Integrated Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird’s eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology’s state-of-the-art and presents relevant opportunities and challenges.

Joint communications and sensing (JCAS) is regarded as a major technology building block in 6G networks. The combination of communications and radar sensing systems benefits from saved hardware costs, reduced energy consumption, and less spectrum occupation. phase-modulated continuous waveform (PMCW) is regarded as an attractive candidate waveform for future automotive JCAS systems due to its perfect anti-interference properties and radar sensing performance. However, PMCW-based JCAS systems usually suffer from extremely low data rates and undesirable communications reliability in dynamic environments, limiting their applicability in traffic scenarios, where the propagation channel may suffer from a high Doppler shift and spread and a large data throughput is expected. To overcome the shortcomings, in this work, a novel PMCW approach with simultaneously transmitted pilot and data sequences, named code-orthogonal PMCW (CO-PMCW), is introduced. The presented CO-PMCW approach benefits from significantly improved data rate and robustness in dynamic environments. Regarding the differences in system architecture between PMCW and CO-PMCW, the authors verified the advantages and investigated the potential side effects of the CO-PMCW system. The properties including the interference between the pilot and data, influence on transmission power, behavior in multipath fading channels, etc. are measured. The result shows that the presented CO-PMCW approach significantly improves the communications performance with slightly influenced radar properties. Based on the simulation result, the application feasibility and scenarios of the CO-PMCW-based JCAS system are analyzed.

The sixth-generation wireless communications (6G) is often labeled as “connected intelligence”. Radio sensing, aligned with machine learning (ML) and artificial intelligence (AI), promises, among other benefits, breakthroughs in the system’s ability to perceive the environment and effectively utilize this awareness. This article offers a panoramic view of radio sensing by unifying physical object sensing and spectrum sensing. To this end, while staying in the framework of integrated sensing and communication (ISAC), we expand the term “sensing” from radar, via spectrum sensing, to miscellaneous applications of radio sensing like non-cooperative transmitter localization. We formulate the problems, explain the state-of-the-art approaches, and detail AI-based techniques to tackle various objectives in the context of wireless sensing. Finally, we discuss the potential integration of various radio sensing functions into a common AI-enhanced framework, emphasizing the possible benefits and the challenges to overcome. In addition to the tutorial-style core of this work based on direct authors’ involvement in 6G research problems, we review the related literature, and provide both a good start for those entering this field of research, and a topical overview for a general reader with a background in wireless communications.

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F₁-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

The steadily increasing number of wireless Internet of Things devices imposes new challenges on spectrum monitoring and spectrum management tasks. In this paper, we design a multifunctional cooperative sensor network to analyze electromagnetic (EM) situation in the monitored area. Specifically, we introduce a framework based on a long short-term memory (LSTM) deep neural network (DNN) to jointly localize a single transmitter (TX) and classify the waveform of the TX signal. The performance capability of separating fifth-generation New Radio (5G NR) communications standard signals from fourthgeneration Long Term Evolution (4G LTE) communications standard signals and Wi-Fi signals is investigated as a classification problem. Furthermore, the associated TX localization accuracy is measured regarding mean squared error (MSE). We show that in a basic scenario, it is possible to yield a reasonable TX localization performance without any prior knowledge of the signals' structure with just four sensors, in parallel getting reliable wireless waveform classification. Finally, we suggest future research directions for joint signal classification and TX localization in cooperative sensor networks.

This paper examines a novel data-driven framework for spectrum monitoring in the context of non-public networks. To this end, we model an indoor scenario with a single non-cooperative transmitter (TX) and a spatially distributed network of sensor units (SUs) capable of recording the broadcasted signal as I/Q samples. Then, a dual-task deep neural network (DNN) is designed to jointly classify wireless signal waveform and estimate the TX coordinates, using raw data from the SUs as input. The numerical simulations focus on the model's scalability, generalizability, and robustness. In this context, we study the effect of the number of SUs and varying signal-to-noise ratio (SNR) on the accuracy of the developed DNN-based joint signal classification and TX localization algorithm. In particular, we show that an increased number of SUs improves the localization accuracy, whereas the performance of waveform classification remains nearly unchanged. Finally, we assess the model's complexity regarding the number of learnable parameters compared to single-task DNNs, demonstrating that extra functionality can be obtained at a low additional complexity cost.