Cognitive radio (CR) is an important technology to provide high-efficiency data communication for the IoT (Internet of Things) era. Signal detection is a key technology of CR to detect communication opportunities. Energy detection (ED) is a signal detection method that does not have high computational complexity. It, however, can only estimate the presence or absence of signal(s) in the observed band. Cyclostationarity detection (CS) is an alternative signal detection method. This method detects some signal features like periodicity. It can estimate the symbol rate of a signal if present. It, however, incurs high computational complexity. In addition, it cannot estimate the symbol rate precisely in the case of single carrier signal with a low Roll-Off factor (ROF). This paper proposes a method to estimate coarsely a signal's bandwidth and carrier frequency from its power spectrum with lower computational complexity than the CS. The proposed method can estimate the bandwidth and carrier frequency of even a low ROF signal. This paper evaluates the proposed method's performance by numerical simulations. The numerical results show that in all cases the proposed coarse bandwidth and carrier frequency estimation is almost comparable to the performance of CS with lower computational complexity and even outperforms in the case of single carrier signal with a low ROF. The proposed method is generally effective for unidentified classification of the signal i.e. single carrier, OFDM etc.

This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.