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This content will become publicly available on April 15, 2025

Title: Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression
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.  more » « less
Award ID(s):
2142978
NSF-PAR ID:
10513590
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
USENIX
Date Published:
Journal Name:
21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)
ISBN:
978-1-939133-39-7
Format(s):
Medium: X
Location:
Santa Clara
Sponsoring Org:
National Science Foundation
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