Affiliation:
1. Beijing University of Posts and Telecommunications, Beijing, China
Abstract
The massive connection of LoRa brings serious collision interference. Existing collision decoding methods cannot effectively deal with the adjacent collisions that occur when the collided symbols are adjacent in the frequency spectrum. The decoding features relied on by the existing methods will be corrupted by adjacent collisions. To address these issues, we propose
Paralign
, which is the first LoRa collision decoder supporting decoding LoRa collisions with confusing symbols via parallel alignment. The key enabling technology behind
Paralign
is tha there is no spectrum leakage with periodic truncation of a chirp.
Paralign
leverages the precise spectrum obtained by aligning the de-chirped periodic signals from each packet in parallel for spectrum filtering and power filtering. To aggregate correlation peaks in different windows of the same symbol,
Paralign
matches the peaks of multiple interfering windows to the interested window based on the time offset between collided packets. Moreover, a periodic truncation method is proposed to address the multiple candidate peak problem caused by side lobes of confusing symbols. We evaluate
Paralign
using USRP N210 in a 20-node network. Experimental results demonstrate that
Paralign
can significantly improve network throughput, which is over 1.46× higher than state-of-the-art methods.
Funder
National Key Research and Development Program of China
A3 Foresight Program of NSFC
National Natural Science Foundation of China
Funds for Creative Research Groups of China
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
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