Research Papers - Department of Electrical and Electronic Engineering
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Publication Open Access A general framework for MIMO transceiver design with imperfect CSI and transmit correlation(IEEE, 2009) Blostein, S. D; Ding, M— Assuming perfect channel state information (CSI), linear precoding/decoding for multiple-input multiple-output (MIMO) systems has been considered in the literature under various performance criteria, such as minimum total meansquare error (MSE), maximum mutual information, and minimum average bit error rate (BER). It has been shown that these criteria belong to a set of reasonable Schur-concave or Schur-convex objective functions of the diagonal entries of the system mean-square error (MSE) matrix. In this paper, assuming only the knowledge of channel mean and transmit correlation at both ends, a general theoretical framework is presented to derive the optimum precoder and decoder for MIMO systems using these objective functions. It is shown that for all these objective functions the optimum transceivers share a similar structure. Compared to the case with perfect CSI, a linear filter is added to both ends to balance the suppression of channel noise and the additional noise induced from channel estimation error. Simulation results are provided.1Publication Embargo A general framework for MIMO transceiver design with imperfect CSI and transmit correlation(IEEE, 2009-09-13) Ding, M; Blostein, S. D; Mow, W. H; Siriteanu, CAssuming perfect channel state information (CSI), linear precoding/decoding for multiple-input multiple-output (MIMO) systems has been considered in the literature under various performance criteria, such as minimum total mean-square error (MSE), maximum mutual information, and minimum average bit error rate (BER). It has been shown that these criteria belong to a set of reasonable Schur-concave or Schur-convex objective functions of the diagonal entries of the system mean-square error (MSE) matrix. In this paper, assuming only the knowledge of channel mean and transmit correlation at both ends, a general theoretical framework is presented to derive the optimum precoder and decoder for MIMO systems using these objective functions. It is shown that for all these objective functions the optimum transceivers share a similar structure. Compared to the case with perfect CSI, a linear filter is added to both ends to balance the suppression of channel noise and the additional noise induced from channel estimation error. Simulation results are provided.Publication Embargo MIMO LMMSE transceiver design with imperfect CSI at both ends(IEEE, 2007-06-24) Blostein, S. D; Ding, MThis paper presents a new result on minimum total mean squared error (MSE) joint transceiver design for multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at both ends and subject to a total power constraint. The channel knowledge here is the channel mean and transmit correlation information. The joint design is formulated into an optimization problem, to which the closed-form optimum solution is found. The optimum precoder consists of a linear filter, a matrix which collects effective channel eigenmodes, and a diagonal power allocation matrix. The linear filter balances the suppression of channel noise and the additional noise induced by channel estimation error. Given the precoder, the optimum decoder is simply a linear minimum MSE (LMMSE) data estimator. The performance degradations due to imperfect channel estimation and/or transmit correlation are demonstrated by simulation results. The relation between the minimum total MSE design and the maximum mutual information design is determined as well, under the above mentioned imperfect CSI.Publication Embargo Joint optimization for multiuser MIMO uplink systems with imperfect CSI(IEEE, 2008-05-24) Ding, M; Blostein, S. DIn this paper, a joint linear minimum sum meansquared error transceiver optimization problem is formulated for multiuser MIMO uplink systems under a sum power constraint assuming imperfect channel state information (CSI). Two methods are proposed to solve this problem. One is based on the associated Karush-Kuhn-Tucker conditions. The other is to solve an equivalent problem, approaching the solution by solving a sequence of semi-definite programming problems. After obtaining the solution to the optimization problem, we investigate the effects of channel estimation errors and antenna correlation at the base station on system performance. Simulation results are provided.Publication Embargo Uplink-downlink duality in normalized MSE or SINR under imperfect channel knowledge(IEEE, 2007-11-26) Ding, M; Blostein, S. DDuality between the multi-antenna multi-user uplink and the downlink has been discovered in terms of sum rate, capacity region, signal-to-interference-plus-noise-ratio (SINR) region or normalized mean-squared error (MSE) region. Previous work on duality has assumed perfect channel knowledge. However, channel estimation is never perfect in practice. In this paper, channel estimation error as well as antenna correlation at the base station (BS) is taken into account. A multi-user system with multiple antennas at the BS and with single-antenna users is studied. Joint detection and transmission are used in the uplink and the downlink, respectively. It is analytically shown that with imperfect channel state information (CSI), under the same sum power constraint, the achievable SINR regions or the normalized MSE regions in both links are the same, as in the case with perfect CSI. Monte Carlo simulation results and discussions are also provided to complement the analysis.Publication Embargo Relation between joint optimizations for multiuser MIMO uplink and downlink with imperfect CSI(IEEE, 2008-03-31) Ding, M; Blostein, S. DJoint linear minimum sum mean-squared error (referred to as MSMSE) transmitter and receiver (transceiver) optimization problems are formulated for multiuser MIMO systems under a sum power constraint assuming imperfect channel state information (CSI). Both the uplink and the dual downlink are considered. Based on the Karush-Kuhn-Tucker (KKT) conditions associated with both problems, a relation between the two problems is discovered, which is termed the uplinkdownlink duality in sum MSE under imperfect CSI. As a result, the MSMSEs in both links are the same and any admissible uplink design satisfying the KKT conditions can be translated for application to the downlink, and vice versa. Simulation results are provided to demonstrate the duality and show the impact of imperfect CSI.Publication Embargo Maximum mutual information design for MIMO systems with imperfect channel knowledge(IEEE, 2010-09-13) Ding, M; Blostein, S. DNew results on maximum mutual information design for multiple-input multiple-output (MIMO) systems are presented, assuming that both transmitter and receiver know only an estimate of the channel state as well as the transmit and receive correlation. Since an exact capacity expression is difficult to obtain for this case, a tight lower-bound on the mutual information between the input and the output of a MIMO channel has been previously formulated as a design criterion. However, in the previous literature, there has been no analytical expression of the optimum transmit covariance matrix for this lower-bound. Here it is shown that for the general case with channel correlation at both ends, there exists a unique and globally optimum transmit covariance matrix whose explicit expression can be conveniently determined. For the special case with transmit correlation only, the closed-form optimum transmit covariance matrix is presented. Interestingly, the optimal transmitters for the maximum mutual information design and the minimum total mean-square error design share the same structure, as they do in the case with perfect channel state information. Simulation results are provided to demonstrate the effects of channel estimation errors and channel correlation on the mutual information.Publication Embargo MIMO minimum total MSE transceiver design with imperfect CSI at both ends(IEEE, 2008-10-31) Ding, M; Blostein, S. DThis paper presents new results on joint linear transceiver design under the minimum total mean-square error (MSE) criterion, with channel mean as well as both transmit and receive correlation information at both ends of a multiple-input multiple-output (MIMO) link. The joint design is formulated into an optimization problem. The optimum closed-form precoder and decoder are derived. Compared to the case with perfect channel state information (CSI), linear filters are added at both ends to balance the suppression of channel noise and the noise from imperfect channel estimation. The impact of channel estimation error as well as channel correlation on system performance is assessed, based on analytical and simulation results.