Detection of multipart DS CDMA signals using neural networks

dc.contributor.authorSoujeri, Ebrahim Ali
dc.contributor.authorBilgekul, Hüseyin
dc.date.accessioned2026-02-06T17:58:30Z
dc.date.issued2006
dc.departmentDoğu Akdeniz Üniversitesi
dc.description2006 IEEE 14th Signal Processing and Communications Applications --
dc.description.abstractMulti-user interference (MAI) cancellation using hysteretic Hopfield Neural Network (HHNN) receiver for Direct Sequence Code-Division Multiple Access (DS-CDMA) in multipath fading channels is investigated. We have shown that by applying the phenomenon of "hysteresis" to the HNN detector, we may enhance the performance of this detector in all near-far situations, for different number of multipath rays. The introduction of hysteresis concept into HNN has made the CDMA HNN detector (abbreviated as HHNN) even closer to the CDMA optimum multiuser detector. As shown by simulation results, the BER performance achieved by the HHNN detector outperforms the classical HNN detector with a good margin and is promising. © 2006 IEEE.
dc.identifier.doi10.1109/SIU.2006.1659809
dc.identifier.isbn9781424402397
dc.identifier.isbn1424402395
dc.identifier.scopus2-s2.0-34247268617
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU.2006.1659809
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/
dc.identifier.urihttps://hdl.handle.net/11129/7612
dc.identifier.volume2006
dc.indekslendigikaynakScopus
dc.language.isotr
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260204
dc.subjectMulti-user interference (MAI)
dc.subjectMultipath rays
dc.subjectCode division multiple access
dc.subjectFading channels
dc.subjectHopfield neural networks
dc.subjectMultiuser detection
dc.subjectUser interfaces
dc.subjectSignal detection
dc.titleDetection of multipart DS CDMA signals using neural networks
dc.title.alternativeÇokyol DD KBÇE i?şaretlerinin yapay sinir a?lari kullanarak sezimi
dc.typeConference Object

Files