Resource Allocation
Resource allocation research focuses on optimizing the distribution of limited resources—computational power, bandwidth, energy—to maximize efficiency and fairness across diverse applications. Current research emphasizes developing sophisticated algorithms, including deep reinforcement learning, graph neural networks, and dynamic programming, to address complex, real-time resource allocation problems in areas like federated learning, edge computing, and wireless networks. These advancements are crucial for improving the performance and scalability of various systems, from mobile communications to large-scale AI training, and for ensuring equitable access to resources.
Papers
Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications
Christo Kurisummoottil Thomas, Walid Saad
Multi-UAV Enabled MEC Networks: Optimizing Delay through Intelligent 3D Trajectory Planning and Resource Allocation
Zhiying Wang, Tianxi Wei, Gang Sun, Xinyue Liu, Hongfang Yu, Dusit Niyato
DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
Xueying Gu, Qiong Wu, Pingyi Fan, Qiang Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning
Rung-Hung Gau, Ting-Yu Wang, Chun-Hung Liu