RaSeCoL: Radar Sensing, Communication, and Learning for Next Generation Wireless Networks

The increasing demand for bandwidth hungry applications in modern communication networks has led to the proliferation of radar and communication systems in integrated radar-sensing and communication networks in order to improve the effectiveness of the limited spectral resources.

However, it turns out that, much more benefits could be harvested regarding both the sensing and communication services through such integration between the two systems.

The overarching goal of the radar sensing, communication, and learning (RaSeCoL) project is to design, analyze and validate innovative dual functional radar sensing and communication networks, to achieve improved communication quality, spectrum efficiency, and energy efficiency for the communication service while achieving accurate estimation for the sensing parameters for the sensing service through learning from the environment and massive data collected from such integrated networks.

The project will introduce an integrated network architecture enabling both radar sensing and communication capabilities by implementing devices that act as both radar sensor and communication base station.

Novel transceiver algorithms and signal coordination procedures will be conceived, and new sensing parameters estimation approaches from the complex combined sensing-communication signals will be targeted taking into consideration different modulation and multiple access schemes.

The project will be carried out by the Experienced Researcher (ER) at the University of Cassino and Lazio Meridionale (Italy), under the supervision of Prof. Stefano Buzzi.

Furthermore, Huawei Mathematical and Algorithmic Research Center in Paris (France) will host the ER for a six-months secondment. The applying ER is Dr.

Mohamed Elmeligy, currently a post-doctoral researcher at the college of information engineering, Shenzhen University, China.