Timing and channel estimation in multiple-antenna communication systems

There is an increasing demand for next generation wireless networks, including wireless local area networks and the third generation cellular networks, that can provide high data rate for broadband services, improve quality of service (QoS), and support more users. The use of multiple transmit and receive antennas can offer substantial performance improvement to a wireless communication system by making the use of the extra degrees of freedom in the spatial domain and thus is a promising technique to satisfy this demand. Many of the current space-time coding schemes proposed for multiple-antenna systems assume perfect timing estimation and channel estimation to achieve the expected performance gain. The lack of timing synchronization between the transmit and receive signals and the inaccuracy of channel estimation could degrade the system performance.; In the first half of this work, we investigate the problem of timing estimation in multiple-antenna systems with the aid of training signals. A slow, independent and identically distributed Rayleigh flat-fading channel model is considered. We derive two maximum likelihood timing estimators based on two different approaches, namely treating the channel as deterministic and random, and present the corresponding Cramer-Rao bounds (CRBs). Then the optimal designs of training signals based on some figures of merit associated with the CRBs are discussed.; In the second half of this work, we study the problem of the estimation of correlated multiple-input multiple-output (MIMO) channels with colored interference. The Bayesian channel estimator is derived and the optimal training sequences are designed based on the mean square error of channel estimation. We propose an algorithm to estimate the long-term channel statistics in the construction of the optimal training sequences. We also design an efficient scheme to feed back the required information to the transmitter where we can approximately construct the optimal sequences. Numerical results show that the optimal training sequences provide substantial performance gain for channel estimation when compared with other training sequences.