# Deep Learning Methods for Universal MISO Beamforming

arXiv: https://arxiv.org/abs/2007.00841 · HTML: https://mlsanity.com/paper/2007.00841/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Learning Robust Beamforming for MISO Downlink Systems](https://mlsanity.com/paper/2103.01602/) (2021)
- [Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks](https://mlsanity.com/paper/2103.11284/) (2021)
- [A Model-based GNN for Learning Precoding](https://mlsanity.com/paper/2212.00860/) (2022)
- [Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding](https://mlsanity.com/paper/2209.10332/) (2022)
- [Deep Learning Methods for Joint Optimization of Beamforming and Fronthaul Quantization in Cloud Radio Access Networks](https://mlsanity.com/paper/2107.02520/) (2021)
- [BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization](https://mlsanity.com/paper/2509.11056/) (2025)
- [Robust Millimeter Beamforming via Self-Supervised Hybrid Deep Learning](https://mlsanity.com/paper/2303.12653/) (2023)
- [Neural Precoding in Complex Projective Spaces](https://mlsanity.com/paper/2603.07811/) (2026)
- [Association-Aware GNN for Precoder Learning in Cell-Free Systems](https://mlsanity.com/paper/2603.13035/) (2026)

## Abstract (arXiv)

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.