Accurate radar cross section (RCS) modeling is crucial for characterizing target scattering and improving the precision of Integrated Sensing and Communication (ISAC) channel modeling. Existing RCS models are typically designed for specific target types, leading to increased complexity and lack of generalization. This makes it difficult to standardize RCS models for 3GPP ISAC channels, which need to account for multiple typical target types simultaneously. Furthermore, 3GPP models must support both system-level and link-level simulations, requiring the integration of large-scale and small-scale scattering characteristics. To address these challenges, this paper proposes a unified RCS modeling framework that consolidates these two aspects. The model decomposes RCS into three components: (1) a large-scale power factor representing overall scattering strength, (2) a small-scale angular-dependent component describing directional scattering, and (3) a random component accounting for variations across target instances. We validate the model through mono-static RCS measurements for UAV, human, and vehicle targets across five frequency bands. The results demonstrate that the proposed model can effectively capture RCS variations for different target types. Finally, the model is incorporated into an ISAC channel simulation platform to assess the impact of target RCS characteristics on path loss, delay spread, and angular spread, providing valuable insights for future ISAC system design.

Smart factory scenarios in the Industrial Internet of Things (IIoT) introduce novel challenges to wireless communication, characterized by large-scale device interaction, strong reflection, and dense scattering, diverging from typical factories. To ascertain the channel disparities between smart factories and typical factories, we analyze channel characteristics across sub-6 GHz (e.g., 6 GHz) and mmWave frequencies (e.g., 28 and 39 GHz) in smart factory scenarios, establishing a multi-frequency path loss model. Furthermore, the comparison between the large-scale fading characteristics, including path loss and root mean square delay spread (RMS DS), observed in smart factory scenarios and those in the typical indoor factory (InF) scenarios defined by the 3rd-Generation Partnership Project (3GPP). Additionally, channel characteristics of the metal and non-metal scatterer areas in the smart factories are extracted to analyze the impact of metal scatterers on angle spread (AS) and RMS DS.

With the development of GPS technology, location-based information services are becoming more and more diverse. Using the trajectory data generated by GPS can analyze and study the various needs of taxi drivers. Big data technology such as interpreting, manipulating data and extracting nugget of information from data is crucial in Intelligent Transportation System (ITS). In order to enhance cruising efficiency of drivers, this paper proposes a Taxi-cruising Recommendation strategy based on Real-time information and Historical Trajectory data (TR-RHT). Primarily, we construct a Passenger-Demand predict model based on Historical Hotspot (PDHH) to predict the passengers’ demand in hotspot area. Then, an Improved Decision Tree (IDT) predicting algorithm is proposed to construct spatio-temporal index to select suitable historical data. Furthermore, we introduce Hotspot Recommendation based on Historical Trajectory (HRHT), wherein it defines cruising event as the process of taxi searching for passengers. The HRHT model performs statistics and analysis on the different states of taxi operation, which can obtain the probability of catching passengers at each hotspot and calculate the travel time between hotspots. The model selects the statistics result of cruising efficiency and driving time between hotspots based on spatio-temporal index, then analyzes the selected result to provide optimal pick-up hotspots for drivers. Experiment results show that TR-RHT can precisely suggest a cruising path to reduce cruising time for drivers.