Signal model for OFDM sensing in cognitive radio

In this paper we investigate the detection of OFDM primary signals using autocorrelation. The conventional model for signal detection in cognitive radio assumes that the primary signal is either absent or present with a fixed level. We show that the conventional model is the source of significant discrepancy between the analytical prediction of the detection algorithm and the simulations. We propose to design the autocorrelation-based detector using the uniform approximation for the distribution of the primary signal power. The uniform approximation allows to derive the optimal decision threshold by using the Lagrangian optimization. Finally, we are able to design the autocorrelation-based detector such that the spectrum reuse is enhanced and the analytical prediction of the detection algorithm achieves a better match with the simulation results.