LoRa as a representative of Low-Power Wide Area Networks (LPWAN) technologies has emerged as an attractive communication platform for the Internet of Things. Since its dense deployment, signal collisions at base stations caused by concurrent transmissions degrade network performance. Existing approaches utilize the signal feature, e.g., frequency, to separate packets from collisions. They do not work well in burst traffic networks because the feature is not stable or fine-grained enough and the information for directed signal separation is not sufficient. In this paper, we leverage multidimensional information and propose a novel PHY layer approach called SCLoRa to decode collided LoRa transmissions. SCLoRa utilizes cumulative spectral coefficient, which integrates both frequency and power information, to separate symbols in the overlapped signal. The practical factors of channel fading, similar symbol boundary, and spectrum leakage are taken into account. The SCLoRa design requires neither hardware nor firmware changes in commodity devices-a feature allowing fast deployment on LoRa base stations. We implement and evaluate SCLoRa on USRP B210 base stations and commodity LoRa devices (i.e., SX1278). The experiment results in different scenarios with different radio parameters show that the throughput of SCLoRa is 3× than the state-of-the-art.
|Original language||English (US)|
|Title of host publication||28th IEEE International Conference on Network Protocols, ICNP 2020|
|Publisher||IEEE Computer Society|
|State||Published - Oct 13 2020|
|Event||28th IEEE International Conference on Network Protocols, ICNP 2020 - Madrid, Spain|
Duration: Oct 13 2020 → Oct 16 2020
|Name||Proceedings - International Conference on Network Protocols, ICNP|
|Conference||28th IEEE International Conference on Network Protocols, ICNP 2020|
|Period||10/13/20 → 10/16/20|
Bibliographical noteFunding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant No. 6167219, BK20190336, and China National Key RD Program 2018YFB2100302.