## 1 Introduction
### Related Works  
Localization and positioning will be interchangeably used for estimation of the state (position, orientation) of a connected device in a global frame of reference [15]. For the investigations of wireless localization, the authors in [16] show that the Cramer-Rao lower bound (CRLB) is commonly used as the lower bound of the variance of unbiased estimation parameters in wireless localization networks. The authors in [17] introduce some basic concepts such as FIM, equivalent Fisher information matrix (EFIM), and so on, and also provide the derivation of the localization FIM expressions in wireless localization networks. The authors in [18] provide analysis for the error lower bound of localization in non-cooperative and spatial cooperative wireless localization networks, respectively. For radar localization networks that have difficult-to-meet trilateration, a method is provided to assist anchors by mutual ranging between radars to achieve accurate localization of radars. The authors in [14] provide specific FIM expressions of non-cooperative, spatial cooperative, and spatiotemporal cooperative wireless localization networks, respectively. It also provides another two lower bounds--the Ziv-Zakai Lower Bound and the Weiss-Weinstein Lower Bound--and compares their performance with traditional CRLB's performance in wireless localization networks. The authors in [19] focus on the localization performance bound in uplink massive MIMO with few-bit ADCs. They elucidate that massive MIMO equipped with low-resolution ADCs can still achieve high-precision localization performance. Additionally, the desired localization CRLB is also derived, guiding the design for localization in quantized massive MIMO.  
Compared with localization and positioning, sensing has a broader scope, from channel parameter estimation and carrier sensing to presence detection [20]. For the investigations of target sensing, the authors in [15] discuss the geometric information brought by the different components in the sensing channel decomposition, such as channel gain, AOA angle, TSOA delay, and so on. The authors in [21] state the impossibility of equipping each target with communication devices in some circumstances, such as intrusion detection and wildlife monitoring, and propose an inexpensive and efficient target sensing approach called RSS distribution-based localization (RDL), compared with other target sensing techniques, such as GPS and channel state information (CSI) [22]. Although the aforementioned achievements include the geometric information of the sensing links and the implementation methods of target sensing, there is little research with respect to analysis of the fundamental limits in target sensing networks. So far, the majority of research on fundamental limits has focused on wireless localization networks, and there is still little research on the fundamental limits of ISAL networks.  
In terms of research on network resource optimization, the authors in [23] propose power optimization schemes between nodes for wireless localization networks and target sensing networks, respectively. The authors in [24] show that, in wireless localization networks, the problem of anchor power optimization can be transformed into a semi-definite program (SDP), and specific SDP solutions are provided. The authors in [25]find the regularity of node power optimization in single-slot synchronous ISAL networks, indicating that, in order to minimize target sensing error in the power-limited networks when the anchor power allocation in the network remains unchanged, more power will be allocated to the radars closer to the target. Additionally, in [25], the optimization of power in ISAL networks is achieved by simultaneously optimizing all the variables. However, when the number of variables is large, the optimization problem is often too complicated to solve. Therefore, we need to explore a new optimization solution, and there has been no relevant research on this issue so far.