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D-LoRa: a Distributed Parameter Adaptation Scheme for LoRa Network

Ruiqi Wang, Tongyu Song, Jing Ren, Xiong Wang, Shizhong Xu, Sheng Wang

TL;DR

D-LoRa tackles the problem of jointly optimizing LoRa network performance—PDR, EE, and TH—under a coupled parameter landscape by introducing a CMAB-based distributed parameter adaptation framework. Each node runs a lightweight CMAB agent that selects a four-parameter configuration (SF, BW, CF, TP) for each packet, with per-parameter rewards designed to reflect nonlinear performance trade-offs. A comprehensive analytical model of LoRa networks (path loss, SF quasi-orthogonality, and collisions) underpins the optimization and informs the reward design, achieving asymptotic optimality. Experimental results show up to 28.8% improvements in PDR over strong baselines and demonstrate D-LoRa’s ability to tailor configurations to application needs, suggesting meaningful practical impact for scalable IoT deployments.

Abstract

The deployment of LoRa networks necessitates joint performance optimization, including packet delivery rate, energy efficiency, and throughput. Additionally, multiple LoRa parameters for packet transmission must be dynamically configured to tailor the performance metrics prioritization across varying channel environments. Because of the coupling relationship between LoRa parameters and metrics, existing works have opted to focus on certain parameters or specific metrics to circumvent the intricate coupling relationship, leading to limited adaptability. Therefore, we propose D-LoRa, a distributed parameter adaptation scheme, based on reinforcement learning towards network performance. We decompose the joint performance optimization problem into multiple independent Multi-Armed Bandit (MAB) problems with different reward functions. We have also built a comprehensive analytical model for the LoRa network that considers path loss, quasi-orthogonality of spreading factor, and packet collision. Experimental results show that our scheme can increase packet delivery rate by up to 28.8% and demonstrates superior adaptability across different performance metrics.

D-LoRa: a Distributed Parameter Adaptation Scheme for LoRa Network

TL;DR

D-LoRa tackles the problem of jointly optimizing LoRa network performance—PDR, EE, and TH—under a coupled parameter landscape by introducing a CMAB-based distributed parameter adaptation framework. Each node runs a lightweight CMAB agent that selects a four-parameter configuration (SF, BW, CF, TP) for each packet, with per-parameter rewards designed to reflect nonlinear performance trade-offs. A comprehensive analytical model of LoRa networks (path loss, SF quasi-orthogonality, and collisions) underpins the optimization and informs the reward design, achieving asymptotic optimality. Experimental results show up to 28.8% improvements in PDR over strong baselines and demonstrate D-LoRa’s ability to tailor configurations to application needs, suggesting meaningful practical impact for scalable IoT deployments.

Abstract

The deployment of LoRa networks necessitates joint performance optimization, including packet delivery rate, energy efficiency, and throughput. Additionally, multiple LoRa parameters for packet transmission must be dynamically configured to tailor the performance metrics prioritization across varying channel environments. Because of the coupling relationship between LoRa parameters and metrics, existing works have opted to focus on certain parameters or specific metrics to circumvent the intricate coupling relationship, leading to limited adaptability. Therefore, we propose D-LoRa, a distributed parameter adaptation scheme, based on reinforcement learning towards network performance. We decompose the joint performance optimization problem into multiple independent Multi-Armed Bandit (MAB) problems with different reward functions. We have also built a comprehensive analytical model for the LoRa network that considers path loss, quasi-orthogonality of spreading factor, and packet collision. Experimental results show that our scheme can increase packet delivery rate by up to 28.8% and demonstrates superior adaptability across different performance metrics.
Paper Structure (17 sections, 28 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 17 sections, 28 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Network Model
  4. Packet Collision Model
  5. Packet Propagation Model
  6. Energy Consumption Model
  7. Optimal Problem Formulation
  8. Distributed Parameter Adaptation Algorithm
  9. D-LoRa Design
  10. Rewards design
  11. Complexity, Regret and Overhead Analysis
  12. Training for Agents
  13. Scheme Performance
  14. Experiment Setting
  15. Baseline Algorithms
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Framework of the D-LoRa algorithm
  • Figure 2: Environment
  • Figure 3: Network PDR
  • Figure 4: Network EE
  • Figure 5: Network TH