Abstract: Disclosed is a method performed by a user equipment (UE) for Artificial Intelligence (AI) based Channel State Information (CSI) encoding. The method comprises receiving, a signaling message including at least one encoding parameter from a Base Station (BS), wherein the encoding parameter comprises an AI model. The method comprises segmenting a precoder data based on the signaling message into a plurality of data segments. The method comprises selecting at least one AI model from a plurality of AI models based on the at least one encoding parameter received from the BS. The method comprises encoding each data segment of the plurality of data segments of the precoder data using the at least one selected AI model. The method comprises sending at least one encoded data segment of the precoder data to the BS for decoding.

Abstract: A network device may include an active supervisor having processing circuitry that generates hardware-agnostic feature tables that are replicated for a standby supervisor. The processing circuitry of the active supervisor may execute a dedicated state replication process that facilitates the sharing of a packet forwarding state of the active supervisor with the standby supervisor. The shared packet forwarding state may be defined by and include hardware-agnostic feature tables and other types of packet forwarding state information.

Abstract: Methods and Wi-Fi devices that utilize the target wait time (TWT) feature of the Wi-Fi specification are disclosed. These methods reduce power consumption by selectively listening to only a portion of the beacons that are transmitted by the access point. In one scenario, the Wi-Fi device only listens to one beacon per TWT wake interval. In another embodiment, the Wi-Fi device may have knowledge of the application that is being executed, such as its allowable latency. The Wi-Fi device may use this allowable latency to determine which when to exit low power mode to receive a beacon. Further, mechanisms to ensure that the connection between the Wi-Fi device and the access point are also disclosed.

Abstract: Embodiments herein disclose a method for controlling a non-linear effect of a power amplifier by an apparatus. The method includes acquiring an input data of the power amplifier of the apparatus and an output data of the power amplifier. Further, the method includes determining an inverse function using a neural network. The inverse function maps normalized output data of the PA to the input data of the PA, where the neural network comprises at least one sub-network for at least one memory tap from a plurality of memory taps in the neural network. Further, the method includes modifying the input data based on the determined inverse function value by dynamically changing a usage of the at least one memory tap from the plurality of memory taps. Further, the method includes compensating the non-linear effect in the output data of the power amplifier.

Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. According to an embodiment, a user equipment (UE) in a communication system includes a transceiver; and a processor coupled with the transceiver and configured to: receive, from a base station, configuration information associated with channel state information (CSI) report; identify, based on the configuration information, that a first precoding matrix indicator (PMI) component on basis information and N second PMI components on combining coefficients are to be transmitted for the CSI report; and transmit, to the base station, the first PMI component and the N second PMI components.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for retrieving a subgraph that is used to generate one or more answer outputs responsive to an input query by: (i) generating one or more context embeddings that are associated with an input query, (ii) identifying one or more candidate node paths and one or more node relations based on a knowledge graph, (iii) identifying, using a predictive machine learning model, one or more context-relationship rankings based on the one or more candidate node paths, the one or more node relations, and the one or more context embeddings, and (iv) generating one or more subgraph data objects based on the one or more context-relationship rankings.

Abstract: Systems and methods for handling resource requests in programming of network device tables. A feature agent of a network device enters a resource request for a resource in a resource table and a resource server returns a response to the request, where the response includes a resource value and a dependency constraint encapsulating validity criteria for utilizing the resource value. When the resource value and dependency constraint are returned, the feature agent writes an entry to a software table, where the software table entry includes the resource and the corresponding dependency constraint. A hardware examines the software table entry and determines whether the dependency constraint has been satisfied. When the dependency constraint has been satisfied, the hardware agent writes an entry corresponding to the software table entry in a hardware table. The network device then processes packets according to the hardware table with the new entry.

Abstract: Systems and methods for maintaining order consistency for asynchronous writes in a network device. In one embodiment, a method includes issuing, by a feature agent of a network device, a plurality of writes to software tables which may be contained in the feature agent. For each write, a corresponding entry is added to a log associated with the software tables, the log entry identifying an order of the write among the plurality of writes. A hardware agent accesses the log to select a next one of the entries in the identified write order and then accesses the software table to retrieve content identified by the selected log entry. The hardware agent then translates the information from the software table to a different form if necessary and writes the information to one or more hardware tables that are indicated by the write associated with the selected log entry.

Abstract: Methods and Wi-Fi devices that utilize the target wait time (TWT) feature of the Wi-Fi specification are disclosed. These 5 methods reduce power consumption by selectively listening to only a portion of the beacons that are transmitted by the access point. In one scenario, the Wi-Fi device only listens to one beacon per TWT wake interval. In another embodiment, the Wi-Fi device may have knowledge of the application that is being executed, such as its allowable latency. The Wi-Fi device may use this allowable latency to determine which when to exit low power mode to receive a beacon. Further, mechanisms to ensure that the connection between the Wi-Fi device and the access point are also disclosed.

Abstract: Systems and methods to identify a growth classification/categorization for a geographic area that helps a network provider to solve for what types of planning opportunities are available at various area granularities is disclosed. The system computes values for a set of growth criteria for a geographic area. The growth criteria are related to planning, usability, customer experience, sales, population, and so on. Based on the growth-criteria values, the system identifies a classification/categorization for the area. For example, the system classifies an area as an invest area (e.g., requiring engineering action), a grow area (e.g., requiring sales action/being sales ready), a defend area (e.g., requiring engineering and sales actions to continue current trend), or a fix area (e.g., likely requiring both engineering and sales actions to improve current trend). Based on the area classification, the system can then provide actionable insights to drive improvement in network coverage and customer experience.

Abstract: Systems and methods for handling resource requests in programming of network device tables. A feature agent of a network device enters a resource request for a resource in a resource table and a resource server returns a response to the request, where the response includes a resource value and a dependency constraint encapsulating validity criteria for utilizing the resource value. When the resource value and dependency constraint are returned, the feature agent writes an entry to a software table, where the software table entry includes the resource and the corresponding dependency constraint. A hardware examines the software table entry and determines whether the dependency constraint has been satisfied. When the dependency constraint has been satisfied, the hardware agent writes an entry corresponding to the software table entry in a hardware table. The network device then processes packets according to the hardware table with the new entry.

Abstract: Disclosed is a method performed by a user equipment (UE) for Artificial Intelligence (AI) based Channel State Information (CSI) encoding. The method comprises receiving, a signaling message including at least one encoding parameter from a Base Station (BS), wherein the encoding parameter comprises an AI model. The method comprises segmenting a precoder data based on the signaling message into a plurality of data segments. The method comprises selecting at least one AI model from a plurality of AI models based on the at least one encoding parameter received from the BS. The method comprises encoding each data segment of the plurality of data segments of the precoder data using the at least one selected AI model. The method comprises sending at least one encoded data segment of the precoder data to the BS for decoding.

Abstract: Systems and methods for maintaining order consistency for asynchronous writes in a network device. In one embodiment, a method includes issuing, by a feature agent of a network device, a plurality of writes to software tables which may be contained in the feature agent. For each writes, a corresponding entry is added to a log associated with the software tables, the log entry identifying an order of the write among the plurality of writes. A hardware agent accesses the log to select a next one of the entries in the identified write order and then accesses the software table to retrieve content identified by the selected log entry. The hardware agent then translates the information from the software table to a different form if necessary and writes the information to one or more hardware tables that are indicated by the write associated with the selected log entry.

Abstract: Systems and methods for enhancing customer service are disclosed. The system can perform operations including detecting individuals and performing facial recognition on the individuals using visitor profiles stored by a local database. The operations can further include determining known visitors and unknown visitors among the individuals. The operations can further include generating new visitor profiles for the unknown visitors, obtaining identity information for the unknown visitors, and associating the identity information of the unknown visitors with the new visitor profiles. The operations can further include accessing identify information of the known visitors, obtaining personalized information for the known visitors from a central database, and providing the personalized information to the known visitors.

Abstract: A method for performing a channel state information (CSI) prediction by a user equipment (UE) is provided. The method includes receiving a plurality of reference signals from a base station, obtaining a channel quality information (CQI) estimation for an interval based on the received plurality of reference signals, wherein obtaining the CQI estimation includes obtaining at least one of mean mutual information per bit (MMIB) or effective exponential signal to noise ratio mapping (EESM), predicting the CSI based on the CQI estimation, and reporting the predicted CSI to the base station.

Abstract: Systems and methods for enhancing customer service are disclosed. The system can perform operations including detecting individuals and performing facial recognition on the individuals using visitor profiles stored by a local database. The operations can further include determining known visitors and unknown visitors among the individuals. The operations can further include generating new visitor profiles for the unknown visitors, obtaining identity information for the unknown visitors, and associating the identity information of the unknown visitors with the new visitor profiles. The operations can further include accessing identify information of the known visitors, obtaining personalized information for the known visitors from a central database, and providing the personalized information to the known visitors.

Abstract: This disclosure describes techniques for optimizing the allocation of physical resource blocks (PRBs) for user equipment (UE), such as a Narrow-Band Internet of Things (NB-IoT) device, within a Long-Term Evolution (LTE) spectrum. More specifically, a resource allocation controller that is configured to select one of at least two resource allocation algorithms that allocate physical resources within an LTE spectrum, based on an identity of the UE and an analysis of real-time or near real-time physical resource utilization and control utilization within the LTE spectrum. In one example, the resource allocation controller may analyze the physical resource utilization and control utilization within a subframe of an LTE spectrum to determine whether to allocate an exact or near-exact number of PRBs for data communications of a UE (i.e. NB-IoT device), or whether to allocate a PRB group (at least four PRBs) for data communications of the UE.

Abstract: Embodiments herein provide a method for channel state information (CSI) prediction by a user equipment (UE) in a wireless network. The method comprises determining a CSI; inputting the determined CSI to at least one machine learning (ML) based CSI prediction model to obtain at least one predicted precoder; encoding the at least one predicted precoder into at least one bit stream using at least one ML based CSI encoding model or at least one non-ML based CSI encoding model; and transmitting the at least one encoded bit stream to a network apparatus in the wireless network for the ML based CSI prediction at the network apparatus.

Abstract: Systems and methods to identify a growth classification/categorization for a geographic area that helps a network provider to solve for what types of planning opportunities are available at various area granularities is disclosed. The system computes values for a set of growth criteria for a geographic area. The growth criteria are related to planning, usability, customer experience, sales, population, and so on. Based on the growth-criteria values, the system identifies a classification/categorization for the area. For example, the system classifies an area as an invest area (e.g., requiring engineering action), a grow area (e.g., requiring sales action/being sales ready), a defend area (e.g., requiring engineering and sales actions to continue current trend), or a fix area (e.g., likely requiring both engineering and sales actions to improve current trend). Based on the area classification, the system can then provide actionable insights to drive improvement in network coverage and customer experience.

Abstract: A device, that provides serverless computing, receives a request to execute multiple jobs, and determines criteria for each of the plurality of jobs, wherein the criteria for each of the multiple jobs includes at least one of job posting criteria, job validation criteria, job retry criteria, or a disaster recovery criteria. The device stores information associated with the multiple jobs in a repository, wherein the information associated with the multiple jobs includes the criteria for each of the multiple jobs. The device provides a particular job, of the multiple jobs, to a cluster computing framework for execution, determines modified criteria for the particular job, and provides the modified criteria for the particular job to the cluster computing framework. The device receives, from the cluster computing framework, information indicating that execution of the particular job is complete, and validates a success of completion of the execution of the particular job.