https://tatcataittn.github.io/en/tags/communication/CommunicationHugoBlox Kit (https://hugoblox.com)en-usMon, 01 Jun 2026 00:00:00 +0000https://tatcataittn.github.io/media/icon.svghttps://tatcataittn.github.io/en/tags/communication/https://tatcataittn.github.io/en/blog/quantum-satellite-communication/Mon, 01 Jun 2026 00:00:00 +0000https://tatcataittn.github.io/en/blog/quantum-satellite-communication/<p>Quantum Key Distribution lets two parties share an encryption key whose security is guaranteed by quantum physics, not by computational hardness the way classical schemes like RSA or ECC work. The biggest weakness of terrestrial fiber-based QKD is loss: single photons are absorbed and scattered exponentially with distance, which in practice limits fiber QKD to a few hundred kilometers.</p> <p>Satellites solve this in a completely different way. Instead of sending photons through hundreds of kilometers of fiber, the signal travels through the near-vacuum of space, where loss is very low, and only experiences meaningful attenuation over a short atmospheric segment near the ground. This is why satellite QKD experiments, China&rsquo;s Micius mission among them, have already demonstrated key distribution over thousands of kilometers, a range fiber alone simply cannot reach.</p> <h2 id="what-degrades-the-signal-over-a-free-space-channel">What degrades the signal over a free-space channel</h2> <p>Satellite-to-ground QKD links typically rely on Free-Space Optics, sending photons through the air via a narrow, collimated laser beam. Three sources of loss and noise matter most. The first is atmospheric loss, from absorption and scattering by air molecules, aerosols, and especially clouds; thick cloud cover alone can introduce more than 20 to 30 dB of loss, enough to fail a session outright. The second is atmospheric turbulence, refractive-index fluctuations driven by temperature gradients that produce scintillation and beam wander, usually modeled with a Gamma-Gamma distribution. The third is background noise, sunlight or ground light entering the receiver, which directly raises the Quantum Bit Error Rate, the metric that ultimately decides whether a session stays secure enough to extract a key.</p> <h2 id="a-problem-specific-to-tropical-asean">A problem specific to tropical ASEAN</h2> <p>Most existing satellite QKD research, whether from China or Europe, has been conducted or simulated in temperate climates, where clear-sky ratios are relatively high and stable across seasons. Tropical ASEAN, Vietnam included, looks quite different. Cloud cover stays high nearly year-round, especially through the rainy season, a challenge similar to what optical remote sensing runs into, something I wrote about in my post on . The gap between wet and dry seasons is sharp, directly shrinking the available window for a satellite session. And tropical humidity and aerosols make atmospheric loss hard to estimate using models calibrated for temperate climates in the first place.</p> <p>This is precisely the research gap I am working on with the team at PTIT&rsquo;s Quantum Technology &amp; Applications Lab.</p> <h2 id="a-three-state-weather-model">A three-state weather model</h2> <p>Rather than simulating the atmosphere continuously, which is computationally expensive and hard to calibrate against real data, we formalize the satellite FSO channel&rsquo;s weather into three discrete states. A clear state carries low atmospheric loss and mild turbulence, producing a high and stable SKR. A cloud state introduces moderate to high loss that varies with cloud thickness, dropping SKR sharply, sometimes close to zero. A rain state is the harshest, combining the highest loss with increased turbulence, pushing SKR near zero.</p> <p>For each state we estimate how often it occurs, using regional climate data, along with its corresponding atmospheric loss, and from there compute an effective Secret Key Rate, the time-averaged secure key rate rather than the instantaneous SKR under ideal conditions that much of the literature still assumes.</p> <h2 id="from-model-to-a-working-sikd-system">From model to a working SIKD system</h2> <p>This weather model feeds into a larger pipeline: a Simultaneous Information and Key Distribution system, transmitting classical data and quantum keys at the same time over the same satellite optical hardware. The pipeline has three stages: channel modeling based on loss per weather state combined with Gamma-Gamma turbulence, QBER estimation from the signal-to-noise ratio and receiver characteristics, and finally QKD performance analysis, computing SKR per scenario along with the probability that a session fails outright.</p> <p>Early results show a clear gap between dry-season and wet-season performance across the ASEAN region. That points to a practical conclusion: real satellite QKD deployments in the tropics need weather-adaptive routing, choosing ground stations or time windows with a higher chance of clear conditions, rather than assuming a fixed channel the way many existing models still do.</p> <p>I am now extending this to compare several candidate ground stations across Vietnam and the region, and looking at whether real satellite weather data, rather than historical statistics alone, could enable short-term forecasting of usable communication windows. It is a fairly interesting intersection of the two fields I work across in parallel: Earth observation and quantum communication.</p>