Samsung Electronics Co., Ltd.

Nokia Corporation

Parion Sciences, Inc.

E.i. Dupont De Nemours and Company

Siemens Medical Solutions USA, Inc.

Intrexon Corporation

Qualcomm Incorporated

Other

Google Inc.

Motorola Mobility LLC

Jianzhong Zhang

Soenghun Kim

Young-Han Nam

Boon Loong Ng

Farooq Khan

Zhouyue Pi

Jin-Kyu Han

Aris Papasakellariou

Joonyoung Cho

Yang Li

Juho Lee

Krishna Kamal Sayana

Ju-Ho Lee

Michael Ross Johnson

Young-Bum Kim

Eko Nugroho Onggosanusi

Hwan-Joon Kwon

Joon-Young Cho

Ying Li

Rui Wang

Younsun Kim

Md Saifur Rahman

Hyojin Lee

Won Suk Choi

Sridhar Rajagopal

Yingyang Li

Jeongho Jeon

Joon Young Cho

Vikram Chandrasekhar

Giridhar Dhati Mandyam

Youn Hyoung Heo

Kenneth Anderson Stewart

Zhijun Cai

Himke Van Der Velde

Kyeong-In Jeong

Jin Kyu Han

Li Guo

Youn-Sun Kim

Ju Ho Lee

Yan E Wang

Balaji B Raghothaman

Bruno Clerckx

Hoondong Noh

Taeyoung Kim

Soeng-hun Kim

Hongbo Si

Soeng-Hun Kim

Ji-Yun Seol

Ari T Hottinen

Qiong Cheng

Richard Stirling-Gallacher

Wonsuk Choi

Lingjia Liu

Yeqing Hu

Hao Chen

Bruce Francis Molino

Cornelius Van Rensburg

Jianhua Mo

Thomas David Novlan

Yan Xin

Ahmed Atef Ibrahim Ibrahim

Christian Peter Lenges

Richard C Ebersole

Hartmuth C Kolb

Vishnu Vardhan Ratnam

Gang Xu

Shadi Abu-Surra

Jiann-An Tsai

Subba Reddy Palli

Dean Ervin Cress

Gilwon Lee

Namjeong Lee

Sudhir Ramakrishna

Yuming Zhu

Rubayet Shafin

Tejas Maheshbhai Bhatt

Yinong Ding

Jinkyu Han

Raja Sekhar Bachu

Wenxun Qiu

Zinan Lin

R Michael Van Dam

Youn Sun Kim

Richard C Boucher

Saidhiraj Amuru

Hyoung Ju Ji

Kaushik Morapakkam Josiam

Haihong Zheng

Kiran Bynam

Eunyong Kim

Yousuf Saifullah

Hyo Jin Lee

Stephen R Fahnestock

Michael Royce Johnson

Aditya V Padaki

Sangkyu Park

Yongseok Park

Myung Hoon Yeon

A method includes determining estimated features comprising second order statistics based on at least one received signal. The method also includes classifying, using a machine learning network, each channel of the at least one received signal into a channel profile based on the estimated features. The method also includes obtaining multiple minimum mean square error (MMSE) channel estimation weights from a database based on the estimated features, the database storing (i) representative MMSE estimation weights and (ii) channel cluster representatives indexed by the estimated features. The method also includes applying a respective MMSE channel estimation weight for each channel.

AI-augmented channel estimation

A method for performing mobility load balancing includes receiving, at a server, current load data for a plurality of cells of a wireless communication network, selecting, from the plurality of cells, a target cell (s), wherein a value of the current load data for the target cell exceeds a first predefined threshold, and selecting, from a neighbor cell list corresponding to the target cell, a set of neighboring cells for the target cell. The method further includes calculating, a value of at least one utilization parameter for the target cell, determining, a CIO value and an E-tilt value for the target cell based on the value of the at least one utilization parameter for the target cell and configuring one or more physical layer parameters of the target cell based on the determined CIO and E-tilt values for the target cell.

Methods and apparatus for network load balancing optimization

A method includes receiving, by a base station, a sounding reference signal (SRS) symbol from a user equipment (UE). The method also includes estimating, by the base station, an uplink (UL) channel from the UE to a full dimensional multiple-input multiple-output (FD-MIMO) base station base band based on the received SRS symbol. The method also includes receiving, by the base station from the UE, an estimate of a downlink (DL) channel from the FD-MIMO base station base band to the UE. The method also includes performing, by the base station, a joint calibration by applying one or more calibration algorithms using channel state information (CSI) of the UL channel and the DL channel.

System and method for user equipment assisted over-the-air calibration

Methods and apparatuses for automating configuration management in cellular networks. A method of a UE comprises: training, based on historical samples, a regression model y using samples obtained from a set of parameters, wherein the regression model y comprises a function of a first term X and a second term h; and predicting, based on the regression model y, a target KPI to capture parameter impacts corresponding to the second term h.

Automating configuration management in cellular networks

Calibration in distributed multiple input multiple output (MIMO) networks. A method performed by a first base station (BS) includes receiving a first uplink (UL) reference signal (RS) and determining, based on the first UL RS and a second UL RS, a first phase offset for transmission of a first downlink (DL) RS to a user equipment (UE). The second UL RS is associated with a second BS. The first phase offset of the first DL RS is relative to a second DL RS associated with the second BS. The method further includes transmitting the first DL RS with the first phase offset; receiving UE feedback associated with the first DL RS and the second DL RS; and determining, based on the first phase offset and the received feedback, for a DL data transmission to the UE, a second phase offset between transmissions of the first BS and the second BS.

Method and apparatus for calibration in distributed mimo networks

A network entity includes a memory and a processor operable coupled to the memory. The processor is configured to determine a hybrid joint transmission (Hybrid-JT) signal for transmission from a wireless network associated with the network entity to a user equipment (UE). The Hybrid-JT signal includes a diversity joint transmission (Div-JT) component and a multiplexing joint transmission (Mux-JT) component. The processor is further configured to instruct the wireless network to transmit the Hybrid-JT signal to the UE.

Hybrid joint transmission for multiple antenna coordinated multipoint systems

Methods and apparatuses for mobility robustness with joint phase-time arrays (JTPAs). A method for operating a base station (BS), includes determining, based on a wide-beam (WB) alignment procedure, a subset of beams for transmission of physical downlink shared control channels (PDSCHs). The method further includes transmitting a physical downlink control channel (PDCCH) indicating the subset of beams and transmitting the PDSCHs on the subset of beams, respectively, using a JPTA beamforming. The same data is included in each of the PDSCHs.

Mobility robustness using joint phase-time arrays

Methods and devices for transmission symbol modulation that accounts for distortion introduced by a power amplifier (PA). A user equipment (UE) comprises a transceiver and a processor. The transceiver is configured to transmit, to a base station (BS), UE capability information that indicates support for adjusted symbol constellations, wherein the adjusted symbol constellations are adjusted to pre-compensate for distortion effects of a power amplifier (PA) in the transceiver, and receive, from the BS, a modulation and coding scheme (MCS) indication. The processor is configured to determine, based on the MCS indication, an adjusted symbol constellation, and generate modulation symbols from input bits according to the determined adjusted symbol constellation. The transceiver is further configured to transmit, to the BS, the modulation symbols.

Support of power amplifier-aware modulation

Error-correcting performance of polar codes is improved by assigning parity check bits to small, reliable indices. Zeros are assigned to message indices not among a most-reliable set of the message indices, information bits and parity check bits are assigned to the most-reliable set of the message indices, with the parity check bits generated on the information bits assigned to the smallest of the most-reliable set and then assigned to the smallest indices of the most-reliable set that follow the information bits on which the parity check pits are generated. During successive cancellation list decoding, the parity check bits allow error events to be promptly corrected during decoding.

Method for polar code design with parity check bits

A method includes obtaining Wi-Fi channel state information (CSI) parameters measured at an anchor and a target device from Wi-Fi frames transmitted between the anchor and the target device, the CSI parameters comprising, for each of the anchor and the target device, frame transmission and reception times, a carrier phase of the CSI, a crystal offset factor, and a packet detection time error. The method also includes determining a round trip time (RTT) estimate or a relative change in the RTT estimate as a function of the CSI parameters. The method further includes estimating a range between the anchor and the target device as a function of the RTT estimate, or estimating a change in the range between the anchor and the target device as a function of the relative change in the RTT estimate.

Wi-Fi based fine ranging using carrier phase information

A hybrid MIMO system comprises N digital transceiver chains and N analog beamformers, each coupled to one of the digital transceiver chains and comprising M analog transmitter (TX) chains and M analog receiver (RX) chains. One of the analog RX chains is an RX reference node and one of the analog TX chains is a TX reference node. A processor is coupled to a calibration transceiver, the transceiver chains, and the beamformers. For each beamformer the processor measures, using the corresponding transceiver chain and the calibration transceiver, TX and RX phase responses and TX and RX magnitude responses of the TX and RX reference nodes, respectively, determines a reciprocity calibration based on a phase difference between the TX and RX phase responses and a magnitude difference between the TX and RX magnitude responses, and applies the reciprocity calibration to stored beam definitions to correct for the phase and magnitude differences.

Hierarchical beamforming and reciprocity calibration for hybrid MIMO systems

Methods and apparatuses in a wireless communication system. A base station (BS) includes a transceiver and a processor. The processor is configured to receive, via the transceiver, a channel state information (CSI) report from at least one user equipment (UE). The processor is also configured to configure CSI-reference signal (RS) resources for the at least one UE. The processor is further configured to, based on the CSI report from the at least one UE, perform one of: set a transmission power to full power for transmission in a physical downlink shared channel (PDSCH); or apply a back-off percentage to the transmission power in the PDSCH.

Hybrid PDSCH for out-of-band earth station interference cancelation

A two-dimensional constellation for data signals having improved bitwise mutual information of data points is based on a signal-to-noise ratio (SNR) and a code rate where, based on the SNR, data bits are mapped to pre-defined in-phase and quadrature values. The in-phase and quadrature values denote points in the two-dimensional space such that the efficiency of bitwise mutual information is adapted based on the SNR. The mapping is preferably subject to a constraint selected from one of quadrant symmetry Lagrangian (QSL), quadrant symmetry constraint (QSC), or rectangular structure constraint (RSC).

AI/ML empowered high order modulation

A method includes storing multiple signals received from a user equipment (UE) in a queue. The method also includes estimating a sounding reference signal (SRS) signal-to-noise-ratio (SNR) and determining a filtered SNR based on the received signals. The method also includes computing one or more features based on the filtered SNR and at least some of the received signals in the queue. The method also includes determining (i) a channel condition of the UE and (ii) a speed range of the UE based on the one or more computed features, wherein the channel condition of the UE comprises line-of-sight (LoS) or non-line-of-sight (NLoS). The method also includes determining a transmission configuration based on the channel condition of the UE and the speed range of the UE.

Low complexity machine learning based channel classifier

Methods and apparatuses for uplink (UL) coverage enhancements with joint phase-time arrays (JPTAs). A method for operating a base station (BS) includes identifying criteria associated with a set of user equipments (UEs) in a cell of the BS and determining, based on the criteria, a first subset of the set of UEs to use a JPTA beamforming. The method further includes using the JPTA beamforming for signaling with the first subset of UEs and using an analog beamforming for signaling with a second subset of UEs in the set of UEs.

Uplink coverage using joint phase-time arrays

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Plano, TX, United States of America

Jianzhong Zhang