In this paper, we study the transmit and receive beamforming vectors in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) performs the downlink communication with user equipments (UEs) and active sensing tasks simultaneously. While reflected signals are utilized for passive sensing at the receive access points (RAPs). We adopt different fusion strategies based on the backhaul capacity between the RAPs and BS. Specifically, in the scenarios with unlimited backhaul capacity, the sensing signals received by the BS and RAPs are forwarded to the central controller (CC) for signal fusion. In contrast, in the scenarios with limited backhaul capacity, the BS and each RAP make independent decisions and transmit their binary inference results to the CC for result fusion. Furthermore, we explore two cases of the signal-to-interference-plus-noise ratio (SINR) with and without the sensing interference cancellation (Case-1 SINR and Case-2 SINR). By optimizing the beamforming vectors according to different fusion strategies, we aim to maximize sensing performance while ensuring the minimum SINR requirement for the UEs subject to the power budget at the BS. Finally, numerical results demonstrate that the proposed beamforming optimization schemes can reach the upper bound of performance under both fusion strategies. It is also shown that adding the sensing signals generally improve the sensing performance in Case-1 SINR.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.