Optimal Training Design for MIMO Systems With General Power Constraints - 2018

PROJECT TITLE :

Optimal Training Design for MIMO Systems With General Power Constraints - 2018

ABSTRACT:

Coaching design for general multiple-input multiple-output (MIMO) systems is investigated during this Project. Unlike previous styles that are applicable solely for centralized MIMO systems with total power constraints, general power constraints are considered here. They cowl total power constraints, individual power constraints, and mixed individual and per-user total-power constraints as special cases. By writing the MIMO received signals in matrix and vector forms, respectively, and using Kronecker structured channel and noise statistics, three channel estimation schemes, i.e., right estimation, left estimation, and right-left estimation, are mentioned. Their corresponding coaching styles are thought of individually with the general power constraints. Under every channel estimation scheme, optimal training sequences to maximize the mutual information between the true channel and its estimated counterpart, and to attenuate the mean sq. error (MSE) of the channel estimate are, respectively, proposed in semiclosed forms. The relationship between the two design criteria, i.e., the mutual information maximization and also the MSE minimization, is clearly revealed. The optimal training designs beneath the three estimation schemes are also compared exhaustive. It is demonstrated that right estimation exploits less statistical information concerning the channel and noise, and provides worse performance than the left estimation however with lower computational complexity. On the other hand, right-left estimation performs in between the other two and provides a smart compromise between complexity and performance. Finally, the optimality and effectiveness of the proposed coaching designs are verified by extensive simulations.

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