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GenNet: A Generative AI-Driven Mobile Network Simulator for Multi-Objective Network Optimization With RL
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AI
DOI：10.1109/TMC.2025.3637214.png)
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Abstract
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Simulation-based optimization has emerged as a crucial methodology in the field of mobile network optimization, addressing the need for dynamic and predictive network management. To address the scarcity of open-source mobile network simulators for advanced research, we developed GenNet—a generative AI-driven mobile network simulator. GenNet can create virtual replicas of mobile users, base stations, and wireless environments, utilizing generative AI methods to simulate the behaviors of these entities under various network settings with high accuracy. GenNet features a tailor-made API explicitly designed for reinforcement learning environments, enabling researchers to finely adjust network parameters such as tilts, azimuth, and transmitting power. Extensive experiments have utilized GenNet to benchmark multi-objective optimization algorithms, with a focus on enhancing network coverage, throughput, and energy efficiency, thereby validating its effectiveness as a robust platform for advancing network optimization techniques. Through this innovative tool, we aim to empower researchers and practitioners to identify and implement the most effective approaches for network optimization, paving the way for future advancements in mobile network management.
Keywords:
Mobile network simulator
generative AI
multi-objective optimization
reinforcement learning
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