ILoRa: Interleaving-driven neural network for rate adaptation in LoRa communications

Abstract

Rate adaptation in LoRa communications is crucial for improving the channel throughput by adjusting the data rate according to varying channel conditions. Existing methods typically operate at the packet or symbol level, which limits their ability to achieve fine-grained rate adaptation. In this paper, we propose ILoRa, an Interleaving-driven partial transmission method that automatically adjusts transmission rates according to real-time channel conditions. To be specific, we first introduce intra-symbol interleaving that leverages a progressive inorder traversal method to determine the transmission order within a symbol. Then inter-symbol interleaving is applied to coordinate the order across symbols. To manage the interleaving-induced partial transmission and improve communication performance under noisy conditions, we employ a multi-task convolutional recurrent neural network (MT-CRNN). This network leverages advanced data augmentation methods to further enhance channel robustness: time-spectral augmentation to mitigate information loss and synthetic noisy data to simulate various channel conditions. Extensive experimental results demonstrate that ILoRa significantly enhance transmission efficiency while maintaining reliable performance even in challenging environments.