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

Shailesh Patil

Peter S Gaal

Juan Montojo

Jelena Damnjanovic

Lik Hang Silas Fong

Yan Zhou

Wooseok Nam

Xiaojie Wang

Xinzhou Wu

Anupam Chanda

Pankaj Thakkar

Alexander Tessmer

Chong Li

Hong Cheng

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

Saurabha Rangrao Tavildar

Aleksandar Jovicic

Mostafa Khoshnevisan

Taesang Yoo

Qing Li

Chih-Hao Liu

Hamed Pezeshki

Saurabh R Tavildar

Yu Meng Li

Raju Koganty

Tingfang Ji

Naga Bhushan

Gabi Sarkis

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

Ahmed Gheith

Yi Liu

Haim Mendel Weissman

Martin J Hirzel

Xiaowei Shen

Giovanni Chisci

Yi He

Zhe Xiang

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

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an activated UE relay measurement configuration that includes one or more first sidelink measurement parameters. The UE may receive a deactivated UE relay measurement configuration that includes one or more second sidelink measurement parameters, wherein values for the one or more first sidelink measurement parameters and values for the one or more second sidelink measurement parameters are different. The UE may perform one or more sidelink measurements based at least in part on at least one of the activated UE relate measurement configuration or the deactivated UE relay measurement configuration. Numerous other aspects are described.

Sidelink measurement configuration and reporting for a user equipment relay

A base station may instruct a UE to use at least one weighting factor associated with a CR for the UE, and the UE may apply the at least one weighting factor to the one or more resources scheduled for the PSSCH transmission to determine the CR. The UE may transmit the PSSCH in the one or more resources of the at least one slot based on the determined CR being less than or equal to a CR threshold value. The at least one weighting factor may be applied to the one or more resources in each of multiple slots scheduled for transmission of a PSSCH.

Channel occupancy ratio calculation

Some aspects described herein relate to receiving a configuration defining resources for transmitting a first message in a two-step random access procedure, wherein the first message includes a random access preamble and a payload, generating a beacon signal for transmitting as the first message, where the beacon signal includes one or more parameters to facilitate discovery by other UEs for pairing in sidelink communications, and transmitting, based on the configuration, the beacon signal as the first message over the resources.

Techniques for performing sidelink discovery in wireless communications

Certain aspects of the present disclosure provide techniques for configuring sidelink slots with multiple automatic gain control symbols. One aspect provides a method for wireless communication by a user equipment, including enabling a slot configuration comprising at least two symbols configured for use by a receiver for automatic gain control in preconfigured symbol locations within a slot, and transmitting the at least two symbols while transmitting symbols within the slot.

Sidelink slots with multiple automatic gain control symbols

A method of wireless communication performed at a user equipment (UE) includes receiving, at the UE, a set of downlink (DL) reference signals (RSs) associated with one or more first transmission beams. The method also includes measuring, at the UE, the set of DL-RSs. The method further includes performing, at the UE, an uplink transmission having a transmission power that is set by a pathloss estimation function based on a set of pathloss parameters. One or more of pathloss parameters of the set of pathloss parameters is based on measuring the set of DL-RSs.

Machine learning-based power control

Methods, systems, and devices for wireless communications are described. A first user equipment (UE) may receive control signaling indicating a set of component carriers allocated for sidelink communications performed by the first UE. The first UE may receive a message indicating a subset of component carriers from the set of component carriers for use in communicating via a sidelink between the first UE and a second UE, where the subset of component carriers may be supported by the first UE and the second UE. In some cases, a base station or anchor UE may determine or configure the subset of component carriers for use between the first and second UEs. In some cases, the first UE, the second UE, or a both may determine the subset of component carriers. The first UE may communicate with the second UE via a component carrier from the subset of component carriers.

Techniques for configuring component carriers for sidelink communications

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Basking Ridge, NJ, United States of America

Hua Wang