Bounded learned step sizes in FastFP plus a learned priority order for greedy RBG scheduling improve the WSR/runtime trade-off in multi-cell MIMO, but the stationary-point proof has a gap.
Deep reinforcement learning for radio resource allocation and man- agement in next generation heterogeneous wireless networks: A sur- vey
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AI and terahertz networks form a mutual symbiosis where each addresses the limitations of the other across hardware, physical layer, protocols, and services.
A survey compiling DM-enabled DRL algorithms and applications across computation offloading, UAV systems, resource allocation, security, and robotics in wireless networks.
A survey of Transformer-enhanced reinforcement learning fundamentals and applications in communication networks covering resource allocation, computation offloading, routing, trajectory control, and security.
citing papers explorer
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Bounded Deep Unfolding for Joint Beamforming and Scheduling in Multi-Cell MIMO Networks
Bounded learned step sizes in FastFP plus a learned priority order for greedy RBG scheduling improve the WSR/runtime trade-off in multi-cell MIMO, but the stationary-point proof has a gap.
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When AI Meets Terahertz: A Survey on the Symbiosis of Artificial Intelligence and Terahertz Networks
AI and terahertz networks form a mutual symbiosis where each addresses the limitations of the other across hardware, physical layer, protocols, and services.
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From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks
A survey compiling DM-enabled DRL algorithms and applications across computation offloading, UAV systems, resource allocation, security, and robotics in wireless networks.
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Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks
A survey of Transformer-enhanced reinforcement learning fundamentals and applications in communication networks covering resource allocation, computation offloading, routing, trajectory control, and security.