Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things

Göktepe, Barış; Rykova, Tatiana; Fehrenbach, Thomas; Schierl, Thomas; Hellge, Cornelius

doi:10.1109/globecom42002.2020.9322302

Cited by 7 publications

(15 citation statements)

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“…3) Subcode-based feedback prediction: In [6], [18] and [19], the authors proposed a feedback prediction mechanism that incorporates the partial decoding behavior of so-called subcodes. The subcodes reflect dependencies between received symbols arising from the structure of the channel code.…”

Section: A Related Work On Harq Feedback Predictionmentioning

confidence: 99%

“…Proactive HARQ combines a high reliability with extremely short latencies [5]. Nevertheless, it trades these advantages for wasted spectral efficiency due to unnecessary retransmissions, which arrive during a successful decoding attempt [6].…”

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confidence: 99%

“…Hence, many papers in the scientific literature studied ways for reducing the feedback delay using prediction mechanisms. For architectures with a single reception point, different HARQ feedback prediction methods exist [6]- [19]. However, for architectures with multiple reception points, i.e.…”

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confidence: 99%

“…C-RANs, to the best of our knowledge only Khalili and Simeone proposed a Signal-to-Noise Ratio (SNR)-based HARQ prediction approach in [12]. Other prediction mechanisms, such as Log-Likelihood Ratio (LLR)-based and subcode-based approaches, [6] and [14]- [19], have not been adapted yet for C-RAN architectures. Even for the SNR-based approach, no evaluation using realistic link-level simulations in the C-RAN context is available.…”

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confidence: 99%

“…• To tackle the HARQ prediction issue in C-RANs in a holistic manner, we present a novel Dual-Input Denoising Autoencoder (DIDA) that treats the subcode features at the individual RRHs as noisy versions of the joint ones is proposed. Furthermore, to reduce the dimensionality of the input features we propose over [19] and [6] a subcarrier-based averaging of the LLRs for the DIDA and LLR-based approaches.…”

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confidence: 99%

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Enabling sub-THz Cloud RANs: Distributed Machine-Learning for Early HARQ Feedback Prediction

Göktepe¹,

Hellge²,

Schierl³

et al. 2022

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We propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (4 and 8 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding.In particular, we propose a novel dual-input denoising autoencoder that is trained in a joint end-to-end fashion over the whole C-RAN setup. In realistic link-level simulations at 100 GHz in the sub-THz band, we show that a combination of the novel dual-input denoising autoencoder and state-of-the-art SNR-based HARQ feedback prediction achieves the overall best performance in all scenarios compared to other proposed and state-of-the-art single prediction schemes. At very low target error rates down to 1.6 • 10 −5 , this combined approach reduces the number of required transmission rounds by up to 50% compared to always transmitting all redundancy. I. INTRODUCTIONThe need for more bandwidth and higher data rates drives the interest to new and even higher frequency bands. The 3rd Generation Partnership Project (3GPP) has responded to that need with a new work item targeting frequencies up to 71 GHz where large frequency bands are available. In particular, recent advances in hardware have paved the way for using these bands.Lately, these advances shift the focus of next generation mobile standards to even higher bands, i.e. the sub-THz and THz bands, which reach from 100 GHz up to 3 THz [1]. However, such

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Section: A Related Work On Harq Feedback Predictionmentioning

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