Channel State Information
This dataset is shared as part of the paper Towards scalable and low-cost WiFi sensing: preventing animal-vehicle collisions on rural roads, submitted to the IEEE Internet of Things Journal (IoT-J). It contains Wi-Fi Channel State Information (CSI) data from roadway crossings of small and large animals, persons and vehicles in rural environments.
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This paper presents the methodology and outcomes of a comprehensive dataset collection using ESP32-Nodemcu devices and the ESP32-CSI Toolkit. The dataset, designed to explore the capabilities of Channel State Information (CSI) in distinguishing human activities, was collected in a controlled indoor environment under three scenarios: single-user, two-user, and three-user setups.
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To test the reciprocity of V2X channel, bidirectional channel state information (CSI) measurement is conducted between Alice (RSU) and Bob (OBU) through PSSCH signal. We utilized two USRP X310 SDR platforms equipped with the CBX daughter board as Alice and Bob. Despite the designed fast USRP transceiver switching, there is a signal transmission delay of approximately 0.3 ms, resulting in a gap of about 4 to 5 symbols in the PSSCH subframe actually received by Alice and Bob.
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This dataset contains thousands of Channel State Information (CSI) samples collected using the 64-antenna KU Leuven Massive MIMO testbed. The measurements focused on four different antenna array topologies; URA LoS, URA NLoS, ULA LoS and, DIS LoS. The users channel is collected using CNC-tables, resulting in a dataset where all samples are provided with a very accurate spatial label. The user position is sweeped across a 9 squared meter area, halting every 5 millimeter, resulting in a dataset size of 252,004 samples for each measured topology.
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