Cyclostationary feature analysis of CEN-DSRC for cognitive vehicular networks

Cognitive vehicular networks provide the necessary intelligence for vehicular communication networks in order to optimally utilize the limited resources and maximize the performance. One of the important functions of cognitive networks is to learn the radio environment by means of detecting and identifying existing radios. In this context we use the cyclostationarity features of dedicated short range communication (DSRC) signals to blindly detect them in the environment. We present experimental results on the cyclostationarity properties of DSRC wireless transmissions considering the CEN (European) standards for both uplink and downlink signals. By performing cyclostationarity analysis we compute the cyclic power spectrum (CPS) of the CEN DSRC signals which is then used for detecting the presence of the CEN DSRC radios. We obtain CEN DSRC signals from experiments and use the recorded data to perform post-signal analysis to determine the detection performance. The probability of false alarm and the probability of missed detection are computed and the results are presented for different detection strategies. Results show that the cyclostationarity feature based detection can be robust compared to the well known energy based technique for low signal to noise ratio levels.