Project Description

RASECOL is a two-years Marie Curie Individual Fellowship Project funded by the European Union under grant agreement 898354. The project has taken place from July1, 2021 to June 30, 2023. The project has been coordinated by the Università degli Studi di Cassino e del Lazio Meridionale. The project principal investigator was Prof. Stefano Buzzi, while the Experienced Researched carrying out the research project was Dr. Mohamed Rihan.  

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 side while achieving accurate estimation for the sensing parameters for the radar side. Novel transceiver algorithms and signal coordination procedures will be conceived to support integrating the radar sensing capabilities into mobile communication networks. New tools based on machine learning can be employed to optimize the waveform of the combined transmission signals as well as estimating the sensing parameters of the integrated DFSC networks.  Specifically, this project

1. Proposed the use of an active RIS to improve the detection capability of a radar system. The main intuition is that an active RIS can offer a second look at a target illuminated by the radar, thus providing spatial (angular) diversity, and, in addition, can compensate for the product path loss along the indirect target-RIS-radar path. Overall, the proposed RIS-assisted architecture may realize a sort of low-cost distributed radar system, in particular, rather than having a second radar receiver equipped with a full radio-frequency processing chain and using a dedicated data link from each receiver to a common fusion center, an active RIS simply redirects the impinging signal towards a unique destination, which collects both the direct and indirect target echoes through two dedicated spatial beams. Our proposed work was able to choose the number of RIS elements, their amplification gain, and the power split among the radar transmitter and the active RIS to maximize the detection probability for a fixed probability of false alarm. Since this problem is non-convex, we derive a sub-optimal solution based upon an alternating maximization. The results showed that the use of an active RIS can grant a large performance improvement compared to the cases where no RIS or a passive RIS is used. 

2. Proposed a novel RIS-assisted radar communication coexistence framework. The design problem aimed to maximize the radar SINR by jointly optimizing the active radar beamforming, the passive RIS beamforming, and communication transmitter covariance matrices under communication rate, communication and radar power budget constraints. The coexistence design problem is solved first under perfect direct and cascaded channel CSI. Alternating optimization is used to decouple the optimization problem and divide it into three separate sub-problems. The communication covariance optimization sub-problem is solved using Lagrangian dual decomposition. The radar active beamforming sub-problem is solved using successive convex programming approach. Finally, the RIS passive beamforming sub-problem is solved through a linear local search method. We also updated the coexistence design problem to the robust case where we considered a statistical CSI error model and solve the design problem by following the same approach used for the perfect CSI case. Simulation results are presented to validate our analysis and verify the effectiveness of the proposed RIS-assisted radar communication coexistence design with either perfect CSI and CSI uncertainty cases.

3. Developed power allocation and RIS beamforming algorithms in active-RIS-aided integrated sensing and multi-user MISO communication systems. Specifically, a communication system that uses a dual function radar-communication base station (DFRC-BS) to communicate with multiple users using an active RIS and to sense a target. The goal is to maximize the system EE by optimizing power allocation and active beamforming while considering individual user requirements and power constraints. The optimization problem is complex, and two different alternating-maximization algorithms are proposed for its solution. Extensive simulations are then conducted to highlight the merits of the proposed framework.

4. However, several works have analyzed the benefits of passive and active RISs in scenarios where either a stand-alone communication or a radar system is considered. But, the deployment of either passive or active RISs in ISAC-enabled networks is a much less-studied topic, with few exceptions. To fill this gap, we highlighted the key design issues and signal processing solutions associated with deploying  passive RIS in ISAC scenarios, and we addressed the various roles the active and passive RISs can play in improving the figure-of-merits of different ISAC scenarios. Moreover, we presented a summary of recent works on RIS-assisted communication and sensing systems. 

Project Results

[1]  M. Rihan, E. Grossi, L. Venturino and S. Buzzi, "Spatial Diversity in Radar Detection via Active Reconfigurable Intelligent Surfaces," in IEEE Signal Processing Letters , vol. 29, pp. 1242-1246, 2022.   (doi: 10.1109/LSP.2022.3175417 ).

Link : https://ieeexplore.ieee.org/document/9775578

[2]  Mohamed Rihan, A. Zappone, and S. Buzzi,  "Robust RIS-Assisted MIMO Communication-Radar Coexistence: Joint Beamforming and Waveform Design," in  IEEE Transactions on Communications, vol. 71, no. 11, pp. 6647-6661, Nov. 2023, (doi: 10.1109/TCOMM.2023.3298983 ). 

Link: https://ieeexplore.ieee.org/document/10194901 

[3]  Mohamed Rihan, A. Zappone, and S. Buzzi,  "Energy Efficiency Maximization for Active RIS-Based Integrated Sensing and Communication,"  in IEEE Communications Letters, 2023.  [Revised]

[4]  Mohamed Rihan, A. Zappone, S. Buzzi, Gabor Fodor, Merouane Debbah,  "Passive vs. Active Reconfigurable Intelligent Surfaces for Integrated Sensing and Communication: Challenges and Opportunities," IEEE Network,  2023. (doi: 10.1109/MNET.2023.3321542 )  

Link: https://ieeexplore.ieee.org/document/10273405

[5] Mohamed Rihan, Alessio Zappone, Stefano Buzzi, Emanuele Grossi, "Power control and Active RIS Design for Energy-Efficient Integrated Sensing and Communications," 2023 57th Asilomar Conference on Signals, Systems, and Computers,  Pacific Grove, CA, USA, 2023, pp. xxx-xxx.  [Invited Paper].

For inquiries: Prof. Stefano Buzzi (s.buzzi@unicas.it); Dr. Mohamed Rihan (elmeligy@ant.uni-bremen.de)