Matches in SemOpenAlex for { <https://semopenalex.org/work/W3131021570> ?p ?o ?g. }
Showing items 1 to 53 of
53
with 100 items per page.
- W3131021570 abstract "We consider the study of received signal strength indication (RSSI) prediction in an indoor room environment using a small set of actual measurement data. The RSSI prediction in a test environment is important in a network planning strategy. Traditional models are based on either empirical or deterministic models, which are time-consuming depending on many factors such as the room structure, obstacles, and many more. In this paper, we investigate any simple machine learning model like an artificial neural network (ANN) or linear regression model that can do this RSSI prediction and the estimation of environmental-related parameters that affect the prediction of RSSI. We assume that some parameters like transmit power, antenna height, wall material properties are kept fixed. We illustrate the RSSI prediction performance in terms of mean squared error (MSE) and mean absolute error (MAE) for a dataset with 1030 data points collected from the test environment. The path loss exponent of our test environment is estimated as 1.97." @default.
- W3131021570 created "2021-03-01" @default.
- W3131021570 creator A5073889458 @default.
- W3131021570 date "2021-01-05" @default.
- W3131021570 modified "2023-09-23" @default.
- W3131021570 title "Indoor RSSI Prediction using Machine Learning for Wireless Networks" @default.
- W3131021570 cites W2096032932 @default.
- W3131021570 cites W2099878889 @default.
- W3131021570 cites W2616222121 @default.
- W3131021570 cites W2917609517 @default.
- W3131021570 cites W2978631110 @default.
- W3131021570 cites W2986489130 @default.
- W3131021570 cites W595252221 @default.
- W3131021570 doi "https://doi.org/10.1109/comsnets51098.2021.9352852" @default.
- W3131021570 hasPublicationYear "2021" @default.
- W3131021570 type Work @default.
- W3131021570 sameAs 3131021570 @default.
- W3131021570 citedByCount "3" @default.
- W3131021570 countsByYear W31310215702021 @default.
- W3131021570 countsByYear W31310215702022 @default.
- W3131021570 crossrefType "proceedings-article" @default.
- W3131021570 hasAuthorship W3131021570A5073889458 @default.
- W3131021570 hasConcept C108037233 @default.
- W3131021570 hasConcept C119857082 @default.
- W3131021570 hasConcept C154945302 @default.
- W3131021570 hasConcept C31258907 @default.
- W3131021570 hasConcept C41008148 @default.
- W3131021570 hasConcept C555944384 @default.
- W3131021570 hasConcept C76155785 @default.
- W3131021570 hasConceptScore W3131021570C108037233 @default.
- W3131021570 hasConceptScore W3131021570C119857082 @default.
- W3131021570 hasConceptScore W3131021570C154945302 @default.
- W3131021570 hasConceptScore W3131021570C31258907 @default.
- W3131021570 hasConceptScore W3131021570C41008148 @default.
- W3131021570 hasConceptScore W3131021570C555944384 @default.
- W3131021570 hasConceptScore W3131021570C76155785 @default.
- W3131021570 hasLocation W31310215701 @default.
- W3131021570 hasOpenAccess W3131021570 @default.
- W3131021570 hasPrimaryLocation W31310215701 @default.
- W3131021570 hasRelatedWork W1908608100 @default.
- W3131021570 hasRelatedWork W1938339660 @default.
- W3131021570 hasRelatedWork W1969993354 @default.
- W3131021570 hasRelatedWork W2061273503 @default.
- W3131021570 hasRelatedWork W2123174207 @default.
- W3131021570 hasRelatedWork W2295679373 @default.
- W3131021570 hasRelatedWork W2512844215 @default.
- W3131021570 hasRelatedWork W2537178846 @default.
- W3131021570 hasRelatedWork W2601964555 @default.
- W3131021570 hasRelatedWork W2607247987 @default.
- W3131021570 isParatext "false" @default.
- W3131021570 isRetracted "false" @default.
- W3131021570 magId "3131021570" @default.
- W3131021570 workType "article" @default.