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Vlad Avramut
Vlad Avramut

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Understanding Meshtastic Range: What Field Data Really Shows

This is an excerpt.

Read the full analysis:
https://www.vladavramut.com/articles/meshtastic-range.html


Meshtastic is often described as a “long-range mesh network,” yet real-world deployments show a wide gap between expectations and reality.

Field data and RF propagation theory both point to a simple truth: antenna placement, Fresnel zone clearance, and local RF noise dominate usable range, not transmit power or firmware settings.


What Actually Limits Meshtastic Range

Despite frequent emphasis on transmit power and tuning, three physical-layer constraints consistently dominate performance:

Antenna placement and height

Raising the antenna even modestly often produces 3–10× improvements in effective range—sometimes more—without changing any radio parameters. This aligns with Fresnel zone and free-space path loss theory.

Fresnel zone clearance

For LoRa frequencies like 868 MHz or 915 MHz, the first Fresnel zone radius at just 2 km can be 8–10 m. Partial obstruction (trees, buildings, terrain) introduces diffraction losses that degrade signal strength far more than tweaks to transmit settings.

Local noise floor and interference

LoRa modulation is highly sensitive to background RF noise. In urban environments the noise floor can rise dramatically, reducing signal-to-noise ratio and collapsing effective range even with strong antennas and clear line-of-sight.

Real deployments with identical hardware show radically different range between:

• dense urban zones

• suburban rooftops

• rural hilltops

because of these physical environment differences.


Why Multi-Hop Isn’t Infinite Range

Meshtastic uses mesh routing — nodes rebroadcast messages — but that does not create infinite coverage.

Field logs show:

• each additional hop increases reliability loss

• latency compounds quickly beyond a few hops

• congestion increases superlinearly in dense networks

• routing loops and stale topology data degrade stability

In practice, Meshtastic performs best as:

• sparse relay networks

• with well-placed high-altitude backbone nodes

• supplemented by short-range local endpoints


Continue reading the full analysis:
https://www.vladavramut.com/articles/meshtastic-range.html

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