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- W4375844984 abstract "In this paper, the ambient intelligence exploration multi-delay deep deterministic policy gradient-based artificial rabbits optimization (AEMDPG-ARO) algorithm is proposed for resolving data rate and the issues of latency in the small base station (SBS) and macro base station (MBS) of the wireless sensor network. The complexity in attaining lower latency and higher data rate is achieved through a novel technique AEMDPG-ARO. The ambient intelligence exploration multi-delay (AIEM) is combined with deep deterministic policy gradient (DDPG) for overcoming the local optimum and diversity issues of DDPG. The data sample for this study is obtained through the WINNER channel model. The proposed AEMDPG-ARO algorithm's efficiency is compared to varied state of art methods." @default.
- W4375844984 created "2023-05-10" @default.
- W4375844984 date "2023-06-01" @default.
- W4375844984 modified "2023-10-16" @default.
- W4375844984 doi "https://doi.org/10.1002/dac.v36.9" @default.
- W4375844984 hasPublicationYear "2023" @default.
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