Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4560
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPanda, Bibekananda-
dc.contributor.authorSenanayake, Dhayan Dhananjaya-
dc.contributor.authorSingh, Poonam-
dc.date.accessioned2024-05-13T10:59:15Z-
dc.date.available2024-05-13T10:59:15Z-
dc.date.issued2024-05-
dc.identifier.citation3rd IEEE International Conference on Artificial Intelligence for Internet of Things (AIIoT 2024), VIT Vellore, India, 3-4 May 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4560-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractIn future generation (5G) wireless networks, nonorthogonal multiple access (NOMA) stands out for its exceptional spectrum efficiency, providing a key resolution for higher connectivity demands. A novel signal detection method employing decision tree techniques is discussed in downlink multiple input and multiple output (MIMO) with the NOMA system. Simulation results for both Rayleigh and Rician fading channels in a multiuser downlink NOMA system exhibit the viability of the proposed approach. Effective resource sharing with users based on various quality of service is made possible by the power domain NOMA, which shows enormous potential in 5G networks. Combining NOMA and MIMO technologies resolves the drawbacks of standard successive interference cancellation (SIC) complexity and latency, and also improves spectral efficiency with system capacity. A decision tree-based technique enhances reliability and efficiency in multi-user scenarios for detecting signals in downlink MIMO-NOMA systems.en_US
dc.subjectMIMO-NOMAen_US
dc.subjectsignal detectionen_US
dc.subjectdecision treeen_US
dc.subjectfading channelen_US
dc.titleA Machine Learning Approach with Decision Tree-based Signal Detection for MIMO-NOMA Systemsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2024_AIIoT_BPanda_AMachine.pdf3.47 MBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.