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

TL;DR

This work tackles TN–NTN coexistence in the FR3 upper mid-band by analyzing INR and NTN throughput under joint TN control of downlink power, uplink power, and antenna downtilt. It formulates a non-convex optimization to maximize NTN throughput R_NTN subject to INR_n ≤ Gamma_th and gNB tilt/power bounds, and solves it with a centralized PPO reinforcement learning agent. The agent observes state features such as {theta_EL, eta, R_NTN, chi, Gamma_th} and outputs actions including P_i^gNB, P_i^UE, theta_d,i and sector muting, with a reward r_t balancing NTN throughput, TN activity, and interference. Empirical results show interference reduction up to 6–8 dB in INR and NTN throughput gains while maintaining TN availability around 87%, outperforming fixed-parameter and exclusion-zone baselines, demonstrating practical FR3 coexistence.

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.

Figures (6)

• Figure 1: Interference Mitigation RL System
• Figure 2: Base Station Map (Frankfurt)
• Figure 3: PPO Training Convergence across Random Seeds
• Figure 4: INR Comparison across NTN Densities
• Figure 5: INR Comparison across NTN User Types