Amplitude Quantization for Type-2 Codebook Based CSI Feedback in New Radio System

TL;DR

This paper proposes amplitude quantization methods for Type-2 codebook CSI feedback in 5G NR, improving accuracy and reducing errors in wideband and subband amplitude estimation.

Contribution

It introduces optimal and sub-optimal amplitude calculation methods to reduce quantization errors in Type-2 codebook feedback, especially for frequency-selective channels.

Findings

Optimal method achieves minimal quantization error but with higher complexity.

Sub-optimal method outperforms linear averaging in error reduction.

Proposed methods enhance CSI accuracy for better MIMO transmission.

Abstract

In 3GPP new radio system, two types of codebook, namely Type-1 and Type-2 codebook, have been standardized for the channel state information (CSI) feedback in the support of advanced MIMO operation. Both types of codebook are constructed from 2-D DFT based grid of beams, and enable the CSI feedback of beam selection as well as PSK based co-phase combining between two polarizations. Moreover, Type-2 codebook based CSI feedback reports the wideband and subband amplitude information of the selected beams. As a result, it is envisioned that more accurate CSI shall be obtained from the Type-2 codebook based CSI feedback so that better precoded MIMO transmission can be employed by the network. To reduce the CSI feedback signaling, 1 bit based subband amplitude with only two quantization levels is supported in combination to 3 bits based wideband amplitude feedback. Typically, wideband amplitude shall be calculated as the linear average amplitude of the beam over all subbands. However, due to the coarse subband amplitude quantization, it has been observed in case of joint wideband and subband amplitude feedback, the average based wideband amplitude can lead to a large amplitude quantization errors. In this paper, we study two methods for joint wideband and subband amplitude calculations. Specifically, both optimal and sub-optimal methods are proposed. The optimal method can achieve the minimum amplitude quantization errors at the cost of a relatively large computation complexity. And by virtue of a derived scaling factor, the sub-optimal method exhibits clearly smaller quantization error than the conventional linear average based method especially for the channel with large frequency selectivity.