Work

Age and Power Minimization via Meta-Deep Reinforcement Learning in AAV Networks

Year: 2025

Type: article

Abstract: 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's 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.

Source: IEEE Transactions on Vehicular Technology

DOI: 10.1109/tvt.2025.3579626

Authors: Sankani Sarathchandra, Eslam Eldeeb, Mohammad Shehab, Hirley Alves, Konstantin Mikhaylov, Mohamed-Slim Alouini

Institutions: University of Oulu, King Abdullah University of Science and Technology

Language: English

Topic: Age of Information Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,645