Min-Hyun Kim

MSE-Based Hybrid RF/Baseband Processing for Millimeter-Wave Communication Systems in MIMO Interference Channels

We consider the design of a hybrid multiple-input multiple-output (MIMO) processor consisting of a radio frequency (RF) beamformer and a baseband MIMO processor for millimeter-wave communications over multiuser interference channels. Sparse approximation problems are formulated to design hybrid MIMO processors approximating the minimum-mean-square-error transmit/receive processors in MIMO interference channels. They are solved by orthogonal-matching-pursuit-based algorithms that successively select RF beamforming vectors from a set of candidate vectors and optimize the corresponding baseband processor in the least squares sense. It is shown that various beamformers can be designed by considering different types of candidate vector sets. Simulation results demonstrate the advantage of the proposed design over the conventional method that designs the baseband processor after steering the RF beams.

Estimation of road surface height for preview system using ultrasonic sensor

The vehicle industry has applied active suspension system for a long time to enhance ride-quality and driving stability. However, only a small improvement is seen in the absence of a preview sensor, and thus it is an integral component in designing active suspension systems. Devices such as stereo vision, lidar, and radar systems thus far have been used when building preview information for active suspension systems. These devices have some drawbacks, however, including costliness and the requirement of complex computations. A high performance data processing device is thus needed to compute complicated calculations in real time, which further increases costs. The goal of this research is to examine the use of a low cost ultrasonic sensor to compute road surface height estimation in real time by using simple algorithms.

LoS Spatial Multiplexing and Beamforming Using Uniform Circular Array of Subarrays

We propose the use of uniform circular array of subarrays (UCA-SAs) for millimeter wave (MMW) communication systems over line of sight (LoS) channels and design beamforming coefficients for each subarray. Specifically, for a UCA-SA consisting of Ns subarrays, which are M × M square arrays, the beamforming coefficients are designed so that the Ns × Ns effective channel becomes circulant. To this end, we first show that the UCA-SA can be decomposed into M2 concentric UCAs having Ns antenna elements. Then, based on this result, we show that the set of beamforming coefficients making the effective channel circulant consist of identical values. The proposed UCA-SA system employs this type of beamformers, called the constant beamformers. The effective channel of the proposed UCA-SA system can be diagonalized by the discrete Fourier transform (DFT) precoder and the inverse DFT (IDFT) combiner, without the knowledge of channel state information (CSI) at the transmitter. This indicates that constant beamformers are optimal beamformers maximizing the Ns × Ns effective channels mutual information. Simulation results show that the proposed UCASA can outperform the conventional UCA with NsM2 antenna elements which are equally spaced on a ring.

Digital Predistortion for Power Amplifiers in Hybrid MIMO Systems with Antenna Subarrays

We consider digital predistortion (DPD) for linearizing power amplifiers (PAs) of hybrid MIMO systems with subarrays. Designing such a DPD scheme is difficult because one DPD should linearize all PAs of a subarray whose beamforming coefficients are time-varying. We develop an adaptive DPD technique for each subarray based on direct learning method minimizing the sum of squared errors between the input and output signals of PAs. Use of the least mean square (LMS) Newton algorithm is proposed to adaptively control the DPD parameters. It is shown that the proposed DPD can simultaneously linearize multiple PAs in a subarray irrespective of its beamforming coefficients. Computer simulation results demonstrate that the proposed scheme can perform better than an alternative scheme that designs the DPD based on one of the PA outputs and uses the resulting DPD for all PAs of the subarray.

Channel estimation via oblique matching pursuit for FDD massive MIMO downlink

We consider channel estimation for massive multiple-input multiple-output (MIMO) systems operating in frequency division duplexing (FDD) mode. By exploiting the sparsity of significant propagation paths in massive MIMO channels, we develop a compressed sensing (CS) based channel estimator that can reduce the pilot overhead as compared with the conventional least squares (LS) and minimum mean square error (MMSE) estimators. The proposed scheme is based on the oblique matching pursuit (ObMP), an extension of the orthogonal matching pursuit (OMP), that can exploit prior information about the sparse signal vector. Given the channel covariance matrix, we obtain the incidence probability that each quantized angle coincides with the angle-of-departure (AoD) and use the incidence probability for deriving the oblique operator of the proposed scheme. The pilot sequence is designed to minimize the MSE of the oracle estimator. The simulation results demonstrate the advantage of the proposed scheme over various existing methods including the LS, MMSE and OMP estimators.

Improving efficiency of ultrasonic distance sensors using pulse interval modulation

In the robot research field, ultrasonic sensors have been highly employed for a long time. However, the sensors have used in the limited applications for near distance measuring, because ultrasonic sensor has some following limitations: low sampling rate, short detection range, and vulnerable to environmental noise. To overcome these limitations, the goal of this research is proposing new operating algorithm for ultrasonic sensors and examining the algorithm in real time by comparing with the conventional algorithm. The proposed algorithm can improve sampling rate of ultrasonic sensor highly. As a result, signal to noise ratio (SNR) is enhanced largely and detection range also becomes longer compared to conventional operating algorithm.

RGBD sensor and mirrors: A practical setup for 3D reconstruction of dynamic objects

In order to get full 3D model from consumer depth cameras, previous approaches used set of synchronized depth cameras or moved the depth sensor around the target object. This on-going work introduces a cost effective and convenient framework for filming dynamic 3D object. This set is comprised of a consumer depth camera, or RGBD sensor and a pair of mirrors. Because of the images reflected from two mirrors in the system, it can capture a depth-map of the target object in an extended view. The practicality and effectiveness of this framework is demonstrated.