# Hierarchical ML Codebook Design for Extreme MIMO Beam Management

arXiv: https://arxiv.org/abs/2312.02178 · HTML: https://mlsanity.com/paper/2312.02178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 44 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MLPerf Inference Benchmark](https://mlsanity.com/paper/1911.02549/) (2019)
- [Sionna: An Open-Source Library for Next-Generation Physical Layer Research](https://mlsanity.com/paper/2203.11854/) (2022)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [OFDM-Autoencoder for End-to-End Learning of Communications Systems](https://mlsanity.com/paper/1803.05815/) (2018)
- [A Survey of Beam Management for mmWave and THz Communications Towards 6G](https://mlsanity.com/paper/2308.02135/) (2023)
- [Deep Learning-based Implicit CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2105.10100/) (2021)
- [An Overview of the 3GPP Study on Artificial Intelligence for 5G New Radio](https://mlsanity.com/paper/2308.05315/) (2023)
- [Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment](https://mlsanity.com/paper/2107.13121/) (2021)
- [Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization](https://mlsanity.com/paper/1906.06021/) (2019)
- [Site-specific online compressive beam codebook learning in mmWave vehicular communication](https://mlsanity.com/paper/2005.05485/) (2020)
- [Learning Robust Beamforming for MISO Downlink Systems](https://mlsanity.com/paper/2103.01602/) (2021)
- [Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays](https://mlsanity.com/paper/2301.13390/) (2023)
- [A review of codebooks for CSI feedback in 5G new radio and beyond](https://mlsanity.com/paper/2302.09222/) (2023)
- [Performance Analysis of Deep Learning Workloads on Leading-edge Systems](https://mlsanity.com/paper/1905.08764/) (2019)
- [Knowledge-driven Meta-learning for CSI Feedback](https://mlsanity.com/paper/2310.15548/) (2023)
- [Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach](https://mlsanity.com/paper/2210.01307/) (2022)
- [Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach](https://mlsanity.com/paper/2308.12198/) (2023)
- [Massive MIMO Beam Management in Sub-6 GHz 5G NR](https://mlsanity.com/paper/2204.06064/) (2022)
- [Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems](https://mlsanity.com/paper/2209.03643/) (2022)

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

- [Neural Codebook Design for Network Beam Management](https://mlsanity.com/paper/2403.03053/) (2024)

## Abstract (arXiv)

Beam management is a strategy to unify beamforming and channel state information (CSI) acquisition with large antenna arrays in 5G. Codebooks serve multiple uses in beam management including beamforming reference signals, CSI reporting, and analog beam training. In this paper, we propose and evaluate a machine learning-refined codebook design process for extremely large multiple-input multiple-output (X-MIMO) systems. We propose a neural network and beam selection strategy to design the initial access and refinement codebooks using end-to-end learning from beamspace representations. The algorithm, called Extreme-Beam Management (X-BM), can significantly improve the performance of extremely large arrays as envisioned for 6G and capture realistic wireless and physical layer aspects. Our results show an 8dB improvement in initial access and overall effective spectral efficiency improvements compared to traditional codebook methods.