LoRaDRL — Deep RL for LoRaWAN Parameter Selection
Overview
A deep reinforcement learning algorithm (DDQN) that selects PHY-layer transmission parameters in LoRaWAN networks. Existing rule-based algorithms cause packet collisions in dense LPWAN deployments; this DRL approach reduces collisions and improves Packet Delivery Ratio by up to 500% in some scenarios. Published at IEEE LCN 2020 with a follow-up paper. Outcome: Demonstrated up to 500% PDR improvement over state-of-the-art LoRaWAN parameter selection, with peer-reviewed publications in IEEE venues.
Architecture & Pipeline
flowchart LR
n0["LoRaWAN Sim EnvCustom Python simulator"]
n1["Network StatePHY-layer conditions"]
n2["DDQN AgentTensorFlow"]
n3["ActionPHY parameters"]
n4["PDR RewardUp to 500% improvement"]
n5["Updated PolicyProduction parameter selection"]
n0 --> n1
n1 --> n2
n2 --> n3
n3 --> n4
n4 --> n5
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step2 fill:#f0fdfa,stroke:#0d9488,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step2;
class n3 step2;
class n4 step3;
class n5 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- Double Deep Q-Network (DDQN) algorithm
- Custom Python simulation environment for LoRaWAN
- Validated against state-of-the-art baselines
- Two peer-reviewed IEEE publications
- Tech Stack:** Python, TensorFlow