Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910798407794688 |
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| author | Sarathchandra, Sankani Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Mikhaylov, Konstantin Alouini, Mohamed-Slim |
| author_facet | Sarathchandra, Sankani Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Mikhaylov, Konstantin Alouini, Mohamed-Slim |
| contents | Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks Sarathchandra, Sankani Eldeeb, Eslam Shehab, Mohammad Alves, Hirley Mikhaylov, Konstantin Alouini, Mohamed-Slim Machine Learning Artificial Intelligence Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall. |
| title | Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.14603 |