Direct-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.

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 well-understood 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 learning-based 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.

Purpose: 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 reactiveschemes cannot always fulfill application demands or have suboptimal performance. However, findingoptimal coding configurations that fulfill application constraints—e.g., tolerate loss and delay—underchanging channel conditions is a computationally challenging task. Methods: Recently, the systems community has started addressing these sorts of problems usinghybrid decomposed solutions, i.e., algorithmic approaches for well-understood formalized parts ofthe problem and learning-based approaches for parts that must be estimated (either for reasons ofuncertainty or computational intractability). For DeepSHARQ, we revisit our own recent work andlimit the learning problem to block length prediction, the major contributor to inference time (andits variation) when searching for hybrid error coding configurations. The remaining parameters arefound 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. Results: DeepSHARQ applies block length regularization in order to reduce the neural networksin comparison to purely learning-based solutions. The hybrid solution is nearly optimal concerningthe channel efficiency of coding configurations it generates, as it is trained so deviations from theoptimum are upper-bound by a configurable percentage. Additionally, DeepSHARQ is capable ofreacting to channel changes in real-time, thereby enabling Cyber-Physical Systems even on resource-constrained platforms. Conclusion: Tightly integrating algorithmic and learning-based approaches allows DeepSHARQ toreact to channel changes faster and with a more predictable time than solutions that rely only oneither of the two approaches.

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