Two-Stage Jamming Detection and Channel Estimation for UAV-based IoT Systems

This work proposes an efficient two-stage jamming detection and channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicles (UAVs) communications. The proposed scheme is designed based on the unique time and frequency domain statistical characteristics of OFDM signals. In the time domain (TD), a likelihood ratio test (LRT)-based decision rule is derived as a function of the inherent correlation between the cyclic prefix (CP) samples and their counterparts in the OFDM symbol. In addition, in the frequency domain (FD), a closed-form joint jamming detection and channel estimation scheme is derived using the maximum a posteriori probability (MAP) principle as a function of the statistics of the received pilots and virtual subcarriers (VSCs) signals, which is then re-expressed using the generalized MAP ratio test (GMAPRT). The system’s complexity is reduced by applying the two stages sequentially, where the possible implementation of the second stage is conditioned on the outcome of the first stage. The performance of the proposed algorithm is evaluated using Monte Carlo simulations, where the results demonstrate its effectiveness compared to the TD-only and FD-only approaches. The results confirm the superior performance of the proposed scheme compared to the cyclostationary feature (CF)-based technique under various operating scenarios.