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Based on the spectrum sensing schemes, the cognitive radio is proposed to reduce the computational complexity in MIMO-OFDM system. On the one hand, apply thematching filter algorithm in a different antenna, and employ the linear property of inverse Fast Fourier transform (IFFT) to increase the number of candidate sequences so as to achieve better SNRand lower MSE performance.
To analyze a signal in the time domain, we apply IFFT (Inverse Fast Fourier Transform) and convert it from parallel to serial OFDM signal to add a cyclic prefix (CP), which helps avoids interference between OFDM symbols.
We then feed this signal through an Additive White Gaussian Noise (AWGN) channel. At the receiver end, the CP is removed and the signal converted from serial to parallel to get the original, with FFT applied to each symbol for analysis in the frequency domain. After demodulation, the signal is cross correlated with that of a time-shifted local oscillator.
Figure 4: Design of MIMO-OFDM System using Matched Filter Spectrum Sensing Cognitive Radio Network
The implementation of system model in MATLAB software, with the main block described below. We generated a random binary signal in serial manner. To analyze a signal in the serial to parallel converter then applied IFFT (inverse fast fourier transform) and convert it from parallel to serial OFDM signal. The OFDM signal is add cyclic prefix (CP) because the remove interference between OFDM symbols. We then feed this signal through an Additive White Gaussian Noise (AWGN) channel. At the receiver site, the OFDM signal is CP removed and signal converted from serial to parallel then applied FFT (fast fourier transform). Received the output of FFT signal then signal converted from parallel to serial converter to each symbol for analysis in the frequency domain is received. After demodulation the signal is cross correlated with that a time shifted in demodulation signal.

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