On January 8th, Iran imposed a full nationwide internet blackout to curb escalating anti-regime protests. Just three days later, on January 11th, reports indicated that Starlink satellites were facing widespread jamming and DDoS attacks throughout the country, reportedly leveraging Russian tech.

While there are not much sources to cite or verify, the NYT reported on this issue, unfortunately behind paywall.

Just 2 months prior, Chinese researchers published a study simulating how to disrupt Starlink’s satellite communications over Taiwan by deploying a network of airborne jammers in a grid pattern. The approach leverages multiple low-power devices working together to overwhelm the downlink signals, effectively creating blackout zones with high efficiency—covering significant areas per jammer at moderate energy levels. This highlights potential vulnerabilities in mega-constellations like Starlink for electronic warfare, offering insights into countermeasures that could inform our strategies for satellite-dependent operations in contested regions.

Link to paper: https://www.spacejournal.cn/xtgcydzjs/article/doi/10.12305/j.issn.1001-506X.2025.11.30

Abstract: Aiming at the problem that conventional single-node jamming methods struggle to counter the spatiotemporal complexity of mega-constellations, a distributed jamming strategy for mega-constellations is proposed, and a mathematical model for jamming analysis targeting the downlink of mega-constellations is established. A grid-based deployment approach for jammers is adopted to enhance the spatial distribution flexibility of the adversarial side, along with a jamming probability calculation method and a jamming effectiveness evaluation metric. Based on actual satellite operation data, taking the Starlink system as an example, the jamming coverage range is calculated under different conditions of radio frequency power, grid spacing, and antenna radiation patterns. Simulation results show that when the node transmission power is 26 dBW, the average jamming coverage per node can reach 38.5 km², providing support for the regulation and management of mega-constellations.

English (AI-based) translation

Detailed Technical Methodology: Simulating Distributed Jamming Against Mega-Constellation Downlink Communications

Introduction

In November 2025, Chinese researchers published a paper titled „Simulation Research of Distributed Jamming Against Mega-Constellation Downlink Communication Transmissions“ in the journal Systems Engineering and Electronics. The study focuses on modeling and simulating electronic warfare techniques to disrupt satellite communications from large-scale low-Earth orbit (LEO) constellations, such as Starlink, over a specific geographic area like Taiwan. Using real orbital data and radio frequency (RF) propagation models, the paper evaluates the effectiveness of a grid-based network of airborne jammers in suppressing downlink signals in the Ku-band.

This article provides a comprehensive English-language breakdown of the paper’s technical methodology, including spatial modeling, antenna patterns, interference calculations, and simulation setups. It draws directly from the original work to explain how the researchers approached the challenge of countering the spatiotemporal advantages of mega-constellations through distributed interference strategies.

Interference Scenario Modeling

The core of the methodology involves constructing a realistic interference model that accounts for the dynamic nature of LEO satellites. The model incorporates three key components: the satellites (denoted as s), distributed jammer nodes (j), and ground user terminals (i).

Spatial Position Relationships

The spatial setup is visualized as a three-dimensional scenario where satellites transmit downlink signals to ground terminals, while jammers emit interfering signals to disrupt these links. Key distances and angles are defined:

• r_{i,s}: Distance from satellite s to terminal i (communication link).
• d_{i,j}: Distance from jammer j to terminal i (interference link).
• ϕ_{i,j}: Off-axis angle between the jammer’s emission beam and the interference link.
• θ_{i,j,s}: Off-axis angle at the terminal between the communication and interference links.

For a network of N jammers, the interference is modeled as the superposition of power flux density (PFD) at the terminal. The PFD for interference signals (normalized to a 1 MHz bandwidth) is calculated as:

Where:

• P_j: Transmission power of jammer j (in watts).
• G_j(ϕ_{i,j}): Emission gain of jammer j at off-axis angle ϕ_{i,j}.
• d_{i,j}: Distance between terminal i and jammer j (in meters).
• G_i(θ_{i,j,s}): Reception gain of terminal i at off-axis angle θ_{i,j,s}.
• B_j: Bandwidth of the jammer’s signal (in MHz; e.g., 2000 MHz for full-band jamming).
• ϒ_{i,j}: Polarization mismatch loss factor.

The carrier-to-interference ratio (C/I) is then derived to assess disruption:

[
(C/I)_{i,s} = frac{mathrm{PFD}c}{mathrm{PFD}{i,s}}
]

Here, PFD_c is the useful signal PFD from the satellite (e.g., -122 dB·W·m⁻²·MHz⁻¹ based on Starlink parameters). A threshold of C/I ≤ 5 dB is used to indicate effective jamming, based on references indicating that this level disrupts digital communications with a 20%–33% duty cycle.

To optimize jammer placement, the researchers considered a Fibonacci grid for uniform distribution in the zenith visibility zone (elevation ≥40°), projecting it onto a plane at 20 km altitude for practical deployment. This accounts for non-uniform satellite visibility, using real Starlink orbital data from July 18, 2024.

Starlink Terminal Reception Antenna Modeling

The ground terminal antenna is modeled as a 0.48 m diameter Ku-band phased array with 1280 elements, following ITU-R recommendations for earth stations. The maximum gain is:

[
G_{max} = 10 cdot lg left( eta left( frac{pi D f}{c} right)^2 right)
]

With D = 0.48 m, f = 11.7 GHz, η = 0.7 (efficiency), and 1 dB losses, G_max ≈ 32.8 dBi.

Off-axis gain thresholds are calculated:

[
theta_m = left( frac{lambda}{D} right) sqrt{ frac{G_{max} – G_1}{0.0025} }
]

[
theta_r = 95 left( frac{lambda}{D} right)
]

[
theta_b = 10^{34/25}
]

[
G_1 = 29 – 25 lg theta_r
]

The full gain pattern G(θ) is piecewise:

[
G(theta) =
begin{cases}
G_{max} – 0.0025 left( frac{theta cdot D}{lambda} right)^2 & 0 leq theta < theta_m
G_1 & theta_m leq theta < theta_r
29 – 25 lg theta & theta_r leq theta < theta_b
-5 & theta_b leq theta < 70^circ
0 & 70^circ leq theta < 180^circ
end{cases}
]

Jammer Node Emission Antenna Modeling

Jammers use simple, non-steerable antennas selected from ITU databases:

• Wide-beam (Antenna A): 5 dBi gain, 63° beamwidth (E/H-plane).
• Narrow-beam (Antenna B): 15 dBi gain, 26.2° beamwidth.

Gain patterns are applied based on off-axis angles, assuming downward-facing orientation at 20 km altitude.

Simulation Analysis

Simulations evaluate jamming effectiveness over a grid-based deployment, using 43,200 orbital snapshots from a 12-hour period. Key parameters include:

• Center frequency: 11.7 GHz.
• Channel bandwidth: 250 MHz.
• Maximum visible satellites: 9.
• Jammer grid spacing: 5–9 km.
• Jammer power: 20–29 dBW.
• Jammer altitude: 20 km.
• Ground area: Uniform grid for terminals.

Disturbance is visualized on a color-coded map, with levels based on the probability (>90%) of interrupting all or most downlink links. Effectiveness is quantified as:

[
I = frac{S}{sum_{j=1}^N P_j}
]

Where S is the disturbed area (km²) with ≥90% interruption probability, and P_j is power in kW. This yields I in km²/kW.

Wide-Beam Jammer Simulations

For Antenna A, varying spacing and power shows increasing coverage with power, but diminishing efficiency at higher levels. Optimal configurations achieve up to 50 km²/kW.

Narrow-Beam Jammer Simulations

Antenna B performs better, with efficiencies up to 96.7 km²/kW (e.g., 26 dBW, 7 km spacing, average 38.5 km² per node).

Temporal Characteristics

Hourly analyses at different terminal locations (center, 20 km offset, 30 km offset) reveal consistent suppression at the center but variability offsets, with narrow-beam setups outperforming wide-beam.

Conclusions and Insights

The methodology demonstrates that distributed jamming can effectively counter mega-constellations by leveraging grid deployments and multi-node superposition. Narrow-beam antennas with moderate power and spacing offer the best efficiency. This simulation-based approach uses real data and RF models to provide actionable insights for electronic warfare strategies.

For the full original paper, refer to the source link.