User equipment and method for optimized utilization of network radio resources

Abstract

A user equipment (UE) (100) and a method (200) for optimizing the utilization of network radio resources are disclosed. The UE (100) includes a processor (102), a memory (104), and a communication interface (106). The memory (104) includes instructions, when executed, cause the processor (102) to determine one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs). The processor is further configured to determine one or more user service parameters and one or more device parameters and compare the parameters with respective predefined criteria. Based on the comparison, the processor enables switching among the one or more SIMs and the one or more RATs after a predetermined time. Herein, the communication interface (106) facilitates communication using the one or more SIMs and the one or more RATs, ensuring optimized network resource utilization.

Description

USER EQUIPMENT AND METHOD FOR OPTIMIZED UTIEIZATION OF NETWORK RADIO RESOURCESTECHNICAE FIELD

[0001] The present disclosure relates to the field of wireless communication. More particularly, the present disclosure relates to a user equipment (UE) and a method for providing optimized utilization of network radio resources.BACKGROUND

[0002] Radio resource utilization in wireless communications refers to the efficient management and allocation of the finite radio frequency spectrum and associated network resources to ensure optimal performance, connectivity, and service quality. In advanced network deployments, such as LTE, 5G, and 5G Advanced, radio resource utilization is increasingly critical due to the diverse range of applications, devices, and demands at varying levels of bandwidth, latency, and reliability. Furthermore, with the deployment of 6G, the diverse range of applications may require radio resource allocation with different service requirements. Balancing radio resource utilization among the RATs (e.g., 4G, 5G, 6G, etc.) may become a critical need in the future to optimize the service experience in the field, based on varying user requirements.

[0003] A Multiple Subscriber Identity Module (MSIM)-capable user equipment (UE) may operate in connected mode on a first subscription (or a first SIM) with a first network while operating in idle mode or active mode on a second subscription (or a second SIM) with a second network. In existing mechanisms, switching between multiple subscriptions for network radio resource utilization and RAT services in advanced network deployments (e.g., 4G, 5G) faces several inefficiencies.

[0004] Current systems often lack the ability to dynamically adapt to the real-time service needs of the UE to optimize spectrum utilization and overall system performance. The reliance on static or preconfigured RAT selection priorities — generally favoring higher RATs — may result in suboptimal utilization of network resources at lower RATs. For example, high-capacity RATs (e.g., 5G or future 6G) may be utilized by low-bandwidth applications, such as voice calls or basic web browsing. The high-capacity RATs may become overloaded with traffic from both low and high-bandwidth-intensive applications, leading to degraded service quality and inefficient resource allocation. Meanwhile, lower-capacity RATs may be underutilized, despite being more suitable for low-bandwidth UEs running on higher-capacity RATs. Hence, there is a need for balanced, optimal RAT service selection with a dynamic RAT selection approach to overall system optimization based on the UE's dynamic needs. For example, a UE running a simple voice call, normal web browsing, or a low- bandwidth application may be switched to a lower-capacity RAT (i.e., a lower RAT), thereby freeing up radio resources on higher-capacity RATs for high-bandwidth or critical- requirement UEs. This is a perfect example of optimal radio resource utilization across RATs.

[0005] The existing mechanisms also struggle to address challenges related to mixed RAT deployments and the diverse requirements of different applications. In many cases, RAT acquisition or subscription switching is governed by predefined settings that do not consider the variability of real-time user requirements, network density, RAT availability, or device mobility. Consequently, existing mechanisms result in either excessive use of high-bandwidth RATs or over-reliance on lower RATs. Furthermore, they fail to achieve a fair distribution of resources. Additionally, the inability to efficiently manage switching between subscriptions can lead to inconsistent user experiences and unnecessary signaling overhead, further complicating the deployment and scalability of advanced network services.

[0006] There is, therefore, a need to overcome the above drawbacks, limitations, and shortcomings associated with existing practices and provide a user equipment (UE) and a method for optimized utilization of network radio resources through optimal dynamic subscriber identity module (SIM) management and RAT switching.OBJECTS OF THE PRESENT DISCLOSURE

[0007] A general object of the present disclosure is to provide a user equipment (UE) and a method for providing optimized utilization of network Radio resources by a user equipment (UE).

[0008] An object of the present disclosure is to enable user equipment (UE) to dynamically switch between multiple SIM associated multiple radio access technologies (RATs) based on real-time user needs and network conditions.

[0009] An object of the present disclosure is to reduce power consumption and operational costs by intelligently selecting energy-efficient RATs when high-speed connections are not necessary.

[0010] An object of the present disclosure is to ensure seamless connectivity and improved user experience by providing uninterrupted service during RAT and subscription switching.

[0011] An object of the present disclosure is to provide machine learning (ML) model for predicting optimal RAT selection based on historical usage patterns and real-time data.

[0012] An object of the present disclosure is to facilitate load balancing and optimal resource allocation between different RATs and network subscriptions.

[0013] An object of the present disclosure is to provide a user equipment (UE) and a method that allows users to set preferences for switching based on factors such as cost, power savings, and application-specific requirements.

[0014] An object of the present disclosure is to provide efficient management of multiple subscriptions within a single user equipment, ensuring fair distribution of network resources.

[0015] An object of the present disclosure is to provide improve overall network performance and service quality by dynamically adapting to the real-time service needs of user equipment.SUMMARY

[0016] The present disclosure relates to the field of wireless communication. More particularly, the present disclosure relates to a user equipment (UE) and a method for providing optimized utilization of network radio resources.

[0017] An aspect of the present disclosure pertains to a user equipment (UE) for optimizing utilization of network radio resources. The UE includes a processor communicatively coupled to a memory, and a communication interface, the memory includes instructions. Herein, when the instructions are executed, the processor is configured to determine one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs). The processor is also configured to determine one or more user service parameters and one or more device parameters. The processor is also configured to compare the one or more user service parameters and the one or more device parameters with respective predefined criteria. The processor is also configured to enable switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time. Herein, the communication interface is responsible for facilitating communication using the one or more SIMs and the one or more RATs.

[0018] In one embodiment, the one or more user service parameters include one or more of: bandwidth requirement, latency requirement, application performance requirement, and user preference.

[0019] In one embodiment, the one or more device parameters include one or more of: battery level, signal strength, current data usage, and device temperature.

[0020] In one embodiment, the predetermined time is a minimum time period to avoid frequent switching and configured based on network condition and user preference.

[0021] In one embodiment, the respective predefined criteria for the switching are dynamically generated using a machine learning (ML) model, based on features extracted from the one or more user service parameters and the one or more device parameters by the ML model. Herein, the features include one or more of: network quality indicators, user activity patterns, device conditions, and environmental context and the ML model is trained based on historical data including, one or more of: user behavior, network performance metrics, and device performance metrics.

[0022] In an additional embodiment, the processor is further configured to categorize one or more applications in the UE into RAT-specific groups based on at least one of: bandwidth, latency, and performance requirements.

[0023] In one embodiment, the one or more SIMs include a first SIMs and a second SIM.

[0024] In an additional embodiment, the processor is further configured to prioritize aRAT acquisition order for the first SIM, from a lower RAT to a higher RAT, and the second SIM, from the higher RAT to the lower RAT.

[0025] In an additional embodiment, the first SIM and the second SIM belong to a same network operator. Herein, the processor is further configured to facilitate an operator-defined optimized load balancing between a first RAT and a second RAT.

[0026] In an additional embodiment, the processor is further configured to apply dynamic allocation of higher RAT resources to the UE based on a predefined priority level assigned by the network operator.

[0027] In an additional embodiment, the first SIM and the second SIM belong to different network operators. Herein, the processor is further configured to switch between the network operators based on at least one of: a RAT density, a capacity agreement and a service agreement between the network operators in a specific deployment area.

[0028] In an additional embodiment, the processor is further configured to enable switching between the first SIM and the second SIM based on a priority override scenario including, one of: an emergency service, and an uninterrupted connectivity.

[0029] In an additional embodiment, the processor is further configured to customize switching between the first SIM and the second SIM based on one or more of: a user preference, cost, power savings, and the one or more applications.

[0030] Another aspect of the present disclosure pertains to a method for optimizing utilization of network radio resources, performed by a processor of a user equipment (UE).The method includes determining one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs). The method also includes determining one or more user service parameters and one or more device parameters. The method also includes comparing the one or more user service parameters and the one or more device parameters with respective predefined criteria. The method also includes enabling switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time. Herein, the communication interface (106) is responsible for facilitating communication using the one or more SIMs and the one or more RATs.

[0031] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.BRIEF DESCRIPTION OF DRAWINGS

[0032] The accompanying drawings are included to provide a better understanding of the present disclosure and form an integral part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the detailed description, help explain its underlying principles. It should be noted, however, that the appended drawings depict only certain representative aspects of the present disclosure and are not intended to limit its scope, as the description may encompass other equally effective embodiments. Identical or similar elements in different drawings may be identified by the same reference numbers.

[0033] FIG. 1 illustrates an exemplary diagram representing the components of the user equipment (UE), in accordance with an embodiment of the present disclosure.

[0034] FIG. 2 illustrates an exemplary flow diagram for implementing the steps of the proposed method, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0035] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0036] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.

[0037] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more.” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more.” Furthermore, as used herein, the terms “set” and “group” are intended to include one or more items (e.g., related items, unrelated items, or a combination of related and unrelated items), and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and / or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of’.

[0038] Embodiments explained herein relate to the field of wireless communication. In particular, the present disclosure provides a user equipment (UE) and a method for providing optimized utilization of network radio resources.

[0039] It should be noted that while aspects may be described herein using terminology commonly associated with a 5G or NR radio access technology (RAT), aspects of the present disclosure may be applied to other RATs, such as a 2G RAT, 3G RAT, a 4G RAT, and / or a RAT subsequent to 5G (e.g., 6G, open radio access network (ORAN) technology).

[0040] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-2.

[0041] Referring to FIG. 1, the proposed user equipment (UE) (100) for optimizing utilization of network radio resources is shown, in accordance with one or more embodiments of the present disclosure. The UE (100) may include a processor (102) communicatively coupled to a memory (104), and a communication interface (106). The memory (104) may include instructions, when executed, the processor (102) may be configured to determine one or more radio access technologies (RATs) associated with one or more subscriber identitymodules (SIMs). The processor (102) may also be configured to determine one or more user service parameters and one or more device parameters. The processor (102) may also be configured to compare the one or more user service parameters and the one or more device parameters with respective predefined criteria. Further, The processor (102) may also be configured to enable switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time. Herein, the communication interface (106) may be responsible for facilitating communication using the one or more SIMs and the one or more RATs.

[0042] In one embodiment, the one or more user service parameters may include, but not limited to, one or more of: bandwidth requirement, latency requirement, application performance requirement, and user preference.

[0043] In one embodiment, the one or more device parameters may include, but not limited to, one or more of: battery level, signal strength, current data usage, and device temperature.

[0044] In one embodiment, the predetermined time is a minimum time period to avoid frequent switching and may be configured based on network condition and user preference.

[0045] In one embodiment, the respective predefined criteria for the switching are dynamically generated using a machine learning (ML) model, based on features extracted from the one or more user service parameters and the one or more device parameters by the ML model. Herein, the features may include, but not limited to, one or more of: network quality indicators, user activity patterns, device conditions, and environmental context. Herein, the ML model being trained based on historical data including, but not limited to, one or more of: user behavior, network performance metrics, and device performance metrics.

[0046] In an exemplary embodiment, the UE (100) may include a wide range of devices that are capable of wireless communication. Examples of the UE (100) include, but are not limited to, a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device or equipment, biometric sensors / devices, wearable devices (smart watches, smart clothing, smart glasses, smart wrist bands, smart jewelry (e.g., smart ring, smart bracelet)), an entertainment device (e.g., a music or video device, or a satellite radio), a vehicular component or sensor, smart meters / sensors, industrial manufacturing equipment, a global positioning system device, any other suitable device thatis configured to communicate via a wireless or wired medium and other portable communication devices.

[0047] In an exemplary embodiment, other important components of the UE (User Equipment) may include a variety of hardware and software modules that enable the described functionalities. In an exemplary embodiment, the UE (100) may include a multicore processor configured to execute complex decision-making algorithms, including those related to dynamic switching and resource optimization across multiple radio access technologies (RATs). The processor (102) may include dedicated cores for handling communication tasks and machine learning (ML) tasks.

[0048] In an exemplary embodiment, the memory (104) of the UE (100) may store instructions for the processor (102) to execute. Additionally, the memory (104) may store a pre-trained machine learning (ML) model and historical data sets that are used by the ML model to make real-time predictions. The memory (104) may also include volatile memory, such as RAM, for real-time processing and non-volatile memory, such as flash storage, for storing long-term data, including the ML model, predefined criteria, and user preferences.

[0049] In an exemplary embodiment, the communication interface (106) may be responsible for establishing and maintaining connections with various RATs, including 4G, 5G, and potentially 6G networks. The communication interface (106) supports multi-SIM operations, allowing seamless switching between different networks or RATs. It also includes transceivers and antennas for handling multiple frequency bands and advanced modulation schemes.

[0050] In an exemplary embodiment, the UE (100) may further include a sensor module including various sensors, such as a gyroscope, accelerometer, GPS, and temperature sensors. The sensor module may provide contextual information to the processor (102), such as mobility status and geographic location, which are utilized by the ML model to enhance the accuracy of switching decisions.

[0051] In an exemplary embodiment, a power management unit (PMU) in the UE (100) may be configured to monitor and manage the device’s power consumption. The PMU may work in conjunction with the processor (102) to optimize energy efficiency by dynamically adjusting the RAT selection and communication settings based on the current battery level and power-saving preferences.

[0052] In an exemplary embodiment, the UE (100) may include a user interface (UI) module that allows users to input preferences related to network usage, cost sensitivity, andapplication prioritization. The UI module may also provide visual feedback to the user regarding the current RAT in use, network conditions, and expected performance metrics.

[0053] In an exemplary embodiment, the UE (100) may further include a network monitoring module configured to collect real-time data regarding network performance, such as signal strength, latency, and data throughput. The data may be periodically fed into the ML model for continuous learning and improvement of the switching criteria.

[0054] In an exemplary embodiment, the UE (100) may be equipped with a subscription management module that handles multiple subscriptions. The subscription management module may be configured to prioritize subscriptions based on predefined user preferences, cost agreements, and network conditions. The subscription management module may interact with the processor to execute seamless switching between different subscriptions while ensuring minimal service interruption.

[0055] In an exemplary embodiment, the network condition may refer to real-time parameters that affect the quality of communication over a wireless network. The parameters may include, but not limited to, signal strength, available bandwidth, network congestion, and latency. Monitoring network conditions may enable the UE to make informed decisions about RAT selection and SIM switching to enhance user experience and maintain optimal connectivity.

[0056] In an exemplary embodiment, the user preference may include customizable settings that influence how the UE prioritizes different RATs and SIMs based on individual requirements. Examples may include, but not limited to, preferences for low-latency connections, minimal data costs, or battery optimization. The UE may allow a user to set predefined preferences, which are factored into dynamic decision-making algorithms, ensuring that switching actions align with user expectations and requirements. By combining real-time network condition monitoring with user-defined preferences, the UE may dynamically balance performance and resource allocation.

[0057] In an exemplary embodiment, the network quality indicators may include, but not be limited to, signal strength, interference level, data throughput, latency, packet loss, and network availability. The UE may leverage the network quality indicators in conjunction with user preferences and device conditions to dynamically select the optimal RAT.

[0058] In an exemplary embodiment, the user activity patterns may refer to the behavioral trends and usage characteristics exhibited by a user when interacting with the UE. The user activity patterns may include, but not limited to, the frequency of application usage, types of applications accessed (e.g., streaming, gaming, browsing), time of day when specificactivities are performed, and the typical duration of usage sessions. By analyzing the patterns, the UE may predict the user's future needs and adjust the RAT and SIM selection accordingly. For example, if a user frequently streams high-definition videos during the evening, the UE may prioritize RATs with higher bandwidth during that time. Similarly, during periods of low activity or simple tasks like messaging, the UE may switch to lower-capacity RATs or SIM associated with lower capacity RAT to conserve network resources and battery life. Analyzing, the user activity patterns may allow the UE to make proactive decisions.

[0059] In an exemplary embodiment, the device conditions may refer to the real-time operational state and environmental factors affecting the user equipment (UE). The conditions include battery level, device temperature, signal strength, and current data usage. For instance, when the battery level is low, the UE may prioritize subscription switch corresponding to RATs or SIMs that consume less power to extend battery life. Similarly, if the device temperature exceeds a predefined threshold, the UE may switch to a lower- capacity RAT or SIM associated with lower capacity RAT to prevent overheating. Monitoring signal strength may help the UE maintain stable connectivity by switching to a more suitable RAT in areas with poor reception. Additionally, by tracking current data usage, the UE may dynamically allocate network resources to balance performance and efficiency.

[0060] In an exemplary embodiment, the environmental context may refer to the external factors and surroundings that influence the performance and behavior of the user equipment (UE). The external factors and surroundings may include, but are not limited to, physical location (e.g., indoors, outdoors, urban, rural), weather conditions (e.g., rain, fog, temperature), and mobility patterns (e.g., stationary, walking, driving). For example, in densely populated urban environments with high signal interference and network congestion, the UE may prioritize RATs with better interference handling and capacity. Similarly, during adverse weather conditions that may affect signal propagation, the UE may adjust its RAT selection to ensure stable connectivity. Analyzing environmental context, the UE may further enhance network performance, reduce service disruptions, and improve user satisfaction, particularly in dynamic and challenging deployment scenarios.

[0061] In one exemplary embodiment, the user behavior may refer to patterns and preferences exhibited by a user when interacting with the UE, including, but not limited to, frequently accessed applications, typical usage times, data consumption, and mobility trends. The network performance metrics may represent measurable attributes of network quality, such as data throughput, latency, jitter, packet loss, and handover success rates. The device performance metrics may include parameters related to the operational health and efficiencyof the UE, including processor usage, memory utilization, thermal levels, and battery consumption.

[0062] In an embodiment, the comparison with the respective predefined criteria may include evaluating real-time parameters, such as user service requirements and device conditions, against pre-established thresholds or rules to determine optimal radio access technology (RAT) and SIM selection. The predefined criteria may include, but not limited to, bandwidth thresholds, latency limits, signal strength ranges, and device-specific constraints like battery level or temperature thresholds. For instance, if the current bandwidth requirement of one or more applications running in the UE exceeds a predefined threshold for a lower-capacity RAT, the UE may trigger a switch to a higher-capacity RAT or a SIM associated with higher capacity RAT. Similarly, when UE is running on high-capacity RAT (or higher RAT) associated SIM, may switch to lower capacity RAT (or lower RAT) associated SIM, when current bandwidth requirement of the one or more applications running currently in the UE is detected to be of low requirement bandwidth. The comparison mechanism ensures that network resources are utilized efficiently, while maintaining a balance between performance, cost, and power consumption. A machine learning model may configure the respective predefined criteria.

[0063] In an exemplary embodiment, training the ML model may, include but not limited to, supervised, unsupervised and reinforcement learning techniques. The supervised learning may be employed to train models using labeled historical data, such as network performance metrics and user activity patterns, enabling the UE to predict optimal RAT selection for specific scenarios. The unsupervised learning may be applied to identify hidden patterns and clusters in large datasets, such as user behavior trends and environmental factors, helping the UE adapt to new or unforeseen conditions without explicit labels. Additionally, reinforcement learning may enable real-time decision-making by continuously learning from the environment through feedback, optimizing switching decisions based on real-time rewards such as improved connectivity, reduced latency, or extended battery life.

[0064] In one embodiment, the processor (102) may be configured to categorize cumulative bandwidth requirement associated with one or more applications in the UE into RAT-specific groups based on at least one of: bandwidth, latency, and performance requirements. For instance, the processor (102) may analyze the bandwidth requirements of running applications installed on the UE. For example, a video streaming or an online gaming along with a messenger application, a social media application and an email application, which require a high bandwidth and low latency, may be cumulatively grouped into a high-performance RAT category, such as 5 G or 6G. Conversely, applications like email along with messaging application, which may have lower bandwidth and moderate latency requirements , may be cumulatively grouped into a lower-performance RAT category, such as 4G.

[0065] In one embodiment, the one or more SIMs may include a first SIMs and a second SIM.

[0066] In one embodiment, the processor (102) may be configured to prioritize a RAT acquisition order for the first SIM, from a lower RAT to a higher RAT, and the second SIM, from the higher RAT to the lower RAT. This means that the first SIM will initially attempt to connect to a lower RAT (e.g., 4G out of 4G, 5G and 6G), and the second SIM will initially attempt to connect to a higher RAT (e.g., 6G out of 4G, 5G and 6G).

[0067] In one embodiment, the first SIM and the second SIM may belong to a same network operator, and the processor (102) may be configured to facilitate an operator-defined optimized load balancing between a first RAT associated with the first SIM and a second RAT associated with the second SIM. In this scenario, the network operator defines specific criteria and policies for distributing the network load between the different RATs to ensure efficient utilization of network resources. The network operator then share data associated with the specific criteria and policies to the UE. Based on the operator-defined criteria, the processor (102) dynamically allocates the UE's connections to either the first RAT or the second RAT. For example, during peak usage times, the processor (102) may direct the UE to connect to the lower RAT (e.g., 4G) for less demanding applications, while reserving the higher RAT (e.g., 5G or 6G) for high-bandwidth and low-latency applications.

[0068] In one embodiment, the processor (102) may be configured to apply dynamic allocation of higher RAT resources to the UE (100) based on a predefined priority level assigned by the network operator. The network operator may assign priority levels to different UEs based on factors such as subscription plans, user profiles, or specific application requirements. The processor (102) may continuously monitor the priority level assigned to the UE (100) and the current network conditions. When the UE (100) requires access to higher RAT resources, such as 5G or 6G, the processor (102) may evaluate the priority level of the UE. If the UE (100) has a high priority level, the processor (102) may dynamically allocate the higher RAT resources to the UE, ensuring that the UE (100) receives the necessary bandwidth and low latency for optimal performance.

[0069] In one embodiment, the first SIM and the second SIM may belong to different network operators. Herein, the processor (102) may be configured to switch between the network operators based on at least one of: a RAT density, a capacity agreement and a serviceagreement between the network operators in a specific deployment area. The processor (102) continuously monitors the network conditions, including the density of available RATs, and evaluates the capacity and service agreements between the network operators. RAT density refers to the availability and distribution of different RATs (e.g., 4G, 5G, 6G) in the deployment area. Capacity agreements define how network resources are shared between operators, while service agreements outline the terms for providing services to users.

[0070] Further, based on the above, the processor (102) may dynamically decide which network operator to connect to for optimal performance. For example, if the RAT density of the first operator is high, meaning there are ample resources available, the processor (102) may choose to connect to the first operator. Conversely, if the second operator has a capacity agreement that allows for better resource allocation during peak times, the processor (102) may switch to the second operator. Additionally, service agreements between operators may dictate preferential treatment for certain types of traffic or users. The processor (102) may take data associated with the service agreements into account to ensure that the UE (100) receives the best possible service based on the current network conditions and agreements in place.

[0071] In one embodiment, the processor (102) may be configured to enable switching between the first SIM and the second SIM based on a priority override scenario including, but not limited to, one of: an emergency service, and an uninterrupted connectivity. For instance, during an emergency service scenario, such as a 911 call or a critical medical alert, the processor (102) may prioritize the connection to the network that offers the best signal strength, lowest latency, and highest reliability, regardless of the current RAT or network operator.

[0072] In one embodiment, the processor (102) may be configured to customize switching between the first SIM and the second SIM based on one or more of: a user preference, cost, power savings, and the one or more applications. A user may specify his preferred network operator or RAT for different scenarios. For example, a user may prefer to use one SIM for personal calls and another for business-related activities. The processor (102) may be configured to switch to the SIM that offers the most cost-effective service. For instance, if one network operator provides cheaper data rates during off-peak hours, the processor (102) will switch to that SIM to reduce costs. Further to extend battery life, the processor (102) may switch to the SIM that connects to a more energy-efficient RAT. For example, during periods of low data usage, the processor (102) may switch to a lower RAT, such as 4G, to conserve power.

[0073] The examples provided herein are intended to illustrate various scenarios and embodiments of the present invention. These examples are not exhaustive and do not limit the scope of the invention. The invention may be practiced in a variety of ways beyond the specific examples disclosed, and modifications, variations, and alternative embodiments are within the scope of the invention as defined by the appended claims. The examples are provided to aid in understanding the principles of the invention and are not intended to restrict the invention to the particular forms or methods disclosed.

[0074] Referring to FIG. 2, a flow diagram for implementing the steps of the proposed method (200) for optimizing utilization of network radio resources is shown. In one or more embodiments, the method (200) may be implemented by the processor (102) of the user equipment (UE) (100).

[0075] At step (202), the method (200) includes determining one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs).

[0076] At step (204), the method (200) includes determining one or more user service parameters and one or more device parameters.

[0077] At step (206), the method (200) includes comparing the one or more user service parameters and the one or more device parameters with respective predefined criteria.

[0078] At step (208), the method (200) includes enabling switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time. Herein, the communication interface (106) may be responsible for facilitating communication using the one or more SIMs and the one or more RATs.

[0079] It should be noted that the steps for executing the method (200) described herein are not limited to the specific steps outlined above. The method may be implemented in various other ways, and the steps may be reordered, combined, or modified without departing from the scope and spirit of the invention. The examples provided are for illustrative purposes only and are not intended to limit the invention to the specific embodiments disclosed. Those skilled in the art will recognize that various modifications and adaptations can be made to the method without departing from the broader inventive concepts disclosed herein.

[0080] While aspects are described in the present disclosure by illustration to some examples, those skilled in the art will understand that such aspects may be implemented in many different arrangements and scenarios. Techniques described herein may be implemented using different platform types, devices, systems, shapes, sizes, and / or packaging arrangements. For example, some aspects may be implemented via integrated chip embodiments or other non-module-component based devices (e.g., end-user devices,vehicles, communication devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, or artificial intelligence-enabled devices). Aspects may be implemented in chip-level components, modular components, non-modular components, non- chip-level components, device-level components, or system-level components. Devices incorporating described aspects and features may include additional components and features for implementation and practice of claimed and described aspects. For example, transmission and reception of wireless signals may include a number of components for analog and digital purposes (e.g., hardware components including antennas, radio frequency (RF) chains, power amplifiers, modulators, buffer, processors, interleavers, adders, or summers). It is intended that aspects described herein may be practiced in a wide variety of devices, components, systems, distributed arrangements, or end-user devices of varying size, shape, and constitution.

[0081] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, the combinations are not intended to limit the disclosure of various aspects. In fact, many of the features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependent claim listed below may directly depend on only one claim, the disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set.

[0082] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

[0083] A person with ordinary skills in the art will appreciate that the user equipment and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above-disclosed user equipment’s elements, modules, and other features and functions, or alternatives thereof, may be combined with creating other different systems or applications.

[0084] Those skilled in the art will appreciate that any of the aforementioned steps and / or user equipment may be suitably replaced, reordered, or removed, and additional steps and / or modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable method and modules, and are not limited to any particular computerhardware, software, middleware, firmware, microcode, and the like, without departing the scope of the present disclosure. The claims can encompass embodiments for hardware and software or a combination thereof.

[0085] While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.ADVANTAGES OF THE PRESENT DISLCOSURE

[0086] The present disclosure provides a system and a method for efficient management and allocation of radio resources, leading to improved network performance and service quality.

[0087] The present disclosure provides a dynamic switching between multiple radio access technologies (RATs) and subscription identity modules (SIMs) based on real-time conditions enhances user experience and network efficiency.

[0088] The present disclosure provides intelligent selection of energy-efficient RATs when high-speed connections are not necessary, the system reduces overall power consumption of user equipment (UE).

[0089] The present disclosure provides a system to minimize operational costs for both users and network operators by optimizing resource allocation and reducing unnecessary high-speed RAT usage.

[0090] The present disclosure provides a machine learning (ML) model for predicting optimal RAT selection based on historical and real-time data enhances decision-making and resource management.

[0091] The present disclosure provides a customized switching preferences based on factors such as cost, power savings, and application-specific requirements, providing a tailored experience.

[0092] The present disclosure provides a system for facilitating load balancing across different RATs and subscriptions, preventing network congestion and ensuring fair resource distribution.

Claims

I Claim:

1. A user equipment (UE) (100) for optimizing utilization of network radio resources, the UE (100) comprising: a processor (102) communicatively coupled to a memory (104), and a communication interface (106), the memory (104) comprising instructions which, when executed by the processor (102), cause the processor (102) to: determine one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs); determine one or more user service parameters and one or more device parameters; compare the one or more user service parameters and the one or more device parameters with respective predefined criteria; and enable switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time, wherein the communication interface (106) is configured for facilitating communication using the one or more SIMs and the one or more RATs.

2. The UE (100) as claimed in claim 1, wherein the one or more user service parameters comprise one or more of: bandwidth requirement, latency requirement, application performance requirement, and user preference.

3. The UE (100) as claimed in claim 1, wherein the one or more device parameters comprise one or more of: battery level, signal strength, current data usage, and device temperature.

4. The UE (100) as claimed in claim 1, wherein the predetermined time is a minimum time period to avoid frequent switching and configured based on network condition and user preference.

5. The UE (100) as claimed in claim 1, wherein the respective predefined criteria for the switching are dynamically generated using a machine learning (ML) model, based on features extracted from the one or more user service parameters and the one or more device parameters by the ML model, the features comprising one or more of: network quality indicators, user activity patterns, device conditions, and environmental context, and wherein the ML model is trained based on historical data comprising, one or more of: user behavior, network performance metrics, and device performance metrics.

6. The UE (100) as claimed in claim 1, wherein the processor (102) is further configured to categorize cumulative bandwidth requirement associated with one or more applications in the UE (100) into RAT-specific groups based on at least one of: bandwidth, latency, and performance requirements.

7. The UE (100) as claimed in claim 1, wherein the one or more SIMs include a first SIM and a second SIM.

8. The UE (100) as claimed in claim 7, wherein the processor (102) is further configured to prioritize a RAT acquisition order for the first SIM, from a lower RAT to a higher RAT, and the second SIM, from the higher RAT to the lower RAT.

9. The UE (100) as claimed in claim 7, wherein the first SIM and the second SIM belong to a same network operator, and wherein the processor (102) is further configured to facilitate an operator-defined optimized load balancing between a first RAT associated with the first SIM and a second RAT associated with the second SIM.

10. The UE (100) as claimed in claim 9, wherein the processor (102) is further configured to apply dynamic allocation of higher RAT resources to the UE (100) based on a predefined priority level assigned by the network operator.

11. The UE (100) as claimed in claim 7, wherein the first SIM and the second SIM belong to different network operators, and wherein the processor (102) is further configured to switch between the network operators based on at least one of: a RAT density, a capacity agreement and a service agreement between the network operators in a specific deployment area.

12. The UE (100) as claimed in claim 7, wherein the processor (102) is further configured to enable switching between the first SIM and the second SIM based on a priority override scenario comprising, one of: an emergency service, and an uninterrupted connectivity.

13. The UE (100) as claimed in claim 7, wherein the processor (102) is further configured to customize switching between the first SIM and the second SIM based on one or more of: a user preference, cost, power savings, and the one or more applications.

14. A method (200) for optimizing utilization of network radio resources, performed by a processor (102) of a user equipment (UE) (100), the method (200) comprising: determining (202) one or more radio access technologies (RATs) associated with one or more subscriber identity modules (SIMs); determining (204) one or more user service parameters and one or more device parameters;comparing (206) the one or more user service parameters and the one or more device parameters with respective predefined criteria; and enabling (208) switching among, at least: the one or more SIMs and the one or more RATs, based on the comparison after a predetermined time, wherein the communication interface (106) is configured for facilitating communication using the one or more SIMs and the one or more RATs.

15. The method (200) as claimed in claim 14, wherein the one or more user service parameters comprise one or more of: bandwidth requirement, latency requirement, application performance requirement, and user preference.

16. The method (200) as claimed in claim 14, wherein the one or more device parameters comprise one or more of: battery level, signal strength, current data usage, and device temperature.

17. The method (200) as claimed in claim 14, wherein the predetermined time is a minimum time period to avoid frequent switching and configured based on network condition and user preference.

18. The method (200) as claimed in claim 14, wherein the respective predefined criteria for the switching is dynamically generated using a machine learning (ML) model, based on features extracted from the one or more user service parameters and the one or more device parameters by the ML model, the features comprising one or more of: network quality indicators, user activity patterns, device conditions, and environmental context, wherein, the ML model is trained based on historical data comprising, one or more of: user behavior, network performance metrics, and device performance metrics.

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