Apple Inc.

Huawei Technologies Co., Limited

Nortel Networks Corporation

Blackberry Corporation

Research in Motion Corporation

Rockstar Bidco, LP

Future Wei Technologies, Inc.

Microsoft Technology Licensing, LLC

Ericsson Ab

Other

Northern Telecom Limited

Huawei Technologies,co., Ltd.

Malikie Innovations Limited

Telefonaktiebolaget Lm Ericsson (publ)

Microsoft Corporation

Wen Tong

Peiying Zhu

Jianglei Ma

Ming Jia

Hang Zhang

Mo-Han Fong

Hua Xu

Dong-Sheng Yu

Shiquan Wu

Yiqun Ge

Leo L Strawczynski

Nimal Gamini Senarath

Shalini Suresh Periyalwar

Kelvin Kar Kin Au

Jian Wang

Liqing Zhang

Amir Keyvan Khandani

Bin Li

David G Steer

Mohammadhadi Baligh

Huazi Zhang

Rong Li

Tao Huang

Hosein Nikopour

Sheng Sun

Aaron Callard

Gongzheng Zhang

Sophie Vrzic

Osama S Aboul-Magd

Carmela Cozzo

Jun Wang

Jianming Wu

Claude Royer

Derek Yu

Wuxian Shi

Rui R Wang

Qifan Zhang

Moussa Abdi

Sarah Boumendil

Ivan Leonidovich Mazurenko

Evelyne Le Strat

Vitali B Kreindeline

Jiajie Tong

Bill W Gage

Israfil Bahceci

Dongsheng Yu

Guo Qiang Wang

Xiaocheng Liu

Gamini Senarath

Chaolong Zhang

Elyar Eldarovich Gasanov

Toufiqul Islam

Pavel Anatolyevich Panteleev

Aleksey Alexandrovich Letunovskiy

Mahmoud Taherzadehboroujeni

Edward Sich

Robert Novak

Kwok Shum Au

Vahid Tarokh

Kelvin Kar-Kin Au

Hosein Nikopourdeilami

Senglee Foo

Jie Jin

Geng Wu

Jun J Li

Hui Shen

Jun Yuan

Keith Russell Edwards

Jiaqi Gu

Yi Bin Wang

Yan Xin

Alireza Bayesteh

Halim Boutayeb

Lan Hu

Xianbin Wang

Paul Robert Watson

Xun Yang

Kunpeng Liu

Jung Hoon Suh

Peter J Ashwood-Smith

Huang Huang

Javad Abdoli

Yu Cao

Jian Li

Lu Rong

Steve Andre Beaudin

Tao Wan

Mingjie Dong

Guangjian Wang

Jiang Li

Qiang Shen

James Mark Naden

Sanjeewa Herath

Sai Mohan Kilambi

Yong Li

Ning Guo

Navid Tadayon

Guo-Qiang Q Wang

A method for sensing sneezing based on a wireless signal includes obtaining a wireless signal, where the wireless signal propagates in space including a first object. Doppler estimation is performed on the wireless signal, to obtain Doppler information of the wireless signal. The Doppler information of the wireless signal may be used for indicating impact of the first object on a frequency of the wireless signal. Whether the first object is sneeze droplets is determined based on the Doppler information of the wireless signal.

Method for sensing sneezing based on wireless signal, and related apparatus

This application discloses a communication method includes: obtaining a plurality of information bits in first transmission; grouping the plurality of information bits into m groups of first information bit sequences K, . . . , K; encoding the m groups of first information bit sequences to obtain a first target encoded codeword; sending the first target encoded codeword to a receive end; performing interleaving processing on n groups of first information bit sequences in the m groups of first information bit sequences in tretransmission if the first transmission fails, to obtain m groups of first bit sequences X, . . . , X; encoding the m groups of first bit sequences to obtain a second target encoded codeword; and sending the second target encoded codeword to the receive end. In this way, in a HARQ-based implementation, interleaving processing is performed on some information bit sequences in a retransmission process.

Hybrid automatic repeat request HARQ-based communication method and apparatus

According to the present disclosure, there are provided methods and devices for estimating a high-dimensional MIMO channel by expressing the MIMO channel in the form of a flow function to determine a change from an input state to an output state. This may be considered similar to a manner in which fluid dynamic problems are solved. Instead of using linearization to estimate all of the coefficients of a matrix representing a MIMO channel, a receiver may determine the MIMO channel rank by detecting leading modes in a subspace of the entire MIMO channel matrix.

Method, apparatus, and system for measuring a MIMO channel

A method and system are provided for scheduling data transmission in a Multiple-Input Multiple-Output (MIMO) system. The MIMO system may comprise at least one MIMO transmitter and at least one MIMO receiver. Feedback from one or more receivers may be used by a transmitter to improve quality, capacity, and scheduling in MIMO communication systems. The method may include generating or receiving information pertaining to a MIMO channel metric and information pertaining to a Channel Quality Indicator (CQI) in respect of a transmitted signal; and sending a next transmission to a receiver using a MIMO mode selected in accordance with the information pertaining to the MIMO channel metric, and an adaptive coding and modulation selected in accordance with the information pertaining to the CQI.

Multi-user MIMO systems and methods

Methods and systems for physical layer network coding based on two-dimensional (2D) joint coding are described. In some methods, first and second packets are obtained. A set of one or more cross-packet check blocks is generated, where each cross-packet check block is generated based on a set of cross-packet bits including at least one bit from each of the first and second packets. At least one cross-packet check block is transmitted to a first communication node.

Methods and systems for network coding using cross-packet check blocks

A low density parity check (LDPC) channel encoding method is used in a wireless communications system. A communication device encodes an input bit sequence by using an LDPC matrix, to obtain an encoded bit sequence for transmission. The LDPC matrix is obtained based on a lifting factor Z and a base matrix. The base matrix may be one of eight exemplary designs. The encoding method can be used in various communications systems including fifth generation (5G) telecommunication systems, and can support various encoding requirements for information bit sequences with different code lengths.

Method and apparatus for low density parity check channel coding in wireless communication system

Some embodiments of the present disclosure relate to the selection of a waveform for an integrated communications and sensing (ICS) signal, where the waveform is suitable for both communication applications and sensing applications. In view of the sensing applications, the waveform selection can be, at least in part, adapted based on capabilities of hardware of nodes involved in the sensing applications. In view of the communication applications, the waveform selection can be, at least in part, adapted based on the extent to which data is to be embedded.

Waveform adaptation for integrated communications and sensing

Embodiments of this application relate to the field of communications technologies, and provide an encoding and decoding method and apparatus, to reduce encoding/decoding complexity and improve encoding/decoding performance. In the method, a transmit device may obtain N to-be-encoded vectors. The transmit device may encode the N to-be-encoded vectors based on a polar code kernel matrix, to obtain N temporary code blocks. The transmit device may respectively perform a mask operation on target bit sequences in an (n+1)temporary code block to an (n+M)temporary code block and a source bit sequence segment of an ntemporary code block, to obtain M mask bit sequences. The transmit device may respectively encode the M mask bit sequences based on the polar kernel matrix, to obtain M encoded mask bit sequences. The transmit device may sum the M encoded mask bit sequences and M temporary code blocks, to obtain M first code blocks.

Encoding and decoding method and apparatus

Methods and devices utilizing artificial intelligence (AI) or machine learning (ML) for customization of a device specific air interface configuration in a wireless communication network are provided. An over the air information exchange to facilitate the training of one or more AI/ML modules involves the exchange of AI/ML capability information identifying whether a device supports AI/ML for optimization of the air interface.

Personalized tailored air interface

A sending device may obtain a first to-be-encoded vector. The sending device may perform first encoding on the first to-be-encoded vector, to obtain a second to-be-encoded vector. The sending device may encode the second to-be-encoded vector based on a first generator matrix, to obtain an encoded codeword. The first generator matrix may include at least N+1 submatrices a, and N of the submatrices a may be located on a main diagonal of the first generator matrix. The first generator matrix may be a block upper triangular matrix, or the first generator matrix may be a block lower triangular matrix. The submatrix a is a polar kernel matrix with a size of 2*2, m is a natural number, and N is a natural number. The sending device may send the encoded codeword.

Encoding and decoding method and related apparatus

A method of non-interactive zero-knowledge crowd verifiable digital contact tracing, system and devices that provides improved accuracy and/or privacy by improving the validity of digital contact tracing sources. Private information associated with a respective user intended for a receiver is uploaded to a data server. The receiver is notified that the private information has been uploaded to the data server. A proof of the private information is generated using a proof function of a non-interactive zero-knowledge cryptographic protocol and added to a contact tracing blockchain for the respective user. A second blockchain transaction is added in response to verification of the proof by a verifier network using a verification function of the non-interactive zero-knowledge cryptographic protocol and the receiver is be notified.

Method of non-interactive zero-knowledge crowd verifiable digital contact tracing

A wireless network can generate candidate signal configurations for physical transmissions to or from a user equipment (UE) in a radio environment. The generation of candidate signal configurations can be performed using a first neural network that is associated with the UE. These signal configurations can then be evaluated using a second neural network that is associated with the radio environment. The second neural network can be trained using measurements from previous physical transmissions in the radio environment. The trained second neural network generates a reward value that is associated with the candidate signal configurations. The first neural network is then trained using the reward values from the second neural network to produce improved candidate signal configurations. When a signal configuration that produces a suitable reward value is generated, this signal configuration can be used for the physical transmission in the radio environment.

Systems and methods for wireless signal configuration by a neural network

The present disclosure relates, in part, to non-terrestrial communication systems, and in some embodiments to the integration of terrestrial and non-terrestrial communication systems. Non-terrestrial communication systems can provide a more flexible communication system with extended wireless coverage range and enhanced service quality compared to conventional communication systems. A method representative of aspects of the present application includes receiving, by an apparatus connected in a first sub-system, from a radio access network, configuration information for performing a channel condition measurement on a second sub-system, reporting, by the apparatus to the radio access network, channel condition measurement of a downlink reference signal received from the second sub-system, transmitting, by the apparatus, a wireless transmission to the second sub-system responsive to the channel condition measurement meeting a predefined condition.

Terrestrial and non-terrestrial communication systems, apparatuses, and methods

In accordance with an embodiment, a network device has an input port for receiving input packets, and an output port for sending output packets, where the input packets and output packets have context layer information. The network device also includes a processor configured to process the input packets and output packets using a network protocol having a context layer.

System and method for a context layer switch

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Ottawa, Canada

Wen Tong