Machine Learning-Assisted PAPR Reduction in Massive MIMO

Kalinov, Aleksei; Bychkov, Roman; Ivanov, Andrey; Osinsky, Alexander; Yarotsky, Dmitry

doi:10.1109/lwc.2020.3036909

Cited by 18 publications

(18 citation statements)

References 16 publications

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“…Finally, the optimal PAPR target can be computed by comparing (18) to the analytically obtained total clipping noise power levels in (17), expressed as λtarget,opt = arg min…”

Section: B Clipping Noise Power-based Papr Target Selectionmentioning

confidence: 99%

“…In Fig. 2(a), clipping noise power levels with respect to several PAPR targets are given for ICF, which is analytically obtained as in (17) and shown with the yellow line. Here, maximum noise power levels that can be supported by the ICWEF masks are 11 dB and 4.2 dB, respectively.…”

Section: A Papr Performancementioning

confidence: 99%

“…In [16], a deep unfolding model was proposed to reduce the PAPR under transmit power constraints. An ML-based hyperparameter optimization method was proposed in [17] to optimize efficiently the various parameters of selected PAPR reduction schemes, with focus on massive multiple-input multiple-output (MIMO) systems. However, none of these works consider the frequency-selective PAPR reduction and the associated optimal target PAPR selection problem which are the technical focus of this article.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Tuner for Frequency-Selective PAPR Reduction

Gökçeli

Riihonen

Levanen

et al. 2023

IEEE Trans. Veh. Technol.

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Frequency-selective peak-to-average power ratio (PAPR) reduction is essential in networks such as 5G New Radio (NR) that support frequency-domain multiplexing of users and services. However, stemming from the frequency-selective shaping of the involved clipping noise, the relation between the intended PAPR target and the actually realized PAPR is known to be heavily nonlinear, which complicates the PAPR reduction. In this article, a novel machine learning (ML)-based solution, called PAPRer, is proposed to automatically and accurately tune the optimal PAPR target for frequency-selective PAPR reduction. This is achieved by utilizing the features related to the used clipping noise filter and minimization of the defined loss function, through supervised learning, which quantifies the PAPR target estimation accuracy. An analytical clipping noise power-based method is also devised for reference purposes. Extensive numerical evaluations in 5G NR context are provided and analyzed, showing that PAPRer can very accurately predict and tune the optimal PAPR target. These results, together with the provided complexity assessment, demonstrate that the proposed PAPRer offers a favorable performance-complexity tradeoff in choosing the optimal PAPR target for frequency-selective PAPR reduction.

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“…Finally, the optimal PAPR target can be computed by comparing (18) to the analytically obtained total clipping noise power levels in (17), expressed as λtarget,opt = arg min…”

Section: B Clipping Noise Power-based Papr Target Selectionmentioning

confidence: 99%

Section: A Papr Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Based Tuner for Frequency-Selective PAPR Reduction

Gökçeli

Riihonen

Levanen

et al. 2023

IEEE Trans. Veh. Technol.

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“…All of the above papers consider a SISO network. A PAPR reduction scheme assisted by DL for a MIMO-OFDM system was suggested in [34]. The authors apply selective tone reservation [35] on each antenna separately and then apply unused beam reservation [36] on all antennas together.…”

Section: Deep-learning-based Schemes (Data Driven) For Papr Reductionmentioning

confidence: 99%

Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder

Huleihel¹,

Permuter²

2023

Preprint

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An enhanced framework for peak-to-average power ratio (PAPR) reduction and waveform design for Multiple-Input-Multiple-Output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems, based on a convolutional-autoencoder (CAE) architecture, is presented. The end-to-end learning-based autoencoder (AE) for communication networks represents the network by an encoder and decoder, where in between, the learned latent representation goes through a physical communication channel.We introduce a joint learning scheme based on projected gradient descent iteration to optimize the spectral mask behavior and MIMO detection under the influence of a non-linear high power amplifier (HPA) and a multipath fading channel. The offered efficient implementation novel waveform design technique utilizes only a single PAPR reduction block for all antennas. It is throughput-lossless, as no side information is required at the decoder. Performance is analyzed by examining the bit error rate (BER), the PAPR, and the spectral response and compared with classical PAPR reduction MIMO detector methods on 5G simulated data. The suggested system exhibits competitive performance when considering all optimization criteria simultaneously. We apply gradual loss learning for multi-objective optimization and show empirically that a single trained model covers the tasks of PAPR reduction, spectrum design, and MIMO detection together over a wide range of SNR levels.

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“…[220] proposed a loss function to increase the performance of neural networks in a communication system, and [221] proposed a multi-antenna and multi-subcarrier channel state information (CSI)-based novel channel sounder architecture to achieve an accuracy better than 75 cm for line of sight (LoS) for indoor user positioning in three dimensions. In [222], the author proposed an ANN-based novel Adaptive Modulation and Coding (AMC) scheme to estimate the signal-to-noise power ratio (SNR) to determine the optimal MCS with a low calculation complexity, and [223] proposed ML-based peak-to-average power ratio (PAPR) reduction using the optimal hyperparameter function and efficient approximation for the downlink channel of mMIMO with an OFDM signal. In addition, ref.…”

Section: Signaling Techniques For 5gmentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

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Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

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Machine Learning-Assisted PAPR Reduction in Massive MIMO

Cited by 18 publications

References 16 publications

Machine Learning Based Tuner for Frequency-Selective PAPR Reduction

Machine Learning Based Tuner for Frequency-Selective PAPR Reduction

Low PAPR MIMO-OFDM Design Based on Convolutional Autoencoder

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

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