# Multiuser Millimeter Wave Beamforming Strategies with Quantized and Statistical CSIT

## TL;DR

This paper proposes a one-stage feedback scheme for multiuser mmWave beamforming that reduces complexity and feedback overhead, leveraging channel statistics and rate splitting to achieve comparable performance to traditional methods.

## Contribution

It introduces a novel one-stage feedback scheme utilizing channel statistics and rate splitting, outperforming conventional two-stage feedback in certain conditions.

## Key findings

- One-stage feedback can outperform two-stage feedback under fixed feedback constraints.
- Rate splitting enhances interference mitigation and rate performance.
- The proposed scheme reduces feedback overhead significantly.

## Abstract

To alleviate the high cost of hardware in mmWave systems, hybrid analog/digital precoding is typically employed. In the conventional two-stage feedback scheme, the analog beamformer is determined by beam search and feedback to maximize the desired signal power of each user. The digital precoder is designed based on quantization and feedback of effective channel to mitigate multiuser interference. Alternatively, we propose a one-stage feedback scheme which effectively reduces the complexity of the signalling and feedback procedure. Specifically, the second-order channel statistics are leveraged to design digital precoder for interference mitigation while all feedback overhead is reserved for precise analog beamforming. Under a fixed total feedback constraint, we investigate the conditions under which the one-stage feedback scheme outperforms the conventional two-stage counterpart. Moreover, a rate splitting (RS) transmission strategy is introduced to further tackle the multiuser interference and enhance the rate performance. Consider (1) RS precoded by the one-stage feedback scheme and (2) conventional transmission strategy precoded by the two-stage scheme with the same first-stage feedback as (1) and also certain amount of extra second-stage feedback. We show that (1) can achieve a sum rate comparable to that of (2). Hence, RS enables remarkable saving in the second-stage training and feedback overhead.