Communication and Radar

Along with the commercial deployment of 5G networks, academic and industry researchers are investigating the next-generation wireless network 6G. Next generation 6G networks are envisioned to achieve higher data rate, higher energy efficiency, lower latency, higher security, and improved quality of service compared to the 5G system. In order to realize these visions, in our group we focus on the study of the following key 6G technologies: Dynamic Metasurface Antennas (DMAs), Near-field wireless communications, Dual-function radar and communications (DFRC), and AI for communication networks. In addition, in the context of radar, we are investigating various applications of autonomous radar, remote patient monitoring using mm-wave radar and sub-Nyquist radar. 

  • Dynamic Metasurface Antennas (DMA): DMA is an emerging technology for efficiently realizing large-scale antenna arrays with reduced cost and power consumption compared to conventional arrays. DMA are a practical implementation of large intelligent surfaces, i.e., they enable programmable control of the transmit/receive beam patterns, which also provide advanced analog signal processing capabilities and naturally implement RF chain reduction without dedicated analog circuitry. Furthermore, DMA facilitates densification of the antenna elements, which can be exploited to improve communication performance.

Communication and Radar.png

                                                                                        Dynamic Metasurface Antennas (DMA)

  • Near-Field Wireless Communications: With the combination of large-scale antennas and high transmission frequencies, future communication devices will likely operate in the near-field (Fresnel) region. Differently from the far-field region, where plane wave propagation holds, in the radiating near-field region the wavefront is spherical and the radiation pattern varies significantly with distance, which brings forth the possibility to generate focused beams (beam focusing) towards a specific location, in contrast to only a specific direction as in far-field conditions via conventional beam steering. Beam focusing gives rise to the possibility to support multiple coexisting orthogonal links, even at similar angles.


                                                                                          Near-Field Wireless Communications

  • Dual-function Radar and Communications (DFRC): In a multitude of practical applications, such as autonomous vehicles, the communication device must also be able to sense its environment using radar. Jointly implementing radar and communications contributes to reducing the number of antennas, system size, weight and power consumption, while allowing spectrum and resource sharing. In this direction, we study the embedding of digital communication methods into radar systems such that both functionalities can operate reliably with minimal mutual interference. In particular, we identify the combination of the notion of agile radar with the usage of index modulation schemes as a potential strategy for realizing dual-function radar communications methods without giving rise to coexistence issues. We also design dedicated precoders to optimally service both communication and radar nodes.

MMWave Radar & Communication Demo
               Extended Line of Site based on Joint Radar and Communication


  • AI for Communication Networks: AI-driven techniques are independent of the underlying stochastic model, and thus can operate efficiently in scenarios where this model is unknown, or its parameters cannot be accurately estimated. Nonetheless, not every problem can and should be solved using deep neural networks (DNNs). In fact, in various communications setups where model-based algorithms exist and are computationally feasible,  these analytical methods are typically preferable over AI schemes due to their theoretical performance guarantees and possible proven optimality. In this direction, we focus on model-based deep learning for communications. These are methods that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data.
  • Remote Patient Monitoring Using mm-Wave Radar: In the last decade, small, sophisticated, and robust millimeter-wavelength (mm-Wave) radar systems have evolved, which opens the door to emerging new and exciting applications.  The healthcare sector is one of the biggest beneficiaries of this development. An essential application of these radar systems is remote patient monitoring of vital signs, without the need for wired connections that produce discomfort or irritations. The proposed method introduces the capability of analyzing very small displacements, enabling real-time monitoring of cardiopulmonary activity by utilizing interpretable mathematical modeling for adaptive sparse recovery methods.

                                                         Remote Patient Monitoring Using mm-Wave Radar