Stefan Schwarz, Robert W. Heath, Jr., and Markus Rupp
IEEE Transactions on Signal Processing, vol. 61, no. 18, pp. 4450-4462, 2013.
This work considers delay limited adaptive quantization on the Grassmann manifold of 1-dimensional subspaces in n-dimensional space. Due to strict delay limits, vector quantization over multiple time instants cannot be used to exploit the temporal correlation of the source signal. Instead, a vector predictive quantization technique is proposed that combines prediction and differential quantization algorithms to achieve an efficient quantization of the correlated Grassmannian source. The proposed predictor is based on adaptive filters to adjust to the temporal statistics of the source signal. It is shown that the prediction error in the tangent space associated with the Grassmann manifold behaves approximately Gaussian, provided its norm is sufficiently small. The proposed quantization algorithm is applied to channel state information quantization in multi-user beamforming wireless communication systems. Large throughput gains are demonstrated in comparison to independent quantization, due to the improved quantization accuracy.
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