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S. Schwarz, M. Rupp:
"Reduced Complexity Recursive Grassmannian Quantization";
IEEE Signal Processing Letters, 27 (2020), 321 - 325.

We propose a novel recursive multi-stage approach to Grassmannian quantization. Compared to the commonly employed single-stage quantization, our method has the advantage of significantly decreasing the number of codebook searches required for quantization and, thus, reducing the complexity. On the downside, the multi-stage approach causes a slight ratedistortion degradation compared to single-stage quantization. We analyze the rate-distortion performance of the proposed recursive quantization approach, considering random vector quantization within the individual stages. We furthermore propose a bit-allocation optimization amongst the stages of the quantizer, given a constraint on the total number of quantization bits.

Grassmannian quantization, CSI feedback, random vector quantization

http://dx.doi.org/10.1109/LSP.2020.2969841

https://publik.tuwien.ac.at/files/publik_287792.pdf