# Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback

arXiv: https://arxiv.org/abs/2209.01436 · HTML: https://mlsanity.com/paper/2209.01436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 3 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://mlsanity.com/paper/2006.08099/) (2020)
- [Deep Learning for Distributed Channel Feedback and Multiuser Precoding in FDD Massive MIMO](https://mlsanity.com/paper/2007.06512/) (2020)
- [Coordinated Sum-Rate Maximization in Multicell MU-MIMO with Deep Unrolling](https://mlsanity.com/paper/2202.10371/) (2022)
- [Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation](https://mlsanity.com/paper/2108.01529/) (2021)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)

## Built on by (most cited first, 1 of 1)

- [Stochastic Unrolled Federated Learning](https://mlsanity.com/paper/2305.15371/) (2023)

## Abstract (arXiv)

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to pre-process its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.