Peilin Liu

Performance Characterization of a Hybrid Satellite-Terrestrial System with Co-Channel Interference over Generalized Fading Channels

The transmission of signals in a hybrid satellite-terrestrial system (HSTS) in the presence of co-channel interference (CCI) is considered in this study. Specifically, we examine the problem of amplify-and-forward (AF)-based relaying in a hybrid satellite-terrestrial link, where the relay node is operating in the presence of a dominant co-channel interferer. It is assumed that direct connection between a source node (satellite) and a destination node (terrestrial receiver) is not available due to masking by obstacles in the surrounding. The destination node is only able to receive signals from the satellite with the help of a relay node located at the ground. In the proposed HSTS, the satellite-relay channel follows the shadowed Rice fading; and the channels of interferer-relay and relay-destination links experience generalized Nakagami-m fading. For the considered AF-based HSTS, we first develop the analytical expression for the moment generating function (MGF) of the overall output signal-to-interference-plus-noise ratio (SINR). Then, based on the derived exact MGF, we derive novel expressions for the average symbol error rate (SER) of the considered HSTS for the following digital modulation techniques: M-ary phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM) and M-ary pulse amplitude modulation (M-PAM). To significantly reduce the computational complexity for utility in system-level simulations, simple analytical approximation for the exact SER in the high signal-to-noise ratio (SNR) regime is presented to provide key insights. Finally, numerical results and the corresponding analysis are presented to demonstrate the effectiveness of the developed performance evaluation framework and to view the impact of CCI on the considered HSTS under varying channel conditions and with different modulation schemes.

Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties

This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed. However, these algorithms cannot be directly extended to use a nonconvex penalty for sparsity inducing. In general, a nonconvex penalty has the capability of ameliorating the bias problem of the popular convex lasso penalty, and thus is more advantageous. In this paper, we propose a class of positive-definite covariance estimators using generalized nonconvex penalties. We develop a first-order algorithm based on the alternating direction method framework to solve the nonconvex optimization problem efficiently. The convergence of this algorithm has been proved. Furthermore, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties. Moreover, extension of this algorithm to covariance estimation from sketched measurements has been considered. The performances of the new estimators have been demonstrated by both a simulation study and a gene clustering example for tumor tissues. Code for the proposed estimators is available at https://github.com/FWen/Nonconvex-PDLCE.git.

2D DOA estimation method based on channel state information for uniform circular array

In orthogonal frequency division multiplexing (OFDM) based communication systems multiple carriers having different frequencies are used to transmit different data at the same time. Complex values that describe attenuation on different subcarriers are called channel state information (CSI). This paper describes a novel method of two dimensional (2D) direction of arrival (DOA) estimation for uniform circular array (UCA) using CSI, available in transmitted OFDM signal. Firstly, using the fact that CSI among subcarriers comprises phase shift due to DOA and Time-of-Flight (ToF) number of antennas is virtually extended. Secondly, beamspace transform is applied to UCAs array manifold for CSI smoothing to virtually extend the number of observations. Finally, multiple signal classification (MUSIC) algorithm is applied on smoothed data for 2D DOA estimation. Comprehensive results and analysis are provided to show the superior performance of the proposed algorithm compared to the previous literature.

Joint Azimuth, Elevation, and Delay Estimation for 3-D Indoor Localization

Accurate localization in harsh indoor environments has long been a challenging problem due to the presence of multipath. Since joint direction-of-arrival (DOA) and time delay (TD) estimation has the capability to separate the line-of-sight signal from multipath signals in the TD space, it has recently become a key technique for accurate indoor localization in next-generation WiFi and 5G networks. This paper addresses the problem of joint azimuth, elevation, and TD estimation of multiple reflections of a known signal. First, we propose an efficient approximate maximum likelihood algorithm for this problem, which updates the DOA and TD parameters alternatingly. This algorithm applies to arbitrarily distributed (planar or 3-D) arrays. Then, we present the closed-form Cramer–Rao bound for joint DOA and TD estimation, based on which we provide further analysis to show the benefit of joint DOA and TD estimation over DOA-only estimation. Although the benefit of joint estimation has been empirically shown long ago, our analysis is the first theoretical proof of it. Finally, simulation results have been provided to demonstrate the theoretical finding and the effectiveness of the new algorithm. Matlab code for the new algorithm is available at https://github.com/FWen/JADE.git.