Abstract:
The utilization of unmanned aerial vehicles (UAVs) in internet of things (IoT) communications has gained significant attention in recent years due to their ability to ada...Show MoreMetadata
Abstract:
The utilization of unmanned aerial vehicles (UAVs) in internet of things (IoT) communications has gained significant attention in recent years due to their ability to adapt to different positions and access areas with limited infrastructure. To support a large number of IoT devices, UAV networks have extensively integrated non-orthogonal multiple access (NOMA) technology. In this study, we propose an innovative Deep Reinforcement Learning (DRL) agent based on Proximal Policy Optimization (PPO) to improve the Energy Efficiency (EE) while considering far-near fairness (FNF) in NOMA-UAV networks. This agent is designed to simultaneously control dynamic factors such as UAV 3D trajectory, downlink transmit power, IoT nodes association, and power allocation (PA). We compare our solution with the Hybrid Decision Framework (HDF) approach through comprehensive simulations. The results clearly demonstrate the superiority of our proposed scheme over HDF in terms of effectiveness.
Published in: IEEE Communications Letters ( Volume: 28, Issue: 5, May 2024)
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- IEEE Keywords
- Index Terms
- NOMA-UAV Networks ,
- Multi-agent ,
- Internet Of Things ,
- Unmanned Aerial Vehicles ,
- Internet Of Things Devices ,
- Deep Reinforcement Learning ,
- Non-orthogonal Multiple Access ,
- Reinforcement Learning Agent ,
- Proximal Policy Optimization ,
- Internet Of Things Nodes ,
- Unmanned Aerial Vehicles Networks ,
- Deep Reinforcement Learning Agent ,
- Optimization Problem ,
- Objective Function ,
- Learning Rate ,
- Service Quality ,
- Reward Function ,
- Trajectory Optimization ,
- Fair Distribution ,
- Dynamic Optimization ,
- Quality Of Service Requirements ,
- Flight Period ,
- Nearest Node ,
- Successive Interference Cancellation ,
- Unmanned Aerial Vehicles Communication ,
- Energy Efficiency Performance ,
- Reinforcement Learning Framework
- Author Keywords
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- NOMA-UAV Networks ,
- Multi-agent ,
- Internet Of Things ,
- Unmanned Aerial Vehicles ,
- Internet Of Things Devices ,
- Deep Reinforcement Learning ,
- Non-orthogonal Multiple Access ,
- Reinforcement Learning Agent ,
- Proximal Policy Optimization ,
- Internet Of Things Nodes ,
- Unmanned Aerial Vehicles Networks ,
- Deep Reinforcement Learning Agent ,
- Optimization Problem ,
- Objective Function ,
- Learning Rate ,
- Service Quality ,
- Reward Function ,
- Trajectory Optimization ,
- Fair Distribution ,
- Dynamic Optimization ,
- Quality Of Service Requirements ,
- Flight Period ,
- Nearest Node ,
- Successive Interference Cancellation ,
- Unmanned Aerial Vehicles Communication ,
- Energy Efficiency Performance ,
- Reinforcement Learning Framework
- Author Keywords
