure, the machine learning models may be used by the server 110 to predict user preferences based on user behavior. The machine learning models may determine the best service providers and technologies best suited for the communication of the UE 104 in the preferred geographical locations. The geographical location may be represented as latitude, longitude, and zip code of the location of the UE 104. The machine learning models may utilize the geographical locations of the UE 104 with latitude and longitude or other geographical coordinates to predict the network information of the available networks. The server 110 may utilize the machine learning models such as, (not limited to) regression, classification, clustering, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine, and Multi-Layer Perceptron Neural Network.
[0088] In some aspects of the present disclosure, the machine learning model may be trained based on the user preferences. The user preferences may be based on the UE 104 choosing the optimal network from the list of the available networks. When the server 110 receives multiple network service request from multiple UEs 104-1 to 104-N for a particular geographical location, the server 110 may send the list of available networks as a response to the multiple UEs 104-1 to 104-N. The multiple UEs 104-1 to 104-N may choose the optimal network from the received list of the available networks. Hence, the server 110 may obtain the user preferences for the geographical location based on the selection of the optimal networks by the multiple UEs 104-1 to 104-N. The server 110 may store the user preferences in the database 228 as the crowd-sourced data. The machine learning models may obtain the user preferences for training and may sort the networks based on the selection of the optimal network.
[0089] In some aspects of the present disclosure, the server 110 may analyze the network usage patterns based on the values of the one or more signal parameters. As the UE 104 chooses the optimal network based on the higher values of the one or more signal parameters of the network, the server 110 may analyze the network usage patterns for each network. The values of the one or more signal parameters may be analyzed for different parts of a day such as, during busy hours of morning, during afternoon, during busy hours of evening, and during night. The server 110 may analyze behavior of the network during the different parts of the day based on the values of the one or signal parameters and store in the database 228. The machine learning models may be trained based on the values of the signal parameters stored for the different parts of the day as the network usage patterns.
[0090] In some aspects of the present disclosure, the user preferences and the network usage patterns may be determined using crowd-sourced data comprising information from the users about network performance and speed testing data in the geographical locations. The crowd-sourced data may be stored in the database 228. The machine learning models may acquire the crowd-sourced data from the database 228 as the training data to predict the user preferences and the network usage patterns. The machine learning models may provide proximity predictions for locations for which no previous data exists. The machine learning models may provide accurate predictions for locations for which historical data exist. The predicted locations may be stored in the database 228 and sorted in the ascending order. The predicted locations may be the network information comprising the list of the best available networks in the geographical locations. The best available networks may be sorted based on the network performance and the speed testing data obtained from the crowd-sourced data stored in the database 228.
[0091] In some aspects of the present disclosure, the server 110 may analyze the network service request and utilize the machine learning models to generate the network information. The network information may comprise the list of names of the available networks. The list of the available networks may be stored in the database 228 based on the training data provided to the machine leaning models. The training data may be obtained based on the user preferences received in the service requirements and the network patterns may be obtained based on network availability, contention ratio, bandwidth utilization, and network latency of each of the network in the geographical location.
[0092] FIG. 5 illustrates a process flow diagram depicting a method 500 for transmitting network information of the available networks, in accordance with an embodiment of the present disclosure. The method 500 comprises a series of operation steps indicated by blocks 502 through 508.
[0093] At block 502, the processor 204 of the server 110 may be configured to receive the network service request from the UE 104.
[0094] In some aspects of the present disclosure, the network service request may be received in the online mode via the application 224 installed on the UE 104. Further, the network service request may be received as the text message in the offline mode.
[0095] At block 504, the processor 204 may be configured to analyze, using the Machine Learning (ML) model based on the network service request, user preferences and network usage patterns associated with the UE 104 to determine the plurality of available networks.
[0096] In some aspects of the present disclosure, the network service request in the online mode may comprise information of one or more locations to identify the optimal communication network at the one or more locations. The processor 204 may be configured to obtain the one or more locations and the service requirements in the network service request.
[0097] In some aspects of the present disclosure, the network service request in the offline mode may comprise information of one or more locations to receive recommendations for available networks at the one or more locations.
[0098] At block 506, the processor 204 may be configured to acquire, from the external database 228, network information of each of the determined plurality of the available networks.
[0099] At block 508, the processor 204 may be configured to transmit the network information of each of the determined plurality of available networks to the UE 104.
[0100] In some aspects of the present disclosure, the processor 204 may be configured to provide intelligent recommendations about the best available networks in a particular location of the UE 104.
[0101] Referring to the technical abilities and advantageous effect of the present disclosure, operational advantages that may be provided by above disclosed system and method may enable a user to find the best network in any location both in the online mode and the offline mode. The present disclosure utilizes the network finding application to find the best network in the online mode and utilizes text messages to find the best network in the offline mode, thereby enabling the user to find the best network in any mode of operation. Another potential advantage of the one or more embodiments may include the utilization of machine learning models to provide intelligent recommendations of the best available network based on the location of the user. The machine learning models are utilized for analyzing the user preferences and the network usage patterns to provide recommendations on the available networks. The intelligent recommendations enable the user to select the optimal network and also reduce significant delays in selecting and establishing appropriate communication service with the network.
[0102] Further, the network finding application may be designed to run in the background so that the operation of the UE is not impacted. The network finding application may offer several advantages to the UE by enhancing their connectivity experience and addressing specific needs. Further, the network finding application may be integrated with location-based services to allow the UE to discover networks available in their vicinity with a simple and intuitive interface.
[0103] Those skilled in the art will appreciate that the methodology described herein in the present disclosure may be carried out in other specific ways than those set forth herein in the above disclosed embodiments without departing from essential characteristics and features of the present disclosure. The above-described embodiments are therefore to be construed in all aspects as illustrative and not restrictive.
[0104] 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. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Any combination of the above features and functionalities may be used in accordance with one or more embodiments.
[0105] In the present disclosure, each of the embodiments has been described with reference to numerous specific details which may vary from embodiment to embodiment. The foregoing description of the specific embodiments disclosed herein may reveal the general nature of the embodiments herein that others may, 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 are intended to be comprehended within the meaning of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and is not limited in scope.
LIST OF REFERENCE NUMERALS
[0106] The following list is provided for convenience and in support of the drawing figures and as part of the text of the specification, which describe innovations by reference to multiple items. Items not listed here may nonetheless be part of a given embodiment. For better legibility of the text, a given reference number is recited near some, but not all, recitations of the referenced item in the text. The same reference number may be used with reference to different examples or different instances of a given item. The list of reference numerals is:
100 - Communication network
102 – Base Station (BS)
102-1 to 102-N – One or more BS
104 - User Equipment (UE)
104-1 to 104-N – One or more UE
106 – Coverage region
106-1 to 106-N – Coverage region
108 - Network
110 - Server
200 - System architecture
202- Communication interface of the server 110
204- Processor of the server 110
206- Memory of the server 110
208- Server database of the server 110
210- User Interface (UI)
212- Processor of the UE 104
214- Communication interface of the UE 104
216- Memory of the UE 104
218- Random Access Memory (RAM) in the memory 216
220- Read-Only Memory (ROM) in the memory 216
222- Operating System (OS) in the memory 216
224- Applications in the memory 216
226- Database in the memory 216
228- External database
230- Network Management System (NMS)
232- Reception module of the server 110
234- Analysis module of the server 110
236- Acquiring module of the server 110
238- Transmission module of the server 110
240- Transmission module of the UE 104
242- Reception module of the UE 104
244- Selection module of the UE 104
300- Communication between the external database 228, the server 110, and one or more UEs 104-1 to 104-N
302- Load balancer
400- Method for monitoring the performance of BS 102
402-408- Operation steps of the method 400
500- Method for transmitting network information of the available networks
502-508- Operation steps of the method 500
,CLAIMS:I/We Claim:

1. A method (400) for selecting an optimal communication network, the method (400) comprising:
transmitting (402), by a transmission module (240) via one of an online mode or an offline mode, a network service request to an application server (110);
receiving (404), by a reception module (242), network information of each of a plurality of available networks from the application server (110) based on the network service request; and
selecting (406), by a selection module (244), an optimal communication network among the plurality of available networks based on the network information of each of the plurality of available networks.

2. The method (400) as claimed in claim 1, wherein, in the online mode, the network service request is transmitted via an application installed on a User Equipment (UE) (104).

3. The method (400) as claimed in claim 1, wherein, in the offline mode, the network service request is transmitted as a text message.

4. The method (400) as claimed in claim 1, wherein, in the online mode, the network service request comprises information of one or more locations to identify the optimal communication network at the one or more locations.

5. The method (400) as claimed in claim 1, wherein, in the offline mode, the network service request comprises information of one or more locations to receive recommendations for available networks at the one or more locations.

6. A method (500) for transmitting network information of a plurality of available networks, the method (500) comprising:
receiving (502), by a reception module (232) of an application server (110), a network service request from a User Equipment (UE) (104);
analyzing (504), by an analyzing module (234) using a Machine Learning (ML) model based on the network service request, user preferences and network usage patterns associated with the UE (104) to determine the plurality of available networks;
acquiring (506), by an acquiring module (236) from a database (228), network information of each of the determined plurality of the available networks; and
transmitting (508), by a transmission module (238) to the UE (104), the network information of each of the determined plurality of available networks.

7. The method (500) as claimed in claim 6, wherein, in an online mode, the network service request is received via an application installed on the UE (104).

8. The method (500) as claimed in claim 6, wherein, in an offline mode, the network service request is received as a text message.

9. The method (500) as claimed in claim 6, wherein, in the online mode, the network service request comprises information of one or more locations to identify the optimal communication network at the one or more locations.

10. The method (500) as claimed in claim 6, wherein, in the offline mode, the network service request comprises information of the one or more locations to receive recommendations for available networks at the one more or locations.

11. A system (200) for selecting an optimal communication network, the system (200) comprising:
a transmission module (240) configured to transmit, via one of an online mode or an offline mode, a network service request to an application server (110);
a reception module (242) configured to receive network information of each of a plurality of available networks from the application server (110) based on the network service request; and
a selection module (244) configured to select an optimal communication network among the plurality of available networks based on the network information of each of the plurality of available networks.

12. The system (200) as claimed in claim 11, wherein, in the online mode, the network service request is transmitted via an application installed on a User Equipment (UE) (104).

13. The system (200) as claimed in claim 11, wherein, in the offline mode, the network service request is transmitted as a text message.

14. The system (200) as claimed in claim 11, wherein, in the online mode, the network service request comprises information of one or more location to identify the optimal communication network at the one or more locations.

15. The system (200) as claimed in claim 11, wherein, in the offline mode, the network service request comprises information of one or more locations to receive recommendations for available networks at the one or more locations.

16. A system (200) for transmitting network information of a plurality of available networks, the system (200) comprising:
a reception module (232) configured to receive, by an application server (110), a network service request from a User Equipment (UE) (104);
an analysis module (234) configured to analyze, using a Machine Learning (ML) model based on the network service request, user preferences and network usage patterns associated with the UE (104) to determine the plurality of available networks;
an acquiring module (236) configured to acquire, from a database (228), network information of each of the determined plurality of the available networks; and
a transmission module (238) configured to transmit, to the UE (104), the network information of each of the determined plurality of available networks.

17. The system (200) as claimed in claim 16, wherein, in an online mode, the network service request is received via an application installed on the UE (104).

18. The system (200) as claimed in claim 16, wherein, in an offline mode, the network service request is received as a text message.

19. The system (200) as claimed in claim 16, wherein, in the online mode, the network service request comprises information of one or more locations to identify the optimal communication network at the one or more locations.

20. The system (200) as claimed in claim 16, wherein, in the offline mode, the network service request comprises information of one or more locations to receive recommendations for available networks at the one or more locations.