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Cited by 3 publications
(4 citation statements)
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References 43 publications
(49 reference statements)
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“…Differences in carbon footprint measurements Miozzo et al (2021) found the emissions computed by Green algorithms to be higher than those computed by ML CO2 Impact. This is explained by the details of the functional criteria reported in table 1, which show that key elements such as carbon intensity values or PUE differ across the tools.…”
Section: Discussionmentioning
confidence: 85%
“…As pointed out by the authors in [21], most of the literature in the field of NMS are focused on single-task learning, e.g., each model is designed and trained to solve one specific learning task such as TC, traffic prediction, or anomaly detection. As a solution, Multi-Task Learning (MTL) approaches have been proposed in [21] and [22], where TC is used as one of the learning tasks, to leverage useful information contained in multiple related tasks aiming to improve the generalization capabilities of all them while learning. The authors in [21] used a MTL approach to jointly solve the TC and traffic Their proposal is a two step procedure where they first use an AE to extract common feature representations among tasks and then use the encoder part of it to train a traffic classifier (softmax layer with a softmax activation function) and a traffic predictor (a dense layer with the Rectified Linear Unit (ReLU) activation function) simultaneously.…”
Section: A Tc Using L2 (And Above) Classification Objectsmentioning
confidence: 99%
“…A preliminary and partial version of this work was presented in [16], and [17]. In [16], we focused only on the single task HO management use case, and used an LSTM RNN to solve the regression problem and estimate the QoE of the users.…”
Section: Introductionmentioning
confidence: 99%
“…This paper further extends our research horizon and includes the second use case of the initial MCS. Moreover, in our previous work [17], we focused on studying the advantages and disadvantages of a decentralized or a centralized ML solution, emphasising the energy aspect. Differently, in this work, we build another architecture where the two RAN tasks share and train a common AE, based on the MTL principles.…”
Section: Introductionmentioning
confidence: 99%