Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323646074> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W4323646074 abstract "In this paper, the influence of Deep Neural Network (DNN) in predicting both the channel parameters and the power factors for users in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In channel prediction based Deep Learning (DL) approach, we integrate the Long Short Term Memory (LSTM) learning network into NOMA system in order that LSTM can be utilized to predict the channel coefficients. In addition, in Deep Learning based power estimation method, we introduce an algorithm based on Convolutional Neural Network (CNN) to predict and allocate the power factor for each user in MISO-NOMA cell. DNN is trained online using channel statistics in order to approximate the channel coefficients and allocate the power factors for each user, so that these parameters can be utilized by the receiver to recover the desired data. Besides, this paper demonstrates the framework where channel prediction based on LSTM layer and power approximation based on CNN can be jointly employed for multiuser detection in MISO-NOMA. In this work, Power factors are optimized analytically based on maximizing the sum-rate of users to derive the optimum power factors. Simulation outcomes for distinct metrics have verified the dominance of the channel estimation and power predication based DNN over standard approaches." @default.
- W4323646074 created "2023-03-10" @default.
- W4323646074 creator A5008549625 @default.
- W4323646074 creator A5063261018 @default.
- W4323646074 creator A5091511508 @default.
- W4323646074 date "2022-10-01" @default.
- W4323646074 modified "2023-09-26" @default.
- W4323646074 title "Hybrid Deep Learning for Channel Estimation and Power Allocation for MISO-NOMA System" @default.
- W4323646074 cites W2601694911 @default.
- W4323646074 cites W2734408173 @default.
- W4323646074 cites W2736068844 @default.
- W4323646074 cites W2788702388 @default.
- W4323646074 cites W2802495557 @default.
- W4323646074 cites W2884853071 @default.
- W4323646074 cites W2886374543 @default.
- W4323646074 cites W2964121960 @default.
- W4323646074 cites W2965808107 @default.
- W4323646074 cites W3129254582 @default.
- W4323646074 cites W3144857097 @default.
- W4323646074 cites W3196101216 @default.
- W4323646074 cites W4206966098 @default.
- W4323646074 cites W4280608426 @default.
- W4323646074 cites W4286247886 @default.
- W4323646074 doi "https://doi.org/10.1109/fnwf55208.2022.00070" @default.
- W4323646074 hasPublicationYear "2022" @default.
- W4323646074 type Work @default.
- W4323646074 citedByCount "0" @default.
- W4323646074 crossrefType "proceedings-article" @default.
- W4323646074 hasAuthorship W4323646074A5008549625 @default.
- W4323646074 hasAuthorship W4323646074A5063261018 @default.
- W4323646074 hasAuthorship W4323646074A5091511508 @default.
- W4323646074 hasConcept C108583219 @default.
- W4323646074 hasConcept C11413529 @default.
- W4323646074 hasConcept C119857082 @default.
- W4323646074 hasConcept C121332964 @default.
- W4323646074 hasConcept C127162648 @default.
- W4323646074 hasConcept C138660444 @default.
- W4323646074 hasConcept C154945302 @default.
- W4323646074 hasConcept C163258240 @default.
- W4323646074 hasConcept C2775918612 @default.
- W4323646074 hasConcept C41008148 @default.
- W4323646074 hasConcept C50644808 @default.
- W4323646074 hasConcept C62520636 @default.
- W4323646074 hasConcept C76155785 @default.
- W4323646074 hasConcept C81363708 @default.
- W4323646074 hasConceptScore W4323646074C108583219 @default.
- W4323646074 hasConceptScore W4323646074C11413529 @default.
- W4323646074 hasConceptScore W4323646074C119857082 @default.
- W4323646074 hasConceptScore W4323646074C121332964 @default.
- W4323646074 hasConceptScore W4323646074C127162648 @default.
- W4323646074 hasConceptScore W4323646074C138660444 @default.
- W4323646074 hasConceptScore W4323646074C154945302 @default.
- W4323646074 hasConceptScore W4323646074C163258240 @default.
- W4323646074 hasConceptScore W4323646074C2775918612 @default.
- W4323646074 hasConceptScore W4323646074C41008148 @default.
- W4323646074 hasConceptScore W4323646074C50644808 @default.
- W4323646074 hasConceptScore W4323646074C62520636 @default.
- W4323646074 hasConceptScore W4323646074C76155785 @default.
- W4323646074 hasConceptScore W4323646074C81363708 @default.
- W4323646074 hasLocation W43236460741 @default.
- W4323646074 hasOpenAccess W4323646074 @default.
- W4323646074 hasPrimaryLocation W43236460741 @default.
- W4323646074 hasRelatedWork W2337926734 @default.
- W4323646074 hasRelatedWork W2731899572 @default.
- W4323646074 hasRelatedWork W3116150086 @default.
- W4323646074 hasRelatedWork W3133861977 @default.
- W4323646074 hasRelatedWork W4200173597 @default.
- W4323646074 hasRelatedWork W4311257506 @default.
- W4323646074 hasRelatedWork W4312417841 @default.
- W4323646074 hasRelatedWork W4320802194 @default.
- W4323646074 hasRelatedWork W4321369474 @default.
- W4323646074 hasRelatedWork W4366224123 @default.
- W4323646074 isParatext "false" @default.
- W4323646074 isRetracted "false" @default.
- W4323646074 workType "article" @default.