# Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

arXiv: https://arxiv.org/abs/2101.01847 · HTML: https://mlsanity.com/paper/2101.01847/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Adaptive Gradient Methods with Dynamic Bound of Learning Rate](https://mlsanity.com/paper/1902.09843/) (2019)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Deep Learning for Launching and Mitigating Wireless Jamming Attacks](https://mlsanity.com/paper/1807.02567/) (2018)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [Generative Adversarial Learning for Spectrum Sensing](https://mlsanity.com/paper/1804.00709/) (2018)
- [Directional Initial Access for Millimeter Wave Cellular Systems](https://mlsanity.com/paper/1511.06483/) (2015)
- [Initial Access in 5G mm-Wave Cellular Networks](https://mlsanity.com/paper/1602.07731/) (2016)
- [Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification](https://mlsanity.com/paper/1910.05765/) (2019)
- [Fast Initial Access with Deep Learning for Beam Prediction in 5G mmWave Networks](https://mlsanity.com/paper/2006.12653/) (2020)
- [Accelerating Beam Sweeping in mmWave Standalone 5G New Radios using Recurrent Neural Networks](https://mlsanity.com/paper/1809.01096/) (2018)
- [Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks](https://mlsanity.com/paper/2009.06974/) (2020)

## Built on by (most cited first, 3 of 3)

- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Efficient Beam Search for Initial Access Using Collaborative Filtering](https://mlsanity.com/paper/2209.06669/) (2022)
- [Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management](https://mlsanity.com/paper/2508.16352/) (2025)

## Abstract (arXiv)

We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA's beam prediction accuracy. We show that the beam prediction accuracy of DeepIA saturates with the number of beams used for IA and depends on the particular selection of the beams. In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%. In NLoS situations, it improves accuracy by up to 35%. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than 95% accuracy in both LoS and NLoS conditions. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the conventional beam sweeping.