4.4 Article

Joint Optimization of Radar and Communications Performance in 6G Cellular Systems

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGCN.2023.3234258

Keywords

Sensors; Radar; Clutter; Array signal processing; Complexity theory; Copper; 6G mobile communication; Detection probability; dual functional radar communication; integrated sensing and communication; resource allocation

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This paper studies a dual functional radar communication (DFRC) system with multiple communication users (CUs) and a radar target. The goal is to optimize the beamforming vectors at the transmitter to enhance radar performance while satisfying the data rate requirements of CUs. Efficient algorithms based on convex optimization techniques are proposed to solve the formulated non-convex optimization problems. The results show that separate probing signals for radar detection are not needed in both clutter scenarios, reducing the complexity of the algorithm.
Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and a radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal-to-clutter-plus-noise-ratio (SCNR) is maximized while satisfying the minimum data rate requirements of the individual CUs. With regard to clutter, we consider two scenarios based on the possibility of clutter removal. Even though the formulated optimization problems are non-convex, we present efficient algorithms to solve them using convex optimization techniques. Specifically, we use duality theory and Karush-Kuhn-Tucker conditions to show the underlying structure of optimal transmit precoders. In the proposed solution, it is observed that there is no need to transmit separate probing signal for the radar detection in both the considered scenarios. This results in reduction in the number of optimization variables in the problem. Moreover, we make use of the asymptotic equivalence between Toeplitz matrices and Circulant matrices to further reduce the complexity of the proposed algorithm. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

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