FTrack: parallel decoding for LoRa transmissions

Xianjin Xia, Yuanqing Zheng, Tao Gu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

61 Citations (Scopus)


LoRa has emerged as a promising Low-Power Wide Area Network (LP-WAN) technology to connect a huge number of Internet-of-Things (IoT) devices. The dense deployment and an increasing number of IoT devices lead to intense collisions due to uncoordinated transmissions. However, the current MAC/PHY design of LoRaWAN fails to recover collisions, resulting in degraded performance as the system scales. This paper presents FTrack, a novel communication paradigm that enables demodulation of collided LoRa transmissions. FTrack resolves LoRa collisions at the physical layer and thereby supports parallel decoding for LoRa transmissions. We propose a novel technique to separate collided transmissions by jointly considering both the time domain and the frequency domain features. The proposed technique is motivated from two key observations: (1) the symbol edges of the same frame exhibit periodic patterns, while the symbol edges of different frames are usually misaligned in time; (2) the frequency of LoRa signal increases continuously in between the edges of symbol, yet exhibits sudden changes at the symbol edges. We detect the continuity of signal frequency to remove interference and further exploit the time-domain information of symbol edges to recover symbols of all collided frames. We implement FTrack on a low-cost software defined radio. Our testbed evaluations show that FTrack demodulates collided LoRa frames with low symbol error rates in diverse SNR conditions. It increases the throughput of LoRaWAN in real usage scenarios by up to 3 times.

Original languageEnglish
Title of host publicationSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
Subtitle of host publicationProceedings of the 17th Conference on Embedded Networked Sensor Systems
EditorsMi Zhang
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages13
ISBN (Electronic)9781450369503
Publication statusPublished - 2019
Externally publishedYes
Event17th Conference on Embedded Networked Sensor
Systems, SenSys 2019
- New York, United States
Duration: 10 Nov 201913 Nov 2019

Publication series

Namein Proc. of the 17th ACM Conference on Embedded Networked Sensor Systems (SenSys 2019)
PublisherWiley-IEEE Press


Conference17th Conference on Embedded Networked Sensor
Systems, SenSys 2019
Abbreviated titleSenSys '19
Country/TerritoryUnited States
CityNew York


  • Collision
  • Internet of things
  • LoRaWAN
  • Parallel decoding


Dive into the research topics of 'FTrack: parallel decoding for LoRa transmissions'. Together they form a unique fingerprint.

Cite this