Elite Gradient Descent Optimization of Antenna Parameters Constrained by Radio Coverage in Green Cellular Networks

Liu, Yaxi; Huangfu, Wei; Zhang, Haijun; Long, Keping

doi:10.1109/globalsip.2018.8646463

Cited by 1 publication

(1 citation statement)

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“…Therefore, this method realized the online parameter optimization of the system containing random factors but it is not ideal in a multi-parameter simulation system. To tackle this problem, a Gradient Descent-based algorithm [21] with a penalty method was proposed. It converted an optimization problem constrained by the coverage condition into a simple form with only lower and upper bound conditions, and thus a discrete coverage index was converted into a continuous coverage index.…”

Section: Introductionmentioning

confidence: 99%

DRESIA: Deep Reinforcement Learning-Enabled Gray Box Approach for Large-Scale Dynamic Cyber-Twin System Simulation

Lin

Yang

et al. 2021

IEEE Open J. Comput. Soc.

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The massive data generated by large-scale dynamic systems makes its optimization facing a tough challenge. Traditional White Box-based methods directly model the internal operating mechanism of the system, so massive amounts of measured data need to be handled, which is costly and time-consuming. The poor interpretability of the Black Box-based methods makes it difficult to adapt to the dynamic environment. Thus we propose a novel Gray Box-based approach namely Deep Reinforcement Learning-enabled Constraint Set Inversion Algorithm (DRESIA), which establishes a quantitative model of the nonlinear interoperability effects of system internal states which simplifies the White Box's complex mechanism of reconstruction and prediction and retains the interpretability of the model, therefore improves the prediction efficiency of feasible region while also improving the generalization ability. It further improves the dynamic adaptability of the modeling environment, which provides a new performance balancing scheme for system modeling. Under the premise that the large-scale 5G Cyber-Twin system satisfies the given Quality of Service(QoS) requirements, we perform DRESIA to realize the efficient and dynamic optimal search of feasible region, the results show that the DRESIA reduces the computational cost, and balances the accuracy and robustness of the feasible region, which validate the effectiveness and superiority of Gray Box-based approach.

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Section: Introductionmentioning

confidence: 99%