(i) Communication. The course begins with RF signals in time and frequency domains, signal fading, propagation-loss models, modulation techniques such as BPSK, QPSK, and m-QAM, and the BER behavior of these schemes. It also covers OFDM, channel estimation, and equalization, supported by demonstrations on SDR platforms including USRP, HackRF, and RTLSDR.
(ii) Sensing. The second component focuses on RF sensing using CIR and CFR representations, with applications built on UWB and WiFi signals that provide different operating points in spatial resolution and coverage. Deep learning on RF data, such as CNN-based classification of amplitude or phase spectrograms, is used to connect signals to applications like navigation, human activity and gesture recognition, and material classification.
(iii) Networking. The final segment studies wireless networking, including the IEEE 802.11 WiFi MAC, and then broadens to current IoT wireless systems such as LoRa, BLE, and RFID. The course closes with LEO satellite networks and their role in modern communication infrastructure.