Qualcomm Incorporated

Massachusetts Institute of Technology

Harvard College

Universidade Do Porto

Jay Kumar Sundararajan

Tingfang Ji

Naga Bhushan

Krishna Kiran Mukkavilli

Wanshi Chen

Joseph Binamira Soriaga

Alexandros Manolakos

Hwan Joon Kwon

Yeliz Tokgoz

Taesang Yoo

Jing Lei

Seyong Park

Wei Jennifer Yang

Jing Jiang

June Namgoong

Yi Huang

Prashanth Haridas Hande

Pavan Kumar Vitthaladevuni

Huilin Xu

Peter Pui Lok Ang

Yuchul Kim

Seyedkianoush Hosseini

Arnaud Meylan

Gokul Sridharan

Linhai He

Peter S Gaal

Ravi Agarwal

Hao Xu

Weimin Duan

Haitong Sun

Gavin Bernard Horn

Renqiu Wang

Hung Dinh Ly

Peerapol Tinnakornsrisuphap

Gerardo Giaretta

Chih-Ping Li

Jing X Sun

Sony Akkarakaran

Yan Zhou

Gabi Sarkis

Sven Fischer

Junyi Li

Xiao Feng Wang

Xinzhou Wu

Mehmet Yavuz

Saurabh R Tavildar

Ying Wang

Yuwei Ren

Satashu Goel

Wei Zeng

Yu Zhang

Thomas J Richardson

Ahmed Elshafie

Andrei Dragos Radulescu

Muriel Medard

Allen Minh-Triet Tran

Rajiv Laroia

Rajat Prakash

Muhammad Sayed Khairy Abdelghaffar

John Edward Smee

Sumeeth Nagaraja

Xipeng Zhu

Joseph Patrick Burke

Rahul Malik

Bo Chen

Kambiz Azarian Yazdi

Ruifeng Ma

Tao Luo

Juan Montojo

Aleksandar Damnjanovic

George Cherian

Alexei Yurievitch Gorokhov

Heechoon Lee

Kuo-Chun Lee

Aziz Gholmieh

Arvind Vardarajan Santhanam

Miguel Griot

Liangping Ma

Guttorm Ringstad Opshaug

Keiichi Kubota

Ning Zhang

Chong Li

Srinivas Katar

Kalle Ahmavaara

Youhan Kim

Yongjun Kwak

Sivaramakrishna Veerepalli

Ajith Tom Payyappilly

Wei Yang

Yiqing Cao

Lorenzo Casaccia

Georgios Tsirtsis

Zhanfeng Jia

Kiran Kumar Somasundaram

Haipeng Jin

Charles Edward Wheatley

Jafar Mohseni

Runxin Wang

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may transmit a request for sets of neural network weights, each set corresponding to a neural network, to one or more network entities. The one or more network entities may transmit multiple sets of neural network weights to the UE in response to the request. The UE may run a classifier neural network on the sets of neural network weights to select a set of neural network weights corresponding to a neural network. The UE may transmit a neural network identifier to the one or more network entities that transmitted neural network weights.

Online training and augmentation of neural networks for channel state feedback

Certain aspects of the present disclosure provide techniques for file delivery feedback and/or application feedback for certain application services. An example method generally includes communicating files with a user equipment (UE), each of the files having a plurality of packets, determining that a delivery failure occurred for at least one of the files, and sending, to a server entity, a notification of the delivery failure.

File delivery failure feedback and application feedback

Various aspects of the present disclosure relate to wireless communication. In some aspects, a client may receive an observed environmental vector feedback configuration associated with a reporting procedure for reporting updates corresponding to at least one observed environmental vector that is based at least in part on one or more features associated with an environment of the client, wherein the observed environmental vector comprises input for a first machine learning component that determines client specific parameters for use by a second machine learning component. The client may transmit an update corresponding to the at least one observed environmental vector based at least in part on the observed environmental vector feedback configuration. Numerous other aspects are provided.

Observed environmental vector feedback for wireless communication

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a transport block (TB). The UE may fail to successfully decode the TB. The UE may transmit a negative acknowledgement (NACK) message based at least in part on the failing to successfully decode the TB. The NACK message may indicate a quantity of repetitions for retransmitting the TB. Numerous other aspects are described.

User equipment feedback on number of repetitions needed for decoding

Disclosed are techniques for wireless communication. In an aspect, a first network node determines at least one positioning measurement based on a channel estimate of a positioning reference signal (PRS) transmitted by a second network node, and obtains measurement error feedback for at least the at least one positioning measurement, wherein the measurement error feedback is based on an expected positioning measurement corresponding to the at least one positioning measurement, and wherein the expected positioning measurement is based on a location of the first network node and a location of the second network node.

Measurement error feedback to enable machine learning-based positioning

Certain aspects of the present disclosure provide techniques for remote interference mitigation between base stations using reference signals. Certain aspects provide a method for wireless communication by a first base station (BS). The method includes receiving a reference signal (RS) from a second BS. The method further includes performing interference measurement corresponding to interference from the second BS based on the RS being associated with the second BS.

Reference signal for remote interference management

Methods, systems, and devices for wireless communications are described. In some systems, a user equipment (UE) may receive, from a base station, a control message including an indication associated with a configuration for an interference measurement (IM) report in a channel state information (CSI) report. The UE may determine a setting of the configuration for the IM report based on the indication, which may include one or both of a resource setting associated with IM information in a frequency domain or a spatial setting associated with IM information in a spatial domain. The UE may generate the IM report based on one or both of the resource setting or the spatial setting and may transmit the CSI report including the IM report to the base station.

Differential mode for interference-specific channel state information report

Aspects presented herein may enhance the accuracy and/or latency of UE positioning based on crowd-sourcing, where a network entity may compute a position estimate of a UE based on neighbor-cell scan data from the UE and one or more reference UEs. In one aspect, a network entity receives a first set of measurements associated with at least one cell from a UE. The network entity performs a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via an ML model, where the UE and the set of reference UEs include at least one common cell.

Similarity learning for crowd-sourced positioning

Certain aspects of the present disclosure provide techniques for scheduling and bandwidth part (BWP) adaptation for extended reality (XR). A method that may be performed by a user equipment (UE) includes obtaining an indication to change at least one of a minimum control-channel-to-data-channel delay or a bandwidth for receiving a first transmission on a BWP; changing the at least one of the minimum control-channel-to-data-channel delay to a new minimum control-channel-to-data-channel delay or the bandwidth on the BWP to a new bandwidth; and receiving the first transmission on the BWP using the at least one of the new minimum control-channel-to-data-channel delay or the new bandwidth of the BWP.

Scheduling and bandwidth part adaptation techniques for extended reality

Disclosed are techniques for wireless positioning. In an aspect, a base station determines a channel profile for a multipath channel between the base station and a user equipment (UE) based on at least one positioning reference signal transmitted to the UE or received from the UE by the base station on one or more radio beams, compresses the channel profile into a compressed representation of the channel profile, and transmits the compressed representation of the channel profile to a network entity. The network entity receives the compressed representation of the time-angle channel profile and determines a location of the UE based on the compressed representation of the channel profile.

Base station-to-server signaling of time-angle channel profile

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may communicate, based at least in part on a physical data channel configuration corresponding to a physical data channel, a first extended reality (XR) data communication corresponding to a first physical data channel occasion associated with the physical data channel configuration, wherein a first priority level is associated with the first XR data communication. The network node may communicate a second XR data communication corresponding to a second physical data channel occasion, wherein a second priority level is associated with the second XR data communication, and wherein the second priority level is higher than the first priority level based at least in part on a packet delay budget associated with the physical data channel configuration being within a specified threshold of packet delay. Numerous other aspects are described.

Delay-dependent priority for extended reality data communications

Methods, systems, and devices for wireless communication are described for user equipment (UE) indication of uplink scheduling information to a base station. The uplink scheduling information may provide information on a periodic traffic pattern, such as for periodic extended reality (XR) data flows. The uplink scheduling information may provide uplink assistance information (UAI) to a base station to assist the base station with providing uplink resources that efficiently serve an XR session. The UAI may indicate one or more of a periodicity of uplink traffic, an offset between uplink traffic and a packet arrival, a data size for uplink traffic for each time period associated with the XR session, a request to enable uplink transmission skipping, or any combinations thereof. A base station may provide one or more dynamic grants, configured grants, or proactive grants to a UE based on the uplink scheduling information.

UE indication of uplink scheduling parameters in wireless communications

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first device may receive, from a second device, multiple code blocks (CBs) of a transport block. The first device may identify a CB bundle of one or more contiguous CBs that include one or more CBs for which negative feedback is to be reported. The first device may transmit, to the second device, an indication of the CB bundle. Numerous other aspects are described.

Control block bundle for retransmission

A communications transceiver system that employs self-interference mitigation techniques can detect static objects by using techniques that introduce angular or Doppler diversity. This can include moving Tx and Rx antennas and/or performing beam sweeping. When processing RF sensing data from reflected RF signals, self-interference mitigation techniques can be used and compensation can be made for the movement and/or beam sweeping to allow for both self-interference mitigation and detection of static objects.

Static target detection for RF sensing in full-duplex communication systems

Disclosed are techniques for addressing relation round trip time (RTT) positioning and timing advance (TA) command with user equipment (UE) receive-transmit (Rx-Tx) measurement reporting in wireless network such as in new radio (NR).

Round trip time (RTT) positioning and timing advance (TA) command with user equipment Rx Tx measurement reporting

Growing community of inventors

San Diego, CA, United States of America

Jay Kumar Sundararajan