In this paper, we make a performance comparison between two different training-based schemes for multiple-input multiple-output (MIMO) channel estimation. The two schemes are the conventional time-multiplexed pilot (CP) scheme and the more recently suggested superimposed pilot (SIP) scheme. Unlike previous comparisons found in the literature, which are mostly based on estimation error performances, the performance comparison in this paper is made by deriving and comparing the maximum data rate (or rather a tight lower bound on the maximum mutual information) achieved by each scheme. By using the maximum mutual information criterion, for each training scheme, we can optimally allocate the time and power spent on transmission of training and data sequences. Once the system parameters (time and power) are tuned to give optimal performance, we can compare their respective maximum data rate. The theory is applied to a blockwise flat-fading MIMO channel, and it is found that in certain scenarios (such as many receive antennas and/or short channel coherence times), it is beneficial to use the SIP. In other scenarios, the SIP scheme suffers from a higher estimation error, and its gain over the CP scheme is often lost.
A low complexity algorithm for parameter estimation in a block fading multipath DS-CDMA system is presented. The main contribution in this paper is a novel technique for minimizing a subspace fitting criterion which is obtained as a large sample approximation of the Maximum Likelihood (ML) estimator. The minimization procedure is based on an approximation of the exact criterion function, allowing a direct analytic solution.For the acquisition phase, an initialization procedure similar to alternating projections is employed which exhibits remarkable global convergence properties. The efficacy of the proposed method is demonstrated by means of numerical simulations.
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