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Browsing by Author Yang, Y.

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  • Estimating Near‐Saturated Soil Hydraulic Conductivity Based on Its Scale‐Dependent Relationships with Soil Properties

    • Authors: Yang, Y.;  Advisor: -;  Participants: Wendroth, O.; Kreba, S.; Liu, B. (2019)

  • Decomposed into four intrinsic mode functions (IMFs) and a residue by NA‐MEMD, each K was found to significantly correlate with all six properties at one spatial scale at least. The variations in K were primarily regulated by soil structure, especially at the relatively small scales. Multiple linear regression (MLR) failed to regress either IMF1 or IMF2 of each K from the soil properties of the equivalent scales and only accounted for 13.7 to 43.6% of the total variance in calibration for the remaining half of the IMF1s and IMF2s. An artificial neural network was then adopted to estimate IMF1 and IMF2, and the corresponding results were added to the MLR estimates at other scales for w...
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  • Machine Learning Inspired Codeword Selection For Dual Connectivity in 5G User-Centric Ultra-Dense Networks

    • Authors: Yang, Y.;  Advisor: -;  Participants: Deng, X.; He, D.; You, Y.; Song, R. (2019)

  • In future 5G user-centric ultra-dense networks (UUDN), demands of high data rate and high spectrum efficiency are effectively met by dual connectivity (DC) technology. However, due to huge increase of base stations (BSs) and mobile users (MUs), it becomes difficult for BSs to quickly and precisely select the codeword and provide DC to MUs. Hence, different from some traditional methods, this correspondence paper aims to improve the network performance using the method of machine learning. First, we model the random distribution of BSs by homogeneous Poisson point processes, where each MU is served by millimeter-wave channel. Second, the probabilities that macro cell BS or small cell BS...