Abstract: In aspects, a base station schedules air interface resources of a wireless communication system using one or more prediction metrics from a user equipment, UE. The base station receives (505), from the user equipment, user-equipment-prediction-metric capabilities. Based on the user-equipment-prediction-metric capabilities, the base station generates (510) a prediction-reporting request and communicates (515) the prediction-reporting request to the user equipment. The base station receives (520) one or more user-equipment-prediction-metric reports from the UE and schedules (525) the one or more air interface resources of the wireless communication system based on the one or more user-equipment-prediction-metric reports.

Abstract: Aspects of the present disclosure describe systems and methods for predicting an intra-aortic pressure of a patient receiving hemodynamic support from a transvalvular micro-axial heart pump. In some implementations, an intra-aortic pressure time series is derived from measurements of a pressure sensor of the transvalvular micro-axial heart pump and a motor speed time series is derived from a measured back electromotive force of a motor of the transvalvular micro-axial heart pump. Furthermore, in some implementations, machine learning algorithms, such as deep learning, are applied to the intra-aortic pressure and motor speed time series to accurately predict an intra-aortic pressure of the patient. In some implementations, the prediction is short-term (e.g., approximately 5 minutes in advance).

Abstract: This document describes techniques and devices for determining a machine-learning architecture for network slicing. A user equipment (UE) executes a first application associated with a first requested quality-of-service level. The UE selects a first machine-learning architecture based on the first requested quality-of-service level. The UE transmits, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use the first machine-learning architecture. The UE receives, from the network-slice manager, a first machine-learning architecture response message that grants permission to use the first machine-learning architecture based on a first network slice. The UE wirelessly communicates data for the first application using the first machine-learning architecture, the first machine-learning architecture being configured to compute an output based on an input using coefficients determined by the user equipment.

Abstract: This document describes techniques and devices for determining a machine-learning architecture. A user equipment (UE) transmits, to a network-slice manager of a wireless network, a first machine-learning architecture request message to request permission to use a first machine-learning architecture.

Abstract: In aspects, a high altitude platform station, HAPS, communicating with a user equipment, UE, using an adaptive phase-changing device, APD. The HAPS receives characteristics of the APD and selects the APD for inclusion in a wireless communication path with the UE based at least in part on the characteristics. The HAPS transmits a resource grant to the APD that includes an indication of air interface resources for an APD-Physical Downlink Control Channel, APD-PDCCH, between the HAPS and the APD, transmits, to the APD, an indication of phase vectors and timing information for a surface of the APD using the APD-PDCCH, and communicates with the UE using wireless transmissions that travel along the wireless communication path that includes the surface of the APD.

Abstract: A method for a cross-platform distillation framework includes obtaining a plurality of training samples. The method includes generating, using a student neural network model executing on a first processing unit, a first output based on a first training sample. The method also includes generating, using a teacher neural network model executing on a second processing unit, a second output based on the first training sample. The method includes determining, based on the first output and the second output, a first loss. The method further includes adjusting, based on the first loss, one or more parameters of the student neural network model. The method includes repeating the above steps for each training sample of the plurality of training samples.

Abstract: Wireless networks may have thousands of configurable parameters, so manual tuning is infeasible. A self-organizing network (SON) can provide automation. However, automated algorithms are not designed to interact with a wireless network, and network experimentation can jeopardize reliability. To address the former, a SON facilitator of a wireless network management node exposes an API that can translate network configuration information for consumption by a SON enhancer, which may implement an AI algorithm for network tuning. The SON facilitator can also transform output from the SON enhancer into directions for controlling a test scenario, including generating a DL SON message describing the test to a UE. To further increase reliability during the test scenario, the UE can be provisioned with two wireless connections. A first connection is unchanged by the test scenario for stability, and a second connection is used for testing.

Abstract: This document describes techniques and apparatuses for optimizing a cellular network using machine learning. A network-optimization controller determines a performance metric to optimize for a cellular network. The network-optimization controller determines at least one network-configuration parameter that affects the performance metric. The network-optimization controller sends a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter. The network-optimization controller receives, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients. The network-optimization controller analyzes the gradients using machine learning to determine at least one optimized network-configuration parameter.

Abstract: This document describes techniques and apparatuses for a user equipment (UE)-coordination set for a wireless network. In aspects, a base station specifies a set of UEs to form a UE-coordination set for joint transmission and reception of data intended for a target UE within the UE-coordination set. The base station selects one of the UEs within the UE-coordination set to act as a coordinating UE for the UE-coordination set and transmits a request message that directs the coordinating UE to coordinate the joint transmission and reception of the data intended for the target UE. Then, the base station transmits a downlink signal to each UE within the UE-coordination set. Each UE within the UE-coordination set demodulates and samples the downlink signal and then forwards the samples to the coordinating UE, which combines the samples and processes the combined samples to provide decoded data.

Abstract: Techniques and apparatuses are described for hybrid wireless communications processing chains that include deep neural networks (DNNs) and static algorithm modules. In aspects, a first wireless communication device communicates with a second wireless device using a hybrid transmitter processing chain. The first wireless communication device selects a machine-learning configuration (ML configuration) that forms a modulation deep neural network (DNN) that generates a modulated signal using encoded bits as an input. The first wireless communication device forms, based on the modulation ML configuration, the modulation DNN as part of a hybrid transmitter processing chain that includes the modulation DNN and at least one static algorithm module. In response to forming the modulation DNN, the first wireless communication devices processes wireless communications associated with the second wireless communication device using the hybrid transmitter processing chain.

Abstract: The disclosure relates to a compound of Formula (I): or a pharmaceutically acceptable salt thereof wherein A, Ra to Rd, and R4 to R7, are as described herein, as well as compositions and methods of using such compounds.

Abstract: The systems, methods, and techniques described in this disclosure allow different wireless systems that operate in accordance with different Radio Access Technologies (RATs) to coexist within a same frequency domain with minimal (if any) inter-RAT interference. Specifically, the described techniques allocate a respective, mutually-exclusive portion of a plurality of Space-Time-Frequency (STF) resources for use in communicating in accordance with each different RAT. For example, mutually-exclusive portions of spatial domain resources, time domain resources, and/or frequency domain resources may be respectively allocated for exclusive use by different RATs. A centralized, third-party controller (120) may perform the allocations, or the allocations may be cooperatively arrived at between systems supporting different RATs, e.g., in a peer-to-peer manner. STF resource allocations may be static and/or dynamic over time, and STF resources may be uniquely identified by respective resource identifiers.

Abstract: The disclosure relates to a compound of Formula (I): or a pharmaceutically acceptable salt thereof wherein A, Ra to Rd, and R4 to R7, are as described herein, as well as compositions and methods of using such compounds.

Abstract: This document describes techniques and apparatuses for joint-transmission over an unlicensed frequency band using a user equipment (UE)-coordination set. In aspects, a first UE in a UE-coordination set acts as a coordinating UE. The coordinating UE receives, using a local wireless network connection, uplink data from a second UE in the UE-coordination set. The coordinating UE distributes, using the local wireless network connection, the uplink data to at least a third UE in the UE-coordination set. The coordinating UE receives, from at least one UE in the UE-coordination set, respective results of a clear channel assessment of the unlicensed frequency band. The coordinating UE determines a specified time to begin joint-transmission of the uplink data based on the results and coordinates the joint-transmission by directing the at least one UE to initiate the joint-transmission of the uplink data based on the specified time.

Abstract: This document describes aspects of multiple active-coordination-set (ACS) aggregation for mobility management. A master base station coordinates aggregation of control-plane and user-plane communications, generated by a first active-coordination-set for a first joint communication between the first ACS and a user equipment, where the first ACS includes the master base station and at least a second base station. The master base station receives, from a second master base station of a second ACS, control-plane information or user-plane data associated with a second joint communication between the second ACS and the UE, the second ACS including the second master base station and at least a third base station. The master base station aggregates the control-plane and user-plane communications with at least a portion of the control-plane information or the user-plane data to coordinate data throughput to the user equipment.

Abstract: This disclosure provides systems, methods, and devices for audio signal processing that support feedback cancellation in a personal audio amplification system. In a first aspect, a method of signal processing includes receiving an input audio signal, wherein the input audio signal includes a desired audio component and a feedback component; and reducing the feedback component by applying a machine learning model to the input audio signal to determine an output audio signal. Other aspects and features are also claimed and described.

Abstract: A wireless system employs neural networks to provide for CSI estimate feedback between a transmitting device and a receiving device. A managing component selects neural network architecture configurations for implementation at the transmitting and receiving devices based on capability information. The receiving device determines CSI estimate(s) from CSI pilot signaling from the transmitting device. The CSI estimate(s) are processed by the neural network(s) at the receiving device to generate a CSF output, which can represent, for example, one or more predicted future CSI estimates and which is wirelessly transmitted to the transmitting device. The one or more neural networks at the transmitting device then process the received CSF output along to generate one or more recovered predicted future CSI estimates, which are then used to control one or more MIMO processes at the transmitting device.

Abstract: Techniques and apparatuses are described for enabling dual connectivity with secondary cell-user equipment. In some aspects, a base station (121) serving as a primary cell forms a base station-user equipment dual connectivity (BUDC) group (410) by configuring a user equipment (UE, 111) as a secondary cell-user equipment (SC-UE, 420) to provide a secondary cell. The base station (121) or SC-UE (420) can then add other UEs (112, 113, 114) to the BUDC group (410) thereby enabling dual connectivity for the UEs through the primary cell or secondary cell provided by the SC-UE (420). In some cases, the SC-UE (420) schedules resources of an air interface (302) by which the other UEs to communicate with the SC-UE (420). By so doing, the SC-UE (420) can communicate data with the other UEs (112, 113, 114) of the BUDC group (410) without communicating through a base station (121, 122), which decreases latency of communications between the UEs (111-114) of the BUDC group (410).

Abstract: Techniques and apparatuses are described for enabling base station-user equipment messaging regarding deep neural networks. A network entity (base station 121, core network server 320) determines a neural network formation configuration (architecture and/or parameter configurations 1208) for a deep neural network (deep neural network(s) 604, 608, 612, 616) for processing communications transmitted over the wireless communication system. The network entity (base station 121, core network server 302) communicates the neural network formation configuration to a user equipment (UE 110). The user equipment (UE 110) configures a first neural network (deep neural network(s) 608, 612) based on the neural network formation configuration. In implementations, the user equipment (UE 110) recovers information communicated over the wireless network using the first neural network (deep neural network(s) 608, 612).