Implementation of MAP Algorithm - Maximum A Posteriori Probability Algorithm

This MATLAB program implements the Maximum A Posteriori Probability (MAP) algorithm, which includes comprehensive modules for convolutional encoding, convolutional decoding, BPSK modulation, and AWGN channel simulation. The implementation features BER (Bit Error Rate) versus SNR (Signal-to-Noise Ratio) performance plotting functionality.

The program serves as a valuable tool for communication system performance analysis and evaluation. By implementing the MAP algorithm, which uses logarithmic domain calculations and forward-backward recursion for probability estimation, we achieve more accurate transmission signal probability estimation, thereby enhancing communication system reliability and performance.

Convolutional encoding and decoding represent fundamental channel coding techniques that improve data transmission reliability and anti-interference capability. The program implements these using polynomial generators and Viterbi-like trellis processing to enhance system error correction capacity through state transition management and path metric calculations.

BPSK (Binary Phase Shift Keying) modulation, implemented through phase switching between 0 and 180 degrees, converts digital data into analog signals for transmission. The program employs BPSK modulation with constellation mapping to prepare signals for channel transmission.

AWGN (Additive White Gaussian Noise) modeling, implemented using random number generation with specified variance, represents a common noise model. The program incorporates AWGN effects through channel simulation and plots BER-SNR relationship curves using Monte Carlo simulation techniques to evaluate system performance under various noise conditions.

In summary, this MATLAB program provides a complete MAP algorithm implementation featuring convolutional coding/decoding, BPSK modulation, and AWGN channel capabilities. It enables comprehensive communication system performance analysis and offers methodologies for improving system reliability through probabilistic decoding techniques and performance metric evaluation.