Electronic device and method for providing a hotspot hosted by the electronic device

Abstract

Disclosed herein is a method (2800) for providing a hotspot　(610) hosted by an electronic device (604). The method (2800) may include operating as the hotspot (610) in a first frequency band. The method (2800) may include receiving one or more network connection requests from one or more client devices (606a, 606b쪋606n) to connect to the hotspot (610) for a wireless network (608). The method may include obtaining one or more parameters corresponding to at least one of the UE, the one or more client devices, or the wireless network.The method may include determining a need to switch the frequency band of the hotspot for the one or more client devices based on the one or more parameters. The method may include switching from the first frequency band of the hotspot to a second frequency band.

Description

ELECTRONIC DEVICE AND METHOD FOR PROVIDING A HOTSPOT HOSTED BY THE ELECTRONIC DEVICE

[0001] The present invention relates generally to wireless networks, and more particularly, to an electronic device and a method for providing a hotspot hosted by the electronic device.

[0002] In the current wireless network technology trends, fixed wired broadband, mobile hotspots (MHS), or soft access points (S-AP) are an integral part of daily connectivity. While fixed wired broadband remains a primary option, the MHS has become a popular and most common choice for on-the-go connectivity.

[0003] With the increase in 5G network coverage, there is an increase in the usage of MHS. Unlike a hard Wireless-Fidelity (Wi-Fi) access point, which is typically a powered device, the MHS is enabled as a battery-operated device. Some of the key performance indicators for MHS are high speed, low power consumption, low latency, and smart connection.

[0004] The MHS can be enabled on a smartphone as a Wi-Fi access point using a cellular network (3rdGeneration, 4thGeneration, or 5thGeneration) or Wi-Fi (Wi-Fi Sharing) as backhaul. The MHS is utilized to provide internet access to client devices, which are devices capable of connecting to a Wi-Fi network to access internet services or communicate with other devices. Examples of client devices include laptops, televisions (TVs), and similar devices.

[0005] Currently, users are required to change the requisite frequency band in the MHS for better speed, low latency, and low power consumption. Further, the users are unable to fully utilize the 5G cellular speeds, and network congestion often results in a poor user experience.

[0006] 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.

[0007] According to an embodiment of the present disclosure, a method for providing a hotspot　hosted by an electronic device is disclosed. The method may include operating, by the electronic device, as the hotspot in a first frequency band. The method may include receiving, by the electronic device, one or more network connection requests from one or more client devices to connect to the hotspot for a wireless network. The method may include obtaining, by the electronic device, one or more parameters corresponding to at least ond of the electronic device, the one or more client devices, or the wireless network. The method may include determining, by the electronic device, a need to switch the frequency band of the hotspot for the one or more client devices based on the one or more parameters obtained by the electronic device. The method may include switching, by the electronic device, from the first frequency band of the hotspot to a second frequency band.

[0008] According to another embodiment of the present disclosure, an electronic device for providing a hotspot hosted by the electronic device is disclosed. The electronic device may include a memory storing instructions and at least one processor coupled to the memory and including processing circuitry. The at least one processor may individually or collectively execute the instructions to casue the electronic device to operate as the hotspot in a first frequency band. The at least one processor may individually or collectively execute the instructions to casue the electronic device to receive one or more network connection requests from one or more client devices to connect to the hotspot for a wireless network The at least one processor may individually or collectively execute the instructions to casue the electronic device to obtain one or more parameters corresponding to at least one of the UE, the one or more client devices, or the wireless network. The at least one processor may individually or collectively execute the instructions to casue the electronic device to determine a need to switch the frequency band of the hotspot for the one or more client devices based on the one or more parameters. The at least one processor may individually or collectively execute the instructions to casue the electronic device to switch from the first frequency band of the hotspot to a second frequency band.

[0009] To further clarify the advantages and features of the present subject matter, 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.

[0010] These and other features, aspects, and advantages of the present subject matter 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:

[0011] Figure 1 illustrates an exemplary user interface (UI) depicting available frequency bands for a current Mobile Hotspot (MHS), according to a related art;

[0012] Figure 2 illustrates an exemplary representation of operating frequency bands of the MHS according to a related art;

[0013] Figure 3 illustrates a graphical representation of the usage of an internet, according to a related art;

[0014] Figure 4A illustrates a user interface (UI) depicting a default frequency band in a leading operator (LO) model, according to a related art;

[0015] Figure 4B illustrates an exemplary representation of a current UI for MHS settings, according to a related art;

[0016] Figure 4C illustrates an exemplary representation of user experience with a default frequency band of the MHS, according to a related art;

[0017] Figure 5A illustrates an exemplary representation of a use case of the MHS when a client device is downloading a file, according to a related art;

[0018] Figure 5B illustrates an exemplary representation of a use case of the MHS when a client device is moving, according to a related art;

[0019] Figure 5C illustrates an exemplary representation of a use case of the MHS during an online gaming session, according to a related art;

[0020] Figure 5D illustrates an exemplary representation of a use case , when the client device is connected to a higher frequency band of the MHS, according to a related art;

[0021] Figure 6 illustrates a block diagram depicting an environment of switching a　frequency band in a hotspot　hosted by a User Equipment (UE), in accordance with an embodiment of the present disclosure;

[0022] Figure 7 illustrates a block diagram of a system for switching the　frequency band in the hotspot hosted by the UE, in accordance with an embodiment of the present disclosure;

[0023] Figure 8 illustrates an exemplary flow diagram depicting an exemplary method for switching the frequency band and estimating a speed limit for the hotspot, in accordance with an embodiment of the present disclosure;

[0024] Figure 9A illustrates an exemplary representation of a connection of client devices based on a received signal strength indicator (RSSI) of each connected client device in accordance with an embodiment of the present disclosure;

[0025] Figure 9B illustrates an exemplary representation of the connection of the client devices based on the frequency band of the hotspot, in accordance with an embodiment of the present disclosure ;

[0026] Figure 10A illustrates an exemplary representation of the data-driven model for predicting the client traffic type, in accordance with an embodiment of the present disclosure;

[0027] Figure 10B illustrates an exemplary representation of a user interface (UI) depicting an icon for prioritizing the real-time traffic features, in accordance with an embodiment of the present disclosure;

[0028] Figure 11A illustrates an exemplary representation of the data-driven model for frequency band selection, in accordance with an embodiment of the present disclosure;

[0029] Figure 11B illustrates an exemplary representation of a user interface (UI) depicting an icon for intelligently selecting the frequency band and speed change, in accordance with an embodiment of the present disclosure;

[0030] Figure 12 illustrates an exemplary flow diagram depicting a method for predicting the frequency band in case of low latency, in accordance with an embodiment of the present disclosure;

[0031] Figure 13 illustrates an exemplary flow diagram depicting a method for predicting the frequency band to handle increased traffic from the client device, in accordance with an embodiment of the present disclosure;

[0032] Figure 14 illustrates another exemplary flow diagram depicting a method for predicting the frequency band to handle increased traffic from the client device and battery low condition of the UE, in accordance with an embodiment of the present disclosure;

[0033] Figure 15 illustrates an exemplary representation of the data-driven model for throttling the speed of the hotspot, in accordance with an embodiment of the present disclosure;

[0034] Figure 16 illustrates an exemplary flow diagram depicting a method for throttling the speed of the hotspot, in accordance with an embodiment of the present disclosure;

[0035] Figures 17A and 17B illustrate exemplary representations of the UI of hotspot settings, in accordance with an embodiment of the present disclosure;

[0036] Figure 18 illustrates an exemplary representation of a use case of the present disclosure when the client device is downloading the file, in accordance with an embodiment of the present disclosure;

[0037] Figure 19 illustrates an exemplary representation of a use case of the present disclosure when 2.4GHz or compatible mode is required, in accordance with an embodiment of the present disclosure;

[0038] Figure 20 illustrates an exemplary representation of a use case of the present disclosure during the online gaming session, in accordance with an embodiment of the present disclosure;

[0039] Figure 21 illustrates an exemplary representation of a use case of the present disclosure when the client is connected to the higher frequency band, in accordance with an embodiment of the present disclosure;

[0040] Figure 22 illustrates an exemplary representation of a table depicting a dataset for training and testing the data-driven model, in accordance with an embodiment of the present disclosure;

[0041] Figure 23 illustrates an exemplary representation of a neural network model or artificial neural network (ANN) used in the present disclosure, in accordance with an embodiment of the present disclosure;

[0042] Figure 24A illustrates an exemplary representation of a result depicting a test accuracy of the data-driven model, in accordance with an embodiment of the present disclosure;

[0043] Figure 24B illustrates a graphical representation of the test accuracy of the ANN model with respect to the epoch of the ANN model, in accordance with an embodiment of the present disclosure;

[0044] Figure 25 illustrates an exemplary flowchart depicting a method for switching a　frequency band of android devices, in accordance with an embodiment of the present disclosure;

[0045] Figure 26 illustrates an exemplary representation of a high-level architecture of the hotspot, in accordance with an embodiment of the present disclosure;

[0046] Figure 27 illustrates an exemplary representation of an environment of the present disclosure depicting enhanced user experience, in accordance with an embodiment of the present disclosure;

[0047] Figure 28 illustrates a flowchart depicting a method for switching the　frequency band in the hotspot　hosted by the UE, in accordance with an embodiment of the present disclosure;

[0048] Figure 29 illustrates a flowchart depicting a method for switching the　frequency band in the hotspot hosted by the UE, in accordance with an embodiment of the present disclosure; and

[0049] Figure 30 illustrates a flowchart depicting a method for switching the　frequency band in the hotspot hosted by the UE, in accordance with an embodiment of the present disclosure.

[0050] 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 in terms of the most prominent steps involved to help to improve understanding of aspects of the present subject matter. 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 subject matter 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. It should be appreciated that the blocks in each flowchart and combinations of the flowcharts may be performed by one or more computer programs which include computer-executable instructions. The entirety of the one or more computer programs may be stored in a single memory or the one or more computer programs may be divided with different portions stored in different multiple memories.

[0051] 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.

[0052] 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.

[0053] 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 subject matter. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0054] The terms "comprises", "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 operations does not include only those operations but may include other operations 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.

[0055] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more of such surfaces.

[0056] Any of the functions or operations described herein can be processed by one processor or a combination of processors. The one processor or the combination of processors is circuitry performing processing and includes circuitry like an application processor (AP), a communication processor (CP), a graphical processing unit (GPU), a neural processing unit (NPU), a microprocessor unit (MPU), a system on chip (SoC), an IC, or the like.

[0057] The processor may include various processing circuitry and / or multiple processors. For example, as used herein, including the claims, the term "processor" may include various processing circuitry, including at least one processor, wherein one or more of at least one processor, individually and / or collectively in a distributed manner, may be configured to perform various functions described herein. As used herein, when "a processor", "at least one processor", and "one or more processors" are described as being configured to perform numerous functions, these terms cover situations, for example and without limitation, in which one processor performs some of recited functions and another processor(s) performs other of recited functions, and also situations in which a single processor may perform all recited functions. Additionally, the at least one processor may include a combination of processors performing various of the recited / disclosed functions, e.g., in a distributed manner. At least one processor may execute program instructions to achieve or perform various functions.

[0058] A function related to AI according to the disclosure is implemented through a processor and a memory. The processor may be configured as one or more processors. In this case, the one or more processors may be a general-purpose processor such as a central processing unit (CPU), an application processor (AP), or a digital signal processor (DSP), a dedicated graphics processor such as a graphics processing unit (GPU) or a vision processing unit (VPU), or a dedicated AI processor such as a neural processing unit (NPU). The one or more processors may control input data to be processed according to predefined operation rules or an AI model stored in a memory. Alternatively, when the one or more processors are AI-dedicated processors, they may be designed with a hardware architecture specialized for processing specific AI models.

[0059] The predefined operation rules or AI model may be generated via training. When the predefined operation rules or AI model are generated via training, it means that the predefined operation rules or AI model set to perform desired characteristics (or purposes) is generated by training a basic AI model with a learning algorithm that utilizes a large number of training data. The training may be performed by a device itself (e.g., the cleaner body 1000) where AI according to the disclosure is being performed, or performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

[0060] An AI model may include a plurality of neural network layers. Each of the neural network layers includes a plurality of weight values, and performs a neural network arithmetic operation via an arithmetic operation between an arithmetic operation result of a previous layer and the plurality of weight values. A plurality of weight values in each of the neural network layers may be optimized by a result of training the AI model. For example, the plurality of weight values may be refined to reduce or optimize a loss value or a cost value obtained by the Al model during the training. An artificial neural network may include, for example, but is not limited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or a deep Q-network.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skilled in the art to which this invention belongs. The electronic device, system, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0062] Figure 1 illustrates an exemplary user interface (UI) 100 depicting available frequency bands for a current MHS, according to a related art. Currently, the MHS is provided with 2.4GHz which may lead to slow data downloading and buffering problems in 4K streaming in 5GHz or 6GHz enabled devices utilizing the 2.4GHz MHS. The 2.4 GHz and 5 GHz frequency bands provide maximized compatibility and better performance at the same time. However, to save power consumption, one of the frequency bands is turned off, if the same is not being used. Further, in 5GHz or 6GHz frequency bands, devices that do not support 5GHz or 6GHz frequency bands are unable to find or connect to the MHS.

[0063] Figure 2 illustrates an exemplary representation 200 of operating frequency bands of the MHS according to a related art. The operating frequency bands may include 2.4GHz, 5GHz, and 6GHz. The throughput (speed) of the frequency bands and average current consumption are represented in Table 1 and Table 2 below:

[0064] [Table 1]

[0065]

[0066] [Table 2]

[0067]

[0068] Further, the latency of the frequency bands may be in the following manner:

[0069] 6 GHz (low) << 5 GHz < 2.4 GHz (high)

[0070] The 2.4GHz frequency band may have a wider coverage range than the 5GHz and 6GHz frequency bands. The 5GHz frequency band may have a wider coverage range than the 6GHz frequency band.

[0071] Figure 3 illustrates a graphical representation 300 of the usage of the internet, according to a related art. As depicted in the figure 3, the represented data depicts approximately 95% of usage of MHS in the 2.4GHz frequency band (e.g., Channel 1, Channel 6, Channel 11, and Channel 0 representing auto channel) and less than 3% usage in the 5 GHz frequency band (e.g., Channel 149). Figure 4A illustrates a user interface (UI) 400a depicting the default frequency band in a leading operator (LO) model, according to a related art. The default frequency band may be, for exmaple, but is not limited to, 2.4 GHz frequency band. Current devices may support dual bridge access points (APs). However, power consumption is high in the dual bridge AP as compared to the 2.4GHz frequency band. Therefore, the 2.4GHz is kept as a default frequency band.

[0072] Further, insufficient backhaul network speed on a host device 402 can lead to network congestion when multiple devices stream HD content simultaneously through the MHS enabled on the host device 402. Further, when a high-priority activity is performed on the host device 402, it becomes desirable to allocate more speed to the host's internet activities than to MHS traffic. Therefore, there is a need to enable dynamic speed throttling for the MHS. Therefore, there is a need to enable dynamic speed throttling for MHS.

[0073] Further, enabling MHS with full available speed may lead to fast battery drainage. Table 3 below represents the proportionality of the speed limit to current consumption.

[0074] [Table 3]

[0075]

[0076] Figure 4B illustrates an exemplary representation 400b of a current UI for MHS settings, according to a related art. As depicted in Figure 4B, in the current MHS settings, a user is allowed to manually change 404 the frequency band of the MHS according to compatibility, thereby degrading the user experience.

[0077] Figure 4C illustrates an exemplary representation 400c of the user experience of the MHS, according to a related art. For example, the MHS may be connected to the default 2.4 GHz frequency band, for example, 2.4 GHz. The limitations of the default 2.4 GHz of the MHS impact data experience for the users connecting with 5 GHz or 6 GHz-capable client devices 406. When the MHS operates solely on the 2.4 GHz frequency band, the client devices that are capable of using 5 GHz or 6 GHz frequency bands are unable to utilize the faster data rates these frequency bands support. Consequently, the data download rate on the 2.4 GHz frequency band is typically lower due to limited bandwidth and increased interference, especially in environments with high device density. This leads to a slower, suboptimal user experience, particularly for high-speed applications. For applications requiring high data throughput, such as 4K video streaming, the limitations of the 2.4 GHz frequency band often result in frequent buffering or lag. Since 4K streaming demands sustained high-speed data transfer, the congestion and slower speeds of 2.4 GHz cause delays, interrupting the viewing experience. Modern devices designed to work on the faster 5 GHz or 6 GHz frequency bands, are unable to perform optimally when limited to the 2.4 GHz frequency band. This not only affects download speeds and streaming quality but also fails to provide the low-latency connectivity needed for real-time applications, such as online gaming. Thus, the default 2.4 GHz MHS fails to meet the needs of advanced 5 GHz or 6 GHz frequency band devices, limiting the effective use of the device's capabilities and degrading the overall user experience for bandwidth-intensive applications.

[0078] Figure 5A illustrates an exemplary representation 500a of a use case of the MHS when the user is downloading a file, according to a related art. The MHS may be enabled in the default 2.4 GHz frequency band, with an upstream backhaul speed of approximately 800 Mbps. In this case, the file downloading speed is slow.

[0079] Figure 5B illustrates an exemplary representation 500b of a use case of the MHS, when a client device is moving, according to a related art. The client device is connected to the MHS. For exmaple, the client device may be connected to the 5GHz or 6GHz frequency band. However, as the user with the client device moves away from the MHS, the internet gets disconnected due to weak signals.

[0080] Figure 5C illustrates an exemplary representation 500c of a use case of the MHS during an online gaming session, according to a related art. The MHS may be enabled in the default 2.4 GHz frequency band, and an upstream backhaul speed is around 800 Mbps. The Wi-Fi may be connected to the MHS 2GHz, thereby leading to high latency during the online gaming session.

[0081] Figure 5D illustrates an exemplary representation 500d of a fourth use case, when the client device is connected to a higher frequency band of the MHS, according to a related art. The MHS is enabled in the default 5 GHz frequency band, and an upstream backhaul speed is around 800 Mbps. The client device is connected to the MHS on the 6 GHz frequency band, which leads to increased battery drainage on the host device.

[0082] Accordingly, there is a need to overcome the above-mentioned limitations. Further, there is a need to provide techniques for switching a frequency band in the hotspot hosted by the UE.

[0083] Figure 6 illustrates a block diagram 600 depicting an environment of switching a　frequency band in a hotspot　hosted by an electronic device, in accordance with an embodiment of the present disclosure. The environment 600 may include a system 602, the electronic device 604, one or more client devices 606a, 606b...606n, and a wireless network 608. The wireless network 608 may refer to a communication system that enables data exchange between devices without requiring physical wired connections. The wireless network 608 may be facilitated by the hotspot 610 hosted by the electronic device 604, which serves as the central point for network connectivity. The hotspot　610 may include, but is not limited to, a mobile hotspot, and the like. The wireless network 608 may include, but is not limited to, frequency bands (e.g., 2.4 GHz, 5 GHz, or 6 GHz), network protocols, hotspot functionality, client connectivity, dynamic management, and the like. The electronic device 604 may refer to a computing device or communication terminal that provides the hotspot 610. For example, the electronic device 604 may be a User Equipment (UE). Hereinafter, when describing the UE, the same drawing reference number as those for the electronic device 604 will be used for the description. The description of the operation of the UE 604 described below can be equally applied to the electronic device 604. The UE 604 may include, but is not limited to, smartphones, tablets, laptops, dedicated hotspot devices capable of creating a wireless access point (e.g., the mobile hotspot), and the like. The electronic device 604 may act as a central hub for wireless network connectivity, enabling the client devices 606a, 606b...606n to access the internet or other network resources through the electronic device's upstream backhaul connection. The electronic device 604 may be configured to dynamically manage operational parameters, such as frequency band selection, data rate allocation, and network configuration, to optimize performance and user experience. The one or more client devices 606a, 606b...606n may connect to the hotspot 610 created by the electronic device 604 to access the network. Each of the client devices 606a, 606b...606n may include, but are not limited to, laptops, televisions (smart TVs), gaming consoles, IoT devices, virtual reality (VR) headsets, augmented reality (AR) devices, smartphones, and any other device capable of connecting to the mobile hotspot(s). The client devices 606a, 606b...606n may utilize the electronic device's network connection for tasks such as streaming, online gaming, file downloads, or general internet browsing. The client devices 606a, 606b...606n may have varying capabilities, such as supported frequency bands (e.g., 2.4 GHz, 5 GHz, 6 GHz), traffic requirements (e.g., real-time or non-real-time), and communication standards (e.g., Single-Input Single-Output (SISO) / Multiple-Input Multiple-Output (MIMO)).

[0084] In an embodiment of the disclosure, the system 202 may be a part of the electronic device 604. In an embodiment of the disclosure, the system 602 may reside in a server and communicate with the electronic device 604. The system 602 may include a data-driven model. The data-driven model may include, but is not limited to, an Artificial Intelligence (AI) model, a Machine Learning (ML) model, and the like. The data-driven model may be configured to switch the　frequency band in the hotspot　610 without the hotspot 610 restart. In an embodiment of the disclosure, the electronic device 604 may operate as the hotspot 610 in the frequency band. The data-driven model may be configured to predict the probability for each frequency band for one or more parameters associated with the hotspot　610. The one or more parameters associated with the hotspot　610 may comprise at least one of the one or more parameters associated with the UE 604, the one or more parameters associated with the one or more client devices 606a, 606b...606n, or the one or more parameters associated with the wireless network. For example, the one or more parameters associated with the hotspot　610 may include the one or more parameters associated with the UE 604. The one or more parameters associated with the hotspot　610 may include the one or more parameters associated with the one or more client devices 606a, 606b...606n. The one or more parameters associated with the hotspot　610 may include the one or more parameters associated with the wireless network. The one or more parameters associated with the hotspot　610 may include any two of the one or more parameters associated with the UE 604, the one or more parameters associated with the one or more client devices 606a, 606b...606n, and the one or more parameters associated with the wireless network. The one or more parameters associated with the hotspot　610 may include the one or more parameters associated with the UE 604, the one or more parameters associated with the one or more client devices 606a, 606b...606n, and the one or more parameters associated with the wireless network. The frequency band may be predicted based on various conditions. The prediction of the frequency band has further been explained in detail with reference to Figure 12, Figure 13, and Figure 14.

[0085] The one or more parameters associated with the hotspot　610 may include, but are not limited to, at least one of one or more characteristics of the one or more client devices 606a, 606b...606n, one or more traffic conditions, signal strength, an operational status of the electronic device 604, or network performance metrics. For example, the one or more parameters associated with the hotspot　610 may include the one or more characteristics of the one or more client devices 606a, 606b...606n. The one or more parameters associated with the hotspot　610 may include the one or more traffic conditions. The one or more parameters associated with the hotspot　610 may include the signal strength. The one or more parameters associated with the hotspot　610 may include the operational status of the electronic device 604. The one or more parameters associated with the hotspot　610 may include the network performance metrics. A speed limit for the hotspot 610 may be estimated. The speed limit of the hotspot 610 has further been explained in detail with reference to Figure 8, Figure 11B, Figure 15, and Figure 16.

[0086] The one or more characteristics of the one or more client devices 606a, 606b...606n may include the capabilities of the one or more client devices 606a, 606b...606n (Single Input Single Output (SISO) or Multiple Input Multiple Output (MIMO) devices.), the received signal strength of the one or more client devices 606a, 606b...606n, and the like. The one or more traffic conditions may include, but are not limited to, traffic load, upstream network speed, quality of network signal, available bandwidth, and the like. The operational status of the electronic device 604 may include, but is not limited to, the battery level of the electronic device 604, the charging status of the electronic device 604, the traffic speed of the electronic device 604, the number of client devices 606a, 606b...606n connected to UE 604, the upstream network speed of the electronic device 604, and the like.

[0087] In an embodiment of the present disclosure, the electronic device 604 may be configured to receive network connection requests from the one or more client devices 606a, 606b...606n to connect to the hotspot 610 for the wireless network 608. The network connection requests may refer to communication signals initiated by the one or more client devices 606a, 606b...606n seeking to establish a connection to the hotspot 610 managed by the electronic device 604. The network connection requests may include, but are not limited to, authentication requests, association requests, probe requests, connection negotiation requests, service requests, and the like.

[0088] The electronic device 604 may be configured to operate as the hotspot 610 in a first frequency band. In an embodiment of the present disclosure, the first frequency band may include one of the 2.4 GHz frequency band, the 5 GHz frequency band, or the 6 GHz frequency band. For example, the first frequency band may include, but is not limited to, the 2.4 GHz frequency band, and the like. The electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to at least one of the electronic device 604, the one or more client devices 606a, 606b...606n, or the wireless network 608. For example, the electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to the UE 604. The electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to the one or more client devices 606a, 606b...606n. The electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to wireless network 608. The electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to any two of the UE 604, the one or more client devices 606a, 606b...606n, and the wireless network 608. The electronic device 604 may be configured to obtain (e.g., measure) the one or more parameters corresponding to the UE 604, the one or more client devices 606a, 606b...606n, and the wireless network 608. The electronic device 604 may be configured to determine a need to switch the frequency band of the hotspot 610 for the one or more client devices 606a, 606b...606n based on the one or more parameters obtained (e.g., measured) by the electronic device 604. The electronic device 604 may be configured to obtain (e.g., receive) information on a supportable frequency band of the one or more client devices 606a, 606b...606n from the one or more client devices 606a, 606b...606n. In an embodiment of the present disclosure, the electronic device 604 may be configured to switch from the first frequency band of the hotspot 610 to a second frequency band. In an embodiment of the present disclosure, the second frequency band may include one of the 2.4 GHz frequency band, the 5 GHz frequency band, or the 6 GHz frequency band, different from the first frequency band. For example, the second frequency band may include, but is not limited to, 5 GHz frequency band, 6 GHz frequency band, and the like.

[0089] In an embodiment of the present disclosure, the electronic device 604 may be configured to detect (or predict, determine, estimate, measure, monitor) changes in activity and proximity of the one or more client devices 606a, 606b...606n. The electronic device 604 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon detecting (or predicting, determining, estimating, measuring, monitoring) the changes in the activity and the proximity of the one or more client devices 606a, 606b...606n. The electronic device 604 may be configured to determine that the one or more client devices 606a, 606b...606n are　connected to the second frequency band. The electronic device 604 may be configured to predict (or determine, estimate, measure, detect, monitor) a battery level of the electronic device 604 and a traffic condition associated with the one or more client devices 606a, 606b...606n. The traffic condition associated with the one or more client devices 606a, 606b...606n may include, but is not limited to, non-real-time traffic, and the like. The battery level of the electronic device 604 may be below a defined (e.g., predefined) threshold. The electronic device 604 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition of the one or more client devices 606a, 606b...606n, and the battery level of the electronic device 604. The electronic device 604 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition associated with the one or more client devices 606a, 606b...606n includes non-real-time traffic or the battery level of the electronic device 604 is below a defined (e.g., predefined) threshold.

[0090] In an embodiment of the present disclosure, the UE 604 may be configured to determine that the one or more client devices 606a, 606b...606n are　connected to the second frequency band. The electronic device 604 may be configured to predict (or determine, estimate, measure, detect, monitor) the battery level of the electronic device 604 and the traffic conditions associated with the electronic device 604. The traffic conditions associated with the electronic device 604 may include, but are not limited to, real-time traffic, and the like. The battery level of the electronic device 604 may be below the defined (e.g., predefined) threshold. The electronic device 604 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition and the battery level of the electronic device 604. The electronic device 604 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition associated with the electronic device 604 includes real-time traffic or the battery level of the electronic device 604 is below a defined (e.g., predefined) threshold. The electronic device 604 may be configured to determine the data rate for the one or more client devices 606a, 606b...606n upon predicting the traffic condition associated with the electronic device 604.

[0091] Figure 7 illustrates a block diagram of the system 602 for switching the　frequency band in the hotspot　610 hosted by the electronic device 604 (e.g., the UE 604), in accordance with an embodiment of the present disclosure. It may be apparent that the system 602 may be a part of the electronic device 604 (e.g., the UE 604). In an embodiment, the system 602 may be outside electronic device 604 (e.g., the UE 604), and may be coupled to the electronic device 604 (e.g., the UE 604). In an embodiment, the system 602 may be a device designed for switching the　frequency band in the hotspot　hosted by the electronic device 604 (e.g., the UE 604).

[0092] Referring to Figure 7, the system 602 may include, but is not limited to, memory 702, a processor (or processors) 704, an interface 706, and a plurality of modules 708. The memory 702, the interface 706, and the plurality of modules 708 may be coupled to the processor 704. In an embodiment of the present disclosure, the plurality of modules 708 may include a receiving module 710, a parameter measuring module 712, a frequency band determining module 714, a switching module 716, and the data-driven model 718.

[0093] The processor 704 can be a single processing unit or several units, all of which could include multiple computing units. The processor 704 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any device that manipulates signals based on operational instructions. Among other capabilities, the processor 704 may fetch and execute computer-readable instructions and data stored in the memory 702.

[0094] In an embodiment of the present disclosure, the processor 704 may be configured to receive the one or more network connection requests from the one or more client devices 606a, 606b...606n to connect to the hotspot 610 for the wireless network 608. The UE 604 may operate as the hotspot 610 in the first frequency band. The processor 704 may be configured to obtain (e.g., measure) the one or more parameters corresponding to at least one of the UE 604, the one or more client devices 606a, 606b...606n, or the wireless network 608. The processor 704 may be configured to determine the need to switch the frequency band of the hotspot 610 for the one or more client devices 606a, 606b...606n based on the one or more parameters measured by the UE 604. The processor 704 may be configured to switch from the first frequency band of the hotspot 610 to the second frequency band. The processor 704 may be configured to obtain (e.g., receive) the information on the supportable frequency band of the one or more client devices 606a, 606b...606n from the one or more client devices 606a, 606b...606n.

[0095] The processor 704 may be configured to detect (or predict, determine, estimate, measure, monitor) changes in activity and proximity of the one or more client devices 606a, 606b...606n. The processor 704 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon detecting (or predicting, determining, estimating, measuring, monitoring) the changes in the activity and the proximity of the one or more client devices 606a, 606b...606n. The processor 704 may be configured to determine that the one or more client devices 606a, 606b...606n are　connected to the second frequency band. The processor 704 may be configured to predict (or determine, estimate, measure, detect, monitor) the battery level of the UE 604 and the traffic condition associated with the one or more client devices 606a, 606b...606n. The traffic condition associated with the one or more client devices 606a, 606b...606n may include, but is not limited to, the non-real-time traffic, and the like. The battery level of the UE 604 may be below a defined (e.g., predefined) threshold. The processor 704 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition associated with the one or more client devices 606a, 606b...606n, and the battery level of the UE 604. The processor 704 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition of the one or more client devices 606a, 606b...606n includes non-real-time traffic or the battery level of the UE 604 is below a defined (e.g., predefined) threshold.

[0096] The processor 704 may be configured to determine that the one or more client devices 606a, 606b...606n are　connected to the second frequency band. The processor 704 may be configured to predict (or determine, estimate, measure, detect, monitor) the battery level of the UE 604 and the traffic condition associated with the UE 604. The traffic condition associated with the UE 604 may include the real-time traffic, and the like. The battery level of the UE 604 may be below the defined (e.g., predefined) threshold. The processor 704 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition associated with the UE 604, and the battery level of the UE 604. The processor 704 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition associated with the UE 604 includes the real-time traffic or the battery level of the UE 604 is below the defined (e.g., predefined) threshold. The processor 704 may be configured to determine the data rate for the one or more client devices 606a, 606b...606n upon predicting the traffic condition associated with the UE 604.

[0097] The memory 702 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The computer-readable medium may be non-transitory. A "non-transitory" computer-readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device. The memory 702 may include an operating system 718 for performing one or more tasks of the system 602, as performed by a generic operating system in the communications domain. The system 602 may be configured to switch the　frequency band in the hotspot　610 hosted by the UE 604. The database 720 may include the one or more parameters, the data-driven models, Machine Learning (ML) models, and the like. The plurality of modules 708 amongst other things, includes routines, programs, objects, components, data structures, etc., which perform particular tasks or implement data types. The plurality of modules 708 may also be implemented as signal processor(s), state machine(s), logic circuitries, and / or any other device or component that manipulates signals based on operational instructions.

[0098] The plurality of modules 708 can be implemented in hardware, instructions executed by a processing unit, or by a combination thereof. The processing unit can comprise a computer, a processor, such as the processor, a state machine, a logic array, or any other suitable wearable device capable of processing instructions. The processing unit can be a general-purpose processor which executes instructions to cause the general-purpose processor to perform the required tasks or, the processing unit can be dedicated to performing the required functions. In an embodiment of the present disclosure, the plurality of modules 708 may be machine-readable instructions (software) that, when executed by a processor / processing unit, perform any of the described functionalities.

[0099] In an embodiment of the present disclosure, the plurality of modules 708 may include a set of instructions that may be executed to cause the system 602 to perform any one or more of the methods disclosed herein. The plurality of modules 708 may be configured to perform the steps of the present disclosure using the data stored in the memory 702 for switching the　frequency band in the hotspot　610 hosted by the UE 604, as discussed throughout this disclosure. In an embodiment of the present disclosure, each of the plurality of modules 708 may be hardware units that may be outside the memory 702.

[0100] In an embodiment of the present disclosure, the receiving module 710 may be configured to receive the one or more network connection requests from the one or more client devices 606a, 606b...606n to connect to the hotspot 610 for the wireless network 608. The receiving module 710 may be configured to receive information on the supportable frequency band of the one or more client devices 606a, 606b...606n from the one or more client devices 606a, 606b...606n.

[0101] In an embodiment of the present disclosure, the parameter measuring module 712 may be configured to measure the one or more parameters corresponding to the UE 604 and the wireless network 608.

[0102] In an embodiment of the present disclosure, the determining module 714 may be configured to determine the need to switch the frequency band of the hotspot 610 for the one or more client devices 606a, 606b...606n based on the one or more parameters measured by the UE 604. For example, the hotspot 610 may obtain a Received Signal Strength Indicator (RSSI) of each connected client device 606a, 606b...606n. If any client device 606a, 606b, ...606n has a low RSSI, then the determining module 714 may determine that the frequency band of the hotspot 610 needs to switch to the lower frequency band. A connection of the client devices 606a, 606b...606n has further been explained in detail with reference to Figure 9A, Figure 9B, and Figure 18.

[0103] In an embodiment of the present disclosure, the determining module 714 may be configured to determine the data rate for the one or more client devices 606a, 606b...606n upon predicting the traffic condition associated with the UE 604. The determining module 714 may be configured to determine that the one or more client devices 606a, 606b...606n are　connected to the second frequency band.

[0104] In an embodiment of the present disclosure, the switching module 716 may be configured to switch from the first frequency band of the hotspot 610 to the second frequency band. In an embodiment of the present disclosure, the switching module 716 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon detecting the changes in the activity and the proximity of the one or more client devices 606a, 606b...606n. In an embodiment of the present disclosure, the switching module 716 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition of the one or more client devices 606a, 606b...606n and the battery level of the UE 604. In an embodiment of the present disclosure, the switching module 716 may be configured to switch from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting the　traffic condition of the UE 604 and the battery level of the UE 604.

[0105] The plurality of modules 708 may be in communication with each other. In an embodiment of the present disclosure, the plurality of modules 708 may be a part of the processor 704. In an embodiment of the present disclosure, the processor 704 may be configured to perform the functions of the plurality of modules 708.

[0106] At least one of the receiving module 710, the parameter measuring modue 712, the determining module 714, or the switching module 716 may be implemented through the data-driven model 718. A function associated with the　data-driven model 718 may be performed through the non-volatile memory, the volatile memory, and the processor 704. In an embodiment of the present disclosure, the processor 704 may include a plurality of processors. At this time, the plurality of processors may be 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 AI-dedicated processor such as a neural processing unit (NPU). The plurality of processors controls the processing of the input data in accordance with a predefined operating rule or data-driven model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0107] Here, being provided through learning means that, by applying a learning technique to a plurality of learning data, a predefined operating rule or data-driven model 718 of a desired characteristic is made. The learning may be performed in a device itself in which data-driven model 718 according to an embodiment of the present disclosure is performed, and / or may be implemented through a separate server / system.

[0108] The data-driven model 718 may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through the 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.

[0109] The processor may perform a pre-processing operation on the data to convert the data into a form appropriate for use as an input for the data-driven model 718. The data-driven model 718 may be obtained by training. Here, "obtained by training" means that a predefined operation rule or data-driven model 718 configured to perform a desired feature (or purpose) is obtained by training a basic data-driven model 718 with multiple pieces of training data by a training technique. The data-driven model 718 may include a plurality of neural network layers. Each of the plurality of neural network layers includes a plurality of weight values and performs neural network computation by computation between a result of computation by a previous layer and the plurality of weight values. The data-driven model 718 has further been explained in detail with reference to Figure 10A, Figure 11A, Figure 15, Figure 22, Figure 24A, and Figure 24B.

[0110] Reasoning prediction is a technique of logical reasoning and predicting by determining information. It includes knowledge-based reasoning, optimization prediction, preference-based planning, and recommendation.

[0111] Figure 8 illustrates an exemplary flow diagram 800 depicting an exemplary method for switching the frequency band and estimating a speed limit for the hotspot　610, in accordance with an embodiment of the present disclosure.

[0112] At operation 802, the method 800 may include starting the hotspot　610 of the UE 604. At operation 804, the method 800 may include obtaining (e.g., taking) the input parameters to make the decision on the frequency band and estimate the speed limit for the hotspot　610. Examples of the input parameters are described below in the below paragraphs:

[0113] 1. UE Wi-Fi chip concurrency support or not: If station (STA) and Access Point (AP) concurrency (e.g., Real Simultaneous Dual Band (RSDB)) may be supported then the STA and the AP (or HOTSPOT 610) may operate on different frequency bands. For example, the STA may connect to 2.4GHz, and the AP may be enabled in 5GHz. For example, if the STA and the AP want to operate on the same frequency band, the STA and the AP may use the same channel.

[0114] 2. UE Wi-Fi connected or not: In a non-RSDB Wi-Fi chip, the STA and the AP may share the same channel. So, the STA and AP may be on the same band.

[0115] 3. UE Wi-Fi connected frequency: Wi-Fi connected frequency may be 2.4GHz, 5GHz, or 6GHz.

[0116] 4. UE battery level: As the hotspot 610 consumes more power, in a low battery state, the UE 604 may not be switched to a higher band. The battery level may be in a range of 1-100.

[0117] 5. UE charging or not: In the low battery state, if the UE 604 is charging, the UE 604 can switch to a higher frequency band.

[0118] 6. The UE supports VLP 5GHz frequency band or not: All UEs may not support 5GHz because of different country regulations. In a specific ecosystem (e.g., watch, virtual studio technology (VST), etc.) using mDns protocol, the UE may get capabilities.

[0119] 7. The UE supports VLP 6GHz frequency band or not: All the UEs may not support 6GHz because of different country regulations.

[0120] 8. UE's hotspot 610 Egress traffic speed in Mbps: If the traffic speed is increasing, the hotspot 610 needs to switch to the higher frequency band. The UE 604 of the hotspot 610 Egress traffic speed may be in a range of 0-2100 Mbps.

[0121] 9. Host upstream (backhaul) network speed in Mbps: If the backhaul network has high speed, then the UE 604 may switch to the higher frequency band.

[0122] 10. Received Signal Strength Indicator (RSSI) of connected client devices: the firmware / driver of the hotspot 610 may know (e.g., obtain) the RSSI of each connected client device 606a, 606b, or 606c. If any client 606a, 606b, or 606c has the low RSSI then the frequency band of the hotspot 610, the UE 604 may determine need to switch to the lower frequency band as illustrated in Figure 9A.

[0123] 11. Connected Wi-Fi client device 606a, 606b...606n may scan 5GHz or not: If the connected client device 606a, 606b..606n may support 5GHz, then only the frequency band of the hotspot 610 may be switched to 5GHz which is associated with the client devices 606a, 606b..606n. In this case, the client devices 606a, 606b..606n may support 5GHz. In the devices (e.g., the client devices 606a, 606b, or 606c such as watch, VST, smart TV, etc.) using mDns protocol, the UE 604 may get client capabilities of client devices 606a, 606b, or 606c as illustrated in Figure 9B.

[0124] 12. Connected Wi-Fi client device 606a, 606b...606n may scan 6GHz or not: If the connected client device 606a, 606b..606n may support 6GHz, then only the frequency band of the hotspot 610 may be switched to 6GHz which is associated with the client devices 606a, 606b..606n. In this case, the client devices 606a, 606b..606n may support 6GHz.

[0125] 13. Connected Wi-Fi client device 606a, 606b...606n may be Single-Input Single-Output (SISO) or Multiple-Input Multiple-Output (MIMO): Throughput threshold values may be different for switching to the higher frequency band.

[0126] 14. Connected Wi-Fi client traffic pattern: For example, the client traffic pattern may include, but is not limited to, packet maximum size, packet average size, packet minimum size, maximum interarrival time, minimum interarrival time.

[0127] 15. Number of connected client devices 606a, 606b...606n.

[0128] In an embodiment of the present disclosure, the input parameters may include at least one of the examples described above. The method 800 may include obtaining (e.g., taking) the input parameters including at least one of the exampled described above.

[0129] Referring to Figure 8, at operation 806, the method 800 may include checking that the hotspot 610 Egress traffic speed exceeds a defined (e.g., predefined) threshold. In an embodiment of the present disclosure, if the hotspot 610 Egress traffic speed exceeds the predetermined threshold then the method 800 may follow operation 808. At operation 808, the method 800 may include enabling the data-driven model 718 to predict a traffic type in the client device 606a, 606b...606n i.e. live high definition (HD) video streaming or RT or gaming.

[0130] In an embodiment of the present disclosure, if the the hotspot 610 egress traffic speed does not exceed the predefined threshold, then the method 800 follows operation 810. At operation 810, the method 500 may include enabling the data-driven model 718 (or) program to predict the frequency band.

[0131] In an embodiment of the present disclosure, at operation 812, the method 800 may include checking the application traffic of the UE 604 and the low battery status of the UE 604. In an embodiment of the present disclosure, if the application traffic of the UE 604 is congested and the UE 604 has low battery, then the method 800 may follow operation 814. At operation 814, the method 800 may include enabling the data-driven model 718 (or) program to estimate the speed limit for the hotspot 610.

[0132] Figure 9A illustrates an exemplary representation 900a of a connection of the client devices 606a, 606b, and 606c based on the RSSI (e.g., RSSI 1, RSSI 2, RSSI 3) of each connected client device 606a, 606b, or 606c, in accordance with an embodiment of the present disclosure. Figure 9B illustrates an exemplary representation 900b of the connection of the client devices 606a, 606b, or 606c based on the support of the frequency band of each connected client device 606a, 606b, or 606c, in accordance with an embodiment of the present disclosure. For example, the client device 606a may support only 2.4 GHz. The client device 606b may support only 2.4 GHz, 5 GHz, and 6 GHz. The client device 606c may support 2.4 GHz and 5 GHz. For the sake of brevity, Figure 8 is described with reference to Figures 9A and 9B.

[0133] Figure 10A illustrates an exemplary representation 1000a of the data-driven model 718 for predicting the traffic type of the client devices (client traffic type), in accordance with an embodiment of the present disclosure. In an embodiment the present disclosure, the data-driven model 718 may be configured to prioritize real-time traffic features. Preferably, the prediction of the traffic type of the client devices is one of the parameters for the frequency band selection.

[0134] For example, the client devices such as a VST 1001, a smart watch 1002, a gaming device 1003, a smart TV 1004, a tablet 1005, and a laptop 1006 may be connected to the UE 604. For example, the VST 1001 may transmit and receive video call traffic. The tablet 1005 may transmit and receive online game traffic. The laptop 1006 may transmit and receive internet browsing traffic. The data-driven model 718 may predict the client traffic type as video call traffic, online game traffic, or internet browsing traffic. The data-driven model 718 may predict wheter the client traffic type is real-time traffic or video straming traffic. The data-driven model 718 may predict the client traffic type based on parameters including at least one of uplink / downlink avgerage packet size, uplink / downlink minimun packet size, uplink / downlink maximum packet size, uplink / downlink minimum packet interarrival time, uplink / downlink maximum packet interarrival time, uplink / downlink average packet interarrival time, a source port, or a destination port. For example, the data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink avgerage packet size. The data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink minimun packet size. The data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink maximum packet size. The data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink minimum packet interarrival time. The data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink maximum packet interarrival time. The data-driven model 718 may obtain (e.g., take, predict) the uplink / downlink average packet interarrival time. The data-driven model 718 may obtain (e.g., take, predict) the source port. The data-driven model 718 may obtain (e.g., take, predict) the destination port.

[0135] Figure 10B illustrates an exemplary representation 1000b of a user interface (UI) depicting an icon for prioritizing the real-time traffic features, in accordance with an embodiment of the present disclosure. As depicted in Figure 10B, the UI may include a first option 1002 for prioritizing the real-time traffic features. The real-time traffic may be prioritized upon activation of the first option 1002 based on the user input. The UE 604 may prioritize the real-time traffic upon activation of the first option 1002 based on the user input.

[0136] Figure 11A illustrates an exemplary representation 1100a of the data-driven model 718 for frequency band selection, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the data-driven model 718 may be configured to receive the input parameters which are discussed above with reference to Figure 8. The data-driven model 718 may be configured to select the optimum band based on the input parameters. Preferably, the data-driven model 718 with artificial neural networks (ANN) may use the input parameters to predict the frequency band to switch. In an embodiment of the present disclosure, the data-driven model 718 may be configured to predict the probability for each frequency band based on the input parameters including given parameters associated with the hotspot 602 and parameters associated with the connected client devices (e.g., connected Wi-Fi client can scan 5GHz or not, connected Wi-Fi client can scan 6GHz or not, or connected Wi-Fi client is SISO or MIMO). Thereafter, the hotspot 610 may be configured to switch to the highest probability frequency band using a channel switch announcement (CSA).

[0137] Figure 11B illustrates an exemplary representation 1100b of a user interface (UI) depicting an icon for intelligently selecting the frequency band and speed change, in accordance with an embodiment of the present disclosure. As depicted in Figure 11B, a second option 1102 for changing the frequency band and speed of the hotspot 610 may be provided. The frequency band and speed of the hotspot 610 may be changed upon activation of the second option 1102 based on the user input. The UE 604 may change the requency band and speed of the hotspot 610 upon activation of the second option 1102 based on the user input.

[0138] Figure 12 illustrates an exemplary flow diagram 1200 depicting a method for predicting the frequency band in case of low latency, in accordance with an embodiment of the present disclosure.

[0139] At operation 1202, the client device 606a, 606b, or 606c may be connected to the hotspot 610 on the 2.4 GHz frequency band. The UE 604 may operate the hotspot 610 in the 2.4 GHz frequency band. At operation 1204, the user of the client device 606a, 606b, or 606c may start playing an online game and the client device 606a, 606b, or 606c may be near the hotspot 610. The client device 606a, 606b, or 606c may start running the online game. The online game may require low latency. In this case, at operation 1204a, the data-driven model 718 (or the UE 604) may predict the client traffic type as gaming and check other input parameters (i.e., input parameters except the client traffic type) to switch to the higher frequency band. The data-driven model 718 (or UE 604) may determine a need to switch from the current frequency band of the hotspot 610 to the higher frequency band based on the client traffic type as gaming and the other input parameters.

[0140] In an embodiment of the present disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is unnecessary, then the method 1200 may follow operation 1204b. In operation 1204b, the UE 604 may continue operating (or may maintain) the hotspot 610 in the 2.4 GHz frequency band. The hotspot 610 may continue the frequency band of 2.4 GHz.

[0141] In an embodiment of the present disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is necessary, then the method 1200 may follow operation 1204c. At operation 1204c, the UE 604 may switch from the currency frequency band of the hotspot 610 to the higher frequency band (e.g., 5GHz frequency band). The hotspot 610 may be switched to the 5GHz frequency band. At operation 1206, the client device 606a, 606b, or 606c may be moved to the higher frequency band of 5GHz.

[0142] At operation 1208, the user of the client device 606a, 606b, or 606c may stop playing the online game and move away from the hotspot 610 of the UE 604, and this may lead to the low RSSI. The client device 606a, 606b, or 606c may stop running the online game and be far from the hotspot 610 of the UE 604. In this case, the client device 606a, 606b, or 606c may have the low RSSI. At operation 1210, the UE 604 may switch from the 5GHz frequency band to lower frequency band (e.g., 2.4GHz frequency band). The frequency band of the hotspot 610 may be switched from 5GHz to 2.4GHz.

[0143] Figure 13 illustrates an exemplary flow diagram 1300 depicting a method for predicting the frequency band to handle increased traffic from the client device 606a, 606b, or 606c, in accordance with an embodiment of the present disclosure.

[0144] At operation 1302, the client device 606a, 606b, or 606c may be connected to the hotspot 610 on the frequency band 2.4 GHz. At operation 1304, the client device 606a, 606b, or 606c may start downloading the application from an application store, thereby leading to an increase in traffic. In this case, at operation 1304a, the data-driven model 718 (or UE 604) may predict that the traffic may be increased, and the data-driven model 718 (or UE 604) may check the input parameters to switch to the higher frequency band. The data-driven model 718 (or UE 604) may determine a need to switch from the current frequency band of the hotspot 610 to the higher frequency band based on the input parameters.

[0145] In an embodiment of the disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is unnecessary, then the method 1300 may follow operation 1304b. At operation 1304b, the UE 604 may continue operating (or may maintain) the hotspot 610 in the 2.4 GHz frequency band. The hotspot 610 may continue the frequency band of 2.4GHz.

[0146] In an embodiment of the disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is necessary, then the method 1300 may follow operation 1304c. At operation 1304c, the UE 604 may switch from the currency frequency band of the hotspot 610 to the higher frequency band (e.g., 5GHz frequency band). The hotspot 610 may be switched to the frequency band of 5GHz. At operation 1306, the client device 606a, 606b, or 606c may be moved to the higher frequency band of 5GHz.

[0147] At operation 1308, the user of the client device 606a, 606b, or 606c may stop playing the online game and move away from the hotspot 610, this may lead to the low RSSI. The client device 606a, 606b, or 606c may stop running the online game and be far away from the UE 604. In this case, the client device 606a, 606b, or 606c may have the low RSSI. At operation 1310, the UE 604 may switch from the 5GHz frequency band to lower frequency band (e.g., 2.4GHz frequency band). The frequency band of the hotspot 610 may be switched from 5GHz to 2.4GHz.

[0148] Figure 14 illustrates an exemplary flow diagram 1400 depicting a method for predicting the frequency band to handle increased traffic from the client device 606a, 606b, or 606c and battery low condition of the UE 604, in accordance with an embodiment of the present disclosure.

[0149] At operation 1402, the client device 606a, 606b, or 606c may be connected to the hotspot 610 on the frequency band 2.4 GHz. At operation 1404, the client device 606a, 606b, or 606c may start downloading the application from an application store, thereby leading to an increase in traffic. In this case, at operation 1404a, the data-driven model 718 (or UE 604) may predict that the traffic may be increased, and the data-driven model 718 (or UE 604) may check the input parameters to switch to the higher frequency band. The data-driven model 718 (or UE 604) may determine a need to switch from the current frequency band of the hotspot 610 to the higher frequency band based on the input parameters.

[0150] In an embodiment of the disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is unnecessary, then the method 1400 may follow operation 1404b. At operation 1404b, the UE 604 may continue operating (or may maintain) the hotspot 610 in the 2.4 GHz frequency band. The hotspot 610 may continue the frequency band of 2.4GHz.

[0151] In an embodiment of the disclosure, if the data-driven model 718 (or UE 604) determines that switching to the higher frequency band is necessary, then the method 1400 may follow operation 1404c. At operation 1404c, the UE 604 may switch from the currency frequency band of the hotspot 610 to the higher frequency band (e.g., 5GHz frequency band). The hotspot 610 may be switched to the higher frequency band of 5GHz. At operation 1406, the client device 606a, 606b, or 606c may be moved to the higher frequency band of 5GHz.

[0152] At operation 1408, the low battery condition of the UE 604 enabling the hotspot 610 may be detected. The UE 604 may detect the low battery condition of the UE 604. At operation 1410, the UE 604 may switch from the 5GHz frequency band to lower frequency band (e.g., 2.4GHz frequency band). The hotspot 610 may be switched from 5GHz to 2.4GHz.

[0153] Figure 15 illustrates an exemplary representation 1500 of the data-driven model 718 for throttling the speed of the hotspot 610, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the UE 604 may be configured to monitor if any video streaming or real-time application is running in the foreground or not. If any video streaming or real-time application is running in the foreground, then the speed may be throttled such that foreground applications executing on the UE 604 may get sufficient speed. The UE 604 may throttle the speed such that foreground applications executing on the UE 604 may get sufficient speed.

[0154] The data-driven model 718 may learn the required data rate for each streaming (e.g., video streaming) or real-time application (RT app) in a device under test (DUT) device. For example, the data-driven model 718 may obtain (e.g., estimate, detect, calculate, measure, predict) at least one of DUT backhaul type, traffic speed of the RT app, traffic speed of the video streaming, battery, or internet speed. The data-driven model 718 may estimate the maxium speed of the hotspot 610 based on at least one of DUT backhaul type, traffic speed of the RT app, traffic speed of the video streaming, battery, or internet speed. The maximum speed of the hotspot 610 in Mbps may be given by Max (1, BackhaulSpeed - requiredDataRateforForegroundApps). BackhaulSpeed may refer to backhaul speed of the UE 604, and requiredDataRateforForegroundApps may refer to minimum data rate required for foregroung applications to function smoothly.

[0155] Figure 16 illustrates an exemplary flow diagram 1600 depicting a method 1600 for throttling the speed of the hotspot 610, in accordance with an embodiment of the present disclosure. At operation 1602, the hotspot 610 may be initiated. At operation 1604, method 1600 may include checking if the battery of the DUT is low. In an embodiment of the disclosure, if the battery is low, then the maximum speed of the hotspot 610 in Mbps may be given by Max (1, BackhaulSpeed - requiredDataRateforForegroundApps).

[0156] In an embodiment of the present disclosure, if the battery of the DUT is not low, then at operation 1606, the process 1600 may check for (or detect, estimate, monitor, predict) real-time traffic or live video streaming. In an embodiment of the present disclosure, if real-time traffic is executed or live video is streamed, then the maximum speed of the hotspot 610 in Mbps may be given by Max (1, BackhaulSpeed - requiredDataRateforForegroundApps).

[0157] In this case, if neither real-time traffic nor live video streaming is detected, at operation 1608, throttling of the speed may be unnecessary. At operation 1608, the UE 604 may maintain the speed without throttling of the speed.

[0158] Figures 17A and 17B illustrate exemplary representations 1700a and 1700b of the UI of hotspot settings, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the data-driven model 718 (or the UE 604) may receive the user input corresponding to selecting an icon 1702. The data-driven model 718 (or the UE 604) may be configured to automatically switch the frequency band from 2.4GHz to 5GHz for higher speed on connected client devices as illustrated in Figure 17A based on the user input corresponding to selecting the icon 1702. In an embodiment of the present disclosure, the data-driven model 718 (or the UE 604) may receive the user input corresponding to selecting an icon 1704. The data-driven model 718 (or the UE 604) may be configured to intelligently change the frequency band and change the speed as illustrated in Figure 17B after receiving user input corresponding to selecting the icon 1704.

[0159] Figure 18 illustrates an exemplary representation 1800 of a use case of the present disclosure when the client device 1801 is downloading the file, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the hotspot 610 may be enabled in the default 2.4 GHz frequency band, and an upstream backhaul speed may be around 800 Mbps. In this case, the data-driven model 718 (or the UE 604) may predict that the client device 1801 is downloading the file, and the data-driven model 718 may determine that switching the frequency band of the hotspot to 5GHz is necessary. The UE 604 may switch the frequency band of the hotspot to 5GHz, thereby increasing the downloading speed of the file. When the file is downloaded, the data-driven model 718 may determine that switching the frequency band of the hotspot back to 2.4 GHz is necessary. The UE 604 may switch the frequency band of the hotspot back to 2.4GHz.

[0160] Figure 19 illustrates an exemplary representation 1900 of a use case of the present disclosure when 2.4GHz or compatible mode is required, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the client device 1901 may be connected to the hotspot 610. In an embodiment of the present disclosure, as the client device 1901 moves away from the hotspot 610, the data-driven model 718 (or the UE 604) may predict that switching the frequency band of the hotspot 610 to 2.4GHz is necessary. The UE 604 may switch the frequency band of the hotspot 610 to the 2.4GHz frequency band without restart. The client device 1901 may be connected to the 2.4 GHz frequency band.

[0161] Figure 20 illustrates an exemplary representation 2000 of a use case of the present disclosure during the online gaming session, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the hotspot 610 may be enabled in the default 2.4 GHz frequency band, and an upstream backhaul speed may be around 800 Mbps. In this case, the data-driven model 718 (or the UE 604) may detect online gaming traffic, thereafter the data-driven model 718 (or the UE 604) may determine that switching the frequency band of the hotspot 610 to the 5GHz or 6GHz frequency band is necessary. The UE 604 may switch the frequency band of the hotspot 610 to the 5GHz or 6GHz frequency band. In an embodiment of the disclosure, the client device 2001 may be connected to 5GHz or 6GHz frequency band, thereby leading to less latency during the online gaming session. In case, when the online gaming session is over, the UE 604 may switch the frequency band of the hotspot 610 back to the 2.4GHz frequency band.

[0162] Figure 21 illustrates an exemplary representation 2100 of a use case of the present disclosure when the client device 2101 is connected to the higher frequency band, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the hotspot 610 may be enabled in the default 6GHz frequency band. In this case, the data-driven model 718 (or the UE 604) may detect low battery of the UE 604 in 6GHz frequency band, thereafter, the data-driven model 718 (or the UE 604) may predict to switch the frequency band of the hotspot 610 to the 2.4GHz frequency band. The UE 604 may swtich the frequency band of the hotspot 610 to the 2.4GHz frequency band. After charging, the UE 604 may switch the frequency band of the hotspot 610 back to the higher frequency band, e.g., 5GHz or 6GHz frequency band. This may enable saving of the battery for a longer duration.

[0163] In an embodiment of the present disclosure, the following features / parameters may be selected for proof of concept (POC).

[0164] - Host Wi-Fi chip concurrency (True / False)

[0165] - Host Wi-Fi connected or not (True / False)

[0166] - Host Wi-Fi connection frequency (2, 5, or 6)

[0167] - Host battery Level (1-100)

[0168] - Host device charging (True / False)

[0169] - Host device support for VLP 6GHz (country regulations) (True / False)

[0170] - Host device support for VLP 5GHz (True / False)

[0171] - The hotspot 610 of the UE 604 traffic speed in Mbps (0 - 2100Mbps)

[0172] - Connected Wi-Fi client can scan 5Ghz or not(True / False)

[0173] - Connected Wi-Fi client can scan 6Ghz or not (True / False)

[0174] - RSSI of connected clients observed in the host (-25dBm to -100dBm)

[0175] Figure 22 illustrates an exemplary representation 2200 of a table depicting a dataset for training and testing the data-driven model 718, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, 3 million data samples may be collected or generated with a class balance maintained. In an embodiment of the present disclosure, 80% of the data may be used for training the data-driven model 718, and 20% of the data may be used for testing the data-driven model 718.

[0176] For example, the dataset for training and testing the data-driven model 718 may include isRSDB (indicating whether the UE supports RSDB functionality), WiFiConnected (indicating whether the UE is connected to Wi-Fi), WifiFreqency_MHz (representing the frequency band used by the Wi-Fi network in MHz), isAll6GhzClients (indicating whether all the client devices connected to the network support the 6GHz frequency band), isAll5GhzClients (indicating whether all the client devices connected to the network support the 5GHz frequency band), minRSSIOfClient (representing the lowest RSSI among the client devices connected to the hotspot), TrafficSpeed_Mbps (representing the data transfer speed in Mbps on the hotspot), BatteryLevel (representing the remaining battery charge of the UE), isUSBPluggin (indicating whetehr a USB device is connected to the UE), CurrentBand (representing the frequency band the UE is currently using to connect to the hotspot), support6Ghz (indicating whether the UE supports the 6GHz frequency band), and support5Ghz (indicating whether the UE supports the 5GHz frequency band), and the dataset for training and testing the data-driven model 718 may include TargetBand. For example, TargetBand may represent the specific frequency band that the UE prefers to connect to or determines to be suitable for connection. For example, the values of TargetBand 0, 1, and 2 may indicate that the UE prefers to connect or determines to be suitable for connection to the 2GHz, 5GHz, and 6GHz frequency bands, respectively.Figure 23 illustrates an exemplary representation 2300 of a neural network model or artificial neural network (ANN) used in the present disclosure, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, a last linear layer of the neural network may return　logits　- raw values in [-∞,+∞] - which are passed to a softmax　module. The logits may be scaled to values [0, 1] representing the model's predicted probabilities for each class (e.g., 2.4GHz frequency band, 5GHz frequency band, and 6GHz frequency band) . In an embodiment of the present disclosure, the frequency band with the highest probability may be selected. For example, the neural network model may be configured to receive input data including, but not limited to isRSDB, Wi-Fi connection frequency, Battery Level, MHS Traffic speed, RSSI of clients, Client support VLP 6GHz, and Device charging and provide target band (e.g., 2.4GHz, 5GHz, or 6GHz) as output.

[0177] The following presents an example of the neural network architecture implemented in the present invention.

[0178] NeuralNet(

[0179] (flatten): Flatten(start_dim=1, end_dim=-1)

[0180] (linear_relu_stack): Sequential(

[0181] (0): Linear(in_features=12, out_features=50, bias=True)

[0182] (1): ReLU()

[0183] (2): Linear(in_features=50, out_features=50, bias=True)

[0184] (3): ReLU()

[0185] (4): Linear(in_features=50, out_features=50, bias=True)

[0186] (5): ReLU()

[0187] (6): Linear(in_features=50, out_features=3, bias=True)

[0188] )

[0189] )

[0190] Trainable Parameters:

[0191] linear_relu_stack.0.weight torch.Size([50, 12])

[0192] linear_relu_stack.0.bias torch.Size(

[0050] )

[0193] linear_relu_stack.2.weight torch.Size([50, 50])

[0194] linear_relu_stack.2.bias torch.Size(

[0050] )

[0195] linear_relu_stack.4.weight torch.Size([50, 50])

[0196] linear_relu_stack.4.bias torch.Size(

[0050] )

[0197] linear_relu_stack.6.weight torch.Size([3, 50])

[0198] linear_relu_stack.6.bias torch.Size([3])

[0199] Hyper Parameters

[0200] BatchSize = 1024

[0201] Loss function = CrossEntropyLoss

[0202] num_epochs = 20

[0203] Activation function = Relu

[0204] no.hidden layers = 2

[0205] Hidden layer size = 50

[0206] learning_rate = 0.001

[0207] Optimizer = Adam

[0208] Figure 24A illustrates an exemplary representation 2400a of a result depicting a test accuracy of the data-driven model 718, in accordance with an embodiment of the present disclosure. More specifically, the data-driven model 718 used may be the ANN model having a test accuracy of 99.88%.

[0209] Figure 24B illustrates a graphical representation 2400b of the test accuracy of the ANN model with respect to the epoch of the ANN model, in accordance with an embodiment of the present disclosure. The graphical representation 2400b may demonstrate the performance of the ANN model during a training phase, highlighting how accuracy improves (or changes) as the ANN model undergoes multiple iterations (epochs) of learning using a training dataset. The accuracy of the ANN model may generally increase as the the ANN model undergoes multiple iterations (epochs) of learning using a training dataset.

[0210] Figure 25 illustrates an exemplary flowchart 2500 depicting a method for switching a　frequency band of the UE 604, in accordance with an embodiment of the present disclosure. Firstly, at operation 2502, the UE 604 may start to operate the hotspot 610. The hotspot 610 may be initiated. At operation 2504, the UE 604 may collect feature values (e.g., the input features or the parameters). At operation 2506, the UE 604 may predict the frequency band (e.g., target band) by using the ANN model. At operation 2508, the UE 604 may dynamically switch the frequency band based on the predicted frequency band by using the ANN model.

[0211] More preferably, the POC may be implemented on the UE 604. A pytorch framework may be used for training the ANN. The pytorch model may be converted to tflite format as Pytorch → ONNX → tf → tflite. Further, the tflite model may be integrated into semwifi-service and tested in a live network.

[0212] Figure 26 illustrates an exemplary representation 2600 of a high-level architecture of the hotspot 610, in accordance with an embodiment of the present disclosure. In an embodiment of the present disclosure, the architecture may include the UI that may include the semi wifi-service, Network stack, Wi-fi service, the data-driven model 718, a tensorflowlite library, HAL, and hostapd / supplicant, and Linux Kernel (Wi-Fi driver). The semi-wifi-service may include SemWifiApServiceImpl, SemSoftApAutoBand, SemSoftApManager, and SemWifiAp*. In an embodiment of the present disclosure, the architecture may carry out enablement of the present disclosure.

[0213] Figure 27 illustrates an exemplary representation of an environment of the present disclosure depicting enhanced user experience, in accordance with an embodiment of the present disclosure.

[0214] In an embodiment the present disclosure, the present disclosure of the electronic device 604 or the method may reduce gaming latency of the one or more client devices 2701 or 2702, thereby improving the online gaming experience as illustrated in Figure 27.

[0215] In an embodiment the present disclosure, the present disclosure of the electronic device 604 or the method may increase the speed of the internet due to switching the frequency band, thereby the file may be downloaded much faster on the one or more client devices 2701 or 2702 as illustrated in Figure 27.

[0216] In an embodiment the present disclosure, the present disclosure of the electronic device 604 or the method may also improve an online HD movie-watching experience on the one or more client devices 2701 or 2702 as illustrated in Figure 27. Moreover, the present disclosure may enhance the user experience.

[0217] Figure 28 illustrates a flowchart 2800 depicting a method for switching the　frequency band in the hotspot　610 hosted by the electronic device 604, in accordance with an embodiment of the present disclosure.

[0218] At operation 2802, the method 2800 may include receiving, by the electronic device 604, the network connection requests from the client devices 606a, 606b...606n to connect to the hotspot 610 for the wireless network. The electronic device 604 may operate as the hotspot 610 in the first frequency band.

[0219] At operation 2804, the method 2800 may include obtaining (e.g., measuring), by the electronic device 604, the one or more parameters corresponding to at least one of the electronic device 604, the one or more client devices 606a, 606b...606n, or the wireless network. For example, the method 2800 may include obtaining (e.g., measuring), by the electronic device 604, the one or more parameters corresponding to the electronic device 604. The method 2800 may include obtaining (e.g., measuring), by the electronic device 604, the one or more parameters corresponding to the one or more client devices 606a, 606b...606n. The method 2800 may include obtaining (e.g., measuring), by the electronic device 604, the one or more parameters corresponding to the wireless network.

[0220] At operation 2806, the method 2800 may include determining, by the electronic device 604, a need to switch the frequency band of the hotspot 610 for the one or more client devices 606a, 606b...606n based on the one or more parameters obtained (e.g., measured) by the UE 604.

[0221] At operation 2808, the method 2800 may include switching, by the electronic device 604, from the first frequency band of the hotspot 610 to the second frequency band. The method 2800 may include one or more parameters that may correspond to at least one of one or more of characteristics of the one or more client devices 606a, 606b...606n, one or more traffic conditions, the signal strength, the operational status of the UE 604, or network performance metrics. For example, the method 2800 may include one or more parameters that may correspond to the one or more of characteristics of the one or more client devices 606a, 606b...606n. The method 2800 may include one or more parameters that may correspond to the one or more traffic conditions. The method 2800 may include one or more parameters that may correspond to the one or more traffic conditions. The method 2800 may include one or more parameters that may correspond to the operational status of the UE 604. The method 2800 may include one or more parameters that may correspond to the network performance metrics.

[0222] The method 2800 may include obtaining (e.g., receiving), by the electronic device 604, information on a supportable frequency band of the one or more client devices from the one or more client devices 606a, 606b...606n. The method 2800 may include detecting (or predicting, determining, estimating, measuring, monitoring), by the electronic device 604, changes in activity and proximity of the one or more client devices 606a, 606b...606n. The method 2800 may include switching, by the electronic device 604, from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon detecting the changes in the activity and the proximity of the one or more client devices 606a, 606b...606n.

[0223] Figure 29 illustrates a flowchart 2900 depicting a method for switching the　frequency band in the hotspot　610 hosted by the electronic device 604, in accordance with an embodiment of the present disclosure. Operations 2810, 2810, and 2814 illustrated in Figure 29 may be executed after operation 2808 illustrated in Figure 28 is executed, however, this is not limited thereto.

[0224] At operation 2810, the method 2900 may include determining, by the electronic device 604, that the one or more client devices 606a, 606b...606n are　connected to the second frequency band.

[0225] At operation 2812, the method 2900 may include predicting (or determining, estimating, measuring, detecting, monitoring), by the electronic device 604, the battery level of the electronic device 604 and the traffic condition associated with the one or more client devices 606a, 606b...606n. The method 2900 may include predicting (or determining, estimating, measuring, detecting, monitoring), by the electronic device 604, whether the traffic condition includes the non-real-time traffic. The method 2800 may include predicting (or determining, estimating, measuring, detecting, monitoring), by the electronic device 604, whether the battery level is below the defined (e.g., predefined) threshold.

[0226] At operation 2814, the method 2900 may include switching, by the electronic device 604, from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition associated with the one or more client devices 606a, 606b...606n or the battery level of the electronic device 604 is below the predetermined threshold.

[0227] Figure 30 illustrates a flowchart 3000 depicting a method for switching the　frequency band in the hotspot　610 hosted by the electronic device 604, in accordance with an embodiment of the present disclosure. Operations 2816, 2818, and 2820 illustrated in Figure 30 may be executed after operation 2808 illustrated in Figure 28 is executed. At least one of the operations 2810, 2812, 2814 illustrated in Figure 29 may be executed simultaneously with at least one of the operations 2816, 2818, 2820 illustrated in Figure 30, or may be executed before at least one of the operations 2816, 2818, 2820 illustrated in Figure 30, or may be executed after at least one of the operations 2816, 2818, 2820 illustrated in Figure 30 are executed. However, this is not limited thereto.At operation 2816, the method 3000 may include determining, by the electronic device 604, that the one or more client devices 606a, 606b...606n are　connected to the second frequency band.

[0228] At operation 2818, the method 3000 may include predicting (or determining, estimating, measuring, detecting, monitoring), by the electronic device 604, the battery level of the electronic device 604 and the traffic condition associated with th electronic device 604. The method 3000 may include predicting, by the electronic device 604, whether traffic condition associated with the electronic device 604 includes the real-time traffic. The method 3000 may include predicting, by the electronic device 604, whether the battery level is below the defined (e.g., predefined) threshold.

[0229] At operation 2820, the method 2800 may include switching, by the electronic device 604, from the second frequency band to the first frequency band for the one or more client devices 606a, 606b...606n upon predicting that the　traffic condition associated with the electronic device 604 includes real-time traffic or the battery level of the electronic device 604 is below the defined (e.g., predefined) threshold. The method 2800 may include determining, by the electronic device 604, the data rate for the one or more client devices 606a, 606b...606n upon predicting the traffic condition associated with the electronic device 604.

[0230] The present disclosure provides an electronic device (e.g., a UE) of a system that may comprise a memory and at least one processor. The memory stores instructions. The at least one processor individually or collectively executes the instructions to cause the electronic device (or the system) to perform the methods as disclosed herein. The electronic device (or the system) may be integrated with a hardware device or may be implemented on a cloud-based server.

[0231] The present disclosure may provide technical advancements such as switching the　frequency band in the hotspot　hosted by the UE. The electronic device (or the system) may enable dynamic switching between Wi-Fi frequency bands (e.g., 2.4 GHz, 5 GHz, and 6 GHz) based on contextual parameters, ensuring optimal performance without requiring user intervention. The electronic device (or the system) may eliminate manual band selection, improve ease of use and user experience. The electronic device (or the system) may ensure better network utilization by selecting the most suitable frequency band for the current conditions, such as traffic type, network speed, and connected device capabilities. The electronic device (or the system) may reduce latency, especially for real-time applications like gaming and video conferencing.

[0232] The electronic device (or the system) may dynamically adjust the bandwidth and the speed allocation to prioritize critical foreground tasks on the host device (e.g., streaming or real-time applications). The electronic device (or the system) may help maintain a balance between host device performance and client device connectivity. The electronic device (or the system) may monitor the host device's battery level and charging status, enabling intelligent decisions to save battery life. For example, The electronic device (or the system) may switch to a lower power-consuming band during low battery conditions. The electronic device (or the system) may support uninterrupted 4K streaming, online gaming, and faster downloads by automatically adapting to the required parameters.

[0233] The electronic device (or the system) may prevent buffering and may ensure seamless connectivity. The electronic device (or the system) may employ the data-driven model to predict optimal bands based on historical and real-time data, and offer a personalized and efficient network experience. The electronic device (or the system) may enhance reliability by ensuring stable connectivity even in challenging network environments. The prevent disclosure may support a wide range of devices, including those requiring higher frequency bands for high-speed applications, ensuring backward compatibility with 2.4 GHz devices. The electronic device (or the system) may minimize the need for users to understand or manually select between the frequency bands, by simplifying the process of optimizing network connectivity.

[0234] The effects obtainable from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by a person skilled in the art to which the present disclosure belongs from the above description.

[0235] According to an embodiment of the disclosure, a method (2800) for switching a　frequency band in a hotspot　(610) hosted by an electronic device (604) may be provided. The method (2800) may include operating as the hotspot (610) in a first frequency band. The method (2800) may include receiving (2802), by the electronic device (604), one or more network connection requests from one or more client devices (606a, 606b...606n) to connect to the hotspot (610) for a wireless network (608). The method (2800) may include obtaining (2804), by the electronic device (604), one or more parameters corresponding to at least one of the electronic device (604), the one or more client devices (606a, 606b...606n), or the wireless network (608). The method (2800) may include determining (2806), by the electronic device (604), a need to switch the frequency band of the hotspot (610) for the one or more client devices (606a, 606b...606n) based on the one or more parameters obtained by the electronic device (604). The method (2800) may include switching (2808), by the electronic device (604), from the first frequency band of the hotspot (610) to a second frequency band.

[0236] According to an embodiment of the disclosure, the one or more parameters may correspond to at least one of one or more of characteristics of the one or more client devices (606a, 606b...606n), one or more traffic conditions, signal strength, an operational status of the electronic device (604), or network performance metrics.

[0237] According to an embodiment of the disclosure, the method (2800) may include obtaining, by the electronic device (604), information on a supportable frequency band of the one or more client devices (606a, 606b...606n) from the one or more client devices (606a, 606b...606n).

[0238] According to an embodiment of the disclosure, the first frequency band may be one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band, wherein the second frequency band may be another one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band.

[0239] According to an embodiment of the disclosure, the method (2800) may include detecting, by the electronic device (604), changes in activity and proximity of the one or more client devices (606a, 606b...606n). The method (2800) may include switching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon detecting the changes in the activity and the proximity of the one or more client devices (606a, 606b...606n).

[0240] According to an embodiment of the disclosure, the method (2800) may include determining, by the electronic device (604), that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band. The method (2800) may include predicting, by the electronic device (604), a battery level of the electronic device (604) and a traffic condition associated with the one or more client devices (606a, 606b...606n). The method (2800) may include switching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the one or more client devices (606a, 606b...606n) comprises non-real-time traffic or the battery level of the electronic device (604) is below a defined threshold.

[0241] According to an embodiment of the disclosure, the method (2800) may include determining, by the electronic device (604), that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band. The method (2800) may include predicting, by the electronic device (604), the battery level of the electronic device (604) and the traffic condition associated with the electronic device (604). The method (2800) may include switching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the electronic device (604) comprises real-time traffic or the battery level of the electronic device (604) is below a defined threshold.

[0242] According to an embodiment of the disclosure, the method (2800) may include determining, by the electronic device (604), data rate for the one or more client devices (606a, 606b...606n) upon predicting the traffic condition associated with the electronic device (604)

[0243] According to an embodiment of the disclosure, an electronic device (604) for switching a　frequency band in a hotspot (610) hosted by the electronic device (604) may be provided. The electronic device (604) may include memory (702) storing instructions and at least one processor (704) operatively coupled to the memory (702) and comprising processing circuitry. The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to operate as the hotspot (610) in a first frequency band. The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to receive one or more network connection requests from one or more client devices (606a, 606b...606n) to connect to the hotspot (610) for a wireless network (608). The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to obtain one or more parameters corresponding to at least one of the electronic device (604), the one or more client devices (606a, 606b...606n), or the wireless network (608). The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to determine a need to switch the frequency band of the hotspot (610) for the one or more client devices (606a, 606b...606n) based on the one or more parameters. The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to switch from the first frequency band of the hotspot (610) to a second frequency band.

[0244] According to an embodiment of the disclosure, the one or more parameters may correspond to at least one of one or more of characteristics of the one or more client devices (606a, 606b...606n), one or more traffic conditions, signal strength, an operational status of the electronic device (604), or network performance metrics.

[0245] According to an embodiment of the disclosure, the at least one processor individually or collectively executes the instructions to cause the electronic device (604) to obtain information on a supportable frequency band of the one or more client devices (606a, 606b...606n) from the one or more client devices (606a, 606b...606n).

[0246] According to an embodiment of the disclosure, the first frequency band may be one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band, wherein the second frequency band may be another one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band.

[0247] According to an embodiment of the disclosure, the at least one processor individually or collectively executes the instructions to cause the electronic device (604) to detect changes in activity and proximity of the one or more client devices (606a, 606b...606n). The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to switch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon detecting the changes in the activity and the proximity of the one or more client devices (606a, 606b...606n).

[0248] According to an embodiment of the disclosure, the at least one processor individually or collectively executes the instructions to cause the electronic device (604) to determine that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band. The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to predict a battery level of the UE (604) and a traffic condition associated with the one or more client devices (606a, 606b...606n). The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to switch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the one or more client devices (606a, 606b...606n) comprises non-real-time traffic or the battery level of the UE (604) is below a defined threshold.

[0249] According to an embodiment of the disclosure, the at least one processor individually or collectively executes the instructions to cause the electronic device (604) to determine that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band. The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to predict the battery level of the electronic device (604) and the traffic condition associated with the electronic device (604). The at least one processor individually or collectively executes the instructions to cause the electronic device (604) to switch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the electronic device (604) comprises real-time traffic or the battery level of the electronic device (604) is below a defined threshold.

[0250] According to an embodiment of the disclosure, the at least one processor individually or collectively executes the instructions to cause the electronic device (604) to determine data rate for the one or more client devices (606a, 606b...606n) upon predicting the traffic condition associated with the UE (604).According to an embodiment of the disclosure, a computer-readable medium containing instructions, wherein the instructions, when executed by at least one processor, cause an electronic device (604) to perform the above method, may be provided.

[0251] 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 in order to implement the inventive concept as taught herein. The drawings and the foregoing 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.

Claims

1.A method (2800) for providing a hotspot　(610) hosted by an electronic device (604), comprising:operating (2801), by the electronic device (604), as the hotspot (610) in a first frequency band;receiving (2802), by the electronic device (604), one or more network connection requests from one or more client devices (606a, 606b...606n) to connect to the hotspot (610) for a wireless network (608);obtaining (2804), by the electronic device (604), one or more parameters corresponding to at least one of the electronic device (604), the one or more client devices (606a, 606b...606n), or the wireless network (608);determining (2806), by the electronic device (604), a need to switch the frequency band of the hotspot (610) for the one or more client devices (606a, 606b...606n) based on the one or more parameters obtained by the electronic device (604); andswitching (2808), by the electronic device (604), from the first frequency band of the hotspot (610) to a second frequency band.2.The method as claimed in claim 1, wherein the one or more parameters correspond to at least one of one or more of characteristics of the one or more client devices (606a, 606b...606n), one or more traffic conditions, signal strength, an operational status of the electronic device (604), or network performance metrics.3.The method as claimed in any one of claims 1 and 2, further comprising:obtaining, by the electronic device (604), information on a supportable frequency band of the one or more client devices (606a, 606b...606n) from the one or more client devices (606a, 606b...606n).4.The method as claimed in any one of claims 1 to 3, wherein the first frequency band is one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band, wherein the second frequency band is another one of 2.4 GHz frequency band, 5 GHz frequency band, or 6 GHz frequency band.5.The method as claimed in any one of claims 1 to 4, further comprising:detecting, by the electronic device (604), changes in activity and proximity of the one or more client devices (606a, 606b...606n); andswitching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon detecting the changes in the activity and the proximity of the one or more client devices (606a, 606b...606n).6.The method as claimed in any one of claims 1 to 5, further comprising:determining, by the electronic device (604), that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band;predicting, by the electronic device (604), a battery level of the electronic device (604) and a traffic condition associated with the one or more client devices (606a, 606b...606n); andswitching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the one or more client devices (606a, 606b...606n) comprises non-real-time traffic or the battery level of the electronic device (604) is below a defined threshold.7.The method as claimed in any one of claims 1 to 6, further comprising:determining, by the electronic device (604), that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band;predicting, by the electronic device (604), the battery level of the electronic device (604) and the traffic condition associated with the electronic device (604); andswitching, by the electronic device (604), from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the electronic device (604) comprises real-time traffic or the battery level of the electronic device (604) is below a defined threshold.8.The method as claimed in claim 7, further comprising:determining, by the electronic device (604), data rate for the one or more client devices (606a, 606b...606n) upon predicting the traffic condition associated with the electronic device (604).9.An electronic device (604) forproviding a hotspot (610) hosted by the electronic device (604), comprising:memory (702) storing instructions; andat least one processor (704) operatively coupled to the memory (702) and comprising processing circuitry, wherein the at least one processor (704) individually or collectively executes the instructions to cause the electronic device (604) to:operate as the hotspot (610) in a first frequency band;receive one or more network connection requests from one or more client devices (606a, 606b...606n) to connect to the hotspot (610) for a wireless network (608);obtain one or more parameters corresponding to at least one of the electronic device (604), the one or more client devices (606a, 606b...606n), or the wireless network (608);determine a need to switch the frequency band of the hotspot (610) for the one or more client devices (606a, 606b...606n) based on the one or more parameters ; andswitch from the first frequency band of the hotspot (610) to a second frequency band.10.The electronic device (604) as claimed in claim 9, wherein the one or more parameters correspond to at least one of one or more of characteristics of the one or more client devices (606a, 606b...606n), one or more traffic conditions, signal strength, an operational status of the electronic device (604), or network performance metrics.11.The electronic device (604) as claimed in any one of claims 9 to 10, wherein the at least one processor (704) individually or collectively executes the instructions to cause the electronic device (604) to:obtain information on a supportable frequency band of the one or more client devices (606a, 606b...606n) from the one or more client devices (606a, 606b...606n).12.The electronic device (604) as claimed in any one of claims 9 to 11, wherein the at least one processor (704) individually or collectively executes the instructions to cause the electronic device (604) to:detect changes in activity and proximity of the one or more client devices (606a, 606b...606n); andswitch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon detecting the changes in the activity and the proximity of the one or more client devices (606a, 606b...606n).13.The electronic device (604) as claimed in any one of claims 9 to 12, wherein the at least one processor (704) individually or collectively executes the instructions to cause the electronic device (604) to:determine that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band;predict a battery level of the UE (604) and a traffic condition associated with the one or more client devices (606a, 606b...606n); andswitch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the one or more client devices (606a, 606b...606n) comprises non-real-time traffic or the battery level of the UE (604) is below a defined threshold.14.The electronic device (604) as claimed in any one of claims 9 to 13, wherein the at least one processor (704) individually or collectively executes the instructions to cause the electronic device (604) to:determine that the one or more client devices (606a, 606b...606n) are　connected to the second frequency band;predict the battery level of the electronic device (604) and the traffic condition associated with the electronic device (604); andswitch from the second frequency band to the first frequency band for the one or more client devices (606a, 606b...606n) upon predicting that the　traffic condition associated with the electronic device (604) comprises real-time traffic or the battery level of the electronic device (604) is below a defined threshold.15.A computer-readable medium containing instructions, wherein the instructions, when executed by at least one processor, cause an electronic device (604) to perform the method of any one of claims 1 to 8.

Images