Qualcomm Incorporated

Other

Naga Bhushan

Tingfang Ji

Joseph Binamira Soriaga

Krishna Kiran Mukkavilli

Peter S Gaal

Wanshi Chen

John Edward Smee

Tao Luo

Alexei Yurievitch Gorokhov

Yongbin Wei

Durga Prasad Malladi

Aleksandar Damnjanovic

Jay Kumar Sundararajan

Jing Jiang

Ravi Palanki

Aamod Dinkar Khandekar

Gavin Bernard Horn

Taesang Yoo

Hao Xu

Seyong Park

June Namgoong

Peter Pui Lok Ang

Alexandros Manolakos

Srinivas Yerramalli

Juan Montojo

Renqiu Wang

Avneesh Agrawal

Jing Lei

Peter J Black

Wei Zeng

Hwan Joon Kwon

Guttorm Ringstad Opshaug

Junyi Li

Siddhartha Mallik

Sven Fischer

Ashwin Sampath

Shimman Arvind Patel

Parag Arun Agashe

Jelena Damnjanovic

Sony Akkarakaran

Hung Dinh Ly

Stephen William Edge

Kapil Gulati

Wei Jennifer Yang

Huilin Xu

Sudhir Kumar Baghel

Yi Huang

Haitong Sun

Linhai He

Xiaoxia Zhang

Santosh Paul Abraham

Shailesh Patil

Bilal Sadiq

Ahmed Kamel Sadek

Arjun Bharadwaj

Muhammad Sayed Khairy Abdelghaffar

Tien Viet Nguyen

Gabi Sarkis

Tamer Adel Kadous

Rajat Prakash

Gokul Sridharan

Seyedkianoush Hosseini

Muhammad Nazmul Islam

Fatih Ulupinar

Ozcan Ozturk

Tianyang Bai

Vasanthan Raghavan

Juergen Cezanne

Kapil Bhattad

Rajarshi Gupta

Soo Bum Lee

Jilei Hou

Navid Abedini

Makesh Pravin John Wilson

Hua Wang

Mehmet Yavuz

Ayman Fawzy Naguib

Qiang Wu

Xiao Feng Wang

Shuanshuan Wu

Vikram Makam Gupta

Thomas J Richardson

Jun Ma

Sumeeth Nagaraja

Aziz Gholmieh

Mark S Wallace

Fuyun Ling

Yu Zhang

Arumugam Chendamarai Kannan

Hong Cheng

Heechoon Lee

Yuchul Kim

Zhibin Wu

Jun Wang

Ahmed Attia Abotabl

Ahmed Elshafie

Samir Salib Soliman

Madhavan Srinivasan Vajapeyam

Soumya Das

Ramin Rezaiifar

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

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

Methods, systems, and devices for wireless communication are described. Some wireless communications systems may support orbital angular momentum (OAM) communications. A transmitting network node may generate signals for transmission via a first circular antenna array that includes a first quantity of antenna subarrays. The transmitting network node may transmit the signals using the first circular antenna array and based on OAM vectors. The receiving network node may receive and decode the signals using a second circular antenna array that includes a second quantity of antenna subarrays and based on the OAM vectors. The OAM vectors may be based on a numerical relationship between the first quantity and the second quantity. The numerical relationship may include the first quantity being equal to a product of the second quantity and a non-integer rational number and a greatest common divisor of the first and second quantities being an integer greater than one.

Orbital angular momentum multiplexing based on a common factor between quantities of transmit and receive antenna subarrays

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

A method of wireless communication by a user equipment (UE) includes receiving different sets of parameters from different sources as input to a receiver neural network. The method also includes receiving, from a base station, a set of target long-term energy values associated with the receiver neural network. The method further includes calculating a scaling factor for each of the different sets of parameters based on the set of target long-term energy values, and separately scaling each of the different sets of parameters based on the scaling factor calculated for that set in order to generate multiple sets of scaled parameters. The method still further includes transmitting the multiple sets of scaled parameters to the receiver neural network.

Gain scaling of input to neural network for end-to-end learning in wireless communication system

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive an indication of one or more encoding operations to use for encoding a compressed dataset, the one or more encoding operations including a differential encoding operation, or an entropy encoding operation, or both. In some examples, using a neural network, the UE may first encode a dataset based on an additional encoding operation to generate a compressed dataset and then quantize the compressed dataset encoded based on the additional encoding operation. Subsequently, after the dataset has been initially encoded and then quantized, the UE may use the indication of the one or more encoding operations to further encode and compress the dataset. The UE may then transmit the dataset to a second device based on the one or more encoding operations.

Encoding techniques for neural network architectures

Disclosed are techniques for determining round-trip times (RTTs) between a user equipment (UE) and multiple base stations. In an aspect, the UE transmits an RTT measurement signal whose arrival time is measured by each of the base stations, and each of the base stations returns an RTT response signal whose arrival times are measured by the UE. In another aspect, the base stations each transmit an RTT measurement signal and the UE returns an RTT response signal. The receiver of the RTT measurement signal may include the measured arrival time in a payload of the RTT Response signal. Alternatively, the measured arrival time(s) of the RTT Measurement signal(s) and the transmission time(s) of the RTT Response signal(s) are sent in a separate message. The RTT signals can be wideband signals using low reuse resources.

Systems and methods for multiple round trip time (RTT) estimation in wireless networks

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

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

A method of wireless communication, by a transmitting device, includes encoding information packets with an outer code to generate a first outer codeword. The first outer codeword includes information packets in addition to parity packets. The method also includes transmitting, to a receiving device, the information packets and the parity packets of the first outer codeword, according to a sequence. A method of wireless communication by a receiving device, includes receiving packets belonging to a first outer codeword, the packets including information packets and parity packets. The method also includes storing the packets in a buffer; and flushing the packets from the buffer in response to satisfying a condition.

Outer code for upper layer data units

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

In a wireless access network, a false base station (FBS) may imitate a legitimate base station by repeating the transmissions of the legitimate base station at a higher power level such that one or more user equipment (UEs) synchronize with the FBS instead of the legitimate base station. The present disclosure provides a UE that detects an FBS. The UE may estimate a time of arrival of different multipath components of a downlink signal corresponding to a physical cell identity. The UE may determine an existence of FBS based on a difference between the times of arrival of two of the different multipath components exceeding a threshold amount of time. The UE may perform a mitigation operation in response to determining the existence of the FBS.

Wireless channel power profile false base station detection

A user equipment (UE) operating in a discontinuous reception (DRX) mode may receive a wake-up signal indicating that the UE may skip the next ON duration of the DRX cycle, i.e., the UE is instructed to not wake up during a next ON time during the DRX cycle to monitor communication signals, such as data signals or control signals. The UE may be configured to receive downlink (DL) positioning reference signals (PRSs), e.g., during the next ON duration of the DRX cycle. The UE respond to the PRS configuration and the wake-up signal by remaining in DRX sleep mode and not receiving the PRS or transitioning to DRX ON mode to receive the PRS during which the UE may monitor or not monitor communication signals. The location server may receive indications of the wake-up signal configuration and status from a base station or the UE.

Interaction of the wake-up signal (WUS) with downlink positioning reference signal (PRS) reception in a wireless network

Methods, systems, and devices for wireless communications are described. A first device and a second device may communicate via a channel. The first device may generate and transmit a reference signal, which may be a distortion probing reference signal with a high peak to average power ratio. In one implementation, the first device may use the reference signal as an input for a neural network model to learn a nonlinear response of the second device transmission components. In another implementation, the second device may sample the generated reference signal, and use the samples as inputs for a neural network model to learn the nonlinear response. The first device and the second device may exchange signaling based on learning the nonlinear response, and each device may compensate for the nonlinear response when communicating via the channel.

Distortion probing reference signals

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a client may select, based at least in part on a classifier, an autoencoder of a set of autoencoders to be used for encoding an observed wireless communication vector to generate a latent vector. The client may transmit the latent vector and an indication of the autoencoder. Numerous other aspects are provided.

Federated learning for classifiers and autoencoders for wireless communication

Growing community of inventors

San Diego, CA, United States of America

Naga Bhushan