Joint Optimization of Networking and Computing Resources for Green M2M Communications Based on DRL
作者： Meng Li , Le Yang , F. Richard Yu , Wenjun Wu , Zhuwei Wang
DOI: 10.1109/GLOBECOM38437.2019.9013366
关键词: Efficient energy use 、 Data transmission 、 Computer network 、 Computer science 、 Server 、 Reinforcement learning 、 Access network 、 Mobile edge computing 、 Energy consumption
摘要: Recent advances in Internet of Things (IoT) provide plenty opportunities for various areas. Nevertheless, the machine-to-machine (M2M) communications-based IoT develops rapidly but suffers from extra energy consumption, large data transmission latency as well overmuch network cost, because machine-type communication devices (MTCDs) are deployed network. To meet requirements efficient M2M communications, this paper, we introduce a promising technology named mobile edge computing (MEC), and propose performance optimization framework with MEC communications based on deep reinforcement learning (DRL). According to dynamic decision process by DRL, appropriate access networks servers can be determined selected minimum system which includes lower time cost consumption tasks execution. Extensive simulation results different parameters show that our proposed effectively improve compared existing schemes.
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