FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3216409906> ?p ?o ?g. }

• W3216409906 endingPage "11674" @default.
• W3216409906 startingPage "11656" @default.
• W3216409906 abstract "We propose a framework that enables the cluster head (CH) to harvest energy from uplink transmission by Internet of Things (IoT) nodes employing data compression under nonorthogonal multiple access (NOMA) scheme. Our framework enables the CH to maximize the harvested energy while meeting constraints on outage probability, consumed energies by the transmitting IoT nodes and compression and distortion ratios. We provide necessary analysis for our framework and derive an expression for the outage probability and average harvested energy under the NOMA scheme. We formulate an optimization problem with NOMA factors, simultaneous wireless information and power transfer (SWIPT) factors, and NOMA user distances as optimization parameters. We first solve the optimization problem and find the optimized values using a grid-based search. Then, we exploit a deep reinforcement learning algorithm to solve the optimization problem more efficiently. Throughout this work, we prove the feasibility of such framework and deliver key observation that will help the CH scheduling different IoT nodes such that the harvested energy is maximized while the constraints are met." @default.
• W3216409906 created "2021-12-06" @default.
• W3216409906 creator A5021329808 @default.
• W3216409906 creator A5024641789 @default.
• W3216409906 creator A5049937219 @default.
• W3216409906 creator A5069084074 @default.
• W3216409906 creator A5079712565 @default.
• W3216409906 date "2022-07-15" @default.
• W3216409906 modified "2023-10-11" @default.
• W3216409906 title "A Deep Reinforcement Learning Framework for Data Compression in Uplink NOMA-SWIPT Systems" @default.
• W3216409906 cites W1523522871 @default.
• W3216409906 cites W1926815826 @default.
• W3216409906 cites W1958281780 @default.
• W3216409906 cites W1968612158 @default.
• W3216409906 cites W1994934077 @default.
• W3216409906 cites W1997789214 @default.
• W3216409906 cites W2009887633 @default.
• W3216409906 cites W2057161796 @default.
• W3216409906 cites W2076207195 @default.
• W3216409906 cites W2096413202 @default.
• W3216409906 cites W2132870761 @default.
• W3216409906 cites W2274490253 @default.
• W3216409906 cites W2549725021 @default.
• W3216409906 cites W2567302839 @default.
• W3216409906 cites W2761780303 @default.
• W3216409906 cites W2763537990 @default.
• W3216409906 cites W2773197923 @default.
• W3216409906 cites W2799335795 @default.
• W3216409906 cites W2885556790 @default.
• W3216409906 cites W2904649481 @default.
• W3216409906 cites W2912895064 @default.
• W3216409906 cites W2914440067 @default.
• W3216409906 cites W2916732817 @default.
• W3216409906 cites W2916854434 @default.
• W3216409906 cites W2943930927 @default.
• W3216409906 cites W2963336322 @default.
• W3216409906 cites W2996573650 @default.
• W3216409906 cites W2997741285 @default.
• W3216409906 cites W3000290725 @default.
• W3216409906 cites W3004968144 @default.
• W3216409906 cites W3016981818 @default.
• W3216409906 cites W3022265915 @default.
• W3216409906 cites W3026197293 @default.
• W3216409906 cites W3027644164 @default.
• W3216409906 cites W3033348916 @default.
• W3216409906 cites W3039640152 @default.
• W3216409906 cites W3044485544 @default.
• W3216409906 cites W3044669249 @default.
• W3216409906 cites W3045984436 @default.
• W3216409906 cites W3065608242 @default.
• W3216409906 cites W3091075746 @default.
• W3216409906 cites W3099378150 @default.
• W3216409906 cites W3101450547 @default.
• W3216409906 cites W3101650196 @default.
• W3216409906 cites W3165009935 @default.
• W3216409906 cites W4250589301 @default.
• W3216409906 doi "https://doi.org/10.1109/jiot.2021.3131524" @default.
• W3216409906 hasPublicationYear "2022" @default.
• W3216409906 type Work @default.
• W3216409906 sameAs 3216409906 @default.
• W3216409906 citedByCount "3" @default.
• W3216409906 countsByYear W32164099062023 @default.
• W3216409906 crossrefType "journal-article" @default.
• W3216409906 hasAuthorship W3216409906A5021329808 @default.
• W3216409906 hasAuthorship W3216409906A5024641789 @default.
• W3216409906 hasAuthorship W3216409906A5049937219 @default.
• W3216409906 hasAuthorship W3216409906A5069084074 @default.
• W3216409906 hasAuthorship W3216409906A5079712565 @default.
• W3216409906 hasConcept C11413529 @default.
• W3216409906 hasConcept C120314980 @default.
• W3216409906 hasConcept C121332964 @default.
• W3216409906 hasConcept C126255220 @default.
• W3216409906 hasConcept C137836250 @default.
• W3216409906 hasConcept C138660444 @default.
• W3216409906 hasConcept C154945302 @default.
• W3216409906 hasConcept C163258240 @default.
• W3216409906 hasConcept C206729178 @default.
• W3216409906 hasConcept C2775918612 @default.
• W3216409906 hasConcept C31258907 @default.
• W3216409906 hasConcept C33923547 @default.
• W3216409906 hasConcept C41008148 @default.
• W3216409906 hasConcept C555944384 @default.
• W3216409906 hasConcept C62520636 @default.
• W3216409906 hasConcept C67186554 @default.
• W3216409906 hasConcept C76155785 @default.
• W3216409906 hasConcept C97541855 @default.
• W3216409906 hasConceptScore W3216409906C11413529 @default.
• W3216409906 hasConceptScore W3216409906C120314980 @default.
• W3216409906 hasConceptScore W3216409906C121332964 @default.
• W3216409906 hasConceptScore W3216409906C126255220 @default.
• W3216409906 hasConceptScore W3216409906C137836250 @default.
• W3216409906 hasConceptScore W3216409906C138660444 @default.
• W3216409906 hasConceptScore W3216409906C154945302 @default.
• W3216409906 hasConceptScore W3216409906C163258240 @default.
• W3216409906 hasConceptScore W3216409906C206729178 @default.
• W3216409906 hasConceptScore W3216409906C2775918612 @default.
• W3216409906 hasConceptScore W3216409906C31258907 @default.
• W3216409906 hasConceptScore W3216409906C33923547 @default.

• next