MATLAB Code Implementation for Simulation Assignment

This MATLAB simulation assignment comprises three major components with detailed technical implementations: 1. Huffman Coding Implementation: Huffman coding is a fundamental data compression algorithm that assigns shorter binary codes to more frequent characters, enabling efficient data compression and decompression. The MATLAB implementation involves building a Huffman tree using priority queues, where the algorithm calculates character frequencies, constructs the binary tree, and generates optimal prefix codes. Key functions would include probability calculation, tree construction, and encoding/decoding routines. 2. QPSK Modulation and Demodulation: Quadrature Phase Shift Keying (QPSK) is a widely used digital modulation scheme that maps two bits per symbol by varying the phase of the carrier signal. The MATLAB simulation should implement constellation mapping, where digital bits are converted to complex symbols (e.g., 00→1+1j, 01→1-1j, 10→-1+1j, 11→-1-1j), followed by pulse shaping and AWGN channel simulation. The demodulation part involves symbol detection using maximum likelihood decision boundaries and bit demapping. 3. Hamming Channel Coding: Hamming codes are error-correcting codes that add redundant parity bits to detect and correct single-bit errors during data transmission. The MATLAB implementation requires generating parity-check and generator matrices based on the desired code parameters (typically (7,4) or (15,11) Hamming codes). The simulation should demonstrate encoding by matrix multiplication, error injection, syndrome calculation for error detection, and correction logic using the parity-check matrix. These enhanced technical descriptions provide implementation insights for developing a comprehensive MATLAB simulation that demonstrates practical understanding of fundamental communication systems concepts, including proper algorithm structure, key mathematical operations, and system integration approaches.