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

TL;DR

This paper proposes a reinforcement learning-based adaptive interference management framework for coexistence of terrestrial and non-terrestrial networks in the upper mid-band, reducing interference while maintaining network performance.

Contribution

It introduces a novel optimization framework combined with reinforcement learning to dynamically control power and antenna tilt for TN-NTN coexistence, overcoming limitations of static methods.

Findings

Achieves up to 8 dB reduction in median INR.

Maintains over 87% TN base station activity.

Outperforms conventional interference mitigation methods.

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.