Employing Machine Learning Approach in Cavity Resonator Sensors for Characterization of Lossy Dielectrics

• DOI: 10.52547/ijict.13.3.1
• Publication Date: 2022-01-31T16:16:01Z
• Authors: Kianoosh Kazemi; ; Gholamreza Moradi
• Keywords: Lossy compression; Characterization; Dielectric resonator

In this paper, we study the spectral efficiency (SE) and energy (EE) of wireless-powered full-duplex (FD) heterogeneous networks (HetNets).In particular, consider a two-tire HetNet with half duplex (HD) massive multiple-input multiple-output (MIMO) macrocell base stations (MBSs), FD small cell (SBSs) user equipments (UEs).UEs rely on harvesting (EH) from radio frequency signals to charge their batteries for communication serving stations.During phase, UEs are associated MBSs/SBSs based mean maximum received power (MMP) scheme.In consecutive data transmission each UE downloads packets same MBSs/SBSs, while uploads nearest SBSs using harvested energy.We use tools stochastic geometry develop an analytical framework average UL transfer DL coverage probability analysis.We further investigate EE proposed DUDe scheme demonstrate impact different system parameters EE.Finally, validate results through simulation discuss significance association improve SE in HetNets.