Jiyong Pang

Optimal Training Sequences for MIMO Channel Estimation with Spatial Correlation

Optimal design of training sequences is considered for multiple-input multiple-output (MIMO) systems in the presence of spatial fading correlation. By the method of minimum mean square error (MMSE) channel estimation, we first evaluate the estimation accuracy and the training sequence length under correlated fading, based on which we then design optimal training sequences that have controllable length to make effective use of the correlation properties. It is investigated that the impacts of spatial correlation are helpful not only to improve channel estimation but also to decrease the training length, moreover, that the proposed training sequences have good performance via simulations.

Total Least-Squares Solution of Active Target Localization Using TDOA and FDOA Measurements in WSN

Active target localization is one of the study emphases in wireless sensor network (WSN). When time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements are used to target localization, the measurements are nonlinearly related to the target location parameters. By the analysis of TDOA and FDOA linearized equations, when there exists sensor location error, active target localization using TDOA and FDOA measurements is a typical total least-squares (TLS) problem. The TLS solution for active target localization is presented in this paper. Its minimum square error ( MSE) is compared with Cramer-Rao lower bound (CRLB) through the simulation results.

Iterative parameter estimation in MIMO flat-fading channels with frequency offsets

We address the problem of frequency offset and channel estimation in multi-input multi-output (MIMO) flat-fading channels. By considering MIMO system as equivalent single-input single-output (SISO) sub-systems with multi-antenna interference, a novel iterative parameter estimation method with interference cancellation is proposed. Numerical results show that this iterative method achieves a satisfactory performance.

Optimal Training Sequences for Frequency-Selective MIMO Correlated Fading Channels

Optimal design of training sequences for multiple-input multiple-output (MIMO) channel estimation is considered. The channel is assumed to be frequency-selective and obey block-fading law with both spatial fading correlation and multipath tap gain correlation known at both the transmitter and receiver. To minimize the channel estimation error, optimal training sequences are designed to exploit full information of channel correlation under the criterion of minimum mean square error (MMSE). It is investigated that channel correlation is helpful to decrease the estimation error and the proposed training sequences have good performance via simulations.

Performance Analysis of Active Target Localization Using TDOA and FDOA Measurements in WSN

Target localization is one of the basic functions of wireless sensor networks. In the target localization algorithms, the problem of how many sensors are needed and how they are deployed is what we are concerned about in this paper. The authors of (K.C. Ho et al., 2007) present the mathematical expression of Cramer-Rao lower bound for the localization error, which is the smallest lower bound of any unbiased estimation. However it is difficult to be used to find out the relation between sensor number and Cramer-Rao lower bound directly. In this paper the relation between sensor number and Cramer- Rao lower bound is revealed by simulation. And how the topology affects the Cramer-Rao lower bound is also presented.

Optimal Training Length for MIMO Systems with Transmit Antenna Correlation

By maximizing a derived lower bound on the ergodic capacity, the optimal design of training length is given for Multiple-Input Multiple-Output (MIMO) systems. The channel is assumed to be flat fading in presence of spatial fading correlation only at the transmitter, which is known a priori by both ends. Different from the case of independent and identically distributed channel, the optimal training length no longer equals the number of transmit antennas but is just the same as the rank of transmit correlation matrix. The stronger the channel correlation is, the less the optimal training length is. This coincides with the fact that the accuracy of channel estimation is being improved when the channel correlation increases under equal training intervals.

Carrier-frequency offsets estimation based on ML and ESPRIT method for OFDMA uplink

Carrier frequency offsets (CFOs) in orthogonal frequency division multiplexing access (OFDMA) system will introduce intercarrier interference (ICI) and consequently degrade the system performance. A new frequency offsets estimation algorithm based on the combination of ESPRIT (estimation of signal parameters by rotational invariance techniques) and the maximum likelihood (ML) estimation methods for OFDMA system is proposed. The subset of the frequency offsets is first estimated by ESPRIT then the frequency offset of each user is identified in the subset by ML estimation method. The complexity of the combination algorithm is significantly decreased compared with the ML estimator. The algorithm efficiently solves the problem of multi-frequency offsets estimation.