DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
METHOD AND SYSTEM FOR UPDATING CHANNEL QUALITY INFORMATION (CQI) OF UEs IN A NETWORK
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN Office-101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India
3. PREAMBLE TO THE DESCRIPTION
The following specification particularly describes the invention and the manner in which it is to be performed.

RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and/or trade dress protection, belonging to JIO PLATFORMS LIMITED or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.
TECHNICAL FIELD
[0002] The present disclosure generally relates to the field of wireless communication. More particularly, the present disclosure relates to a method and a system for updating channel quality information (CQI) of UEs in a network.
DEFINITION
[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.
[0004] The expression ‘uplink Timing Advance (TA) measurement parameter’ used hereinafter in the specification refers to a measure depicting a length of time a signal takes to reach a base station from a user equipment (UE). The uplink TA measurement parameter is used to control an uplink transmission timing of an individual UE. The uplink TA measurement parameter helps to ensure that uplink transmissions from all UE are synchronized when received by the base station.
[0005] The expression ‘pathloss measurement parameter’ used hereinafter in the specification refers to a measure depicting an amount of a reduction in a signal strength as the signal travels from the base station to the UE. The pathloss measurement parameter is affected based on various factors such as a distance, obstacles (e.g., high rise buildings), and environmental conditions (e.g., a rainy weather).
[0006] The expression ‘Signal-to-Noise Ratio (SNR) measurement parameter’ used hereinafter in the specification refers to a measure of a strength of a signal relative to a background noise. The SNR measurement parameter indicates a quality of a received signal. A higher SNR implies better signal quality and less interference from noise.
[0007] The expression ‘a Channel State Information (CSI) measurement parameter’ used hereinafter in the specification is a report (i.e., a CSI report) providing a detailed information about a channel (i.e., a network channel) conditions between the base station and the UE. The CSI report provides a comprehensive view of a current channel conditions, enabling optimization of data transmission and network performance.
[0008] The expression ‘Channel Quality Information (CQI)’ used hereinafter in the specification is a quantized measure of a channel's quality from the UE to the base station. The CQI helps in selecting an appropriate modulation and coding scheme for data transmission, aiming to optimize a balance between a data rate and a reliability based on the channel conditions.
[0009] The expression ‘channel’ used hereinafter in the specification refers to a designated path or a frequency range used for transmitting data between the UEs device and the base station. The channel manages a flow of information, ensuring effective communication and minimizing interference, to enable maintain reliable data exchange.
[0010] These definitions are in addition to those expressed in the art.
BACKGROUND
[0011] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0012] In modern wireless communication systems, particularly in Fifth Generation (5G) and Sixth Generation (6G) networks, Channel State Information (CSI) plays a critical role in optimizing data transmission and enhancing network performance. The CSI provides detailed insights into a channel condition, including signal strength, phase shift, and interference levels. These detailed insights are essential for making real-time decisions regarding resource allocation, modulation schemes, and overall network management.
[0013] Currently used techniques for CSI reporting and channel estimation involve periodic updates with relatively long time intervals. Examples of some currently used techniques include a periodic CSI reporting technique, an event-based CSI reporting technique, a semi-persistent CSI reporting, an on-demand CSI reporting technique, etc. Despite the utility of these currently used techniques for the CSI reporting, these techniques have various drawbacks especially in an environment with rapidly changing channel conditions. These currently used techniques often result in a delay between an actual change in channel conditions and the CSI report received by the network. Consequently, the network's ability to adapt dynamically to fluctuations in the channel quality is hindered, potentially leading to suboptimal performance and reduced efficiency.
[0014] Due to this sparse nature of the CSI reporting, particularly during rapidly changing channel conditions or high mobility, the delay in updating Channel Quality Information (CQI) can result in degraded communication performance, increased error rates, and inefficient resource utilization. As a result, there is a growing need for improved techniques that offer more frequent and accurate updates of the channel conditions, enabling more responsive and adaptive network management.
[0015] There is, therefore, a need in the art to provide a method and a system that can mitigate the disadvantages of the prior art.
OBJECTIVE
[0016] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies, are as follows:
[0017] An objective of the present disclosure is to provide a method and a system for updating Channel Quality Information (CQI) of UEs within a network.
[0018] Another objective of the present disclosure is to provide a method and a system that employs a clustering algorithm to group UEs into a plurality of clusters based on an associated set of measurement parameters.
[0019] Another objective of the present disclosure is to provide a method and a system that employs a reinforcement learning technique for updating the CQI of a plurality of UEs present within a cluster. The usage of the reinforcement learning technique to adjust the CQI ensures that the system quickly adapts to fluctuations in channel conditions.
[0020] Another objective of the present invention is to provide timely adjustments in resource allocation and service provisioning by using Channel State Information (CSI) reports of the UEs to estimate channel conditions, thereby enhancing overall network efficiency.
[0021] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
[0022] In an exemplary embodiment, a method for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network is disclosed. The method includes receiving a set of measurement parameters from at least one UE. The method includes adding the at least one UE to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE. The method includes determining a current Channel Quality Information (CQI) corresponding to the at least one UE based on the set of measurement parameters. The method includes updating an existing CQI associated with remaining UEs in the cluster based on the current CQI determined for the at least one UE.
[0023] In an embodiment, the set of measurement parameters includes an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter.
[0024] In an embodiment, the method for adding the at least one UE to the cluster comprises mapping each of the set of measurement parameters received from the at least one UE with a corresponding set of cluster measurement parameters associated with each cluster.
[0025] In an embodiment, the method further includes creating the plurality of clusters including a set of UEs from a plurality of UEs based on a corresponding set of measurement parameters associated with each of the plurality of UEs, using a clustering algorithm.
[0026] In an embodiment, the existing CQI of each of the remaining UEs in the cluster is updated using at least one reinforcement learning technique.
[0027] In another exemplary embodiment, a system for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network is disclosed. The system includes a processing engine, and a memory coupled to the processing engine and configured to store instructions executable by the processing engine causing the processing engine to receive a set of measurement parameters from at least one UE. The processing engine is further configured to add the at least one UE to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE. The processing engine is further configured to determine a current Channel Quality Information (CQI) corresponding to the at least one UE based on the set of measurement parameters. The processing engine is further configured to update an existing CQI associated with remaining UEs present within the cluster based on the current CQI determined for the at least one UE
[0028] In an embodiment, the set of measurement parameters includes an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter.
[0029] In an embodiment, to add the at least one UE to the cluster, the processing engine is further configured to map each of the set of measurement parameters received from the at least one UE with a corresponding set of cluster measurement parameters associated with each cluster.
[0030] In an embodiment, the processing engine is further configured to create the plurality of clusters comprising a set of UEs from a plurality of UEs based on a corresponding set of measurement parameters associated with each of the plurality of UEs, using a clustering algorithm.
[0031] In an embodiment, the existing CQI of each of the remaining UEs in the cluster is updated using at least one reinforcement learning technique.
[0032] In yet another exemplary embodiment, a User Equipment (UE) communicatively coupled with a network is disclosed. The coupling includes a step of receiving, by the network, a connection request from the UE. The coupling includes a step of sending, by the network, an acknowledgment of the connection request to the UE. The coupling includes a step of transmitting a plurality of signals in response to the connection request. Based on the connection request, an updation of Channel Quality Information (CQI) of UEs within the network is performed.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
[0033] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0034] FIG. 1 illustrates an exemplary network architecture for implementing a system for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network, in accordance with an embodiment of the present disclosure.
[0035] FIG. 2 illustrates an exemplary block diagram of the system configured for updating the CQI of the UEs within the network, in accordance with an embodiment of the present disclosure.
[0036] FIG. 3 illustrates an exemplary flow diagram of a method for updating the CQI of the UEs within the network, in accordance with an embodiment of the present disclosure.
[0037] FIG. 4 illustrates an exemplary representation of a plurality of clusters including a plurality of UEs, in accordance with an embodiment of the present disclosure.
[0038] FIG. 5 illustrates an exemplary process flow diagram depicting a method of adding UEs to a cluster, in accordance with an embodiment of the present disclosure.
[0039] FIG. 6 illustrates an exemplary process flow diagram depicting a method of updating existing CQIs of remaining UEs in the cluster, in accordance with an embodiment of the present disclosure.
[0040] FIG. 7 illustrates an exemplary computer system in which or with which the embodiments of the present disclosure may be implemented.
[0041] The foregoing shall be more apparent from the following more detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100 – Network architecture
102-1, 102-2…102-N – Plurality of Users
104-1, 104-2…104-N – Plurality of User Equipments
106 – Network
108 – System
200 – Block Diagram
202 – Processor(s)
204 - Memory
206 – Plurality of Interfaces
208 – Processing Engine
210 – Database
300 – Exemplary flow diagram
400 – Exemplary representation
500 – Process flow diagram
600 – Process flow diagram
700 – Computer System
710 - External Storage Device
720 – Bus
730 – Main Memory
740 – Read Only Memory
750 - Mass Storage Device
760 - Communication Port
770 – Processor
DETAILED DESCRIPTION
[0042] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.
[0043] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0044] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0045] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0046] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.
[0047] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0048] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.
[0049] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical and computing device. The user device is capable of receiving and/or transmitting one or parameters, performing function/s, communicating with other user devices and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery and an input-means such as a hard keypad and/or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
[0050] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
[0051] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.
[0052] Radio Access Technology (RAT) refers to the technology used by mobile devices/ user equipment (UE) to connect to a cellular network. It refers to the specific protocol and standards that govern the way devices communicate with base stations, which are responsible for providing the wireless connection. Further, each RAT has its own set of protocols and standards for communication, which define the frequency bands, modulation techniques, and other parameters used for transmitting and receiving data. Examples of RATs include a GSM (Global System for Mobile Communications), a Code Division Multiple Access (CDMA), a Universal Mobile Telecommunications System (UMTS), a Long-Term Evolution (LTE), a Fifth Generation (5G) technology, and a Sixth Generation (6G) technology. The choice of RAT depends on a variety of factors, including the network infrastructure, the available spectrum, and the mobile device's/device's capabilities. Mobile devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.
[0053] Wireless communication technology has rapidly evolved over the past few decades. The first generation of wireless communication technology was analog, offering only voice services. Further, text messaging and data services became possible when a Second Generation (2G) technology was introduced. A Third Generation (3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. A Fourth Generation (4G) technology revolutionized the wireless communication with faster data speeds, improved network coverage, and security. Currently, the 5G technology is being deployed, offering significantly faster data speeds, lower latency, and the ability to connect many devices simultaneously. These advancements represent a significant leap forward from previous generations, enabling enhanced mobile broadband, improved Internet of Things (IoT) connectivity, and more efficient use of network resources. The 6G technology promises to build upon these advancements, pushing the boundaries of wireless communication even further. While the 5G technology is still being rolled out globally, research and development into the 6G are rapidly progressing, with the aim of revolutionizing the way of connecting and interacting with technology.
[0054] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
[0055] Wireless communication technology has been continuously developing, resulting in a significant increase in the number of User Equipments (UEs), service volume, and data throughput. Therefore, it is necessary to distribute the load within a cell to achieve a balanced load distribution among the UEs. To achieve even distribution of the load, a base station should be able to receive multiple information from the UEs to maintain a network channel quality. However, receiving information from a large number of UEs is a complex and time-consuming process.
[0056] A 5G New Radio (NR) technology requires that signals transmitted by different UEs associated with the base station arrive at the base station approximately at the same time. This is necessary to ensure that the base station can receive uplink frames within a range of one cyclic prefix (CP) in the same subframe. To avoid timing misalignment interference, especially in terrestrial networks, the 5G NR also employs an uplink Timing Advance (TA) scheme during a random access procedure. However, when there are many UEs, retrieving information from received signals can be a difficult task. To overcome the shortcomings of existing techniques, the present disclosure discloses a technique that focuses on grouping multiple UEs based on various measurement parameters associated with each UE.
[0057] Embodiments herein relate to a method for updating Channel Quality Information (CQI) of the UEs within a network (i.e., a wireless network) is disclosed. In particular, the method includes receiving a set of measurement parameters from at least one UE. The set of measurement parameters includes an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter, and the like. Upon receiving the set of measurement parameters, the at least one UE is added to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE using a clustering algorithm. Once the at least one UE is added to the cluster, a current Channel Quality Information (CQI) corresponding to the at least one UE within the cluster is determined based on the set of measurement parameters using the clustering algorithm. In an embodiment, the current CQI for the at least one UE is determined based on the CSI measurement parameter. The CSI measurement parameter may be received in a form of a report (also referred to as a CSI report). Upon determining the current CQI, an existing CQI associated with remaining UEs in the cluster is updated based on the current CQI determined for the at least one UE, using at least one reinforcement learning technique.
[0058] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.
[0059] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 7.
[0060] FIG. 1 illustrates an exemplary network architecture 100 for implementing a system 108 for updating Channel Quality Information (CQI) of the user equipments within a network (106), in accordance with an embodiment of the present disclosure.
[0061] As illustrated in FIG. 1, the network architecture 100 may include one or more computing devices or UEs 104-1, 104-2…104-N associated with one or more users 102-1, 102-2…102-N in an environment. A person of ordinary skill in the art will understand that one or more users 102-1, 102-2…102-N may be individually referred to as the user 102 and collectively referred to as the users 102. Similarly, a person of ordinary skill in the art will understand that one or more UEs 104-1, 104-2…104-N may be individually referred to as the UE 104 and collectively referred to as the UEs 104. A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three UEs 104 are depicted in FIG. 1, however, any number of the UEs 104 may be included without departing from the scope of the ongoing description.
[0062] In an embodiment, the UE 104 may include smart devices operating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the UE 104 may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., a mechanical sensor, a thermal sensor, an electrical sensor, a magnetic sensor, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart televisions (TVs), computers, smart security systems, smart home systems, other devices for monitoring or interacting with or for the user 102 and/or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE 104 may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[0063] In an embodiment, the UE 104 may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the UE 104 may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device. Further, the UE 104 may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user 102 or an entity such as a touch pad, a touch enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE 104 may not be restricted to the mentioned devices and various other devices may be used.
[0064] In FIG. 1, the UE 104 may communicate with the system 108 through the network 106. In particular, the UE 104 may be communicatively coupled with the network 106. The coupling including steps of receiving, by the network 106, a connection request from the UE 104. Upon receiving the connection request, the coupling includes steps of sending, by the network 106, an acknowledgment of the connection request to the UE 104. Further, the coupling includes steps of transmitting a plurality of signals in response to the connection request. The plurality of signals is responsible for establishing communication of the UE 104 with the system 108 to manage the trace data for the users (i.e., the user 102) in the network 106.
[0065] In an embodiment, the network 106 may include at least one of the 4G network, the 5G network, the 6G network, or the like. The network 106 may enable the UE 104 to communicate with other devices in the network architecture 100 and/or with the system 108. The network 106 may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network 106 may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. In another embodiment, the network 106 includes, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth.
[0066] In another exemplary embodiment, the network architecture 100 may include a centralized server (not shown) may include or comprise, by way of example but not limitation, one or more of a stand-alone server, a server blade, a server rack, a bank of servers, a server farm, a hardware supporting a part of a cloud service or a system, a home server, a hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.
[0067] Although FIG. 1 shows exemplary components of the network architecture 100, in other embodiments, the network architecture 100 may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture 100 may perform functions described as being performed by one or more other components of the network architecture 100.
[0068] FIG. 2 illustrates an exemplary block diagram 200 of the system 108 configured for updating the CQI of the UEs within the network (e.g., the network 106), in accordance with an embodiment of the disclosure. FIG. 2 is explained in conjunction with FIG. 1.
[0069] In an embodiment, the system 108 may include one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) 202 may be configured to fetch and execute computer-readable instructions stored in a memory 204 of the system 108. The memory 204 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as a Random-Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read Only Memory (EPROM), a flash memory, and the like.
[0070] In an embodiment, the system 108 may include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices (I/O), storage devices, and the like. The interface(s) 206 may facilitate communication through the system 108. The interface(s) 206 may also provide a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, a processing engine 208 and a database 210.
[0071] In an embodiment, the processing engine 208 is configured to receive a set of measurement parameters from at least one UE (e.g., the UE 104). The set of measurement parameters may be received from the at least one UE as a Physical Uplink Shared Channel (PUSCH) block. The PUSCH block in the 5G New Radio (NR) serves as a primary channel for the at least one UE. The PUSCH block is designated to carry multiplexed control information (such as the set of measurement parameters) and user application data (such as text messaging data, voice data, video data, etc.) transmitted by the at least one UE to the network (e.g., the network 106). The set of measurement parameters may include, but are not limited to, an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter. The uplink TA measurement parameter is a measure used to synchronize the timing of uplink transmissions from a UE to a base station (or a network node, i.e., gNodeB (gNB)). In an embodiment, the system 108, including the processing engine 208, may be present at the base station. The uplink TA measurement parameter compensates for a propagation delay that occurred due to a distance between the UE and the base station, ensuring that signals from multiple UEs arrive at the base station at a same time or with minimal timing differences. The pathloss measurement parameters is a measure depicting an amount of a reduction in a signal strength as the signal travels from the base station (also referred to as a transmitter) to the UE (also referred to as a receiver). The pathloss measurement parameter is affected based on various factors such as a distance, obstacles (e.g., high rise buildings), and environmental conditions (e.g., a rainy weather, or a stormy weather). The SNR measurement parameter is a measure of a strength of the signal relative to a background noise. The SNR measurement parameter indicates a quality of the received signal. A higher SNR implies better signal quality and less interference from noise. The AoA measurement parameter is a measure depicting an angle at which a signal arrives at the UE relative to a reference direction (usually a direction of an antenna array or a horizontal plane) associated with the base station. The PRS measurement parameter is a measure used to determine a location of the UE based on signals transmitted by the network (e.g., the network 106).
[0072] The CSI measurement parameter provides detailed information about a channel (i.e., a network channel) conditions between the base station and the UE. The CSI measurement parameter may include details such as, a channel’s frequency response, a channel’s signal strength, a Channel Impulse Response (CIR), a phase shift, a level of interference or noise, etc. The channel’s frequency response is a measure to determine how a channel behaves when different frequencies of a signal are transmitted through the channel between the at least one UE and the base station. The channel’s signal strength is a measure of a power level of the signal received by the base station from the at least one UE. A stronger signal strength indicates a better link quality, which enhances data transmission reliability and reduces errors. The CIR is a measure used to determine how the signal is transformed as it travels from the at least one UE to the base station, capturing delays and reflection. The phase shift is a measure of a change in a phase of the signal as the signal propagates from the at least one UE to the base station. The level of the interference or the noise indicates the presence of unwanted signals or background noise affecting a communication link between the at least one UE and the base station. Higher levels of the interference or the noise represents degrade signal quality and impact an overall communication performance. In an embodiment, the CSI measurement parameter may correspond to a CSI report. The CSI report provides a comprehensive view of the current channel conditions, enabling optimization of data transmission and network performance. Further, apart from the above-listed measurement parameters, various other hybrid positioning measurement parameters may be used, for example, a Time Difference of Arrival (TDOA) measurement parameter, a Frequency Difference of Arrival (FDOA) measurement parameter, a Received Signal Strength Indicator (RSSI) measurement parameter, Angle of Departure (AoD) measurement parameter, and the like. In an embodiment, apart from the CSI measurement parameter used for determining the channel condition, various other similar measurement parameters, such as, a Sounding Reference Signal (SRS) measurement parameter, an interference measurement parameter, and the like, may be used.
[0073] Upon receiving the set of measurement parameters, the processing engine 208 is configured to add the at least one UE to a cluster from a plurality of clusters. The at least one UE may correspond to a new UE or an existing UE present within one of the plurality of clusters. In an embodiment, initially, a set of UEs from a plurality of UEs may be grouped together to generate the cluster based on a corresponding set of measurement parameters. In an embodiment, the at least one UE may be added to the cluster based on the set of measurement parameters associated with the at least one UE using a clustering algorithm. Examples of the clustering algorithm may include, but are not limited to a k-Nearest Neighbors (k-NN) clustering algorithm, a K-means clustering algorithm, a hierarchical clustering algorithm, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN), a spectral clustering, and the like. In an embodiment, the processing engine 208 may include a Machine Learning (ML) model that may employ one or more clustering algorithms for generating the plurality of clusters including the plurality of UEs and for adding the at least one UE to the cluster from the plurality of clusters.
[0074] Once the at least one UE is added to the cluster, the processing engine 208 is configured to determine a current CQI corresponding to the at least one UE added to the cluster. In an embodiment, the current CQI for the at least one UE is determined based on the CSI measurement parameter. The current CQI corresponding to the at least one UE is determined by processing the CSI report including the CSI measurement parameter. Further, based on the processing, the current CQI corresponding to the at least one UE is extracted from the CSI report. In other words, the current CQI is determined by analyzing the CSI measurement parameter that describe a quality of the channel used for connecting the at least one UE to the network. In some embodiments, the CQI is determined using each of the set of measurement parameters. In other words, by processing the values received for each of the set of measurement parameters, the current CQI of the channel associated with the at least one UE is determined.
[0075] Upon determining the current CQI of the at least one UE, the processing engine 208 is configured to update an existing CQI associated with remaining UEs in the cluster based on the current CQI determined for the at least one UE. The existing CQI of each of the remaining UEs within the cluster is updated based on at least one reinforcement learning technique. Examples of the at least one reinforcement learning technique may include, a Q-learning technique, Deep Q-Networks, a policy gradient learning technique, a State-Action-Reward-State-Action (SARSA) learning technique, and the like. In some embodiments, the processing engine 208 may include another ML model employing one or more reinforcement learning techniques for updating the CQI of the remaining UEs within the cluster. This complete method of updating the CQI of the UEs is further explained in detail in conjunction with FIG. 3 to FIG. 6.
[0076] In an embodiment, the processing engine 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine 208. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine 208 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine 208 may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine 208. In such examples, the system 108 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 108 and the processing resource. In other examples, the processing engine 208 may be implemented by electronic circuitry.
[0077] In an embodiment, the database 210 includes data (e.g., the set of measurement parameters associated with the at least one UE, the cluster assigned to the at least one UE, the plurality of clusters, the set of measurement parameters associated with each UE present within each of the plurality of clusters, etc.) that may be either stored or generated as a result of functionalities implemented by any of the components of the processor 202 or the processing engine 208.
[0078] FIG. 3 illustrates an exemplary flow diagram of a method 300 for updating the CQI of the UEs within the network (e.g., the network 106), in accordance with an embodiment of the present disclosure. FIG. 3 is explained in conjunction with FIGS. 1 and 2.
[0079] In order to update the CQI of the UEs, initially, at step 302, the set of measurement parameters may be received from the at least one UE (e.g., the UE 104). The at least one UE may correspond to the new UE added to the network or the existing UE present within the plurality of clusters associated with the network. The set of measurement parameters may include the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, and the CSI measurement parameter. The uplink TA measurement parameter is the measure depicting the length of the time the signal takes to reach the base station from the UE (e.g., the at least one UE). In other words, the uplink TA measurement parameter compensates for the propagation delay occurred due to the distance between the UE and the base station, ensuring that signals from multiple UEs arrive at the base station at the same time or with the minimal timing differences. The pathloss measurement parameters is the measure depicting the amount of the reduction in the signal strength as the signal travels from the base station to the UE. The pathloss measurement parameter is affected based on various factors such as the distance, the obstacles (e.g., the high rise buildings), and the environmental conditions (e.g., the rainy weather). The SNR measurement parameter is the measure of the strength of the signal relative to the background noise. The higher SNR implies better signal quality and less interference from noise. The AoA measurement parameter is the measure depicting the angle at which the signal arrives at the UE relative to the reference direction associated with the base station. The PRS measurement parameter is the measure used to determine the location of the UE based on the signals transmitted by the network.
[0080] The CSI measurement parameter provides the detailed information about the channel (i.e., the network channel) conditions between the base station and the UE. The CSI measurement parameter may include details such as, the channel’s frequency response, the channel’s signal strength, the CIR, the phase shift, the level of interference or noise, etc. In an embodiment, the CSI measurement parameter may correspond to the CSI report.
[0081] Upon receiving the set of measurement parameters associated with the at least one UE, at step 304, the at least one UE is added to the cluster from the plurality of clusters. The at least one UE may be added to the cluster based on the set of measurement parameters associated with the at least one UE using the clustering algorithm. In order to add the at least one UE to the cluster, the set of measurement parameters associated with the at least one UE may be compared with a corresponding set of cluster measurement parameters associated with each cluster. In an embodiment, the corresponding set of cluster measurement parameters associated with each cluster may include a range of each corresponding measurement parameter associated with each UE present in each cluster. In some embodiments, to add the at least one UE to the cluster, each of the set of measurement parameters received from the at least one UE may be mapped with each corresponding measurement parameter associated with each UE present within each of the plurality of clusters. In an embodiment, initially, the plurality of UEs associated with the network may be grouped together to create the plurality of clusters based on a corresponding set of measurement parameters using the clustering algorithm. Each cluster from the plurality of clusters may include a set of UEs from the plurality of UEs. In an embodiment, the clustering algorithm is a machine-learning method used to group similar data points with similar values or attributes in one cluster. In other words, the clustering algorithm identifies patterns or natural attributes in data to group entities with similar values, such as grouping UEs from the plurality of UEs with similar values of the corresponding set of measurement parameters in one cluster. Examples of the clustering algorithm may include, but are not limited to the k-NN clustering algorithm, the K-means clustering algorithm, the hierarchical clustering algorithm, the DBSCAN, the spectral clustering, and the like.
[0082] Once the UE is added to the cluster, at step 306, the current CQI corresponding to the at least one UE may be determined based on the set of measurement parameters. In an embodiment, the current CQI for the at least one UE is determined based on the CSI measurement parameter. The current CQI is determined based on the CSI measurement parameter received for the at least one UE. In an embodiment, the current CQI determined for the at least one UE corresponds to a CQI recently calculated based on a most recent CSI measurement parameter received corresponding to the at least one UE at a current time interval. The current CQI reflects a current network channel quality specifically for the at least one UE when the at least one UE is added the cluster. Upon determining the current CQI for the at least one UE, at step 308, the existing CQI associated with the remaining UEs present within the cluster may be updated based on the current CQI determined for the at least one UE. The existing CQI (i.e., the old CQI) associated with the remaining UEs in the cluster corresponds to a CQI previously determined for the remaining UEs already present in the cluster based on the latest previous CSI measurement parameter received for each of the remaining UEs at a previous time-interval. The existing CQI represents the current network channel quality of all the remaining UEs determined based on the previous CSI measurement parameter received for each of the remaining UEs. The existing CQI of each of the remaining UEs within the cluster is updated using the at least one reinforcement learning technique. In an embodiment, to update the existing CQI of each of the remaining UEs, the existing CQI (also referred to as an old CQI) of each of the remaining UEs may be determined based on the corresponding set of measurement parameters. In other words, the existing CQI for each remaining UE is determined based on an existing CSI report associated with each another UE. Examples of the at least one reinforcement learning technique may include, the Q-learning technique, the Deep Q-Networks, the policy gradient learning technique, the SARSA learning technique, and the like. This method of adding the at least one UE to the cluster and the method of updating the CQI of the remaining UEs in the cluster is further explained in detail in conjunction with FIG. 4 to FIG. 6.
[0083] FIG. 4 illustrates an exemplary representation 400 of the plurality of clusters including the plurality of UEs, in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with FIGS. 1, 2 and 3.
[0084] In FIG. 4, three clusters, i.e., a cluster M, a cluster N, and a cluster O are shown. The three clusters may be generated, upon receiving the set of measurement parameters corresponding to each UE. The three clusters are shown for ease of explanation, however any number of clusters can be generated based on a number of UEs associated with the network. Each of the three clusters may include the set of UEs of the plurality of UEs. The set of UEs within the cluster M is represented via a plurality of shaded circles as class (i). The set of UEs within the cluster N is represented via a plurality of shaded square boxes as class (ii). The set of UEs within the cluster O is represented via a plurality of shaded rectangular boxes as class (iii).
[0085] Each UE is assigned to a cluster based on a proximity of the UE to a cluster center determined using the set of measurement parameters associated with each UE. The cluster center for each of the three clusters may be selected randomly. In other words, based on the set of measurement parameters received from the plurality of UEs, UEs with similar values of the set of measurement parameters may be grouped together to generate each cluster. The grouping of the UEs from the plurality of UEs may be done using the clustering algorithm. Examples of the clustering algorithm may include, but are not limited to the k-NN clustering algorithm, the K-means clustering algorithm, the hierarchical clustering algorithm, the DBSCAN, the spectral clustering, and the like. For example, the k-NN clustering algorithm may be used to group the UEs together based on the values obtained for the set of measurement parameters, for example, the uplink TA measurement parameter, the pathloss measurement parameters, the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, and the CSI measurement parameter. The k-NN clustering algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point (i.e., a UE).
[0086] In FIG. 3, ‘Pt’ represents a center point used for calculating a distance between each of the three clusters. Further, upon receiving the set of measurement parameters corresponding to the new UE, the new UE may be assigned one of the three clusters based on mapping the set of measurement parameters of the new UE with the corresponding set of cluster measurement parameters associated with each cluster, i.e., the cluster M, the cluster N, and the cluster O. In an embodiment, the corresponding set of cluster measurement parameters associated with each of the set of three clusters may include a range of each corresponding measurement parameter associated with each UE present in each cluster. The mapping corresponds to the matching of the values of the set of measurement parameters of the new UE with the range of each corresponding measurement parameter associated with the cluster M, the cluster N, and the cluster O. The range of each corresponding measurement parameter for each cluster may be determined based on a measurement parameter of the corresponding set of measurement parameters associated with each UE present within the cluster. For instance, upon determining similarity in the values of the set of measurement parameters of the new UE with the corresponding set of cluster measurement parameters associated with each of the plurality of UEs in the cluster M, the new UE may be assigned the cluster M. In other words, the new UE may be added to the cluster M. In other words, when the values of the set of measurement parameters lie within the range of the corresponding set of cluster measurement parameters associated with the cluster M, then the new UE may be added to the cluster M.
[0087] For example, suppose the values of the set of measurement parameters, i.e., the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, and the CSI measurement parameter, received for the new UE may be 96 microseconds, 8 decibel (dB), 29 dB, and 23 dB, respectively. Further, suppose the values of the corresponding set of cluster measurement parameters, i.e., the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, and the CSI measurement parameter, associated with the cluster M may lie between a range of 94 – 100 microseconds, 7-10 dB, 25 – 34 dB, 20 – 25 dB. Similarly, suppose the values of the corresponding set of cluster measurement parameters associated with the cluster N may lie between a range of 100 – 110 microseconds, 11-15 dB, 19 – 24 dB, and 14 – 19 dB. Similarly, suppose the values of the corresponding set of measurement parameters associated with the cluster O may lie between a range of 111 – 125 microseconds, 16 - 21 dB, 11 – 18 dB, and 11 – 18 dB. In this case, based on matching of the values of the set of measurement parameters associated with the new UE with the corresponding set of cluster measurement parameters associated with the cluster M, the new UE may be assigned the cluster M. In other words, the new UE may be added to the cluster M.
[0088] FIG. 5 illustrates an exemplary process flow diagram depicting a method 500 of adding UEs to the cluster, in accordance with an embodiment of the present disclosure. FIG. 5 is explained in conjunction with FIGS. 1, 2, 3, and 4.
[0089] In FIG. 5, a network node 502, a UE 1, and a UE 2 are shown. In an aspect, the system 108, including the processing engine 208, may be integrated with or within the network node 502. In an example, the network node 502 may correspond to the gNB. In an example, only the UE 1 and the UE 2 are shown in FIG. 5 for the sake of brevity. The UE 1 and the UE 2 may correspond to the at least one UE.
[0090] Initially, at step 504 and at step 506, the network node 502, integrating the system 108, is configured to receive the set of measurement parameters from the UE 1. The set of measurement parameters may include the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, and the CSI measurement parameter. In particular, at step 504, the system 108 may receive the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, from the UE 1 as a PUSCH block. The PUSCH block in the 5G NR serves as the primary channel for the UE 1. The PUSCH block is designated to carry the multiplexed control information and the user application data associated with the UE 1 to the network (e.g., the network 106). In particular, the PUSCH block is designated to carry the multiplexed control information and the user application data from the UE 1 to the network node 502.
[0091] At step 506, the network node 502 is configured to receive the CSI measurement parameter as the CSI report from the UE 1. In an embodiment, the CSI report includes three major components, e.g., the CQI, a Precoding Matrix Index (PMI), and a Rank Indicator (RI). The three major components of the CSI report may include details such as the channel’s frequency response, the channel’s signal strength, the CIR, the phase shift, the level of interference or noise, etc. As per the situation and configuration from the network, a UE (e.g., the UE 1) performs a different combination of measurements and generates the CSI report. The CQI provides an estimate of the channel quality and the SNR, guiding the network node 502 on an optimal modulation and coding scheme for data transmission. The PMI specifies an optimal precoding matrix that should be used for beamforming to improve signal quality and reduce interference, based on current channel conditions. The RI reveals a number of spatial layers or data streams that can be effectively used for transmission, helping the base station determine the appropriate Multiple Input Multiple Output (MIMO) configuration for the UE 1.
[0092] Upon receiving the set of measurement parameters from the UE 1, at step 508, the network node 502 may be configured to assign the cluster to the UE 1 from the plurality of clusters by applying the clustering algorithm. The network node 502 may assign the cluster to the UE 1 by mapping the set of measurement parameters received from the UE 1 with the corresponding set of cluster measurement parameters associated with each of the plurality of clusters. In conjunction with the FIG. 3, suppose based on the mapping, the values of the set of measurement parameters received from the UE 1 matches with the values of the corresponding set of cluster measurement parameters associated with the cluster M. In other words, suppose based on the mapping, the values of the set of measurement parameters received from the UE 1 lie within the range of the values of the corresponding set of cluster measurement parameters associated with the cluster M. In this case, the UE 1 may be assigned the cluster M. In other words, based on similarity in the values of the uplink TA measurement parameter, the pathloss measurement parameter, and the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, and the CSI measurement parameter associated with the UE 1, with the range of the values of the corresponding set of cluster measurement parameters associated with the cluster M, the UE 1 may be placed in the cluster M. In an embodiment, the range of the corresponding set of cluster measurement parameters associated with the cluster M may be determined based on the corresponding set of measurement parameters associated each UE present within the cluster M.
[0093] Similarly, at step 510, the network node 502 is configured to receive the uplink TA measurement parameter, the pathloss measurement parameter, and the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, as the PUSCH block from the UE 2. Further, at step 512, the network node 502 is configured to receive the CSI measurement parameter as the CSI report from the UE 2. Upon receiving the set of measurement parameters from the UE 2, the network node 502 is configured to assign a cluster to the UE 2 based on the clustering algorithm. The network node 502 is configured to map the set of measurement parameters received from the UE 2 with the corresponding set of cluster measurement parameters associated with each of the plurality of clusters. In conjunction with the FIG. 3, suppose based on the mapping, the values of the set of measurement parameters received from the UE 2 lie within the range of the values of the corresponding set of cluster measurement parameters associated with the cluster N. In this case, at step 510, the network node 502 is configured to assign the cluster N to the UE 2. In other words, based on the set of measurement parameters received at step 510 and step 512, the network node 502 is configured to place the UE 2 in the cluster N, including UEs having similar CSI report and near about values of the uplink TA measurement parameter, the pathloss measurement parameter, the SNR measurement parameter, the AoA measurement parameter, and the PRS measurement parameter.
[0094] In an aspect, a TA associated with the uplink TA transmission is a command issued by the network node 502 (or the base station) to a UE (e.g., the UE 1 or the UE 2) to synchronize the timing of uplink transmissions of the UE. This adjustment of the uplink transmissions ensures that the UE sends uplink symbols, such as those used in the PUSCH block, a Physical Uplink Control Channel (PUCCH), and Sounding Reference Signal (SRS) transmissions, at the correct time. By advancing the timing of these uplink transmissions, the TA helps to align the UE's signals with the base station's expected timing, thereby reducing potential interference and improving overall network performance. In other words, the TA is used to control the uplink transmission timing of an individual UE, ensuring that uplink transmissions from all UE are synchronized when received by the base station. For example, an uplink frame number ‘I’ for transmission from the UE shall start TTA = (NTA + NTA_offset)·Tc before the start of the corresponding downlink frame at the UE. The ‘TTA’ represents a TA for the uplink frame number ‘I’. The ‘NTA’ a nominal timing advance representing a nominal adjustment in time that the UE initially uses. The ‘NTA_offset’ is an additional adjustment value of the time for the UE that accounts for any specific variations or errors that could occur due to factors such as varying distances or environmental conditions affecting the timing. The ‘Tc’ is a timing cycle representing a time increment or unit of time used in the system 108 to measure and apply the timing adjustment.
[0095] In an aspect, UEs closer to the base station has shorter propagation delay, and hence smaller TA. UEs further away from the base station has longer propagation delay, and hence larger TA. The TA shall account for a round trip propagation delay, represented as ‘2.t_prop’ to account for a complete round trip of the signal. The round trip of the signal represents a time for the signal to travel from the UE to the BS counted as ‘t_prop’, and an acknowledgment or a response signal to travel back from the base station to the UE, counted as another ‘t_prop’. In addition, the TA also includes a timing offset toffset = NTA,offset Tc. The purpose of considering the complete round trip is for a Time Division Duplex (TDD) base station to activate its transmitter after an uplink frame.
[0096] In an aspect, from a perspective of the UE, a reference point for the UE initial transmit timing control requirement is a downlink timing of a reference cell, adjusted by subtracting the TA, which is calculated as NTA + NTA,offset · Tc. The downlink timing is defined as a time when a first detected path (in time) of a corresponding downlink frame is received from the reference cell.
[0097] In an aspect, from a perspective of the base station (i.e., the network node 502), the time difference between an uplink radio frame and the corresponding downlink radio frame is toffset, which is the same for all UEs attached to the base station. The propagation delay is already compensated at the UE side by the TA.
[0098] In a network, the UE is configured to transmit the CSI Report (CSI measurement parameter) to the network node 502 periodically in pre-defined intervals. The UE reports the CQI, the PMI, and the RI with reporting periods configured by a higher layer.
[0099] In an aspect, the system 108 is configured to estimate the CQI for each UE based on the received CSI measurement parameter correspondingly. The CQI is the information that the UE sends to the network (e.g., the network 106) or the network node 502 and includes current channel quality. In an embodiment, the CQI may be affected by a SNR, a Signal-to-Interference plus Noise Ratio (SINR), and a Signal-to-Noise plus Distortion Ratio (SNDR). For example, if the CSI measurement parameters show a high SNR and a low path loss, the channel is considered to have good quality, leading to a higher current CQI. On the other hand, if the CSI measurement parameter show a significant interference or a path loss, the current CQI will be lower, indicating poorer channel quality.
[00100] In an embodiment, once the cluster, i.e., the cluster M and the cluster N is assigned to the UE 1 and the UE 2 respectively, the CQI of the UE 1 and the UE 2 may be determined to update the existing CQI of the remaining UEs present within the cluster M and the cluster N. This is further explained in detail in conjunction with FIG. 6.
[00101] FIG. 6 illustrates an exemplary process flow diagram depicting a method 600 of updating existing CQIs of the remaining UEs present within the cluster, in accordance with an embodiment of the present disclosure. FIG. 6 is explained in conjunction with FIGS. 1, 2, 3, 4, and 5.
[00102] Once the UE 1 is placed in the cluster M, the network node 502 is configured to update the existing CQI of each of the remaining UEs within the cluster M. For this, initially, at step 602, the CSI report, i.e., the CSI measurement parameter may be received by the network node 502 (gNB) from the UE 1 present within the cluster M. Upon receiving the CSI report, at step 604, the network node 502 is configured to update the existing CQI associated with each of the remaining UEs present within the cluster M based on the current CQI determined for the at least one UE, i.e., the UE 1, using the at least one reinforcement learning technique. To update the existing CQI of the remaining UEs, the network node 502 implementing the system 108 is configured to determine the current CQI of the UE 1, upon receiving the CSI report from the UE 1. Further, the network node 502 may determine the existing CQI of each of the remaining UEs present within the cluster M. Once the existing CQI of each of the remaining UEs and the current CQI of the UE 1 is determined, the network node 502 is configured to estimate a CQI for each of the remaining UEs within the cluster based on the current CQI of the UE 1 using the at least one reinforcement learning technique. For example, if the UE 1 reports a high CQI, then using the at least one reinforcement learning technique, the system 108 predicts that the remaining UEs within the cluster M are likely to have similar high CQI as of the UE 1. For example, suppose the cluster M includes a set of three UEs, a UE A, a UE B, and a UE C each with an existing CQI of 8, 9, and 7 respectively. Further, suppose the CQI of the UE 1 added to the cluster M is determined to be 11. In this case, to determine the estimated CQI for the remaining UEs, the values of the CQI of the remaining UEs and the UE 1 may be aggregated to obtain the estimated CQI for the remaining UEs. Once the estimated CQI and the existing CQI for each of the remaining UEs are determined, then the existing CQI of each of the remaining UEs is updated by calculating a new CQI for each of the remaining UEs in the cluster.
[00103] In an embodiment, the network node 502 is configured to estimate the current CQI for the UE 1 based on the received CSI report. Once the CSI report is received for the UE 1 in the cluster M, all remaining UEs in the cluster M will update their CQI using a formula ‘New CQI = W*est CQI + (1-W)*old CQI’ where, weight ‘W’ (of each remaining UE) is calculated based on the estimated CQI and the old CQI (i.e., the existing CQI) associated with each remaining UE. The at least one reinforcement learning technique employs a reward function and a reward value to enhance an accuracy of the at least one learning technique. In other words, when an actual CQI for a UE within a cluster is received at a periodic interval (e.g., 1 hour). Then, the actual CQI of the UE is matched with the estimated CQI of the UE. Based on the matching, when the actual CQI matches with the estimated CQI or is similar to the estimated CQI, a high reward score (e.g., 90 % match) may be provided to at least one reinforcement learning technique. However, when the actual CQI does not match with the estimated CQI, a low reward score (e.g., 1% match) may be provided to the at least one reinforcement learning technique. In an embodiment, the at least one reinforcement learning algorithm uses reward values to improve its predictions over time. Specifically, after several iterations of updating the existing CQI and receiving feedback (i.e., the reward value) based on the estimated CQI, the reinforcement learning algorithm adjusts its decision-making process to more accurately predict the estimated CQI for each UE, considering the overall network conditions. This results in better network performance and optimized resource allocation for all UEs within the cluster. Over time, as the algorithm receives feedback from each iteration, its prediction accuracy increases. Eventually, the estimated CQI predicted by the reinforcement learning technique may closely match the actual CQI, improving the algorithm’s performance and its ability to adapt to dynamic network conditions.
[00104] Similarly, at step 606, the network node 502 is configured to receive the CSI report from the UE 2 present within the cluster N. Upon receiving the CSI report, at step 608, the network node 502 is configured to update the existing CQI of the remaining UEs present within the cluster N by determining the current CQI for the UE 2 within the cluster N. The existing CQI of each of the remaining UEs present within the cluster N may updated in the same way as of the existing CQI updated for each of the remaining UEs in the cluster M explained using the steps 602 and 604.
[00105] In an aspect, the method 600 include updating the existing CQI of each of the remaining UEs within the cluster M and the cluster N in real time by continuously observing the current CSI reports of the UE 1 and the UE 2 respectively. By considering the CSI reports having high periodicity, the channel condition at the UE (e.g., the UE 1 and the UE 2) can be estimated more frequently. Further, since the channel reciprocity is present in TDD systems, an overall idea of a UE location can be done based on the uplink TA measurement parameter, the pathloss measurement parameter, and the SNR measurement parameter. The present disclosure is configured to cluster of such UEs that have similar CSI reports and near about values of the uplink TA measurement parameter, the pathloss measurement parameter, and the SNR measurement parameter, the AoA measurement parameter, the PRS measurement parameter, such that frequent estimates of the channel associated with can be done based on the CSI reports of the UE and the remaining UEs within the cluster.
[00106] FIG. 7 illustrates an exemplary computer system 700 in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 7, the computer system 700 may include an external storage device 710, a bus 720, a main memory 730, a read-only memory 740, a mass storage device 750, communication port(s) 760, and a processor 770. A person skilled in the art will appreciate that the computer system 700 may include more than one processor and communication ports. The processor 770 may include various modules associated with embodiments of the present disclosure. The communication port(s) 760 may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) 760 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 700 connects.
[00107] The main memory 730 may be Random-Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 740 may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input/Output System (BIOS) instructions for the processor 770. The mass storage device 750 may be any current or future mass storage solution, which can be used to store information and/or instructions. The mass storage device 750 includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, a Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks.
[00108] The bus 720 communicatively couples the processor 770 with the other memory, storage, and communication blocks. The bus 720 may be, e.g. a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 770 to the computer system 700.
[00109] Optionally, operator and administrative interfaces, e.g. a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus 720 to support direct operator interaction with the computer system 700. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) 760. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system 700 limit the scope of the present disclosure.
[00110] In another exemplary embodiment, the present disclosure discloses a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network. The method includes receiving a set of measurement parameters from at least one UE. The method includes adding the at least one UE to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE using a clustering algorithm. The method includes determining a current Channel Quality Information (CQI) corresponding to the at least one UE based on the set of measurement parameters. The method includes updating an existing CQI associated with remaining UEs in the cluster based on the current CQI determined for the at least one UE.
[00111] 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.
[00112] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
[00113] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.
[00114] The present disclosure provides technical advancement related to updation of the CQI of the UEs within the network. This advancement addresses the limitations of existing solutions by facilitating grouping of UEs in one or more clusters using the clustering algorithm and by employing the reinforcement learning technique to determine the CQI of the at least one UE and update the exiting CQI of the remaining UEs within the same cluster as of the at least one UE. In other words, the present disclosure facilitates grouping of the UEs having similar uplink TA, pathloss, and SNR values with similar CSI reports. The present disclosure also enables estimation of the channel condition of the UEs in a cluster based on a received CSI report. In other words, the present disclosure facilitates estimation of the channel condition of the UE till the time an actual CSI report (i.e., a report including a real-time data about the channel condition experienced by the UEs, leading to better service and resource allocation for the UE.
ADVANTAGES OF THE PRESENT DISCLOSURE
[00115] The present disclosure provides a method and a system for updating Channel Quality Information (CQI) of UEs within a network.
[00116] The present disclosure provides a method and a system that employs a clustering algorithm to group UEs into a plurality of clusters based on an associated set of measurement parameters.
[00117] The present disclosure provides a method and a system that employs a reinforcement learning technique for updating the CQI of a plurality of UEs present within a cluster. The usage of the reinforcement learning technique to adjust the CQI ensures that the system quickly adapts to fluctuations in channel conditions.
[00118] The present disclosure provides timely adjustments in resource allocation and service provisioning by using Channel State Information (CSI) reports of nearby UEs to estimate channel conditions, thereby enhancing overall network efficiency. ,CLAIMS:CLAIMS
We claim:
1. A method (300) for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network (106), the method (300) comprising:
receiving (302), by a processing engine (208), a set of measurement parameters from at least one UE;
adding (304), by the processing engine (208), the at least one UE to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE;
determining (306), by the processing engine (208), a current CQI corresponding to the at least one UE based on the set of measurement parameters; and
updating (308), by the processing engine (208), an existing CQI associated with remaining UEs in the cluster associated with the at least one UE based on the current CQI determined for the at least one UE.

2. The method (300) as claimed in claim 1, wherein the set of measurement parameters includes an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter.

3. The method (300) as claimed in claim 1, wherein adding the at least one UE to the cluster comprises:
mapping, by the processing engine (208), each of the set of measurement parameters associated with the at least one UE with a corresponding set of cluster measurement parameters associated with each cluster.

4. The method (300) as claimed in claim 1, further comprising:
creating, by the processing engine (208), the plurality of clusters comprising a set of UEs from a plurality of UEs based on the set of measurement parameters associated with each of the plurality of UEs, using a clustering algorithm.

5. The method as claimed in claim 1, wherein the existing CQI of each of the remaining UEs in the cluster is updated using at least one reinforcement learning technique.

6. A system (108) for updating Channel Quality Information (CQI) of User Equipments (UEs) within a network (106), the system (108) comprising:
a memory (204); and
a processing engine (208) coupled to the memory (204), configured to:
receive (302) a set of measurement parameters from at least one UE;
add (304) the at least one UE to a cluster from a plurality of clusters based on the set of measurement parameters associated with the at least one UE;
determine (306) a current Channel Quality Information (CQI) corresponding to the at least one UE based on the set of measurement parameters; and
update (308) an existing CQI associated with remaining UEs in the cluster based on the current CQI determined for the at least one UE.

7. The system (108) as claimed in claim 6, wherein the set of measurement parameters includes an uplink Timing Advance (TA) measurement parameter, a pathloss measurement parameter, a Signal-to-Noise Ratio (SNR) measurement parameter, an Angle of Arrival (AoA) measurement parameter, a Positioning Reference Signal (PRS) measurement parameter, and a Channel State Information (CSI) measurement parameter.

8. The system (108) as claimed in claim 6, wherein, to add the at least one UE to the cluster, the processing engine is further configured to:
map each of the set of measurement parameters received from the at least one UE with a corresponding set of measurement parameters with each cluster.

9. The system (108) as claimed in claim 6, wherein the processing engine is further configured to:
create the plurality of clusters comprising a set of UEs from a plurality of UEs based on a corresponding set of cluster measurement parameters associated with each of the plurality of UEs, using a clustering algorithm.

10. The system (108) as claimed in claim 1, wherein the existing CQI of each of the remaining UEs in the cluster is updated using at least one reinforcement learning technique.

11. A User Equipment (UE) (104) communicatively coupled with a network (106), the coupling comprises steps of:
receiving, by the network (106), a connection request from the UE (104);
sending, by the network (106), an acknowledgment of the connection request to the UE (104); and
transmitting a plurality of signals in response to the connection request, wherein based on the plurality of signals, an updation of Channel Quality Information (CQI) of UEs within the network (106) is performed by the method (300) as claimed in claim 1.