IEEE Network · 2022 · 133 citations · 13 references
ComputingComputer ScienceComputer SystemsArtificial IntelligenceMachine LearningMobile ComputingCognitive ArchitectureDeep LearningMobile NetworkingMobile NetworksNetwork DynamicsNeural NetworksAction Space FormulationArtificial Intelligence In Self-serviceCognitive Control ArchitectureComplexityIntelligent ControlMachine IntelligenceSelf-configuring NetworksSystem Dynamics Simulation
Mobile networks promise unprecedented connectivity but their growing complexity creates deployment and management challenges that require full cognition of network state, a role AI is well positioned to fill. The article proposes an AI‑powered mobile network architecture to address cognition, high‑dimensional decision spaces, and self‑adaptation to dynamic system behavior. The authors present a deep‑learning framework that maps network state directly to perceived QoS, integrating cognition with decision making. The approach enables operators to make smarter decisions that guarantee QoS, as shown by improved performance on a real‑world dataset of 31,261 users across 77 stations over five days.
Mobile networks (MNs) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MNs are becoming more and more complex, driven by ever increasing complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require complete understanding and cognition of MNs. Artificial intelligence (AI), which deals with the simulation of intelligent behavior in computers, has demonstrated enormous success in many application domains, suggesting its potential in cognizing the state of an MN and making intelligent decisions. In this article, we first propose an AI-powered MN architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaptation to system dynamics. Then potential solutions associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of an MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators to make more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset involving 31,261 users over 77 stations within 5 days.
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Engineering Radio Maps for Wireless Resource Management
Suzhi Bi, Jiangbin Lyu, Zhi Ding et al. · IEEE Wireless Communications · 2019 · 198 citations