Signal Classification of Second Order Cyclostationarity Signals Using Bt-scld and Vbt-scld Techniques

Signal Classification and parameter estimation of cyclostationary signal is an vital issue for various security applications like civil and military applications such as LTE, spectrum detection, and spectrum maintaining in cognitive radio systems. Cyclostationary signals is one, that exhibits a statistical property to categories whether the signal is belong to be a probabilistic approach or a deterministic approach. However there are many criteria’s to be developed, led to investigate the problem in digital modulation. The problems are overcome by using different algorithms which includes Single Carrier Linearly Digitally modulated signals (SCLD), Orthogonal Frequency Division Multiplexing (OFDM), Block Transmitted-Single Carrier Linearly Digitally modulated signals (BT-SCLD), Variable Block Transmitted-Single Carrier Linearly Digitally Modulated signals (VBTSCLD). Analytical expressions are resulting for the cyclic autocorrelation function (CAF), cyclic spectrum (CS), and equivalent cycle frequencies (CFs). The block transmitted single carrier linear digitally modulated signals to accomplish a sensibly good performance at signal-to-noise ratios(SNR) for different channel conditions, still using a short sensing time. But the Block transmission system which is used for only fixed block size. So, in order to overcome this problem, Variable block transmitted single carrier modulated signals system consists of several different block size and used to enable a transmitter in variable blocks based on the bits. That is the size varies dynamically based on incoming signal. Furthermore, the conditions for avoiding aliasing in the cycle aliasing and spectral aliasing domains are obtained. The paper demonstrates that the effectiveness of proposed algorithm under less signal-to-noise ratios (SNRs), various short sensing times, and reduced Bit error rate. Keywords— Signal classification, Cyclostationarity statistics, Blind parameter estimation, Cyclic autocorrelation function and Signal-to-Noise Ratio. --------------------------------------------------------------------***----------------------------------------------------------------------