Maximilian Bauhofer, M.Sc.

Abstract

Integrated Sensing And Communication (ISAC) forms a symbiosis between the human need for communication and the need for increasing productivity, by extracting environmental information leveraging the communication network. As multiple sensory already create a perception of the environment, an investigation into the advantages of ISAC compare to such modalities is required. Therefore, we introduce MaxRay, an ISAC framework allowing to simulate communication, sensing, and additional sensory jointly. Emphasizing the challenges for creating such sensing networks, we introduce the required propagation properties for sensing and how they are leveraged. To compare the performance of the different sensing techniques, we analyze four commonly used metrics used in different fields and evaluate their advantages and disadvantages for sensing. We depict that a metric based on prominence is suitable to cover most algorithms. Further we highlight the requirement of clutter removal algorithms, using two standard clutter removal techniques to detect a target in a typical industrial scenario. In general a versatile framework, allowing to create automatically labeled datasets to investigate a large variety of tasks is demonstrated.

Abstract

As the research community starts to address the key features of 6G cellular standards, one of the agreed bridge topics to be studied already in 5G advanced releases is Integrated Sensing and Communication (ISAC). The first efforts of the research community are focusing on ISAC enablers, fundamental limits, and first demonstrators, that show that the time has come for the deployment of sensing functionalities in cellular standards. This survey paper takes a needed step towards ISAC deployment, providing an analytical toolkit to model cellular systems' sensing performance, accounting for both their fundamental and practical constraints. We then elaborate on the likely features of 6G systems to provide the feasible sensing key performance indicators (KPIs) in the frequency ranges spanned by cellular networks, including the potential new bands available in 6G, the Frequency Range 3 (FR3). We further validate our framework by visually investigating ISAC constraints with simulation examples. Finally, we assess the feasibility of few selected scenarios that can be enabled by ISAC, highlighting in each of them the limiting factor and, thus, which gaps should be filled by the research and standardization communities in the next years.

Abstract

In future wireless communication networks, existing active localization will evolve into more sophisticated sensing functionalities. One main enabler for this process is the merging of information collected from the network's nodes, sensing the passive environment in a multi-static deployment. In this work we propose an ensemble of techniques for processing the information gathered from multiple sensing nodes, jointly observing an environment with multiple target objects. We validate our approach in an indoor scenario at millimeter-wave frequencies, sweeping a variety of parameter to analyze the KPIs sensitivity with respect to each of them. The proposed algorithms to fuse information by multiple nodes show significant gains in terms of targets' localization performance, with up to 35 % for the probability of detection, compared to the baseline with a mono-static setup.

Abstract

Higher-order solitons inherently possess a spatial periodicity along the propagation axis. The pulse expands and compresses in both, frequency and time domain. This property is exploited for a bandwidth-limited receiver by sampling the optical signal at two different distances. Numerical simulations show that when pure solions are transmitted and the second (i.e., further propagated) signal is also processed, a significant gain in terms of required receiver bandwidth is obtained. Since all pulses propagating in a nonlinear optical fiber exhibit solitonic behavior given sufficient input power and propagation distance, the above concept can also be applied to spectrally efficient Nyquist pulse shaping and higher symbol rates. Transmitter and receiver are trainable structures as part of an autoencoder, aiming to learn a suitable predistortion and post-equalization using both signals to increase the spectral efficiency.

Abstract

The sixth-generation (6G) of mobile networks is envisioned to join the state of the physical world with the digital world by enabling sensing abilities at the communication device. In this paper we investigate the task of radio-based parking lot detection using a (potential) 6G base station setting. Exploiting the workhorse of communication, namely orthogonal frequency division multiplex (OFDM), we formalize the Radio Detection And Ranging (RADAR) task. Further, the challenge of projection from a single base station, creating overlapping parking-lots in range and angle, is demonstrated. Leveraging a ray-tracing framework allowing to randomize different realistic parking scenarios, a state-of-the art Neural Network (NN) is optimized and rigorously investigated using classification metrics. Different hardware configurations and occupancy levels are investigated, concluding, that radio-based parking lot detection will be a viable task for 6G communication devices.