Morph: ChirpTransformer-based Encoder-decoder Co-design for Reliable LoRa Communication

TL;DR

Morph introduces a neural network-based encoder-decoder system for LoRa communication that significantly enhances reliability in low SNR conditions and improves decoding efficiency, compatible with existing LoRa hardware.

Contribution

It proposes a novel SF-configuration based encoder and a DNN decoder that extend LoRa's SNR tolerance and boost decoding efficiency without hardware modifications.

Findings

Reliable decoding at -28.8 dB SNR, 6.4 dB lower than standard LoRa.

DNN decoder achieves 3x higher efficiency than existing methods.

Compatible with off-the-shelf LoRa nodes.

Abstract

In this paper, we propose Morph, a LoRa encoder-decoder co-design to enhance communication reliability while improving its computation efficiency in extremely-low signal-to-noise ratio (SNR) situations. The standard LoRa encoder controls 6 Spreading Factors (SFs) to tradeoff SNR tolerance with data rate. SF-12 is the maximum SF providing the lowest SNR tolerance on commercial off-the-shelf (COTS) LoRa nodes. In Morph, we develop an SF-configuration based encoder to mimic the larger SFs beyond SF-12 while it is compatible with COTS LoRa nodes. Specifically, we manipulate four SF configurations of a Morph symbol to encode 2-bit data. Accordingly, we recognize the used SF configuration of the symbol for data decoding. We leverage a Deep Neural Network (DNN) decoder to fully capture multi-dimensional features among diverse SF configurations to maximize the SNR gain. Moreover, we customize the input size, neural network structure, and training method of the DNN decoder to improve its efficiency, reliability, and generalizability. We implement Morph with COTS LoRa nodes and a USRP N210, then evaluate its performance on indoor and campus-scale testbeds. Results show that we can reliably decode data at -28.8~dB SNR, which is 6.4~dB lower than the standard LoRa with SF-12 chirps. In addition, the computation efficiency of our DNN decoder is about 3x higher than state-of-the-art.