Qualcomm Incorporated

Nicira, Inc.

International Business Machines Corporation

Other

Sino Ic Technology Co., Ltd.

Sap Se

Huazhong University of Science and Technology

Vmware, Inc.

Tencent Technology (Shenzhen) Company Limited

Lenovo (Singapore) Pte. Ltd.

Sap Ag

Semiconductor Manufacturing International (Shanghai) Corporation

Semiconductor Manufacturing International (Beijing) Corporation

Beijing Stomatology Hospital, Capital Medical University

Beijing Sh Bio-Tech Corporation

Hua Wang

Junyi Li

Tao Luo

Sony Akkarakaran

Piyush Gupta

Jung Ho Ryu

Jianjun Shen

Jing X Sun

Tianyang Bai

Donghai Han

Xiaoxia Zhang

Jelena Damnjanovic

Shailesh Patil

Peter S Gaal

Juan Montojo

Yan Zhou

Lik Hang Silas Fong

Wooseok Nam

Xiaojie Wang

Xinzhou Wu

Anupam Chanda

Pankaj Thakkar

Alexander Tessmer

Hong Cheng

Chong Li

Caixia Jiang

Mahmoud Taherzadeh Boroujeni

Thomas J Richardson

Yisheng Xue

Mukesh Hira

Arumugam Chendamarai Kannan

Ram Dular Singh

Vasanthan Raghavan

Xin Wang

Kun Wang

Kapil Gulati

Seyed Ali Akbar Fakoorian

Karl Georg Hampel

Qing Li

Saurabha Rangrao Tavildar

Aleksandar Jovicic

Mostafa Khoshnevisan

Taesang Yoo

Gabi Sarkis

Chih-Hao Liu

Hamed Pezeshki

Saurabh R Tavildar

Yu Meng Li

Raju Koganty

Tingfang Ji

Naga Bhushan

Juergen Cezanne

Kiran Venugopal

Shuo Li

Ronghua Zhang

Ganesan Chandrashekhar

Jin Liu

Sheng Yan Sun

Kun-Lung Wu

Chidambareswaran Raman

Hao Kai Zhang

Georgios Tsirtsis

Bugra Gedik

Pablo Alejandro Anigstein

Yi Ge

Henrique Andrade

Akhila Naveen

Rahul Korivi Subramaniyam

Navid Abedini

Shuanshuan Wu

Vincent Douglas Park

Xin Peng Liu

Vinay Chande

Rong Yan

Sourjya Dutta

Wei Lu

Bo Feng

Jun Li Wang

Aleksandar Damnjanovic

Yongbin Wei

Yu Zhang

Sundar Subramanian

Qian Zhang

Zhibin Wu

Yan Li

Xiaobo Wang

Sungwoo Park

Danlu Zhang

Igor Gutman

Yu Cao

Gang Tang

Konstantinos Dimou

Ahmed Gheith

Yi Liu

Haim Mendel Weissman

Martin J Hirzel

Giovanni Chisci

Xiaowei Shen

Yi He

Aspect are direction to techniques for predicting future beams (e.g., transmit beams and/or receive beams) for communications between a network node and a user equipment (UE). In some examples, the UE may output, for transmission to a network node, beam information of a receive beam. The UE may obtain, via a beam pair comprising the receive beam and a transmit beam, signaling from the network node. In some examples, the UE may output, for transmission to the network node, a measurement of the signaling from a perspective of the receive beam.

Transmit-beam prediction

Wireless communications systems, apparatuses, and methods are provided. A method of wireless communication performed by a sidelink user equipment (UE) includes receiving, from a first candidate relay UE, a plurality of beam failure detection reference signals (BFD RSs) associated with a first plurality of beams, detecting, based on at least one BFD RS of the plurality of BFD RSs, a beam failure associated with a first beam of the first plurality of beams based on a measurement associated with the first beam, and transmitting, to at least one of the first candidate relay UE or a network unit, an indicator indicating the detected beam failure.

Beam failure recovery in sidelink communications

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for retransmission and selection of HARQ and MCS based on partial sensing. A UE may perform a partial sensing procedure to identify one or more available resources for a sidelink transmission of the UE and transmit the sidelink transmission on a resource selected from the one or more available resources. The sidelink transmission may include at least one of a HARQ retransmission parameter or an MCS selected for the sidelink transmission based on at least one of a number of candidate slots for the partial sensing procedure, a quantity of the one or more available resources within the number of candidate slots, or a number of times that a threshold for a minimum amount of the one or more available resources is increased.

Retransmission and selection of HARQ and MCS based on partial sensing

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may establish, with a second UE, a first sidelink associated with a first layer 2 (L2) identifier of the second UE and a second sidelink associated with a second L2 identifier of the second UE, wherein the first L2 identifier is different than the second L2 identifier. The first UE may receive an indication associating the second UE with the first L2 identifier and the second L2 identifier. Numerous other aspects are described.

Indications associating layer 2 identifiers with a user equipment for sidelink

Methods, systems, and devices for wireless communication are described. A first user equipment (UE) may select a sidelink beam for a second UE configured to be connected to the first UE based on a sidelink transmission configuration indicator (TCI). The sidelink TCI may include a sidelink pathloss reference signal identifier, or a sidelink power control identifier, or both. The first UE may transmit a sidelink pathloss reference signal to the second UE configured to be connected to the first UE. The sidelink pathloss reference signal may be associated with the sidelink pathloss reference signal identifier of the sidelink TCI.

Sidelink power control with unified transmission configuration indicators

Methods, systems, and devices for wireless communications are described. For example, the described techniques provide for performing and reporting suitable measurements, at a first user equipment (UE), on reference signals from relay UEs to determine which relay UEs to activate or deactivate for relaying communications for the first UE. The first UE may be configured to perform measurements on deactivated relay UEs, and the first UE may report these measurements (or other measurements) when one or more events are triggered. The events may depend on the measurements performed on the deactivated relay UEs. Once the events are triggered, the first UE may report the measurements, and a second UE or a network entity may use the measurements to efficiently determine which relay UEs to activate or deactivate for relaying communications for the first UE.

Sidelink measurement reporting for sidelink relays

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a relay user equipment (UE) may receive, from a network node, a first message, the first message indicating beam switching information for a target UE. The relay UE may transmit, to the target UE, a second message based at least in part on the first message, the second message indicating the beam switching information for the target UE. The relay UE may receive, from the target UE, an acknowledgement (ACK) associated with a receipt of the second message. Numerous other aspects are described.

Relaying beam switching information

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitting user equipment (UE) may receive, from a network node, a sidelink grant that indicates a sidelink resource and indicates a set of sidelink beams associated with a sidelink communication using the sidelink resource. The UE may select, based at least in part on the set of sidelink beams indicated in the sidelink grant, a sidelink beam for the sidelink communication. The UE may transmit, via the selected sidelink beam, the sidelink communication to one or more receiving UEs using the sidelink resource indicated in the sidelink grant. Numerous other aspects are provided.

Network indication to control sidelink beam selection

Methods, systems, and devices for wireless communications at a user equipment (UE) are described. A UE may identify one or more measurement parameters to use for input in a machine learning (ML) model. In some cases, the measurement parameters may include reference signal received power measurements or a past set of timing advance (TA) values for a specific node. The UE may predict the TA based on inputting the measurement parameters into the ML model. The UE may transmit an uplink communication from the UE to a network entity using the TA predicted from the ML model. In some examples, the UE may transmit a capability report to indicate that the UE may support autonomous update of the TA based on the predictions of the TA values produced from the ML model.

Machine learning based timing advance updates

Certain aspects of the present disclosure provide techniques for sending a retransmission via a sidelink wireless communication channel. A method that may be performed by a first user equipment (UE) includes receiving, from a base station, a first data transmission. The method also includes receiving, from the base station, a signal indicating to retransmit at least a portion of the first data transmission to a second UE. The method further includes transmitting, to the second UE, based at least in part on the signal, a second data transmission via one or more resources, the second data transmission comprising at least the portion of the first data transmission.

Sidelink identification for multi-path downlink retransmission

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may transmit, to a second UE, an indication of at least one sidelink unified transmission configuration indicator (TCI) state, wherein the at least one sidelink unified TCI state includes at least one of: a joint forward link and reverse link TCI state, a separate forward link TCI state, or a separate reverse link TCI state. The first UE may communicate with the second UE on at least one of the forward link or the reverse link based at least in part on the indication of the sidelink unified TCI state. Numerous other aspects are described.

Sidelink unified transmission configuration indicator state

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, a configuration of a beam prediction model that is trained to predict beam measurements for a set of beams. The UE may receive, from the network node, an indication of a subset of beams, from the set of beams, that are to be associated with a measurement report. The UE may transmit, to the network node, the measurement report indicating one or more predicted beam measurements that are based at least in part on an output of the beam prediction model, the output of the beam prediction model including beam predictions associated with the set of beams, and the one or more predicted beam measurements including information associated with the subset of beams. Numerous other aspects are provided.

Disabling beam prediction outputs

Methods, systems, and devices for wireless communications are described. In one aspect, a first user equipment (UE) may transmit an indication of non-preferred resources to a second UE and an indication of an amount of transmit power reduction for the second UE to observe for a sidelink transmission on the non-preferred resources. In another aspect, a first UE may transmit an indication of an amount of transmit power reduction for a second UE to observe for a sidelink transmission on conflicting resources (e.g., resources reserved by the second UE and a third UE). Because the second UE may transmit on the non-preferred resources or the conflicting resources with reduced power (e.g., rather than suppressing transmissions on the non-preferred resources or the conflicting resources), the utilization of the non-preferred resources or the conflicting resources may be improved.

Transmit power adjustment based on inter-user equipment coordination

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first user equipment (UE) may determine, based at least in part on a channel sensing using a receive beam of the first UE, preferred resources or non-preferred resources of the first UE for a sidelink transmission by a second UE. The UE may transmit, to the second UE, inter-UE coordination (IUC) information that indicates: a transmit beam of the second UE, and the preferred resources or the non-preferred resources of the first UE, wherein the preferred resources or the non-preferred resources of the first UE are to be applied to the transmit beam of the second UE indicated in the IUC information. Numerous other aspects are described.

Transmitting inter-user-equipment coordination information based at least in part on beamforming

Methods, systems, and devices for wireless communications are described. A device such as a user equipment (UE) may receive, via network signaling, an assistance information message that includes network configuration information corresponding to one or more inputs of an interference-prediction machine learning model. The network configuration information may include a set of parameters associated with one or more neighboring cell configurations. The UE may then generate, using the interference-prediction machine learning model, a channel interference prediction corresponding to one or more communication links at the UE based on the network configuration information included in the assistance information message. The UE may then communicate with a network entity based on the generated channel interference prediction.

Interference prediction with network configuration information

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Basking Ridge, NJ, United States of America

Hua Wang