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This paper proposes a linear transmitter design that aims at minimizing the packet error rate (PER) using partial channel state information at the transmitter (CSIT). The design is based on the maximization of the channel cut-off rate, which provides an upper bound on the codeword error probability over the ensemble of random codes. The advantage of this optimization criterion is that it allows to get a low PER while keeping the design independent of the specific channel code used in the system. The algorithm is derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, and adopts a Bayesian approach to design an algorithm robust to CSI errors. The proposed algorithm converges to the open-loop transmission scheme for the case of no channel knowledge and it converges to the closed-loop design with perfect CSI for the case of no uncertainty. Simulation results evidence the performance gain in terms of PER and throughput of the proposed algorithm when compared to more commonly used optimization criteria. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in Closed-loop schemes ro- bust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimization criteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed, and their relationship with existing algorithms is indicated. As with other previously proposed solutions, the minimum bit error rate algorithm converges to the open-loop transmission scheme for very poor CSI estimates. [ABSTRACT FROM AUTHOR]