Deepdetangle: Deep Learning-Based Fusion of Chirp-Level and Packet-Level Features for Lora Parallel Decoding

LoRa has been widely adopted in Internet of Things (IoT) due to its long distance and low cost. With the largescale deployment of LoRa devices, it is not uncommon that multiple nodes transmit concurrently, leading to packet collisions and degraded performance. Previous studies have focused on examining single or multiple features in the received raw signals, named as chirp-level features, to separate collided packets for parallel decoding. However, the accuracy of feature extraction turns out to be vulnerable to interference, which can lead to incorrect packet decoding as network concurrency increases. Our study reveals that, other than the chirp-level features, a standard LoRa packet encoder introduces coding correlations across symbols of the same packet and thus leaves packet-level features to the symbols that can be utilized to disentangle symbols of collided packets in a new dimension. In this work, we introduce DeepDetangle, a Deep-learning-based feature fusion framework that efficiently fuses both packet-level and chirp-level features of symbols to enhance LoRa parallel decoding. DeepDetangle utilizes Complex-CNN and LSTM structures to capture multidimensional chirp-level features, their joint distributions, and temporal patterns. An MLP is employed to aggregate the chirplevel features with the packet-level features, thereby enabling the decoding of symbols on a per-block basis. By integrating features from both levels, erroneous symbols that cannot be recovered with chirp-level features alone can now be effectively corrected with other valid ones in the same block. Therefore, it demonstrates a strong capability to combat interference from other packets. Extensive evaluations have been conducted to assess the performance of DeepDetangle. The results indicate that DeepDetangle achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 8. 1 \%}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 0. 5 \%}$</tex> higher network throughput compared to existing works of similar complexity.