Experiment-based Models for Air Time and Current Consumption of LoRaWAN LR-FHSS

Long Range - Frequency Hopping Spread Spectrum (LR-FHSS) is an emerging and promising technology recently introduced into the LoRaWAN protocol specification for both terrestrial and non-terrestrial networks, notably satellites. The higher capacity, long-range and robustness to Doppler effect make LR-FHSS a primary candidate for direct-to-satellite (DtS) connectivity for enabling Internet-of-things (IoT) in remote areas. The LR-FHSS devices envisioned for DtS IoT will be primarily battery-powered. Therefore, it is crucial to investigate the current consumption characteristics and Time-on-Air (ToA) of LR-FHSS technology. However, to our knowledge, no prior research has presented the accurate ToA and current consumption models for this newly introduced scheme. This paper addresses this shortcoming through extensive field measurements and the development of analytical models. Specifically, we have measured the current consumption and ToA for variable transmit power, message payload, and two new LR-FHSS-based Data Rates (DR8 and DR9). We also develop current consumption and ToA analytical models demonstrating a strong correlation with the measurement results exhibiting a relative error of less than 0.3%. Thus, it confirms the validity of our models. Conversely, the existing analytical models exhibit a higher relative error rate of -9.2 to 3.4% compared to our measurement results. The presented in this paper results can be further used for simulators or in analytical studies to accurately model the on-air time and energy consumption of LR-FHSS devices.

Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario

The integration of subterranean LoRaWAN and non-terrestrial networks (NTN) delivers substantial economic and societal benefits in remote agriculture and disaster rescue operations. The LoRa modulation leverages quasi-orthogonal spreading factors (SFs) to optimize data rates, airtime, coverage and energy consumption. However, it is still challenging to effectively assign SFs to end devices for minimizing co-SF interference in massive subterranean LoRaWAN NTN. To address this, we investigate a reinforcement learning (RL)-based SFs allocation scheme to optimize the system's energy efficiency (EE). To efficiently capture the device-to-environment interactions in dense networks, we proposed an SFs allocation technique using the multi-agent dueling double deep Q-network (MAD3QN) and the multi-agent advantage actor-critic (MAA2C) algorithms based on an analytical reward mechanism. Our proposed RL-based SFs allocation approach evinces better performance compared to four benchmarks in the extreme underground direct-to-satellite scenario. Remarkably, MAD3QN shows promising potentials in surpassing MAA2C in terms of convergence rate and EE.