A Joint PAPR Reduction and Digital Predistortion Based on Real-Valued Neural Networks for OFDM Systems

Liu, Zhijun; Hu, Xin; Wang, Weidong; Ghannouchi, Fadhel M.

doi:10.1109/tbc.2021.3132158

Cited by 16 publications

(6 citation statements)

References 28 publications

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“…Teknik linierisasi dengan penerapan sebuah predisorter diterapkan pada sinyal OFDM dapat menaikkan memperluas daerah kerja linier penguat daya dan menghilangkan distorsi sinyal yang disebabkan oleh sifat tak linier penguat daya [6] [7]. Teknik gabungan reduksi PAPR dan linierisasi atau predistorsi kemudian diterapkan untuk menghasilkan kinerja yang optimal sistem OFDM seperti pada makalah [8]- [11].…”

Section: Pendahuluanunclassified

“…Hasil numerik menunjukkan bahwa teknik gabungan memberikan hasil kinerja sistem yang lebih baik dibanding bila diterapkan secara terpisah. Pada makalah [11] peneliti mengajukan algoritma real-valued neural network (RVNN) untuk mengevaluasi kinerja gabungan teknik reduksi PAPR dan digital predistortion (DPD) pada sistem OFDM. Kinerja sistem ditampilkan dalam nilai adjacent channel power ratio (ACPR) and bit error rate (BER).…”

Section: Pendahuluanunclassified

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Eksperimen Gabungan Teknik Reduksi PAPR Dan Predistorsi Pada Sistem OFDM Menggunakan USRP

Gunawan,

Arifin,

Moegiharto

et al. 2024

JTE

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Pada makalah ini dilakukan evaluasi kinerja gabungan teknik reduksi PAPR metode iterative clipping and filtering (ICF) dan predistorsi model inverse Saleh pada sistem OFDM. Evaluasi kinerja dipenerima ditampilkan dalam titik-titik konstelasi simbol QAM untuk kondisi LOS dan NLOS. Dari hasil experimen dapat ditunjukkan bahwa teknik predistorsi dapat memperbaiki kinerja sistem dari makin kecilnya sebaran titik konstelasi simbol. Sedang kinerja teknik reduksi PAPR dinyatakan dengan besarnya nilai penurunan nilai PAPR untuk nilai clipping ratio (CR) = 5, 4 dan 3 masing-masing ditunjukkan sebesar 7,6 dB, 8,4 dB dan 9,4 dB. Dan kinerja predistorsi dapat menurunkan gain PSD sinyal OFDM dengan penguat daya tak linier sebesar 30 dB.

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Section: Pendahuluanunclassified

Eksperimen Gabungan Teknik Reduksi PAPR Dan Predistorsi Pada Sistem OFDM Menggunakan USRP

Gunawan,

Arifin,

Moegiharto

et al. 2024

JTE

View full text Add to dashboard Cite

show abstract

“…On the other hand, in OFDM systems, much ongoing research works jointly optimizes the PAPR reduction and PD matrices, for example, the work published in [261] and references therein to explore the unified PAPR reduction and DPD model that cooperate. However, in pNOMA networks, there is a lack of research work or real applications discussing the integration of PD solutions to strengthen downlink transmissions.…”

Section: B Predistortion Based On Noma Schemementioning

confidence: 99%

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

Haider

You

et al. 2022

IEEE Commun. Surv. Tutorials

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The next-generation (5G/6G) wireless communication aims to leapfrog the currently occupied sub-6 GHz spectrum to the wideband millimeter-wave (MMW) spectrum. However, MMW spectrums with high-order modulation schemes drive the power amplifier (PA) at significant back-off, causing severe nonlinear distortions, thus deteriorating the transceiver's (TRXs) modulation process. Typically, the TRX efficacy is quantified with standardized linearization matrices, which take advantage of different predistortion (PD) schemes to handle deep compression of the PA. In this regard, TRX baseband signals are mostly linearized in the digital domain, where digitally controlled linearization needs higher sampling rates due to increasing MMW bandwidths to compensate for intermodulation (IMD) products, resulting in increased system cost and power consumption. Alternately, the digitally controlled analog-based linearization, i.e., the hybridization of digital predistortion (DPD) and analog predistortion (APD), is highly productive and cost-effective for fulfilling the linearized energy-efficient design vision of MMW networks. Therefore, this paper puts an extensive spotlight on the progress in PD-based linearization for 5G and beyond communications. It first provides background information on the advancements of PD schemes through recent surveys, then classifies the general roadmap of PD waveform processing across the TRX system models as preliminary. After this, we present three prominent PD architectures and their design approaches with intrinsic performance metrics. Finally, we explore four case studies encompassing PD operation under certain nonlinear constraints of different communication schemes. We examine the suitability of PD-based linearization solutions, both existing and proposed till the first quarter of 2022, and identify the potential prospects in this domain.

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“…Measurements show that fewer coefficients and floating‐point operations (FLOPs) are required and better linearization performance than state‐of‐the‐art DPDs. Based on a unified RVNN model with multiobjective optimization, a method for simultaneously training PAPR reduction function and DPD function is proposed for the first time, 9 which overcomes the mutual influences between the PAPR reduction and PA's linearization. The experimental results show that compared with the conventional methods, the proposed joint optimization method has improved in terms of linearization effects and PAPR reduction performance, which has lower computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A Digitally assisted nonlinearity suppression architecture using over‐the‐air beam cancelation

Chen

et al. 2023

Int J Numerical Modelling

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Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two‐step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in‐band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion.

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A Joint PAPR Reduction and Digital Predistortion Based on Real-Valued Neural Networks for OFDM Systems

Cited by 16 publications

References 28 publications

Eksperimen Gabungan Teknik Reduksi PAPR Dan Predistorsi Pada Sistem OFDM Menggunakan USRP

Eksperimen Gabungan Teknik Reduksi PAPR Dan Predistorsi Pada Sistem OFDM Menggunakan USRP

Predistortion-Based Linearization for 5G and Beyond Millimeter-Wave Transceiver Systems: A Comprehensive Survey

A Digitally assisted nonlinearity suppression architecture using over‐the‐air beam cancelation

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