# Deep Learning Beam Optimization in Millimeter-Wave Communication Systems

arXiv: https://arxiv.org/abs/2107.07846 · HTML: https://mlsanity.com/paper/2107.07846/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [Connections between spectral properties of asymptotic mappings and solutions to wireless network problems](https://mlsanity.com/paper/1903.09793/) (2019)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks](https://mlsanity.com/paper/2002.05564/) (2020)
- [Adaptive Beam-Frequency Allocation Algorithm with Position Uncertainty for Millimeter-Wave MIMO Systems](https://mlsanity.com/paper/1711.01800/) (2017)
- [Power and Beam Optimization for Uplink Millimeter-Wave Hotspot Communication Systems](https://mlsanity.com/paper/1908.11313/) (2019)

## Abstract (arXiv)

We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so that the users' rates are allocated fairly in a well-defined sense. In more detail, the discrete variables include user-access point assignments and the beam configurations, while the continuous variables refer to the power allocation. The beam configuration is predicted from user-related information using a neural network. Given the predicted beam configuration, a fixed point algorithm allocates power and assigns users to access points so that the users achieve the maximum fraction of their interference-free rates. The proposed method predicts the beam configuration in a "one-shot" manner, which significantly reduces the complexity of the beam search procedure. Moreover, even if the predicted beam configurations are not optimal, the fixed point algorithm still provides the optimal power allocation and user-access point assignments for the given beam configuration.