dc.contributor.author | Qureshi, HA | en_US |
dc.contributor.author | Oh, S | en_US |
dc.contributor.author | Khan, N | en_US |
dc.contributor.author | Kim, YH | en_US |
dc.contributor.author | Nallanathan, A | en_US |
dc.date.accessioned | 2024-07-11T13:55:35Z | |
dc.date.issued | 2024-01-01 | en_US |
dc.identifier.issn | 2162-2337 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/98025 | |
dc.description.abstract | This letter investigates the effect of an extremely large (XL) simultaneously transmitting and reflecting intelligent reconfigurable surface (STAR-RIS) on the sum rate of the uplink nonorthogonal multiple access (NOMA). For the XL-STAR-RIS supporting NOMA in energy-splitting mode, we develop a sum rate maximization algorithm under the minimum rate constraints that accommodates XL-STAR-RIS in a computationally efficient way. We also analyze upper and lower bounds on the sum rate to identify governing factors of the channel models. The results show that the sum rate gain with respect to the number of STAR-RIS elements is reduced when the channels between the STAR-RIS and users become near-field, which entails the use of the correct channel model to estimate the exact value of an XL-STAR-RIS. | en_US |
dc.relation.ispartof | IEEE Wireless Communications Letters | en_US |
dc.rights | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.title | Sum Rate of Extremely Large STAR-RIS Aided Uplink NOMA | en_US |
dc.type | Article | |
dc.identifier.doi | 10.1109/LWC.2024.3401690 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
rioxxterms.funder.project | b215eee3-195d-4c4f-a85d-169a4331c138 | en_US |