Bremen. He has authored or coauthored over 150 journal and conference publications and is the holder of over 17 patents in the area of wireless communications. He has long-term expertise in the research of wireless communication systems, baseband algorithms, and signal processing.Prof. Dekorsy is a senior member of the IEEE Communications and Signal Processing Society and the VDE/ITG expert committee "Information and System Theory."

We consider the problem of distributed subsurface imaging in seismic receiver networks. This problem is particularly relevant for future planetary exploration missions where multi-agent networks shall autonomously reconstruct a subsurface based on network-wide measurements. To this end, we propose a distributed implementation of the full waveform inversion (FWI) for distributed imaging of subsurfaces in seismic networks. In particular, we show that the gradient of FWI is equivalent to the sum of locally computed gradients. To obtain estimates of the global gradient and subsurface model at each receiver we employ the adapt-then-combine technique that relies on data exchange among neighboring receivers only. Numerical evaluations show that the proposed distributed FWI performs close to its centralized version for different source-receiver constellations.

No abstract

No abstract

Swarm exploration by multi-agent systems relies on stable inter-agent communication. However, so far both exploration and communication have been mainly considered separately despite their strong inter-dependency in such systems. In this paper, we present the first steps towards a framework that unifies both of these realms by a “tight” integration. We propose to make exploration “communication-aware” and communication “exploration-aware” by using tools of probabilistic learning and semantic communication, thus enabling the coordination of knowledge and action in multi-agent systems. We anticipate that by a “tight” integration of the communication chain, the exploration strategy will balance the inference objective of the swarm with exploration-tailored, i.e., semantic, inter-agent communication. Thus, by such a semantic communication design, communication efficiency in terms of latency, required data rate, energy, and complexity may be improved. With this in mind, the research proposed in this work addresses challenges in the development of future distributed sensing and data processing platforms—sensor networks or mobile robotic swarms consisting of multiple agents—that can collect, communicate, and process spatially distributed sensor data.