I built a MATLAB simulation of an extended (16, 11) Hamming Code for error detection and correction over an AWGN channel, modeling the full communication pipeline from encoding to decoding. The system serializes arbitrary ASCII text into binary, encodes it into Hamming codewords with syndrome-based error correction, simulates noisy BPSK transmission, and reconstructs the original message at the receiver. To validate performance, I swept SNR from 0–6 dB and plotted coded vs. uncoded BER curves, demonstrating a significant coding gain — the Hamming-coded curve drops orders of magnitude faster than the uncoded baseline. This project was a hands-on extension of my digital communications coursework (ECE 562/564) at Oregon State University, putting theory like syndrome decoding, AWGN channel modeling, and BER analysis directly into practice.