Power Optimization for Integrated Active and Passive Sensing in DFRC Systems

TL;DR

This work addresses the sensing-communication tradeoff in DFRC systems by introducing integrated active and passive sensing (IAPS) with two backhaul regimes. It develops a centralized fusion framework under unlimited backhaul using GLRT for target detection, and a distributed, voting-based approach under limited backhaul with a whitening-based GLRT and a heuristic power allocation algorithm. The key contributions are the SDP-based reformulation for unlimited backhaul, the GLRT derivations and noncentrality parameter expressions, and the novel voting-based fusion plus algorithmic solution for limited backhaul, validated by simulations showing significant sensing gains from IAPS, especially when backhaul capacity is high. The results highlight the practical advantages of leveraging multi-static sensing in DFRC and provide scalable strategies to optimize transmit power under QoS constraints, with improved performance as RAPs increase.

Abstract

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.

Figures (12)

• Figure 1: Illustration of IAPS in DFRC System comprising $K$ UEs, $R$ RAPs, a DFRC BS and a CC, where the RAPs and the BS are fully synchronized controlled through the CC. Specifically, the BS transmit DFRC signals to serve the $K$ UEs and sense a desired target, while $R$ RAPs and the BS receive echo signal.
• Figure 2: The relation between $\hat{P}_{\rm D}$ and $p_0$ with {$P_{\max}=30$ dBm, $\Gamma=$15dB}.
• Figure 3: The performance gap between the heuristic algorithms and the upper bound.
• Figure 4: The trade-off between the step size $\Delta_p$ and the time cost as well as the sensing performance.
• Figure 5: The 2D locations of the RAPs, DFRC BS, and the target.

Theorems & Definitions (3)

• Lemma 1
• Lemma 2
• Lemma 3