Intel Corporation

Apple Inc.

Qualcomm Incorporated

Intel IP Corporation

Feng Xue

Shilpa Talwar

Qinghua Li

Shu-Ping Yeh

Yang-Seok Choi

Venkatesan Nallampatti Ekambaram

Yuan Zhu

Nageen Himayat

Hooman Shirani-Mehr

Jong-Kae Fwu

Xiaogang Chen

Candy Yiu

Srikathyayani Srikanteswara

Dave A Cavalcanti

Maruti Gupta Hyde

Thomas J Kenney

Bernhard Raaf

Markus Dominik Mueck

Hyung-Nam Choi

Shahrnaz Azizi

Ping Wang

Marta Martinez Tarradell

Christian Drewes

Zhibin Yu

Thorsten Clevorn

John Joseph Browne

Robert Zaus

Biljana Badic

Sumeet Sandhu

Simon R Hunt

Ingolf Karls

Alexei Vladimirovich Davydov

Hosein Nikopour

Tao Luo

Yongbin Wei

Huaning Niu

Seunghee Han

Hwan-Joon Kwon

Gregory Vladimirovich Morozov

Jing Zhu

Wook Bong Lee

Alexander W Min

Xue Yang

Vallabhajosyula S Somayazulu

Maruti Gupta

Jingwen Bai

Roya Doostnejad

Yiting Liao

Wendy C Wong

Ching-En Lee

Juergen H Kreuchauf

Stefan Franz

Masoud Sajadieh

Ralph Hasholzner

Reinhold Schneider

Christopher MacNamara

Sarabjot Singh

Niall Power

Ashish Singh

Chia-Hsiang Chen

Uwe Kliemann

Farhana Sheikh

Ajay G Gupta

Eduardo Alban

Mustafa Akdeniz

Sung-En Chiu

Rakesh Kalathil

Anuja Surendra Vaidya

Mohammad Mamunur Rashid

Jun Shi

Ching-Yu Liao

Victor Sergeev

Javier Perez-Ramirez

Daniel B Schwartz

Kerstin Johnsson

Oner Orhan

Mark C Davis

Ravikumar Balakrishnan

David Arditti Ilitzky

Seong-Youp Suh

Karthik Narayanan

Mikhail T Galeev

Vinayak Honkote

Sreenivas Kasturi

Ehsan Aryafar

Ranganadh Karella

Mi Sun Park

Junyoung Nam

Namyoon Lee

Tiefeng Shi

Sagar Dhakal

Dibyendu Ghosh

Fangze Tu

Chris Macnamara

Vasuki Narasimha Swamy

Ajay Gupta

Vishnusudhan Raghupathy

Ganeshram Nandakumar

Jan Schreck

A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Methods and devices for radio communications

A method can be performed by a first node for determining a parameter of physical (PHY) layer circuitry of a second node. The method can include implementing a cascaded hierarchy of techniques to determine, based on an electrical signal from a second node, a parameter of the PHY layer circuitry of the second node, and causing an antenna of the first node to transmit an electromagnetic wave consistent with the determined parameter.

Feature detection in collaborative radio networks

Various methods and devices for positioning autonomous agents including verifying a reported agent location using physical attributes of the received signal; improving agent formation for iterative localization; selecting agents for distributed task sharing; intelligent beacon-placement for group localization; relative heading and orientation determination utilizing time of flight; and secure Instrument Landing System (ILS) implementation for unmanned agents.

Unmanned vehicle positioning, positioning-based methods and devices therefor

Apparatuses of a user equipment (UE), a cellular base station, and radio access network (RAN) nodes are disclosed. An apparatus of a wireless communication device includes circuitry configured to measure reference signals received from a plurality of antennas of an other wireless communication device, and circuitry configured to cause one or more antennas of the wireless communication device to transmit information regarding the received reference signals back to the other wireless communication device to enable the other wireless communication device to estimate a utility function for different transmit parameter sets.

Management of MIMO communication systems

Systems and methods of beamforming and improving mmWave communications for drones are described. Multiple RF chains are used to adapt the main beam to track changes without the use of pilot signals. To reduce interference, interfering signal power is eliminated by optimizing a non-Gaussian measure to extract the interferers. The AoA of signals from a target drone on neighbouring drones and location of the neighbouring drones and base stations are used to independently corroborate the location reported by the target drone. The base station provides additional synchronization signals below 6 GHz and restricts the search/measurement space in the vertical direction. The inherent sparse structure above 6 GHz is exploited by applying different beamformers on a sounding signal and estimating the AoA and impulse response. Variations of fully digital and hybrid beamforming architectures for multi-cell DL sync and CRS measurement are described.

Techniques for elevated device communication

An unmanned aerial vehicle can be configured to adjust a beam direction, provide path information, act as a base station, act as a cluster head, include an improved directional antenna or array of directional antennas, communicate in a collaboration using belief propagation, receive communications from a serving station aiding in navigation or improved signal performance, or the like.

Unmanned aerial vehicle communication

A wireless communication device includes a processor configured to select an offload processing task for performance by an edge computing device; cause a baseband modem to establish a direct wireless connection between the wireless communication device and the edge computing device; cause the baseband modem to send first data to the edge computing device via the direct wireless connection; and receive second data from the edge computing device, wherein the second data comprise a result of the offload processing task performed on the first data. The edge computing device includes a processor configured to receive, from a user device, offloaded data to be processed according to an offload processing task; execute the offload processing task on the offloaded data; and cause the radio via the interface to wirelessly send a result of the executed offload processing task via a direct wireless connection with the user device.

A communication circuit arrangement includes a first kernel dimension filter circuit configured to apply a first kernel dimension filter to a first input signal to estimate a first kernel dimension interference signal from a first amplifier, a second kernel dimension filter circuit configured to apply a second kernel dimension filter to a second input signal to estimate a second kernel dimension interference signal from a second amplifier, a joint delay tap dimension filter configured to apply a joint delay tap dimension filter to a combination of the first kernel dimension interference signal and the second kernel dimension interference signal to obtain an estimated joint interference signal, and a cancelation circuit configured to remove the estimated joint interference signal from a received signal to obtain a clean signal.

Apparatuses and methods for joint interference cancelation

Devices and methods of providing joint scheduling in a full-duplex LTE system are generally described. The UE measures intra-cell interference at the UE caused by a UL transmission from each intra-cell UE, calculates a ratio of the intra-cell interference over an average inter-cell interference, determines whether the intra-cell UE is an aggressor or non-aggressor based on whether the ratio exceeds a threshold, transmits at least some of the determinations to the eNB as feedback, and communicates with the eNB based on a schedule that is dependent on the feedback such that the UE is neither an aggressor nor is subject to interference from aggressors. The schedule may also be based on wideband CQI feedback based on a ratio of an average overall DL interference plus noise over the average inter-cell interference plus noise and/or subband CQI feedback based on SINR measurements.

Device and method for CQI feedback to enable joint DL-UL scheduling in full duplex systems

To configure a UT for handover between a source evolved Node-B (eNB) and a target eNB using aerial communications in a cellular network, the UE processing circuitry is to decode measurement configuration information from the source eNB. The measurement configuration information includes a plurality of height thresholds associated with aerial height of the UE. A measurement report is encoded for transmission to the source eNB. The measurement report includes the aerial height of the UE and the measurement report generation triggered based on one or more triggering events associated with the plurality of height thresholds. RRC signaling from the source eNB is decoded, the RRC signaling including a handover command. The handover command is based on a handover decision by the source eNB using the measurement report. A handover from the source eNB to the target eNB is performed based on the handover command.

Signaling design of enhanced handover support for drones in a cellular network

Embodiments of interference coordination for aerial vehicle user equipment (UE) are described. In some embodiments, a base station (BS) is configured to select a subset of time-frequency resources dedicated for use in serving aerial vehicle UEs and communicate with the aerial vehicle UEs via the subset of time-frequency resources. In some embodiments, the BS may transmit signaling to a neighbor BS that indicates the BS has dedicated the subset of time-frequency resources for serving aerial vehicle UEs and the subset of time-frequency resources to be used. The BS may receive an indication from the neighbor BS that a neighbor BS is to dedicate the subset of time-frequency resources to serve aerial vehicle UE, and based on the indication, the BS may reduce transmission activities in the subset of time-frequency resources, including refraining from transmitting or reducing an amount of information or power level of signaling to the aerial vehicle UEs.

Interference coordination for networks serving aerial vehicles

Aspects described herein relate to modulating control data in wireless communications. First control data for a first device can be modulated in an enhancement layer of a control data resource based at least in part on whether the control data resource includes second control data for a second device, and transmitted over the control data resource. A signal can be received from an access point comprising at least one control data resource. One of a plurality of modulation schemes utilized to modulate the at least one control data resource and an associated layer can be determined. The signal can be demodulated according to the one of the plurality of modulation schemes and the layer to obtain control data in the at least one control data resource.

Techniques for improving control channel capacity

Technologies for providing signal quality based route management for unmanned aerial vehicles include a device that includes circuitry to produce a data set indicative of a wireless communication signal quality at each of multiple locations in a geographic area. The circuitry is also to produce, as a function of the data set and a target wireless communication signal quality, a planned route for a vehicle through the geographic area.

Technologies for providing signal quality based route management for unmanned aerial vehicles

Techniques are described for improving handover performance in the context of UEs incorporated into unmanned aerial vehicles (UAVs, a.k.a., drones). A database is constructed that relates locations in a three-dimensional flying space to handover information that may include optimum scanning directions, optimum handover parameters, and/or optimum target cells to be monitored for possible handover.

Handover for unmanned aerial vehicles

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Redwood City, CA, United States of America

Feng Xue