The field of mobile network research is moving towards more efficient and adaptive optimization techniques, leveraging advancements in machine learning and artificial intelligence. Recent studies have focused on developing innovative algorithms and frameworks to improve network performance, enhance resource allocation, and reduce energy consumption. Notable trends include the integration of reinforcement learning, graph neural networks, and multi-agent systems to tackle complex network management challenges. These approaches have shown promising results in improving network efficiency, reducing interference, and optimizing resource utilization. Notable papers include: HEAT, which proposes a history-enhanced dual-phase actor-critic algorithm with a shared transformer to improve network performance in LoRaWAN networks. Decentralized Handover Parameter Optimization with MARL, which jointly models the mutual influence of cell handover types and proposes a multi-agent-reinforcement-learning-based scheme to automatically optimize parameters for load balancing in 5G networks.
Advances in Mobile Network Optimization and Resource Allocation
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HEAT:History-Enhanced Dual-phase Actor-Critic Algorithm with A Shared Transformer
Decentralized Handover Parameter Optimization with MARL for Load Balancing in 5G Networks
Deep Learning on Graphs for Mobile Network Topology Generation
5Guard: Isolation-aware End-to-End Slicing of 5G Networks
Uncovering Issues in the Radio Access Network by Looking at the Neighbors
Proactive Radio Resource Allocation for 6G In-Factory Subnetworks
Interference-Aware PMI selection for MIMO systems in an O-RAN scenario
PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning
Optimal Repurchasing Contract Design for Efficient Utilization of Computing Resources
An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective
RRC Signaling Storm Detection in O-RAN
Evaluating energy inefficiency in energy-poor households in India: A frontier analysis approach
An All-Optical Metro Network Architecture and QoS-Aware Wavelength Allocation Study for Converged Fixed, Mobile, and Edge Computing Multi-Granular Traffic
Mitigating xApp conflicts for efficient network slicing in 6G O-RAN: a graph convolutional-based attention network approach