Sub-Nyquist sampling of OFDM signals for cognitive radios

We investigate sampling and detection of orthogonal frequency-division multiplexing (OFDM) signals with unknown carriers at sub-Nyquist rates. Efficient acquisition and processing of such broadcast signals is a challenge but constitutes a crucial part of enabling cognitive radios. In order to alleviate both the analog and digital burden when treating wideband signals, we adapt the modulated wideband converter (MWC), a recently proposed sub-Nyquist sampling system, to fit OFDM signals. In particular, after detecting the active bands using the MWC, we use several different equalization methods in order to improve the bit-error rate (BER). We then show how to process the real sub-Nyquist samples in each band in order to recover the complex OFDM signal. A standard digital OFDM receiver is then used to detect the input symbols. To evaluate the performance of our system, we derive an analytical bound on the BER as a function of the received signal to noise ratio. Simulations validate the proposed system.