CRLB-Driven Beamforming and Power Allocation for Multi-BS Cooperative ISAC Networks

This paper presents a Cramér-Rao lower bound (CRLB)-driven beamforming (BF) and power allocation (PA) framework for cooperative integrated sensing and communication (ISAC) networks, where a set of multi-antenna base stations (BSs) jointly serve multiple users and simultaneously perform multi-static target estimation. In our design, we investigate how the position error bound (PEB) and velocity error bound (VEB) can be exploited and incorporated into BF and PA optimization. The design leveraging PEB and VEB as metrics directly characterizes sensing accuracy. First, we propose a semidefinite programming (SDP)-based BF, where the PEB and VEB constraints are handled by double Schur complements, and the rank-1 constraints of the BF covariance matrices are relaxed by semidefinite relaxation (SDR), whose tightness is proved using the Karush-Kuhn-Tucker (KKT) conditions. Addressing the complexity, we further develop a two-stage PA algorithm, where the communication and sensing beams are formed by the regularized zero-forcing (RZF) and null-space projection (NSP) methods, respectively, and the communication and sensing PAs are solved sequentially by second-order cone programming (SOCP). Although the PA algorithm sacrifices the degrees of freedom, its performance shows a slight gap compared to BF in the simulation, while the execution time of around 25 ms demonstrates its applicability in dynamic environments.

Fundamental Sensing Limits of 6G Cooperative MIMO-ISAC Networks: Joint Position-Velocity CRLB and Decoupling Analysis

This paper presents a Cramér-Rao lower bound (CRLB)-based performance bound analysis of cooperative multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) networks. We first show the CRLB transformation of the signal-level parameters to the state parameters (position and velocity) in cooperative ISAC networks. Unlike existing studies that primarily ignored coupling between position and velocity in the Fisher information matrix (FIM), we derive the full FIM and the corresponding exact CRLB. Particularly, the results of multi-monostatic sensing, multi-bistatic sensing, and their hybrid are discussed. Addressing the complexity and tractability, we simplify the FIM and CRLB by excluding the coupling terms between the position and velocity, and provide a criterion for determining whether the simplification is valid. The simplified CRLB benefits from low computational complexity and provides a tractable and reliable performance metric for optimization problems such as resource allocation and beamforming. Finally, the position and velocity CRLBs and the simplification-induced error are examined in the simulation. The results demonstrate that the simplified CRLB can be applied in general cases. Based on the simulation results, the impact of resource and geometric parameters on position and velocity error bounds, and the validity of the simplified CRLBs is explained through the corresponding CRLB expressions.

On Unified CRLB Framework from Generic Signals to ISAC Waveforms with Virtual Array Sensing

This paper presents a unified Cramér-Rao lower bound (CRLB) framework for signal-level parameters in integrated sensing and communications (ISAC)-enabled radar systems. Starting from the generic signal model, we analyze the coupling between delay and Doppler in the Fisher information matrix (FIM), which is unsolved and often overlooked in relevant studies. Addressing this issue, we derive the conditions under which the coupling terms can be eliminated and demonstrate that these conditions are typically satisfied for ISAC-enabled waveforms. Afterward, the CRLBs of representative ISAC waveforms are derived within the unified framework, enabling consistent and comparable analysis across the waveforms and avoiding model-dependent discrepancies. Further, the framework is extended to virtual array (VA) sensing systems, where the impact of different multiplexing schemes is analyzed. Simulation results demonstrate the consistency between the CRLBs derived from the proposed framework and those obtained from waveform-specific analyses. The proposed framework shows strong generality, waveform-compatibility, and flexibility, offering a versatile tool for the CRLB analysis of various waveforms, including those lacking existing analytical results.