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

arXiv: https://arxiv.org/abs/2311.01743 · HTML: https://mlsanity.com/paper/2311.01743/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Graph Attention Network-based Multi-agent Reinforcement Learning for Slicing Resource Management in Dense Cellular Network](https://mlsanity.com/paper/2108.05063/) (2021)
- [Low Power Wide Area Network Analysis: Can LoRa Scale?](https://mlsanity.com/paper/1610.04793/) (2016)
- [Enhancement of Direct LEO Satellite-to-Smartphone Communications by Distributed Beamforming](https://mlsanity.com/paper/2308.05055/) (2023)
- [LoRa Performance Analysis with Superposed Signal Decoding](https://mlsanity.com/paper/2010.09625/) (2020)
- [Enhancement of Satellite-to-Phone Link Budget by Using Distributed Beamforming](https://mlsanity.com/paper/2308.04818/) (2023)

## Built on by (most cited first, 2 of 2)

- [LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions](https://mlsanity.com/paper/2409.11200/) (2024)
- [Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimization](https://mlsanity.com/paper/2603.20404/) (2026)

## Abstract (arXiv)

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.