Full-Duplex NOMA Enabled Integrated Sensing and Communication: Joint Transmit and Receive Beamforming Optimization

Integrated sensing and communication (ISAC) has emerged as a new paradigm for the sixth generation (6G) mobile communication systems. However, embedding ISAC into a conventional communication system may degrade the mutual benefit of radar sensing and communication due to the low spectral efficiency and weak interference management. Therefore, in this paper, we propose a full-duplex (FD) non-orthogonal multiple access (NOMA) enabled ISAC framework in which a dual functional base station (BS) operates simultaneous target detection and uplink/downlink communication with the same temporal and spectral resources. To exploit some insights into the benefit of such a framework, we investigate the sensing signal processing procedure and communication model. Towards this end, a joint transmit and receive beamforming design is studied in the cases where single-target detection with perfect channel state information (CSI) and multi-target detection with imperfect CSI are considered, respectively. The corresponding optimization problems aim to maximize the sensing signal-to-interference-plus-noise ratio (SINR), subject to uplink communication SINR requirement for each uplink user equipment (UUE) and downlink communication SINR requirement for each downlink user equipment (DUE). We propose an alternating-optimization algorithm to solve the formulated non-convex optimization problems efficiently. Specifically, at each iteration, the closed forms of the optimal sensing and uplink communication receive beamforming vectors are obtained, respectively. Then, the sub-optimal transmit beamforming vector is solved by equivalent transformation and semidefinite relaxation (SDR) method. Numerical results demonstrate that the proposed FD-NOMA ISAC system outperforms the orthogonal multiple access (OMA) based ISAC in terms of both sensing and communication performances. Notably, the efficacy of the proposed scheme is heavily dependent on factors such as self cancellation (SI) and CSI uncertainty.