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.

Multi-Agent Reinforcement Learning for Base Station Placement in TDOA-Based Localization

Accurate localization of devices is a key capability for emerging 5G and 6G networks and depends on effective base station (BS) placement. Conventional geometry-based approaches such as Geometric Dilution of Precision (GDOP) ignore realistic propagation effects such as Non-Line of Sight (NLOS) shadowing and multipath-induced Time of Arrival (TOA) bias caused by buildings. This paper proposes a ray-tracing-assisted Multi-Agent Reinforcement Learning (MARL) framework for environment-aware BS placement in Time Difference of Arrival (TDOA) localization systems. Proximal Policy Optimization (PPO) agents are trained on Channel Impulse Responses (CIRs) generated from a detailed 3D model of a university campus. Each agent cooperatively places one BS while optimizing a shared reward that combines localization accuracy and coverage. The approach is evaluated on five campus segments with varying propagation characteristics. Results show that the learned policy achieves localization accuracy comparable to conventional GDOP-based placement, lowering the average localization Mean Absolute Error (MAE) by about 3 % relative to the stronger (mean-optimized) geometric baseline. The behavior is segment-dependent, with a clear improvement on individual segments (up to about 14 %) and comparable or slightly higher error on the others. These findings indicate that incorporating site-specific propagation data into the placement process can match and selectively improve upon purely geometric strategies, motivating further work toward consistent gains.

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 the Feasibility of Passive Bistatic ISAC Based on Unmodified LoRa

Integrated Sensing and Communication (ISAC) enables sensing capabilities by reusing communication signals, making it particularly attractive for large-scale deployments through signals of opportunity. While most existing ISAC research targets wideband systems, Low Power Wide Area Network (LPWAN) technologies such as LoRa remain largely unexplored from a radar-like sensing perspective. Existing LoRa-based approaches mainly focus on motion detection or require modifications of the communication waveform, limiting their applicability in deployed networks. This paper investigates the feasibility of radar-like sensing using unmodified LoRa communication signals as signals of opportunity in a purely passive bistatic ISAC configuration. The proposed approach focuses on Doppler-based sensing to enable target separation and super-resolved target estimation without interfering with existing LoRa network operation. The analytically derived sensing capabilities are compared against simulation results and validated through bistatic measurements using two USRP B210 software-defined radios, confirming the feasibility of Doppler-based LoRa sensing under practical conditions and revealing relevant implementation challenges. The results demonstrate that LoRa-based ISAC enables highly scalable, large-area, low-resolution sensing by leveraging existing infrastructure, providing a complementary sensing capability to area-limited high-resolution 6G ISAC systems, and a foundation for future multi-node and data fusion extensions.

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.

How connected cars could capture cloud dynamics -- first evidence from two simulation scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image-based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal-based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40 %, the root mean square errors for both sensor networks are still below 5 m/s. In conclusion, the spatio-temporal characteristics of a vehicle network offer some potential for estimating cloud movements.