Utilizing Connectivity Maps to Accelerate V2I Communication in Cellular Network Dead Spots

On many roads in rural and mountainous areas, the cellular network connectivity is intermittent and dead spots, i.e., zones without any coverage, are frequent. In previous work, we developed a data dissemination protocol to accelerate the transmission of messages in dead spots. It combines the cellular network with short-living ad-hoc networks between vehicles. A car in a dead spot can forward messages directed towards the environment, to the peer in its ad-hoc network that will leave the dead spot first, effectively reducing the delay. An issue, however, is to reliably identify the peer that is most likely the first one regaining cellular network coverage. This problem can be solved if the borders of the dead spot, the vehicles are in, are previously known. For that, we use a novel technology named dead spot prediction. Here, vehicles conduct local connectivity measurements that are aggregated to so-called connectivity maps describing the locations of dead spots on a road system. In this article, we introduce the combination of the data dissemination protocol with dead spot prediction. Particularly, our protocol is amended such that connectivity maps are considered when deciding which vehicle leaves a dead spot first. Since currently only few publicly available works about dead spot prediction exist, we further created a prototype of such a predictor ourselves that will be discussed as well

Multi-Band Measurements for Deep Learning-Based Dynamic Channel Prediction and Simulation

Next-generation mobile communication systems are planned to support millimeter Wave (mmWave) transmission in scenarios with high-mobility, such as in private industrial networks. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, high-quality measurement datasets need to be made available to the research community in order to develop and benchmark ML-based methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6 GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel simulators for both sub-6GHz and mmWave bands on real-world data. We conclude this paper by discussing promising experimental results on two illustrative ML tasks leveraging on our dataset, namely, channel impulse response forecasting and synthetic channel transfer function generation, upon which we propose future exploratory research directions. The original dataset employed in this work is available on IEEE DataPort (https://dx.doi.org/10.21227/3tpp-j394), and the code utilized in our numerical experiments is publicly accessible via CodeOcean (https://codeocean.com/capsule/9619772/tree)

Forward Error Correction for Optical Transponders

Forward error correction is an essential technique required in almost all communication systems to guarantee reliable data transmission close to the theoretical limits. In this chapter, we discuss the state-of-the-art forward error correction (FEC) schemes for fiber-optic communications. Following a historical overview of the evolution of FEC schemes, we first introduce the fundamental theoretical limits of common communication channel models and show how to compute them. These limits provide the reader with guidelines for comparing different FEC codes under various assumptions. We then provide a brief introduction to the general basic concepts of FEC, followed by an in-depth introduction to the main classes of codes for soft decision decoding and hard decision decoding. We include a wide range of performance curves, compare the different schemes, and give the reader guidelines on which FEC scheme to use. We also introduce the main techniques to combine coding and higher-order modulation (coded modulation), including constellation shaping. Finally, we include a guide on how to evaluate the performance of FEC in transmission experiments. We conclude the chapter with an overview of the properties of some state-of-the-art FEC schemes used in optical communications and an outlook