Academic Journal
Proactive Handover Decision for UAVs with Deep Reinforcement Learning
| العنوان: | Proactive Handover Decision for UAVs with Deep Reinforcement Learning |
|---|---|
| المؤلفون: | Younghoon Jang, Syed M. Raza, Moonseong Kim, Hyunseung Choo |
| المصدر: | Sensors, Vol 22, Iss 3, p 1200 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Chemical technology |
| مصطلحات موضوعية: | Unmanned Aerial Vehicles (UAV), Deep Reinforcement Learning (DRL), Proximal Policy Optimization (PPO), handover decision, mobility management, Chemical technology, TP1-1185 |
| الوصف: | The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 22031200 1424-8220 |
| Relation: | https://www.mdpi.com/1424-8220/22/3/1200; https://doaj.org/toc/1424-8220 |
| DOI: | 10.3390/s22031200 |
| URL الوصول: | https://doaj.org/article/7f733db47bac4460b0494cf29fffd7ef |
| رقم الانضمام: | edsdoj.7f733db47bac4460b0494cf29fffd7ef |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22031200 14248220 |
|---|---|
| DOI: | 10.3390/s22031200 |