Press Release

Low-latency Imaging and Inference from LoRa-enabled CubeSats

By SpaceRef Editor
June 29, 2022
Filed under , ,

Akshay Gadre, Swarun Kumar, Zachary Manchester

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, primarily for research, education, and Earth observation. The vast majority of these CubeSats experience significant latency (several hours) from the time an image is captured to the time it is available on the ground. This is primarily due to the limited availability of dedicated satellite ground stations that tend to be bulky to deploy and expensive to rent. This paper explores using LoRa radios in the ISM band for low-latency downlink communication from CubeSats, primarily due to the availability of extensive ground LoRa infrastructure and minimal interference to terrestrial communication. However, the limited bandwidth of LoRa precludes rich satellite Earth images to be sent – instead, the CubeSats can at best send short messages (a few hundred bytes).

This paper details our experience in communicating with a LoRa-enabled CubeSat launched by our team. We present Vista, a communication system that makes software modifications to LoRa encoding onboard a CubeSat and decoding on commercial LoRa ground stations to allow for satellite imagery to be communicated, as well as wide-ranging machine learning inference on these images. This is achieved through a LoRa-channel-aware image encoding that is informed by the structure of satellite images, the tasks performed on it, as well as the Doppler variation of satellite signals. A detailed evaluation of Vista through trace-driven emulation with traces from the LoRa-CubeSat launch (in 2021) shows 4.56 dB improvement in LoRa image PSNR and 1.38x improvement in land-use classification over those images.

Subjects: Networking and Internet Architecture (cs.NI); Instrumentation and Methods for Astrophysics (astro-ph.IM); Signal Processing (eess.SP)

Cite as: arXiv:2206.10703 [cs.NI] (or arXiv:2206.10703v1 [cs.NI] for this version)

Submission history

From: Akshay Gadre 

[v1] Tue, 21 Jun 2022 19:50:21 UTC (27,847 KB)

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