Dynamic Interference Management for TN-NTN Coexistence in the Upper Mid-Band

2026-02-11

AI Summary

This work addresses the severe interference caused by densely deployed terrestrial networks (TNs) to non-terrestrial network (NTN) downlink transmissions in the FR3 mid-band. To mitigate this issue, the authors propose a reinforcement learning approach based on Proximal Policy Optimization (PPO) that jointly optimizes the downlink/uplink transmit power and antenna tilt of TN base stations. The method dynamically suppresses interference to NTN while preserving TN performance, without requiring precise channel state information or static coordination—making it well-suited for dynamic NTN environments. Experimental results demonstrate that the proposed scheme reduces the median interference-to-noise ratio (INR) by up to 8 dB while maintaining over 87% of terrestrial base station activity, significantly outperforming conventional baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Privacy-enhancing technologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The coexistence of terrestrial networks (TN) and non-terrestrial networks (NTN) in the frequency range 3 (FR3) upper mid-band presents considerable interference concerns, as dense TN deployments can severely degrade NTN downlink performance. Existing studies rely on interference-nulling beamforming, precoding, or exclusion zones that require accurate channel state information (CSI) and static coordination, making them unsuitable for dynamic NTN scenarios. To overcome these limitations, we develop an optimization framework that jointly controls TN downlink power, uplink power, and antenna downtilt to protect NTN links while preserving terrestrial performance. The resultant non-convex coupling between TN and NTN parameters is addressed by a Proximal Policy Optimization (PPO)-based reinforcement learning method that develops adaptive power and tilt control strategies. Simulation results demonstrate a reduction up to 8 dB in the median interference-to-noise ratio (INR) while maintaining over 87% TN basestation activity, outperforming conventional baseline methods and validating the feasibility of the proposed strategy for FR3 coexistence.

Problem

• TN-NTN coexistence
• dynamic interference management
• upper mid-band
• FR3
• interference mitigation

Innovation

• reinforcement learning
• dynamic interference management
• TN-NTN coexistence
• Proximal Policy Optimization (PPO)
• antenna downtilt control