A Novel Selective Mapping Method based on Cumulative Symbol Optimization for PAPR Minimization in Low Complexity GFDM Transmitter

Year 2022, Issue: 35, 168 - 176, 07.05.2022

Şakir Şimşir , Necmi Taşpınar

https://doi.org/10.31590/ejosat.1078288

Cited By: 1

Abstract

In this paper, the problem of high peak-to-average power ratio (PAPR) in the low complex version of the generalized frequency division multiplexing (LC-GFDM) is handled by developing a novel PAPR reduction method called cumulative symbol optimization-selective mapping (CSO-SLM). With this new method, the disadvantage caused by the symbol addition process that greatly reduces the SLM performance in the LC-GFDM transmitter was eliminated thanks to the combination of SLM technique with the cumulative symbol optimization procedure. In order to demonstrate the benefit of integrating the CSO procedure into the SLM scheme, our proposed CSO-SLM strategy was compared to the conventional SLM technique with regard to both PAPR reduction and power spectral density (PSD) performance in the simulations. Moreover, the CSO-partial transmit sequence (CSO-PTS) technique, which was specially developed for the LC-GFDM system as the CSO-SLM strategy, and a robust PAPR reduction method named GreenOFDM were also used for comparison. According to the simulation results, the proposed CSO-SLM technique clearly outperforms each of the benchmark strategies considered in this paper by leaving them behind with regard to the amounts of PAPR and PSD improvements achieved in the LC-GFDM transmission signal.

Keywords

PAPR, LC-GFDM, 5G, Cumulative symbol optimization, Selective mapping

Supporting Institution

Scientific Research Projects Coordination Unit of Erciyes University

Project Number

FDK-2018-8463

Thanks

We thank the Scientific Research Projects Coordination Unit of Erciyes University for supporting our study

Düşük Karmaşıklı GFDM Vericisinde PAPR Minimizasyonu için Birikimli Sembol Optimizasyonuna Dayalı Yeni Bir Seçici Eşleme Yöntemi

Abstract

Bu çalışmada, birikimli sembol optimizasyonu-seçici eşleme (CSO-SLM) adı verilen yeni bir PAPR düşürme yöntemi geliştirilerek, genelleştirilmiş frekans bölmeli çoğullamanın düşük karmaşıklı versiyonundaki (LC-GFDM) yüksek tepe gücü/ortalama güç oranı problemi ele alınmıştır. Bu yeni yöntemle birlikte, SLM tekniğinin birikimli sembol optimizasyon prosedürü ile birleştirilmesi sayesinde, LC-GFDM vericisinde SLM performansını büyük ölçüde düşüren sembollerin toplanması işleminin yol açtığı dezavantaj ortadan kaldırılmıştır. CSO prosedürünü SLM şemasına entegre etmenin faydasını göstermek amacıyla, önermiş olduğumuz CSO-SLM stratejisi geleneksel SLM tekniği ile simülasyonlarda hem PAPR düşürme hem de güç spektral yoğunluğu (PSD) performansı bakımından karşılaştırılmıştır. Ayrıca, LC-GFDM sistemi için CSO-SLM stratejisi gibi özel olarak geliştirilmiş olan CSO-kısmi iletim dizisi (CSO-PTS) tekniği ve GreenOFDM isimli güçlü bir PAPR düşürme yöntemi de karşılaştırma amaçlı kullanılmıştır. Simülasyon sonuçlarına göre, önerilen CSO-SLM yöntemi, bu çalışmada ele alınan kıyaslama stratejilerini LC-GFDM iletim sinyalinde elde edilen PAPR ve PSD iyileştirme miktarları bakımından geride bırakarak her birinden net olarak daha iyi bir performans göstermiştir.

Keywords

PAPR, LC-GFDM, 5G, Birikimli sembol optimizasyonu, Seçici eşleme

Project Number

FDK-2018-8463

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