Signal Alignment in Frequency-Hopped IoT Networks

This paper introduces a novel method for decoding uplink messages in Internet of Things (IoT) networks that utilize packet repetition, such as Sigfox and LoRa. These protocols use packet repetition at different pseudo-random frequency hops to avoid collisions and channel fades, but they do not coherently combine signals across these random (a priori unknown) hops. This paper introduces a novel frequency alignment strategy that leverages the frequency-hopped repetition structure to decode signals at lower transmission power levels without requiring channel state information, providing a significant advantage over techniques that rely on channel estimates. The proposed approach is validated through extensive simulations and real-world experiments examining its effectiveness in both single-user and multi-user environments under conditions of high system load and interference. The results demonstrate that the proposed approach not only enhances the performance and reliability of IoT networks but also contributes to more energy-efficient communication for IoT Low Power Wide Area Networks (LPWANs), thus offering a substantial advancement in dependable IoT communications.