Abstract
In this letter, we consider time-varying complex-valued n × m matrices H[k] (m ≤ n) and propose a predictive quantizer for the eigenvectors of the Gramian H[k]H[k] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</sup> , which operates on the associated compact Stiefel manifold. The proposed quantizer exploits the temporal correlation of the source signal to provide high-fidelity representations with significantly reduced quantization codebook size compared to memoryless schemes. We apply the quantizer to channel state information quantization for limited feedback based multi-user MIMO, employing regularized block-diagonalization precoding. We demonstrate significant rate gains compared to block-diagonalization precoding using Grassmannian predictive feedback.
Published Version
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