Codebook Design for Millimeter-Wave Channel Estimation with Hybrid Precoding Structure

TL;DR

This paper introduces a new metric called GDP for evaluating and designing hierarchical codebooks for mmWave channel estimation with hybrid precoding, considering power constraints, and proposes two effective design solutions that outperform existing methods.

Contribution

It presents the first GDP metric for mmWave codebook design and proposes two novel hierarchical codebook design methods exploiting BMW-MS technique.

Findings

GDP effectively evaluates codeword quality.

BMW-MS/LCS and BMW-MS/CF outperform existing codebooks.

Proposed methods work well under per-antenna power constraints.

Abstract

In this paper, we study hierarchical codebook design for channel estimation in millimeter-wave (mmWave) communications with a hybrid precoding structure. Due to the limited saturation power of mmWave power amplifier (PA), we take the per-antenna power constraint (PAPC) into consideration. We first propose a metric, i.e., generalized detection probability (GDP), to evaluate the quality of \emph{an arbitrary codeword}. This metric not only enables an optimization approach for mmWave codebook design, but also can be used to compare the performance of two different codewords/codebooks. To the best of our knowledge, GDP is the first metric particularly for mmWave codebook design for channel estimation. We then propose an approach to design a hierarchical codebook exploiting BeaM Widening with Multi-RF-chain Sub-array technique (BMW-MS). To obtain crucial parameters of BMW-MS, we provide two solutions, namely a low-complexity search (LCS) solution to optimize the GDP metric and a closed-form (CF) solution to pursue a flat beam pattern. Performance comparisons show that BMW-MS/LCS and BMW-MS/CF achieve very close performances, and they outperform the existing alternatives under the PAPC.