Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

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 $10^{-5}$ at 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.Comment: 14 pages, 15 figures; accepted versio

Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ

In order to fulfill the stringent Ultra-Reliable Low Latency Communication (URLLC) requirements towards Fifth Generation (5G) mobile networks, early-Hybrid Automatic Repeat reQuest (e-HARQ) schemes have been introduced, aimed at providing faster feedback and thus earlier retransmission. The performance of e-HARQ prediction strongly depends on the classification mechanism, data length, threshold value. In this paper, we propose an analytical model that incorporates e-HARQ and Hybrid Automatic Repeat reQuest (HARQ) functionalities in terms of two phases in discrete time. The model implies a fast and accurate way to get the main performance measures, and apply optimization analysis to find the optimal values used in predictor’s classification. We employ realistic data for transition probabilities obtained by means of 5G link-level simulations and conduct extensive experimental analysis. The results show that at false positive probability of 10−1, the e-HARQ prediction with the found optimal parameters can achieve around 20% of gain over HARQ at False Negative (FN) of 10−1 and around 7.5% at FN of 10−3 in terms of a mean spending time before successful delivery

Channel Measurement and Modeling for 5G Urban Microcellular Scenarios

In order to support the development of channel models for higher frequency bands, multiple urban microcellular measurement campaigns have been carried out in Berlin, Germany, at 60 and 10 GHz. In this paper, the collected data is uniformly analyzed with focus on the path loss (PL) and the delay spread (DS). It reveals that the ground reflection has a dominant impact on the fading behavior. For line-of-sight conditions, the PL exponents are close to free space propagation at 60 GHz, but slightly smaller (1.62) for the street canyon at 10 GHz. The DS shows a clear dependence on the scenario (median values between 16 and 38 ns) and a strong distance dependence for the open square and the wide street canyon. The dependence is less distinct for the narrow street canyon with residential buildings. This behavior is consistent with complementary ray tracing simulations, though the simplified model tends to overestimate the DS

Computational cardiology: A modified Hill model to describe the electro-visco-elasticity of the myocardium

This contribution presents a novel three-dimensional constitutive model which describes the orthotropic electro-visco-elastic response of the myocardium. The model can be regarded as the viscoelastic extension of the recently proposed generalized Hill model for orthotropic active muscle cells. The formulation extends the recent contributions of Cansiz et al. (CMBBE 18: 1160-1172, 2015) and Goktepe et al. (IMPS 72: 20-39, 2014) in a novel rheological description which incorporates the active (electrical) and mechanical (viscous and elastic) deformations in a multiplicative format. To this end, the stress response is additively decomposed into passive (purely mechanical) and visco-active (electro-visco-elastic) contributions in line with a rheological model in which the passive part is connected parallel to a branch that consists of an elastic spring, a dashpot and a contractile element in serial. The former branch is assumed to be a function of the total deformation gradient while the formulation of the latter one is based on a multiplicative decomposition of the total deformation gradient into a mechanical and an active part. The active deformation gradient is devised by means of the prescribed active stretch which arises from the electrical excitation of the myocardial tissue and is governed by the intracellular calcium concentration. Thanks to the proposed rheology, marked differences are observed in isometric and isotonic tests between viscoelastic and elastic cases that are performed on material level. Moreover, novel evolution equations for the description of active stretch and intracellular calcium concentration having superiorities over the existing approaches have been proposed. On the numerical side, a fully implicit finite element formulation along with surface elements accounting for blood pressure evolution in the ventricles during successive phases of the cardiac cycle is presented. The argument of the constitutive equation describing the ventricular blood pressure is specified as the associated ventricular cavity volume which leads to the mutual interaction of the non-adjacent surface elements. The performance of the theory and algorithms are demonstrated by means of representative multi-field initial boundary value problems. The results indicate that viscous effects significantly alter the electromechanical response of the cardiac tissue and is thought to be crucial in the virtual assessment of the cardiac function