Reduced-rank channel estimation for time-slotted mobile communication systems

Abstract

Abstract-In time-slotted mobile communication systems with antenna array at the receiver, the space-time channel matrix is conventionally estimated by transmitting pilot symbols within each data packet (or block). This paper is focused on reduced rank (RR) estimation methods that exploit the low-rank property of the space-time channel matrix to estimate single or multiple user channels from the observation of single or multiple training blocks. The proposed RR methods allow to improve the estimate accuracy by reducing the set of unknown parameters (rank reduction) and extending the training set (multiblock processing). The maximum likelihood RR estimate is obtained as the projection of the prewhitened full-rank (FR) estimate onto the spatial or temporal signal subspace. The paper shows that, even for time varying channels, these subspaces can be considered to be slowly varying, and therefore, they can be estimated with increased accuracy by properly exploiting training signals from several blocks. The analytical and numerical performance in terms of mean square error for the RR estimate shows that the main advantage of the proposed method with respect to the conventional FR one can be ascribed to the reduced complexity of the channel parameterization.

New rank detection methods for reduced-rank MIMO systems

“…When the channel matrix has reduced rank, the number of its entries is larger than its real dimension, and thus designs based on full-rank channels become inefficient. This motivates the research on reduced-rank technologies for MIMO systems [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

“…A more natural and efficient approach is to use SVD-based channel estimation methods [15][16][17][18][19][20][21][22][23]. It was shown in [16,17] that the maximum-likelihood (ML) estimation of the reduced-rank MIMO channel with Gaussian noise is the truncated SVD method. In truncated SVD, if the channel rank is known to be r, the MIMO channel matrix is estimated from the SVD of the received signal-plus-noise matrix, by keeping the largest r singular values and their corresponding singular vectors.…”

“…In truncated SVD, if the channel rank is known to be r, the MIMO channel matrix is estimated from the SVD of the received signal-plus-noise matrix, by keeping the largest r singular values and their corresponding singular vectors. While the truncated SVD method traces back to the 1930s [28], it was rediscovered in [16,17] for MIMO channel estimation and further improved to reduce the mean squared error (MSE) of the estimation [18][19][20][21][22]. One improved scheme is shrinkage-and-threshold SVD, where the truncated singular values are further shrunk to remove the noise effect.…”

“…Various rank detection methods are available in the literature [17][18][19][20][21][22][23]. In [17], a minimum description length (MDL)-based rank detection was used for MIMO channels.…”

“…In [17], a minimum description length (MDL)-based rank detection was used for MIMO channels. A threshold is calculated at each instance of the channel to minimize the MDL.…”