Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression


Muhammad Osama Shahid, Daniel Koch, Jayaram Raghuram, and Bhuvana Krishnaswamy, University of Wisconsin-Madison; Krishna Chintalapudi, Microsoft Research; Suman Banerjee, University of Wisconsin-Madison


The Cloud Radio Access Network (CRAN) architecture has been proposed as a way of addressing the network throughput and scalability challenges of large-scale LoRa networks. CRANs can improve network throughput by coherently combining signals, and scale to multiple channels by implementing the receivers in the cloud. However, in remote LoRa deployments, a CRAN's demand for high-backhaul bandwidths can be challenging to meet. Therefore, bandwidth-aware compression of LoRa samples is needed to reap the benefits of CRANs. We introduce Cloud-LoRa, the first practical CRAN for LoRa, that can detect sub-noise LoRa signals and perform bandwidth-adaptive compression. To the best of our knowledge, this is the first demonstration of CRAN for LoRa operating in real-time. We deploy Cloud-LoRa in an agricultural field over multiple days with USRP as the gateway. A cellular backhaul hotspot is then used to stream the compressed samples to a Microsoft Azure server. We demonstrate SNR gains of over 6 dB using joint multi-gateway decoding and over 2x throughput improvement using state-of-the-art receivers, enabled by CRAN in real-world deployments.

NSDI '24 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {295707,
author = {Muhammad Osama Shahid and Daniel Koch and Jayaram Raghuram and Bhuvana Krishnaswamy and Krishna Chintalapudi and Suman Banerjee},
title = {{Cloud-LoRa}: Enabling Cloud Radio Access {LoRa} Networks Using Reinforcement Learning Based {Bandwidth-Adaptive} Compression},
booktitle = {21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
year = {2024},
isbn = {978-1-939133-39-7},
address = {Santa Clara, CA},
pages = {1959--1976},
url = {},
publisher = {USENIX Association},
month = apr

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