# Standalone and Non-Standalone Beam Management for 3GPP NR at mmWaves

arXiv: https://arxiv.org/abs/1805.04268 · HTML: https://mlsanity.com/paper/1805.04268/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

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

- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach](https://mlsanity.com/paper/2210.01307/) (2022)
- [Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction](https://mlsanity.com/paper/2609.18368/) (2026)

## Abstract (arXiv)

The next generation of cellular networks will exploit mmWave frequencies to dramatically increase the network capacity. The communication at such high frequencies, however, requires directionality to compensate the increase in propagation loss. Users and base stations need to align their beams during both initial access and data transmissions, to ensure the maximum gain is reached. The accuracy of the beam selection, and the delay in updating the beam pair or performing initial access, impact the end-to-end performance and the quality of service. In this paper we will present the beam management procedures that 3GPP has included in the NR specifications, focusing on the different operations that can be performed in Standalone (SA) and in Non-Standalone (NSA) deployments. We will also provide a performance comparison among different schemes, along with design insights on the most important parameters related to beam management frameworks.