Qualcomm Incorporated

Other

Naga Bhushan

Tingfang Ji

Joseph Binamira Soriaga

Krishna Kiran Mukkavilli

Peter S Gaal

Wanshi Chen

John Edward Smee

Tao Luo

Durga Prasad Malladi

Yongbin Wei

Alexei Yurievitch Gorokhov

Aleksandar Damnjanovic

Jay Kumar Sundararajan

Jing Jiang

Ravi Palanki

Aamod Dinkar Khandekar

Gavin Bernard Horn

Taesang Yoo

Hao Xu

June Namgoong

Seyong Park

Peter Pui Lok Ang

Alexandros Manolakos

Srinivas Yerramalli

Renqiu Wang

Juan Montojo

Avneesh Agrawal

Jing Lei

Hwan Joon Kwon

Peter J Black

Wei Zeng

Siddhartha Mallik

Guttorm Ringstad Opshaug

Junyi Li

Shimman Arvind Patel

Sven Fischer

Ashwin Sampath

Parag Arun Agashe

Jelena Damnjanovic

Hung Dinh Ly

Sony Akkarakaran

Stephen William Edge

Kapil Gulati

Huilin Xu

Wei Jennifer Yang

Sudhir Kumar Baghel

Yi Huang

Haitong Sun

Linhai He

Weimin Duan

Xiaoxia Zhang

Santosh Paul Abraham

Muhammad Sayed Khairy Abdelghaffar

Shailesh Patil

Bilal Sadiq

Ahmed Kamel Sadek

Arjun Bharadwaj

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

Qiang Wu

Ayman Fawzy Naguib

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

Ahmed Attia Abotabl

Jun Wang

Ahmed Elshafie

Samir Salib Soliman

Madhavan Srinivasan Vajapeyam

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

Techniques for performing peer discovery to enable peer-to-peer (P2P) communication are disclosed. In an aspect, a proximity detection signal used for peer discovery may be generated based on one or more physical channels and/or signals used in a wireless network. In one design, a user equipment (UE) may generate a proximity detection signal occupying at least one resource block based on a SC-FDMA modulation technique. In another design, the UE may generate a proximity detection signal occupying at least one resource block based on an OFDMA modulation technique. The UE may generate SC-FDMA symbols or OFDMA symbols in different manners for different physical channels. In yet another design, the UE may generate a proximity detection signal including a primary synchronization signal and a secondary synchronization signal. For all designs, the UE may transmit the proximity detection signal to indicate its presence and to enable other UEs to detect the UE.

Transmission and reception of proximity detection signal for peer discovery

Methods, systems, and devices for wireless communications are described. Generally, the described techniques provide for performing initial access procedures including transmitting or receiving synchronization signal blocks (SSBs) based on a capability of a user equipment (UE) to use reconfigurable intelligent surfaces (RISs). In some examples, a base station may transmit SSBs using two synchronization raster grids. For example, the base station may transmit SSBs on a first synchronization raster grid for UEs that do not support RISs and on a second synchronization raster grid for UEs that support RISs. In some examples, a base station may transmit different types of SSBs. For example, a base station may transmit a first type of SSB for UEs that do not support RISs and a second type of SSB for UEs that support RISs. A UE may search for and receive SSBs according to a capability of the UE.

Transmitting synchronization signal block via reconfigurable intelligent surfaces

Methods, systems, and devices for wireless communications are described. A method for wireless communication may include receiving a single frequency networked composite reference signal (SFNed RS) at a first port of a user equipment (UE) and receiving a reference signal (RS) at a second port of the UE that is different than the first port of the UE. The method may also include performing channel estimation for at least one of the first port or the second port based at least in part on receiving the SFNed RS and the RS. A method for wireless communication may include transmitting a first portion of an SFNed RS from a first port of a first transmit/reception point to a UE and transmitting a downlink control information message to the UE that indicates at least one transmission configuration indicator state identifier that identifies the SFNed RS.

Consideration for demodulation reference signal and tracking reference signal quasi co-location relationship

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

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San Diego, CA, United States of America

Naga Bhushan