No abstract

Integrated sensing and communication (ISAC) is a promising technology for 6G, with the goal of providing end-toend information processing and inherent perception capabilities for future communication systems. Within ISAC emerging application scenarios, vehicular ISAC technologies have the potential to enhance traffic efficiency and safety through integration of communication and synchronized perception abilities. To establish a foundational theoretical support for vehicular ISAC system design and standardization, it is necessary to conduct channel measurements, and modeling to obtain a deep understanding of the radio propagation. In this paper, a dynamic statistical channel model is proposed for vehicular ISAC scenarios, incorporating Sensing Multipath Components (S-MPCs) and Clutter Multipath Components (C-MPCs), which are identified by the proposed tracking algorithm. Based on actual vehicular ISAC channel measurements at 28 GHz, time-varying sensing characteristics in front, left, and right directions are investigated. To model the dynamic evolution process of channel, number of new S-MPCs, lifetimes, initial power and delay positions, dynamic variations within their lifetimes, clustering, power decay, and fading of C-MPCs are statistically characterized. Finally, the paper provides implementation of dynamic vehicular ISAC model and validates it by comparing key simulation statistics between measurements and simulations.

No abstract

Simulated and experimental data were used to investigate the particle size distribution (PSD) recovery from a flowing aerosol via dynamic light scattering (DLS). It is found that, for a unimodal PSD, increasing the flow velocity leads to an increase in the error of the peak position, a decrease in the peak height and a broadening of the PSD. This is exacerbated for larger aerosol particles. For a bimodal PSD, the separation of the two peaks decreases and results in a single-peak PSD as the velocity increases. The effect of velocity on the measurement is similar to adding signal noise, which increases with the velocity. This effect is related to the measured aerosol particle size. The larger the particles, the lower the signal-to-noise ratio. By comparing DLS measurement results for flowing and non-flowing aerosol samples, it is possible to determine the maximum flow rate at which measurements can be usefully made.

No abstract