Article

# Transceiver Design for Dual-Hop Nonregenerative MIMO-OFDM Relay Systems Under Channel Uncertainties

Sch. of Inf. & Electron., Beijing Inst. of Technol., Beijing, China

IEEE Transactions on Signal Processing (Impact Factor: 2.81). 01/2011; DOI: 10.1109/TSP.2010.2070797 Source: DBLP

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**ABSTRACT:**The remarkable promise of multiple-input multiple-output (MIMO) wireless channels has motivated an intense research activity to characterize the theoretical and practical issues associated with the design of transmit (source) and receive (destination) processing matrices under different operating conditions. This activity was primarily focused on point-to-point (single-hop) communications but more recently there has been an extensive work on two-hop or multi-hop settings in which single or multiple relays are used to deliver the information from the source to the destination. The aim of this tutorial is to provide an up-to-date overview of the fundamental results and practical implementation issues of designing amplify-and-forward MIMO relay systems.IEEE Journal on Selected Areas in Communications 03/2013; · 3.12 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**For MIMO systems, due to the deployment of multiple antennas at transmitter and receiver the design variables e.g., precoders, equalizers, training sequences, etc are usually matrices. It is natural that matrix operations are usually more complicated compared to their vector counterparts. In order to overcome the high complexity resulting form matrix variables, in this paper we investigate a class of elegant and powerful optimization problems, namely matrix-monotonic optimization problems (MMOPs). Various famous MIMO optimization problems are unified into a framework of MMOPs which includes linear transceiver designs, nonlinear transceiver designs, training sequence designs, radar waveform optimization, their corresponding robust designs and so on as its special cases. Moreover, based on the MMOP framework the optimal structures of the considered matrix variables can be derived first. Then based on the optimal structure, the matrix-variate optimization problems can be greatly simplified into the ones with only vector variables. In particular, the dimensionality of the new vector variable is only the smaller number of column and row of the original matrix variable. Finally, we also extend our work to some more general cases with multiple matrix variables.12/2013; - [Show abstract] [Hide abstract]

**ABSTRACT:**In this paper, we consider robust optimization of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay precoders in presence of deterministic imperfect channel state information (CSI), when the CSI uncertainty lies in a norm bounded region. Two widely used performance metrics, mutual information (MI) and mean square error (MSE), are adopted as design objectives. According to the philosophy of worst-case robustness, the robust optimization problems with respect to maximizing the worst-case MI and minimizing the worst-case MSE are formulated as maximin and minimax problems, respectively. Due to the fact that these two problems do not have a concave-convex or convex-concave structure, we cannot rely on the conventional saddle point theory to find the robust solutions. Nevertheless, by exploiting majorization theory, we show that the formulated maximin and minimax problems both admit saddle points. We further analytically characterize the saddle points, and provide closed-form solutions to robust relay precoder designs. Interestingly, we find that, under both MI and MSE metrics, the robust relay optimization leads to a channel-diagonalizing structure, meaning that eigenmode transmission is optimal from the worst-case robustness perspective. The proposed robust designs can improve the spectral efficiency and reliability of AF MIMO relaying against CSI uncertainties at the similar cost of computational complexity as the existing non-robust schemes.IEEE Transactions on Signal Processing 11/2013; 61(21):5458-5471. · 2.81 Impact Factor

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