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Reduced Complexity Recursive Grassmannian Quantization
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DOI：10.1109/LSP.2020.2969841.png)
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摘要
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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 rate-distortion 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.
Keyword:
Grassmannian quantization
CSI feedback
random vector quantization
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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