Abstract: A method includes receiving a pilot signal or a measurement report from a user equipment (UE) or a base station (BS). The method also includes updating a CSI buffer with channel state information (CSI) obtained from the pilot signal or the measurement report, the CSI buffer configured to store previous uplink or downlink channel estimates. The method also includes providing at least a portion of the CSI buffer to a CSI predictor comprising an artificial intelligence (AI) model that utilizes one or more weight sharing mechanisms, the AI model comprising a sequence of layers. The method also includes predicting temporal CSI using the CSI predictor. Depending on the configured output, the method can also include and/or be used for denoising and frequency extrapolation.

Abstract: Methods and apparatuses for an AI channel prediction using a path-based tracking in wireless communication systems are provided. The methods of BS comprise: receiving one or more SRSs; identifying, based on the one or more SRSs, path clusters of channel instances, wherein each of the path clusters includes a group of paths that have associated impinging angles and propagation delays, respectively; identifying, based on a set of pixels in a channel image, a path from the path clusters; and performing, based on the identified path, a channel tracking operation.

Abstract: A base station (BS) includes a processor configured to generate training data for a shifted window (Swin) transformer-based channel estimation (CE) model, preprocess the training data, and train the Swin transformer-based CE model with the preprocessed training data. The BS also includes a transceiver operably coupled to the transceiver. The transceiver is configured to receive, over a wireless communication channel, a sounding reference signal (SRS). The processor is also configured to provide the SRS as an input image to the trained Swin transformer-based CE model, and receive as output from the trained Swin transformer-based CE model, a CE for the wireless communication channel.

Abstract: Methods and apparatuses for a high speed CSI prediction using a shifted window transformer in wireless communication systems are provided. The methods of BS comprise: receiving an SRS; determining, based on channel pixels including an angle and a delay, at least one attention score associated with an image; identifying, based on the at least one attention score, correlation patterns of the images; performing, based on the correlation patterns, a shifted window attention operation for uplink channel estimation; and predicting, based on the shifted window attention operation, CSI from the SRS for the uplink channel estimation.

Abstract: Joint configuration of cellular communication and bistatic object sensing involves transmitting, to a UE, a bistatic object sensing configuration. The bistatic object sensing configuration configures the UE to one of receive a sensing signal transmitted by a base station or transmit the sensing signal for reception by the base station. The bistatic object sensing configuration indicates sensing transmission power, waveform, and sensing resources and periodicity for the sensing signal, and may configure the UE to one of receive or transmit an object detection report.

Abstract: Apparatuses and methods of artificial intelligence based carrier aggregation in wireless communication systems. A method includes receiving channel state information (CSI) of a first carrier component (CC) from a user equipment; preprocessing the CSI of the first CC; and determining CSI of a second CC based on the preprocessed CSI of the first CC and a signal predicting model.

Abstract: Apparatuses and methods of solving wireless communication tasks. A method includes receiving, at an electronic device, a wireless channel model trained based on task-independent data; obtaining task-dependent data; determining, based on the wireless channel model and the task-dependent data, a task-independent metric and a task-dependent metric; and combining the task-independent metric and the task-dependent metric to solve wireless communication tasks.

Abstract: An apparatus includes a transceiver configured to receive, over a wireless communication channel, at least one controlled-noise signal. The apparatus also includes a processor, operatively coupled to the transceiver. The processor is configured to train, based on the at least one controlled-noise signal, a noise prediction model for the wireless communication channel, and generate, based on the trained noise prediction model, a noise prediction for the wireless communication channel. The processor is also configured to determine, based on the received at least one controlled-noise signal and the noise prediction, a score function for the wireless communication channel.

Abstract: A base station includes a transceiver, and a processor operatively coupled to the transceiver. The processor is configured to estimate a mobility level of a user equipment (UE), and determine whether the estimated mobility level of the UE exceeds a speed threshold. The processor is also configured to generate, from a channel response prediction model, a future channel response prediction based on the estimated mobility level of the UE and whether the estimated mobility of the UE exceeds the speed threshold.

Abstract: An electronic device includes a transceiver configured to receive information related to an action request or rule. The electronic device further includes a processor operatively coupled to the transceiver. The processor is configured to determine that the action is location based, and identify a zone within which the electronic device is located. The identification of the zone is based on at least one of a first reception by the transceiver of at least one signal indicative of a location of the electronic device, and a second reception by the transceiver of at least one response to a transmission of a signal indicative of a location of the electronic device. The processor is further configured to trigger an action based on the action request or rule and the identified zone.

Abstract: A method includes identifying a zone blocked from line of sight (LOS) of a gNB; receiving a 3D spatial map of a first area including the zone and a second area that surrounds the zone and that is within both a coverage area and the LOS of the gNB; determining whether the second area includes a mountable surface to which a passive RF reflective metasurface is attachable; and determining whether the metasurface, if attached to the mountable surface, generates a reflection path to the zone, based on a determination that the second area includes the mountable surface and based on estimated propagation paths from the gNB to the zone. The method includes determining whether to add a metasurface to the second area based on: a determination result whether the reflection path from the metasurface attached to the mountable surface to the zone includes reflected signals satisfying a threshold bandwidth condition.

Abstract: A method includes selecting a subset of access points from among multiple candidate access points associated with a building. The method also includes receiving Wi-Fi signal strength data from the subset of access points. The method further includes using a machine learning (ML) localization algorithm to determine a proximity of a device within the building based on the received Wi-Fi signal strength data.

Abstract: Apparatuses and methods for transmission mode adaptation in New Radio (NR) with AI/ML assistance. A base station includes a transceiver configured to receive a set of input metrics. The set of input metrics comprises at least one metric derived from a channel state information (CSI) report. The base station further includes a processor operably coupled to the transceiver, the processor configured to determine, based on the set of input metrics, a first multiple-input multiple-output (MIMO) mode throughput prediction and a second MIMO mode throughput prediction, generate, based on the first MIMO mode throughput prediction, a predicted first MIMO mode throughput result, generate, based on the second MIMO mode throughput prediction, a predicted second MIMO mode throughput result, and select a MIMO mode based on the predicted first MIMO mode throughput result and the predicted second MIMO mode throughput result.

Abstract: A method includes receiving a pilot signal or a measurement report from a user equipment (UE) or a base station (BS). The method also includes updating a CSI buffer with channel state information (CSI) obtained from the pilot signal or the measurement report, the CSI buffer configured to store previous uplink or downlink channel estimates. The method also includes providing at least a portion of the CSI buffer to a CSI predictor comprising an artificial intelligence (AI) model that utilizes one or more weight sharing mechanisms, the AI model comprising a sequence of layers. The method also includes predicting temporal CSI using the CSI predictor. Depending on the configured output, the method can also include and/or be used for denoising and frequency extrapolation.

Abstract: A time domain resource configuration indicates a time domain resource for sensing operations by a user equipment, and a frequency domain resource configuration indicates a bandwidth part (BWP) for the sensing operations. The user equipment performs the sensing operations using the indicated time domain resource and the indicated bandwidth part. The time domain resource configuration may include a sensing type indicator S for the time domain resource for sensing operations, and may indicate that dynamic triggering of sensing is allowed. The BWP for the sensing operations may comprise BWP(s) selectively activated for the sensing operations, and may indicate BWP(s) that overlap a BWP used for cellular communication. Assistance information for interference between sensing operations and cellular communication may be transmitted by the user equipment, which may subsequently receive a configuration for coexistence of the sensing operations and the cellular communication.

Abstract: Joint configuration of cellular communication and bistatic object sensing involves transmitting, to a UE, a bistatic object sensing configuration. The bistatic object sensing configuration configures the UE to one of receive a sensing signal transmitted by a base station or transmit the sensing signal for reception by the base station. The bistatic object sensing configuration indicates sensing transmission power, waveform, and sensing resources and periodicity for the sensing signal, and may configure the UE to one of receive or transmit an object detection report.

Abstract: A method determining scheduling for packet transmission in a convergecast network. The method includes receiving a request to perform an operation in the network. Initializing a query from the sink-node, the query is transmitted to a plurality of nodes, and in response to the query, the receiver receives information indicative at a period of time. Sorting the nodes by the sink-node based on the received information and using a sorting function that prioritizes each node to obtain a prioritized order of the nodes. Calculating an end-to-end delay for each node based on the time data by determining a difference between the data generation release time for each node and the data delivery time at the sink-node for each node. Scheduling packet transmission for each of the nodes based on a scheduling function having a set of predetermined scheduling criteria. Performing packet transmissions in the tree using the scheduled order.

Abstract: A method determining scheduling for packet transmission in a convergecast network. The method includes receiving a request to perform an operation in the network. Initializing a query from the sink-node, the query is transmitted to a plurality of nodes, and in response to the query, the receiver receives information indicative at a period of time. Sorting the nodes by the sink-node based on the received information and using a sorting function that prioritizes each node to obtain a prioritized order of the nodes. Calculating an end-to-end delay for each node based on the time data by determining a difference between the data generation release time for each node and the data delivery time at the sink-node for each node. Scheduling packet transmission for each of the nodes based on a scheduling function having a set of predetermined scheduling criteria. Performing packet transmissions in the tree using the scheduled order.