Nxp Usa, Inc.

Marvellworld Trade Ltd.

Qualcomm Incorporated

Marvell International Limited

Lokesh Sundaramurthy Satrasala

B Hari Ram

Sudhir Srinivasa

Sri Varsha Rottela

Hongyuan Zhang

Rohit U Nabar

Mao Yu

Gowrisankar Somichetty

Christian Raimund Berger

Niranjan Grandhe

Swaroop Venkatesh

Yanni Chen

Ganesh Nagamani

Chusong Xiao

Yong Ma

Ping Gao

Hong Zhang

Leiter Kang

Manikandan Chandrasekar

Pratik Vinod Agrawal

Sai Aditya Chundi

Ramakrishna Vadlamani

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a receiving device may receive a wireless signal represented as a receive matrix. The wireless signal carries one or more information symbols modified through a wireless channel that is represented as a channel matrix. The receiving device may perform QR decomposition on the channel matrix to generate an upper triangular channel matrix. The receiving device may determine a selected demapping technique using the upper triangular channel matrix. The receiving device may perform the selected demapping technique on the receive matrix and the channel matrix. Numerous other aspects are described.

Selecting a demapping technique based on QR decomposition

The present disclosure describes apparatuses and methods for dynamic interference cancellation in wireless receivers. In some aspects, a signal vector transmitted through a wireless environment is received via multiple antennas of a receiver, the signal vector affected by interfering signals in the wireless environment. The receiver determines an interference channel matrix for the signal vector based on the interference received with the signal vector and selects, from the interference channel matrix, columns of the interference channel matrix to form a noise-cancelling equalization matrix. The receiver then equalizes the signal vector with the noise-cancelling equalization matrix to remove a portion of interference from the signal vector to provide noise-cancelled equalized values of the signal vector for decoding. By so doing, the receiver may reduce effects of interference of the wireless environment (e.g., interfering streams or signals) to improve receive performance for signal vectors that are intended for reception by the receiver.

Apparatus and methods of dynamic interference cancellation for wireless receivers

Embodiments described herein provide a method for time delay estimation in a wireless communication system. A signal received, from a transmitter, is delayed due to hardware components in the receiver. A time domain representation of a computed complex channel impulse response (CIR) is generated. A first set of peaks of a time domain representation of the complex CIR is determined and a first peak, having a lowest time delay of the first set of peaks relative to the arrival time of the signal at the receiver, is identified. Following the interpolation of a region corresponding to the first peak, a second set of peaks is determined, and a more accurate estimation of the delay experienced by the signal through the receiver is determined.

Systems and methods for time delay estimation

Systems and methods for detecting data in a received multiple-input-multiple-output signal are provided. N signals are received from N respective antennas, where the received signals are associated with (i) M sets of data values, (ii) a set of symbols, and (iii) a set of carrier frequencies. The N signals are formed into a received signal vector y, and one or more transformations are performed on the received signal vector y to obtain a transformed vector. A plurality of samples are formed from the transformed vector. For samples of the plurality of samples, a data detection technique of a plurality of data detection techniques is selected. The selecting is based on at least one of a spatial stream, a symbol, and a carrier frequency associated with the given sample. The selected data detection, technique is used to detect data of the given sample.

Systems and methods for detecting data in a received multiple-input-multiple-output (MIMO) signal

Systems and methods for detecting data in a received multiple-input-multiple-output signal are provided. N signals are received from N antennas, with M being greater than or equal to three. The N signals form a vector y and are associated with M sets of data values, where the M sets of data values form a vector x. A channel matrix (H) is estimated, and a QR decomposition of the channel matrix is performed, such that H=QR. The vector y is transformed into a vector z according to z=Qy. The R matrix and the rotated signal vector z are transformed such that one or more elements of the R matrix having complex number values are set equal to zero. Distance values are calculated using the transformed vector z and the vector x. Log likelihood ratio (LLR) values are calculated based on the distance values.

Systems and methods for detecting data in a received multiple-input-multiple-output signal are provided. First, second, and third signals are received and form a vector y. The received signals are associated with first, second, and third data values that form a vector x. A channel matrix (H) is received, and a QR decomposition of the channel matrix is performed, such that H=QR. The vector y is transformed into a vector z according to z=Qy. A distance value between the rector z and the vector x is determined for each possible third data value. A nearest constellation point is calculated based on a first of the possible third data values. The calculating step is repeated for each of the possible third data values to generate a set of constellation point triplets. The distance values are determined using the set of constellation point triplets.

Low complexity technique for detecting data in a received multiple-input-multiple-output (MIMO) signal

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Bengaluru, India

Lokesh Sundaramurthy Satrasala