Leveraging Imperfect-Orthogonality Aware Scheduling for High Scalability in LPWAN

As an emerging Low-Power Wide Area Networks (LPWAN) technology, LoRa is dedicated to providing long-range connections for pervasive Internet-of-Things devices. As LoRa operates in the unlicensed spectrum with an ALOHA-based MAC-layer protocol stack, transmissions from multiple LoRa end-devices inevitably collide with each other, leading to packet losses and increased transmission delay. Targeting at collisions caused by interferences under the <italic>same</italic> spreading factor (SF) settings, researchers introduce multiple lines of techniques. Despite their efforts, these techniques commonly neglect the potential collisions caused by interferences under <italic>different</italic> SF settings, resulting in imperfect orthogonality. Given the disparate transmission power configurations and diverse deployed locations, the collisions under different SFs commonly exist in practical networks and significantly limit the LoRa reliability. This paper presents X-MAC, the first scheduler aware of imperfect orthogonality. Technically, X-MAC detects the collisions under different SFs via tracking historical transmissions, and performs dynamic channel scheduling to avoid collisions caused by interferences under the same and different SFs. Extensive evaluations on testbed devices show that, compared with the state-of-the-art methods, X-MAC boosts the network scalability (number of concurrent end-devices) by 1.26× to 2.41× with packet reception rate requirement of > 95%. IEEE