Zhao Linjing

Optimal training sequences for MIMO systems under correlated fading

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

Ergodic capacity formula of MIMO-OFDM systems under correlated frequency selective Rayleigh fading channels

An explicit formula for the ergodic capacity of Orthogonal Frequency Division Multiplexing (OFDM)-based Multiple-Input Multiple-Output (MIMO) systems under correlated frequency selective Rayleigh channels is derived, by simplifying the channel response matrix in frequency domain into the so-called Kronecker model composed of three kinds of correlations, i.e. multipath tap gain correlation and spatial fading correlations at both transmitter and receiver. The derived formula is very simple and convenient for one to estimate the effects of all three kinds of correlations on MIMO-OFDM capacity. If taps are independent, there is a very simple expression for the ergodic capacity. In case of tap correlation, the capacity formula could be further given in an integral expression. The validity of the new formula is verified and the effects of correlations, delay spread as well as the number of subcarriers on the ergodic capacity are evaluated via Monte Carlo simulations.

Bayesian integer frequency offset estimator for MIMO-OFDM systems

Abstract Carrier frequency offset (CFO) in MIMO-OFDM systems can be decoupled into two parts: fraction frequency offset (FFO) and integer frequency offset (IFO). The problem of IFO estimation is addressed and a new IFO estimator based on the Bayesian philosophy is proposed. Also, it is shown that the Bayesian IFO estimator is optimal among all the IFO estimators. Furthermore, the Bayesian estimator can take advantage of oversampling so that better performance can be obtained. Finally, numerical results show the optimality of the Bayesian estimator and validate the theoretical analysis.