Oct 1, 2023 · In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence.
Dec 29, 2022 · Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to- ...
Notably, the rapid growth of M2M traffic dramatically increases energy consumption, as well as degrades the performance of existing Human-to-Human (H2H) traffic ...
A multi-agent deep reinforcement learning (DRL) based scheme is developed that provides optimal spectrum, transmit power and power splitting (PS) ratio ...
Multi-Agent Deep Reinforcement Learning Based Resource ...
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In this paper, we investigate a resource management problem in cellular networks with H2H/M2M coexistence. First, considering the energy-constrained nature of ...
Bibliographic details on Multi-Agent Deep Reinforcement Learning Based Resource Management in SWIPT Enabled Cellular Networks with H2H/M2M Co-Existence.
Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence · Author Picture Xuehua Li, ...
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Multi-Agent Deep Reinforcement Learning Based Resource Management in SWIPT Enabled Cellular Networks with H2H/M2M Co-Existence · Engineering, Computer Science.
Multi-Agent Deep Reinforcement Learning Based Resource Management in SWIPT Enabled Cellular Networks with H2H/M2M Co-Existence. Preprint. Dec 2022. Xuehua Li ...
Multi-agent deep reinforcement learning based resource management in SWIPT enabled cellular networks with H2H/M2M co-existence · Xuehua Li · Xing Wei · Shuo Chen.