DeepSHARQ: hybrid error coding using deep learning

Cyber-physical systems operate under changing environments and on resource-constrained devices. Communication in these environments must use hybrid error coding, as pure pro- or reactive schemes cannot always fulfill application demands or have suboptimal performance. However, finding optimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—under changing channel conditions is a computationally challenging task. Recently, the systems community has started addressing these sorts of problems using hybrid decomposed solutions, i.e., algorithmic approaches for wellunderstood formalized parts of the problem and learning-based approaches for parts that must be estimated (either for reasons of uncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work and limit the learning problem to block length prediction, the major contributor to inference time (and its variation) when searching for hybrid error coding configurations. The remaining parameters are found algorithmically, and hence we make individual contributions with respect to finding close-to-optimal coding configurations in both of these areas—combining them into a hybrid solution. DeepSHARQ applies block length regularization in order to reduce the neural networks in comparison to purely learningbased solutions. The hybrid solution is nearly optimal concerning the channel efficiency of coding configurations it generates, as it is trained so deviations from the optimum are upper bound by a configurable percentage. In addition, DeepSHARQ is capable of reacting to channel changes in real time, thereby enabling cyber-physical systems even on resource-constrained platforms. Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ to react to channel changes faster and with a more predictable time than solutions that rely only on either of the two approaches

Structural Dynamics of incommensurate Charge-Density Waves tracked by Ultrafast Low-Energy Electron Diffraction

We study the non-equilibrium structural dynamics of the incommensurate and nearly-commensurate charge-density wave phases in 1T-TaS$_2$. Employing ultrafast low-energy electron diffraction (ULEED) with 1 ps temporal resolution, we investigate the ultrafast quench and recovery of the CDW-coupled periodic lattice distortion. Sequential structural relaxation processes are observed by tracking the intensities of main lattice as well as satellite diffraction peaks as well as the diffuse scattering background. Comparing distinct groups of diffraction peaks, we disentangle the ultrafast quench of the PLD amplitude from phonon-related reductions of the diffraction intensity. Fluence-dependent relaxation cycles reveal a long-lived partial suppression of the order parameter for up to 60 picoseconds, far outlasting the initial amplitude recovery and electron-phonon scattering times. This delayed return to a quasi-thermal level is controlled by lattice thermalization and coincides with the population of zone-center acoustic modes, as evidenced by a structured diffuse background. The long-lived non-equilibrium order parameter suppression suggests hot populations of CDW-coupled lattice modes. Finally, a broadening of the superlattice peaks is observed at high fluences, pointing to a nonlinear generation of phase fluctuations.Comment: Main text and Appendice

Uplink Transmission Probability Functions for LoRa-Based Direct-to-Satellite IoT: A Case Study

International audienceDirect-to-Satellite IoT allows devices on the Earth surface to directly reach Low-Earth Orbit (LEO) satellites passing over them. Although an appealing approach towards a truly global IoT vision, scalability issues as well as highly dynamic topologies ask for dedicated protocol adaptations supported by novel models. This paper contributes to this research by introducing estimators and a transmission probability function to dynamically control the contending set of devices on a framed slotted Aloha model compatible with the LoRaWAN specification. In particular, we discuss techniques that account for particularities in the dynamics of sparse DtS-IoT constellations. Simulation analyses of a realistic case study show that >86% of the theoretical throughput is achievable in practice

Structural phase transitions and phase ordering at surfaces probed by ultrafast LEED

We demonstrate the capability of ultrafast low-energy electron diffraction to resolve phase-ordering kinetics and structural phase transitions on their intrinsic time scales with ultimate surface sensitivity

Structural phase transitions and phase ordering at surfaces probed by ultrafast LEED

We demonstrate the capability of ultrafast low-energy electron diffraction to resolve phase-ordering kinetics and structural phase transitions on their intrinsic time scales with ultimate surface sensitivity