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Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2922706
Abstract: Estimation bias seriously affects the performance of reinforcement learning algorithms. The maximum operation may result in overestimation, while the double estimator operation often leads to underestimation. To eliminate the estimation bias, these two operations are…
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Keywords:
estimation bias;
double estimator;
stochastic double;
double deep ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3179720
Abstract: Anomaly detection in smart environments is important when dealing with rare events, which can be safety-critical to individuals or infrastructure. Safety-critical means in this case, that these events can be a threat to the safety…
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Keywords:
detection smart;
detection;
double deep;
anomaly detection ... See more keywords
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Published in 2020 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2020.3022611
Abstract: Mobile crowdsensing (MCS) is a new and promising paradigm of data collection due to the growing number of mobile smart devices. It can be utilized in applications of large-scale sensing by employing a group of…
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Keywords:
tasks mobile;
sensing tasks;
double deep;
mobile users ... See more keywords
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3
Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2023.3238073
Abstract: This letter investigates machine learning approach for the joint optimal phase shift and beamforming in the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) network, consisting of one source node, one RIS panel and…
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Keywords:
phase shift;
shift beamforming;
cascaded channels;
mimo ... See more keywords