TinyTurbo: Efficient Turbo Decoders on Edge

Hebbar, S. Ashwin; Mishra, Rajesh; Ankireddy, Sravan Kumar; Makkuva, Ashok Vardhan; Kim, Hyeji; Viswanath, Pramod

doi:10.1109/isit50566.2022.9834589

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Note that we focus on the AWGN channel in this section. Recently, machine learning based turbo decoders have also been proposed in [108]- [110] to improve the robustness and adaptivity of non-AWGN channels.…”

Section: G Turbo Code Decoding Algorithmsmentioning

confidence: 99%

Channel Coding Toward 6G: Technical Overview and Outlook

Rowshan,

Qiu,

Xie

et al. 2024

IEEE Open J. Commun. Soc.

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Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is paramount for mobile and the Internet of Things (IoT) communications. As the fifth generation (5G) of mobile communications is currently in operation and 5G-advanced is on the horizon, the objective of this paper is to assess prominent channel coding schemes in the context of recent advancements and the anticipated requirements for the sixth generation (6G). In this paper, after considering the potential impact of channel coding on key performance indicators (KPIs) of wireless networks, we review the evolution of mobile communication standards and the organizations involved in the standardization, from the first generation (1G) to the current 5G, highlighting the technologies integral to achieving targeted KPIs such as reliability, data rate, latency, energy efficiency, spectral efficiency, connection density, and traffic capacity. Following this, we delve into the anticipated requirements for potential use cases in 6G. The subsequent sections of the paper focus on a comprehensive review of three primary coding schemes utilized in past generations and their recent advancements: lowdensity parity-check (LDPC) codes, turbo codes (including convolutional codes), and polar codes (alongside Reed-Muller codes). Additionally, we examine alternative coding schemes like Fountain codes (also known as rate-less codes), sparse regression codes, among others. Our evaluation includes a comparative analysis of error correction performance and the performance of hardware implementation for these coding schemes, providing insights into their potential and suitability for the upcoming 6G era. Lastly, we will briefly explore considerations such as higher-order modulations and waveform design, examining their contributions to enhancing key performance indicators in conjunction with channel coding schemes.

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Section: G Turbo Code Decoding Algorithmsmentioning

confidence: 99%

Channel Coding Toward 6G: Technical Overview and Outlook

Rowshan,

Qiu,

Xie

et al. 2024

IEEE Open J. Commun. Soc.

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“…Wodiany et al [379] Efficient implementation of an low-precision NSC decoder. Hebbar et al [380] Novel curriculum learning-based sequential neural decoder.…”

Section: Dl-aided Decoding Of Polar Codesmentioning

confidence: 99%

“…Recently, another approach to tackle the difficulty of learning to decode long polar codes was proposed in [380], where a novel curriculum learning-based sequential neural decoder for polar and PAC codes was proposed. The paper designed a novel curriculum to train RNN, where the problem of joint estimation of information bits is decomposed into a sequence of sub-problems of increasing difficulty.…”

Section: Dl-aided Sclf Decodingmentioning

confidence: 99%

“…The resulting TurboNet+ decoder was shown to achieve state-of-the-art performance and outperform existing DL turbo decoders even with lower computational complexity. Subsequently, the authors in [407] proposed TINYTURBO which significantly reduces the trainable parameters of Tur-boNet+ by sharing the same weight across bit indices in the computation of the posterior LLR. In particular, for a block length of 40, it was demonstrated that TINYTURBO with 18 parameters outperforms TurboNet+ with 720 parameters over AWGN channels.…”

Section: Dl-aided Convolutional and Turbo Decodingmentioning

confidence: 99%

“…He et al [406] A novel model-driven decoder, called TurboNet. Hebbar et al [407] TINYTURBO that reduces the parameters of TurboNet+.…”

Section: E Dl-aided Decoding Of Cyclic Codesmentioning

confidence: 99%

See 2 more Smart Citations

Security Gap Improvement of BICM Systems Through Bit-Labeling Optimization for the Gaussian Wiretap Channel

2022

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This paper provides a comprehensive survey on recent advances in deep learning (DL) techniques for the channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to the physical layer technologies have been extensively studied in recent years, and are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on the channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and mode-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding.

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Turbo polar codes: A review paper

Mohammed,

Hamad

2024

AIP Conference Proceedings

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TinyTurbo: Efficient Turbo Decoders on Edge

Cited by 8 publications

References 18 publications

Channel Coding Toward 6G: Technical Overview and Outlook

Channel Coding Toward 6G: Technical Overview and Outlook

Security Gap Improvement of BICM Systems Through Bit-Labeling Optimization for the Gaussian Wiretap Channel

Turbo polar codes: A review paper

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