TY - GEN
T1 - A low-cost approach to training-based MIMO channel estimation in interference-limited environments
AU - Katselis, Dimitrios
AU - Kofidis, Eleftherios
AU - Theodoridis, Sergios
PY - 2007
Y1 - 2007
N2 - The problem of optimizing the training signal for the estimation of multiple-input multiple-output (MIMO) fading channels has been of a great interest in the last few years, due to its central role in combining accuracy in estimation with bandwidth efficiency. The general case of correlated channels and colored interference was recently addressed [2]. It was shown that the optimal training for the Linear Minimum Mean Squared Error (LMMSE) estimator, in the sense of minimizing the channel estimation MSE subject to a constraint on the total transmit power, consists of a joint water-filling along the eigenmodes of the desired channel and interference covariance matrices. Thus, the resulting scheme relies on the assumption of the availability of these matrices at the transmitter, which is, in practice, realized via a feedback path. In this paper, that scheme is revisited, with the aim of reducing its requirements for side information and transmit beamforming as well as exploring efficient ways of achieving improvements on its performance. Inspired by an interpretation of the LMMSE estimator as a two-step procedure, we investigate possible gains (and tradeoffs) from an alternative scheme, in which the processing of the received data is performed on both of its dimensions, temporal and spatial. The simulation results demonstrate that this approach provides a good trade-off between estimation performance and low feedback communication and beamforming overheads.
AB - The problem of optimizing the training signal for the estimation of multiple-input multiple-output (MIMO) fading channels has been of a great interest in the last few years, due to its central role in combining accuracy in estimation with bandwidth efficiency. The general case of correlated channels and colored interference was recently addressed [2]. It was shown that the optimal training for the Linear Minimum Mean Squared Error (LMMSE) estimator, in the sense of minimizing the channel estimation MSE subject to a constraint on the total transmit power, consists of a joint water-filling along the eigenmodes of the desired channel and interference covariance matrices. Thus, the resulting scheme relies on the assumption of the availability of these matrices at the transmitter, which is, in practice, realized via a feedback path. In this paper, that scheme is revisited, with the aim of reducing its requirements for side information and transmit beamforming as well as exploring efficient ways of achieving improvements on its performance. Inspired by an interpretation of the LMMSE estimator as a two-step procedure, we investigate possible gains (and tradeoffs) from an alternative scheme, in which the processing of the received data is performed on both of its dimensions, temporal and spatial. The simulation results demonstrate that this approach provides a good trade-off between estimation performance and low feedback communication and beamforming overheads.
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U2 - 10.1109/spawc.2007.4401418
DO - 10.1109/spawc.2007.4401418
M3 - Conference contribution
AN - SCOPUS:48049083338
SN - 1424409551
SN - 9781424409556
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
BT - SPAWC 2007 - 8th IEEE Workshop on Signal Advances in Wireless Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Signal Processing Advances in Wireless Communications, SPAWC 2007
Y2 - 17 June 2007 through 20 June 2007
ER -