Samsung Electronics Co., Ltd.

Yanru Tang

Hongbing Cheng

Kee-Bong Song

Qi Zhan

Pranav Dayal

Hou-Shin Chen

Tienyu Chang

Siuchuang Ivan Lu

Pritesh Vora

A system and method are provided in which an input including a measurement output of digital channel information is received. The digital channel information includes an estimated digital channel, a beamforming codebook, and quantized angle of arrival (AoA) values. A basis corresponding to one of the quantized AoA values is selected according to a largest projection power of the measurement output. A partial channel is recovered based on the beamforming codebook and the estimated digital channel. An analog channel is recovered based on the partial channel and the basis.

System and method for providing channel recovery for angle domain sparse channels

A closed-loop power control (CLPC) system is disclosed that includes a first signal path for a first polarization and a second signal path for a second polarization. The first signal path includes a first power amplifier, a first output power detector configured to detect a first output power level of the first power amplifier, and a first processor configured to determine a first analog gain for a first controller and a first gain for a first digital-to-analog converter based on a first accumulated error between the first output power level and a target Effective Isotropically Radiated Power. A second processor is configured to set a first variable gain of a first variable gain amplifier coupled to an input of the first power amplifier. The CLPC can be configured to control the gain of the first signal path separately or as one signal path.

Distributed closed-loop power control with VGA gain update

A wireless communication device includes: a processing circuit configured to: receive, from an antenna array during a previous period, a first directional electromagnetic signal including beam sweeping reference symbols of a previous beam sweeping period; compute an estimated combined channel; estimate a dominant angle-of-arrival (AoA) of the first directional electromagnetic signal based on the estimated combined channel and a previous beamforming codebook including two or more beamforming vectors corresponding to different AoAs; construct an updated beamforming codebook based on the estimated dominant AoA and one or more remaining AoAs spaced apart from the estimated dominant AoA; receive, at the antenna array during a current period, a second directional electromagnetic signal including data symbols; determine a beamforming vector for data reception of the second directional electromagnetic signal based on the updated beamforming codebook; and detect the data symbols in the second directional electromagnetic signal based on the determined beamforming vector.

Systems and methods for updating beamforming codebooks for angle-of-arrival estimation using compressive sensing in wireless communications

A system and method for providing channel recovery for angle domain sparse channels is herein provided. According to one embodiment, a method includes receiving an input including a measurement output, and recovering analog channels utilizing bases derived from the measurement output.

According to one general aspect, a method may include estimating a combined channel. The method may include estimating a channel correlation based, at least in part upon a measurement of a combined channel for a current beam sweeping interval and a previous beam sweeping interval. The method may include recovering a current channel by employing a compressive sensing-based channel recovery employing estimated channel correlation, a previous measurement, and a current measurement.

Compressive sensing based channel recovery considering time variation of the channel

Compressive sensing (CS) channel recovery using history measurements. Both current and history measurements for AoAs estimation, and only use current measurement for coefficient estimation. The dominant angle of arrival (AoA) is estimated using history and current measurements. In Approach 1, the dominant AoA is invariant and the coefficients are uncorrelated. In Approach 2, the dominant AoA is invariant and the coefficients are fully correlated. The remaining AoAs are estimated. The coefficients corresponding to each estimated dominant AoA are estimated. And the channel is recovered.

Angular sparse channel recovery using history measurements

Low-complexity methods of calculating a precoding matrix for use in MIMO transmission. In some embodiments, a method of calculating a precoding matrix includes (i) calculating a first portion of a first singular value decomposition, based on a first portion of a channel matrix; (ii) calculating a second portion of a second singular value decomposition, based on a second portion of the channel matrix; (iii) calculating an intermediate matrix, based on: the first portion of the first singular value decomposition and the second portion of the second singular value decomposition; and (iv) calculating a matrix of approximate right singular vectors. The calculating of the matrix of approximate right singular vectors may include calculating a product of factors, the factors including a first factor based on the conjugate transpose of the intermediate matrix, a second factor based on a unitary matrix, and a third factor based on a diagonal matrix.

Low complexity algorithms for precoding matrix calculation

Disclosed is an apparatus and a method of deriving an optimal precoder, in which a precoder is derived using water filling power allocation, and then is multiplied with some unitary matrix satisfying certain conditions to meet a per layer power constraint. Since the multiplication with the unitary matrix does not change the capacity, the derived precoder is optimal in achieving maximum capacity in a multiple input multiple output (MIMO) wireless communication scheme.

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San Diego, CA, United States of America

Yanru Tang