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

• URL: http://arxiv.org/abs/2602.21116v1
• Date: Tue, 24 Feb 2026 17:12:36 GMT
• Title: Attention-Based SINR Estimation in User-Centric Non-Terrestrial Networks
• Authors: Bruno De Filippo, Alessandro Guidotti, Alessandro Vanelli-Coralli,
• Abstract summary: The signal-to-interference-plus-noise ratio (SINR) is central to performance optimization in user-centric beamforming for satellite-based non-terrestrial networks (NTNs)<n>We propose a low-complexity SINR estimation framework that leverages multi-head self-attention (MHSA)<n>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.
• Score: 79.40703093824894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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