TY - JOUR
T1 - FTrack: Parallel Decoding for LoRa Transmissions
AU - Xia, Xianjin
AU - Zheng, Yuanqing
AU - Gu, Tao
N1 - Funding Information:
Manuscript received October 24, 2019; revised July 19, 2020; accepted July 22, 2020; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor B. Shrader. Date of publication August 27, 2020; date of current version December 16, 2020. This work was supported in part by the National Nature Science Foundation of China under Grant 61702437; in part by the Hong Kong GRF under Grant PolyU 152165/19E; and in part by the Australian Research Council (ARC) Discovery Project under Grant DP190101888 and Grant DP180103932. (Corresponding author: Yuanqing Zheng.) Xianjin Xia and Yuanqing Zheng were with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong. They are now with the Department of Computing, The Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - 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 article 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 substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. 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.
AB - 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 article 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 substantially optimize computation-intensive tasks and meet the real-time requirements of parallel LoRa decoding. 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.
KW - collision resolving
KW - Internet of Things
KW - LoRaWAN
KW - parallel decoding
UR - http://www.scopus.com/inward/record.url?scp=85090470927&partnerID=8YFLogxK
U2 - 10.1109/TNET.2020.3018020
DO - 10.1109/TNET.2020.3018020
M3 - Journal article
AN - SCOPUS:85090470927
SN - 1063-6692
VL - 28
SP - 2573
EP - 2586
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 6
M1 - 9178997
ER -