CMI analysis and precoding designs for correlated multi-hop MIMO channels

Conditional mutual information (CMI) analysis and precoding design for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels are presented in this paper. Although some particular scenarios have been examined in existing publications, this paper investigates a generally correlated transmission system having spatially correlated channel, mutually correlated source symbols, and additive colored Gaussian noise (ACGN). First, without precoding techniques, we derive the optimized source symbol covariances upon mutual information maximization. Secondly, we apply a precoding technique and then design the precoder in two cases: maximizing the mutual information and minimizing the detection error. Since the optimal design for the end-to-end system cannot be analytically obtained in closed form due to the non-monotonic nature, we relax the optimization problem and attain sub-optimal designs in closed form. Simulation results show that without precoding, the average mutual information obtained by the asymptotic design is very close to the one obtained by the optimal design, while saving a huge computational complexity. When having the proposed precoding matrices, the end-to-end mutual information significantly increases while it does not require resources of the system such as transmission power or bandwidth

Superimposed training designs for spatially correlated MIMO-OFDM systems

Only one asymptotic training design for a special case of channel correlation was proposed in the literature for spatially correlated multiple-input multiple-output with orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To fill this gap, this letter applies tractable semi-definite programming (SDP) to obtain the optimal superimposed training signals for the general case of channel correlation. For a more efficient computation, two approximate designs are also proposed. Simulation results demonstrate the efficiency of our approach and its advantage over the asymptotic design. © 2006 IEEE

Superimposed training designs for spatially correlated MIMO-OFDM systems

Optimal training design and channel estimation for spatially correlated multiple-input multiple-output systems with orthogonal frequency-division multiplexing (MIMO-OFDM) is still an open research topic of great interest. Only one asymptotic design for a special case of channel correlations was proposed in the literature. To fill this gap, this paper applies tractable semi- definite programming (SDP) to obtain the optimal superimposed training signals for the general case of channel correlations. To improve computational efficiency, an approximate design in closed-form is also proposed. This approximate design is formed by minimizing an upper bound of the channel estimation mean-square error. Since the superimposed training approach is taken, the derivation of an optimal non-redundancy precoder for data detection enhancement is also given. Analytical and simulation results demonstrate the excellent performance of the proposed designs and their superior performance compared to the previously proposed design. © 2008 IEEE