Maximum likelihood receivers for space-time coded MIMO systems with gaussian estimation errors

Maximum likelihood (ML) receivers for space-time coded multiple-input multiple-output (MIMO) systems with Gaussian channel estimation errors are proposed. Two different cases are considered. In the first case, the conditional probability density function (PDF) of the channel estimate is assumed Gaussian and known. In the second case, the joint PDF of the channel estimate and the true channel gain is assumed Gaussian and known. In addition to ML signal detection for space-time coded MIMO with ML and minimum mean-squared-error channel estimation, ML signal detection without channel estimation is also studied. Two suboptimal structures are derived. The Alamouti space-time codes are used to examine the performances of the new receivers. Simulation results show that the new receivers can reduce the gap between the conventional receiver with channel estimation errors and the receiver with perfect channel knowledge at least by half in some cases

Joint power loading of data and pilots in OFDM using imperfect channel state information at the transmitter

Abstract- The search for optimality in the design of channel precoders and training symbols in block processing communication systems is one of paramount importance. Finding the best tradeoff in terms of power distribution between information and pilot symbols for frequency selective channels, when channel estimation via feedback is available, however, has not been fully addressed. In this paper, we solve the problem of finding the optimal power distribution between pilots and data symbols in the mean-square-error (MSE) sense when a delayless error-free channel feedback path is available to the transmitter. The novel approach adaptively designs the optimal precoders and training vectors based on the frequency domain estimates of the channel