Adaptive quantization on the Grassmann-manifold for limited feedback multi-user MIMO systems

We propose an adaptive quantization algorithm for subspace tracking on the Grassmann-manifold of p-dimensional subspaces in the n-dimensional Euclidean space. This quantization problem arises naturally in limited feedback based wireless communication systems, which apply precoding for interference cancellation and alignment. The proposed algorithm exploits the differential geometry associated with the Grassmann-manifold for efficient differential and predictive quantization. The algorithm is applied to channel state information quantization in a multi-user block-diagonalization based wireless communication system, demonstrating large throughput gains compared to memoryless quantization.