Distributed Uplink Anti-Jamming in LEO Mega-Constellations via Game-Theoretic Beamforming

TL;DR

The paper tackles uplink anti-jamming in LEO mega-constellations, where ground-based jammers threaten terrestrial uplinks and single-satellite mitigation fails due to shared channels. It models the interaction as a convex-concave min-max game between the desired transmitter and a jammer, optimizing spatial covariance matrices $\boldsymbol{Q}_0$ and $\boldsymbol{Q}_1$ to maximize or minimize the rate $J(\boldsymbol{Q}_0,\boldsymbol{Q}_1)$, and proves that a Nash equilibrium exists. A lightweight solver combining alternating best-response updates with projected gradient steps yields fast convergence to the equilibrium, with closed-form water-filling for the transmitter and gradient-based updates for the jammer. Simulations using Starlink geometry and Sionna ray-tracing demonstrate that distributed reception across 3–5 satellites significantly improves resilience to strong interference, achieving notable capacity gains versus a single-satellite link; the work also outlines avenues for channel estimation under adversarial conditions and distributed timing synchronization as future directions.

Abstract

Low-Earth-Orbit (LEO) satellite constellations have become vital in emerging commercial and defense Non-Terrestrial Networks (NTNs). However, their predictable orbital dynamics and exposed geometries make them highly susceptible to ground-based jamming. Traditional single-satellite interference mitigation techniques struggle to spatially separate desired uplink signals from nearby jammers, even with large antenna arrays. This paper explores a distributed multi-satellite anti-jamming strategy leveraging the dense connectivity and high-speed inter-satellite links of modern LEO mega-constellations. We model the uplink interference scenario as a convex-concave game between a desired terrestrial transmitter and a jammer, each optimizing their spatial covariance matrices to maximize or minimize achievable rate. We propose an efficient min-max solver combining alternating best-response updates with projected gradient descent, achieving fast convergence of the beamforming strategy to the Nash equilibrium. Using realistic Starlink orbital geometries and Sionna ray-tracing simulations, we demonstrate that while close-proximity jammers can cripple single-satellite links, distributed satellite cooperation significantly enhances resilience, shifting the capacity distribution upward under strong interference.

Figures (4)

• Figure 1: LEO satellite network topology with legitimate uplink and jamming channels from a malicious ground station. Inter-satellite free-space links enable cooperation among satellites.
• Figure 2: Instantaneous satellite geometry above the ground TX. The five nearest Starlink satellites are shown in color, and other visible ones in gray. Observation elevation: 4090°.
• Figure 3: Capacity degradation under nullforming failure.
• Figure 4: Beampatterns of the jammer and ground TX. Pentagram indicate satellite positions, with red stars denoting the available satellites. The jammer is located 1 km north of the TX, with elevation angles from 40$^\circ$ to 90$^\circ$ (zenith).