In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 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 Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4 bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.

We provide a mathematical framework to analyze the limits of Hybrid Automatic Repeat reQuest (HARQ) and derive analytical expressions for the most powerful test for estimating the decodability under maximum-likelihood decoding and t-error decoding. Furthermore, we numerically approximate the most powerful test for sum-product decoding. We compare the performance of previously studied HARQ prediction schemes and show that none of the state-of-the-art HARQ prediction is most powerful to estimate the decodability of a partially received signal vector under maximum-likelihood decoding and sum-product decoding. Furthermore, we demonstrate that decoding in general is suboptimal for predicting the decodability.

In this paper, we investigate short packet transmissions in a Polar Coded Sparse Code Multiple Access (PC-SCMA) system. Since recently introduced by Arikan Polarization-Adjusted Convolutional (PAC) codes have shown remarkable performance at short block lengths with an ability of reaching theoretical limits, in this work we propose a PC-SCMA system based on the soft output PAC decoder. The proposed decoder is a combination of a sequential stack decoder and an enhanced Belief Propagation (BP) algorithm. The numerical analysis shows its ability to outperform the state-of-the-art PC-SCMA system with a Successive Cancellation List (SCL) decoder at short block lengths in terms of the bit error rate with smaller computational complexity. Furthermore, we enhance the SCL-based scheme by row-merging of the polarization kernel and evaluate its performance.

In this work, we extend two autoencoder-based HARQ prediction schemes to exploit subcode-based features and SNR-based features jointly. We apply the proposed HARQ prediction schemes to Cloud-RAN (C-RAN) and Single-RAN (S-RAN) architectures. Furthermore, we conduct realistic link-level simulations to test the performance and compare to state-of-the-art prediction schemes that rely solely on either subcode-based features or SNR-based features. Compared to the state-of-the-art, we show that the proposed schemes reduce the transmitted redundancy at a target error rate of$5\cdot 10^{-5}$and$2\cdot 10^{-5}$by 12.3% - 27.3% in C-RAN architectures and 10.5% - 11.0% in S- RAN architectures, respectively.

In this work, we investigate proactive Hybrid Automatic Repeat reQuest (HARQ) using link-level simulations for multiple packet sizes, modulation orders, BLock Error Rate (BLER) targets and two delay budgets of 1 ms and 2 ms, in the context of Industrial Internet of Things (IIOT) applications. In particular, we propose an enhanced proactive HARQ protocol using a feedback prediction mechanism. We show that the enhanced protocol achieves a significant gain over the classical proactive HARQ in terms of energy efficiency for almost all evaluated BLER targets at least for sufficiently large feedback delays. Furthermore, we demonstrate that the proposed protocol clearly outperforms the classical proactive HARQ in all scenarios when taking a processing delay reduction due to the less complex prediction approach into account, achieving an energy efficiency gain in the range of 11% up to 15% for very stringent latency budgets of 1 ms at 10-2BLER and from 4% up to 7.5% for less stringent latency budgets of 2 ms at 10-3BLER. Furthermore, we show that power-constrained proactive HARQ with prediction even outperforms unconstrained reactive HARQ for sufficiently large feedback delays.

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC). To this end, we propose machine learning methods to predict the outcome of the decoding process ahead of the end of the transmission. We discuss different input features and classification algorithms ranging from traditional methods to newly developed supervised autoencoders. These methods are evaluated based on their prospects of complying with the URLLC requirements of effective block error rates below 10-5at small latency overheads. We provide realistic performance estimates in a system model incorporating scheduling effects to demonstrate the feasibility of E-HARQ across different signal-to-noise ratios, subcode lengths, channel conditions and system loads, and show the benefit over regular HARQ and existing E-HARQ schemes without machine learning.

5G is envisioned to support three broad categories of services: eMBB, URLLC, and mMTC. URLLC services refer to future applications which require reliable data communications from one end to another, while fulfilling ultra-low latency constraints. In this paper, we highlight the requirements and mechanisms that are necessary for URLLC in LTE. Design challenges faced when reducing the latency in LTE are shown. The performance of short processing time and frame structure enhancements are analyzed. Our proposed DCI Duplication method to increase LTE control channel reliability is presented and evaluated. The feasibility of achieving low latency and high reliability for the IMT-2020 submission of LTE is shown. We further anticipate the opportunities and technical design challenges when evolving 3GPP's LTE and designing the new 5G NR standard to meet the requirements of novel URLLC services.