# Attention-Based SINR Estimation in User-Centric Non-Terrestrial Networks

arXiv: https://arxiv.org/abs/2602.21116 · HTML: https://mlsanity.com/paper/2602.21116/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 24 Feb 2026.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Federated Cell-Free MIMO in Non-Terrestrial Networks: Architectures and Performance](https://mlsanity.com/paper/2302.00057/) (2023)
- [On the Performance of Handover Mechanisms for Non-Terrestrial Networks](https://mlsanity.com/paper/2201.04904/) (2022)
- [On the Distribution of SINR for Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2310.03268/) (2023)

## Abstract (arXiv)

The signal-to-interference-plus-noise ratio (SINR) is central to performance optimization in user-centric beamforming for satellite-based non-terrestrial networks (NTNs). Its assessment either requires the transmission of dedicated pilots or relies on computing the beamforming matrix through minimum mean squared error (MMSE)-based formulations beforehand, a process that introduces significant computational overhead. In this paper, we propose a low-complexity SINR estimation framework that leverages multi-head self-attention (MHSA) to extract inter-user interference features directly from either channel state information or user location reports. The proposed dual MHSA (DMHSA) models evaluate the SINR of a scheduled user group without requiring explicit MMSE calculations. The architecture achieves a computational complexity reduction by a factor of three in the CSI-based setting and by two orders of magnitude in the location-based configuration, the latter benefiting from the lower dimensionality of user reports. We show that both DMHSA models maintain high estimation accuracy, with the root mean squared error typically below 1 dB with priority-queuing-based scheduled users. These results enable the integration of DMHSA-based estimators into scheduling procedures, allowing the evaluation of multiple candidate user groups and the selection of those offering the highest average SINR and capacity.