HybridDeepRx: Deep Learning Receiver for High-EVM Signals

Pihlajasalo, Jaakko; Korpi, Dani; Honkala, Mikko; Huttunen, Janne M. J.; Riihonen, Taneli; Talvitie, Jukka; Brihuega, Alberto; Uusitalo, Mikko A.; Valkama, Mikko

doi:10.1109/pimrc50174.2021.9569393

Cited by 11 publications

(8 citation statements)

References 19 publications

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“…As for hardware-induced nonlinearities, the impact of various hardware impairments on ML-based receivers has also been analyzed in the literature. The preliminary results in [21], [28], [29] demonstrate the effectiveness of fully learned receivers in dealing with amplifier-induced nonlinearities. Additionally, in [24], [30], transmitter-induced clipping effects are considered, with the solution in [30] outperforming a non-ML baseline with similar complexity.…”

Section: A State-of-the-artmentioning

confidence: 77%

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Pihlajasalo

Korpi

Honkala

et al. 2023

IEEE Trans. Wireless Commun.

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In this article, we propose multiple machine learning (ML) based physical-layer receiver solutions for demodulating orthogonal frequency-division multiplexing (OFDM) signals that are subject to high level of nonlinear distortion. Specifically, three novel deep learning based convolutional neural network receivers are devised, containing layers in time-and/or frequency-domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Applicable training procedures are also described, such that the learned layers in the receiver processing properly generalize over different nonlinear distortion and multipath channel characteristics. Extensive set of numerical results is provided, in the context of 5G NR uplink (UL) incorporating also measured terminal power amplifier (PA) characteristics. The obtained results show that the proposed receiver systems are able to clearly outperform the classical linear minimum meansquared error (LMMSE) receiver as well as the existing ML receiver approaches, especially when the EVM is high compared to modulation order. This is particularly so when the devised ML receiver is of hybrid nature with layers both in time and frequency. The proposed ML receivers can thus facilitate pushing the terminal PA systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage. Through combining the obtained radio link performance results with link budget calculations, all carried out at the 28 GHz mmWave band, it is shown that the proposed ML receivers can enhance the network coverage in terms of maximum UL link distances by close to 100%, when compared to classical LMMSE receiver based networks.

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Section: A State-of-the-artmentioning

confidence: 77%

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Pihlajasalo

Korpi

Honkala

et al. 2023

IEEE Trans. Wireless Commun.

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“…In OFDM transmission systems, DL can be also used to combat other impairments such as phase noise and synchronization errors. For example, a DL-based receiver called HybridDeepRx is introduced to detect nonlinearly distorted OFDM signals [219]. Additionally, in order to alleviate nonlinear distortion in a MIMO-OFDM system, authors of [220] have designed both model and data-driven DL-based receivers.…”

Section: Reference Name Ce Sd Nn Type Waveformmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

IEEE Open J. Commun. Soc.

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DLbased multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.

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“…In OFDM transmission systems, DL can be also used to combat other impairments such as phase noise and synchronization errors. For example, a DL-based receiver called HybridDeepRx is introduced to detect nonlinearly distorted OFDM signals [201]. Additionally, in order to alleviate nonlinear distortion in a MIMO-OFDM system, authors of [202] have designed both model and data-driven DL-based receivers.…”

Section: Gfdm-immentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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HybridDeepRx: Deep Learning Receiver for High-EVM Signals

Cited by 11 publications

References 19 publications

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Deep Learning OFDM Receivers for Improved Power Efficiency and Coverage

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

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