On the feasibility of Integrated Sensing and Communication (ISAC) on bluetooth and Wi-fi

Sammanfattning

The study thesis is to determine the possibility of tracking humanmovement in an indoor space using exclusively wireless signals as measurements, received by low-cost ESP32-C3 microcontrollers. The results are two basic scalars that can be found on commodity hardwareknown as the Received Signal Strength Indicator (RSSI) based on thecommunication of the Bluetooth Low Energy (BLE) and the distance estimates based on the Wi-Fi Fine Time Measurement (FTM). Although guided by the vision of the concept of Integrated Sensing andCommunication (ISAC), the piece conformists this paradigm to a miniature of a microcontroller in size, using the non-abundant hardwareand relative low-power of lacking computational capabilities. A prototype system was designed with four ESP32-C3 nodes set totwo independent subsystems namely: a BLE subsystem which wouldsense the RSSI and Wi-Fi subsystem which would range FTM. Theaverages of the RSSI and the FTM distance were recorded block-wisewithin a fixed indoor room on a Python-based logging pipeline running on a personal computer as the presence and movement of oneperson varied systematically. An experimental design was to be usedinvolving a sequence of stationary and motion situations such as anempty room, a motionless person standing in different positions, andthe multiple walking paths which crossed or visited the inter-nodespace. The findings show that there is qualitative distinction between thestationary and the motion conditions in both RSSI and FTM. Eachmetric had variation in a narrow range around a constant value inboth the empty-room and stationary-person conditions. On the otherhand, the motion left behind significant temporal changes- such asdips, spikes, and sustained changes compared with baseline, indicating that it is sufficient that simple threshold based rules based ondeviations with the baseline can be used to distinguish most motionperiods and no-motion periods in the considered setup. The experiments also show that RSSI and FTM provide complementary data,and that combining them, they provide a stronger depiction of channel dynamics than either of them used independently. Although there are drawbacks related to the reliance on the environment, single-room testing, and qualitative analysis, the resultssupport the possibility of the creation of a simple, ISAC-inspired motion detector using the help of the off-the-shelf ESP32-C3 boards andbasic wireless measurements.