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.

Hardware Limitations and Optimization Approach in 1-Bit RIS Design at 28 GHz

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for electromagnetic (EM) wave manipulation, offering unprecedented control over wave reflections compared to traditional metallic reflectors. By utilizing an array of tunable elements, RIS can steer and shape electromagnetic waves to enhance signal quality in wireless communication and radar systems. However, practical implementations face significant challenges due to hardware limitations and phase quantization errors. In this work, a 1-bit RIS prototype operating at 28 GHz is developed to experimentally evaluate the impact of hardware constraints on RIS performance. Unlike conventional studies that model RIS as an ideal phase-shift matrix, this study accounts for physical parameters that influence the actual reflection pattern. In particular, the presence of specular reflection due to hardware limitations is investigated. Additionally, the effects of phase quantization errors, which stem from the discrete nature of RIS elements, are analyzed, and a genetic algorithm (GA)-based optimization is introduced to mitigate these errors. The proposed optimization strategy effectively reduces gain degradation at the desired angle caused by 1-bit quantization, enhancing the overall performance of RIS. The effectiveness of the approach is validated through measurements, underscoring the importance of advanced phase control techniques in improving the functionality of RIS.

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.