Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048558169> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3048558169 endingPage "148540" @default.
- W3048558169 startingPage "148528" @default.
- W3048558169 abstract "Intelligent capabilities are of utmost importance in future wireless communication systems. For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems. We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios. Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution. We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain. The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed. From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice." @default.
- W3048558169 created "2020-08-18" @default.
- W3048558169 creator A5015171644 @default.
- W3048558169 creator A5046282905 @default.
- W3048558169 creator A5085031765 @default.
- W3048558169 date "2020-01-01" @default.
- W3048558169 modified "2023-09-24" @default.
- W3048558169 title "Robust Deep Radio Frequency Spectrum Learning for Future Wireless Communications Systems" @default.
- W3048558169 cites W1972187084 @default.
- W3048558169 cites W2005956500 @default.
- W3048558169 cites W2017795724 @default.
- W3048558169 cites W2091005538 @default.
- W3048558169 cites W2093289735 @default.
- W3048558169 cites W2104094955 @default.
- W3048558169 cites W2111085587 @default.
- W3048558169 cites W2272847350 @default.
- W3048558169 cites W2329664094 @default.
- W3048558169 cites W2586243172 @default.
- W3048558169 cites W2591951844 @default.
- W3048558169 cites W2599116359 @default.
- W3048558169 cites W2614217642 @default.
- W3048558169 cites W2755424905 @default.
- W3048558169 cites W2773170971 @default.
- W3048558169 cites W2775383661 @default.
- W3048558169 cites W2791256362 @default.
- W3048558169 cites W2791279818 @default.
- W3048558169 cites W2893903145 @default.
- W3048558169 cites W2907410281 @default.
- W3048558169 cites W2939757977 @default.
- W3048558169 cites W2962970834 @default.
- W3048558169 cites W2963980515 @default.
- W3048558169 cites W2968197850 @default.
- W3048558169 cites W2980245908 @default.
- W3048558169 cites W2984025449 @default.
- W3048558169 cites W2994978237 @default.
- W3048558169 cites W2995715529 @default.
- W3048558169 cites W3005252141 @default.
- W3048558169 cites W3009850456 @default.
- W3048558169 cites W3010867338 @default.
- W3048558169 cites W3011894868 @default.
- W3048558169 cites W3011975373 @default.
- W3048558169 cites W3027109963 @default.
- W3048558169 cites W3103073819 @default.
- W3048558169 cites W3104028856 @default.
- W3048558169 cites W3104668471 @default.
- W3048558169 doi "https://doi.org/10.1109/access.2020.3015939" @default.
- W3048558169 hasPublicationYear "2020" @default.
- W3048558169 type Work @default.
- W3048558169 sameAs 3048558169 @default.
- W3048558169 citedByCount "5" @default.
- W3048558169 countsByYear W30485581692020 @default.
- W3048558169 countsByYear W30485581692021 @default.
- W3048558169 countsByYear W30485581692022 @default.
- W3048558169 countsByYear W30485581692023 @default.
- W3048558169 crossrefType "journal-article" @default.
- W3048558169 hasAuthorship W3048558169A5015171644 @default.
- W3048558169 hasAuthorship W3048558169A5046282905 @default.
- W3048558169 hasAuthorship W3048558169A5085031765 @default.
- W3048558169 hasConcept C149946192 @default.
- W3048558169 hasConcept C31258907 @default.
- W3048558169 hasConcept C41008148 @default.
- W3048558169 hasConcept C555944384 @default.
- W3048558169 hasConcept C74064498 @default.
- W3048558169 hasConcept C76155785 @default.
- W3048558169 hasConcept C92545706 @default.
- W3048558169 hasConceptScore W3048558169C149946192 @default.
- W3048558169 hasConceptScore W3048558169C31258907 @default.
- W3048558169 hasConceptScore W3048558169C41008148 @default.
- W3048558169 hasConceptScore W3048558169C555944384 @default.
- W3048558169 hasConceptScore W3048558169C74064498 @default.
- W3048558169 hasConceptScore W3048558169C76155785 @default.
- W3048558169 hasConceptScore W3048558169C92545706 @default.
- W3048558169 hasFunder F4320332923 @default.
- W3048558169 hasLocation W30485581691 @default.
- W3048558169 hasOpenAccess W3048558169 @default.
- W3048558169 hasPrimaryLocation W30485581691 @default.
- W3048558169 hasRelatedWork W1978816788 @default.
- W3048558169 hasRelatedWork W2224800885 @default.
- W3048558169 hasRelatedWork W2236567279 @default.
- W3048558169 hasRelatedWork W2279494525 @default.
- W3048558169 hasRelatedWork W2313403597 @default.
- W3048558169 hasRelatedWork W2392764151 @default.
- W3048558169 hasRelatedWork W2564217976 @default.
- W3048558169 hasRelatedWork W2613603939 @default.
- W3048558169 hasRelatedWork W2768004565 @default.
- W3048558169 hasRelatedWork W2188389332 @default.
- W3048558169 hasVolume "8" @default.
- W3048558169 isParatext "false" @default.
- W3048558169 isRetracted "false" @default.
- W3048558169 magId "3048558169" @default.
- W3048558169 workType "article" @default.