Communications Simulation

Total 3898 resources

In channel equalization applications, the original signal distorted by the channel is used as the input to an adaptive filter, with the desired signal being a time-delayed version of the original signal, as shown in Figure 22(a). Typically, the time-

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This implementation demonstrates image compression using 8x8 block-based Discrete Cosine Transform (DCT) and reconstruction via Inverse DCT (IDCT): DCT serves as a separable transform for image compression encoding, while IDCT handles decompression.

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This MATLAB source code by Dr. Xiaohua Li implements a comprehensive MIMO blind equalization system featuring second-order statistics, CMA equalizer, and subspace method implementations with detailed algorithmic structures and modular functions.

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This MATLAB code simulates M-QAM transmission systems with different modulation orders (4-QAM, 16-QAM, 64-QAM, etc.) over AWGN channels, generating comprehensive bit error rate performance curves for analysis.

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Traditional modulation recognition algorithms utilize key statistical features including: maximum value of the zero-centered normalized instantaneous amplitude spectral density, standard deviation of the zero-centered normalized instantaneous amplitu

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