Channel Coding and Source Coding

The document mentions that the purpose for Lee is to conduct communication simulation experiments, primarily involving channel coding and source coding aspects. Additionally, the following related topics can be explored:

1. Importance and application domains of communication simulation experiments, including digital communication system modeling and performance analysis through software-defined radio (SDR) implementations

2. Types and principles of channel coding, such as error correction coding (e.g., Hamming codes, Reed-Solomon codes) and modulation coding, with implementation examples using MATLAB's Communications Toolbox functions like comm.BCHEncoder and comm.ConvolutionalEncoder

3. Types and principles of source coding, including entropy coding (Huffman coding, Arithmetic coding) and dictionary coding (LZW algorithm), demonstrated through compression algorithms that reduce redundancy in source data

4. Evaluation metrics and methods in communication simulation experiments, covering Bit Error Rate (BER) calculation, Frame Error Rate (FER) analysis, and signal-to-noise ratio (SNR) performance testing using Monte Carlo simulation approaches

5. Steps and workflows for communication simulation experiments, detailing the process from signal generation, coding/decoding operations, channel modeling, to performance verification through iterative simulation cycles

By conducting detailed discussions on these topics, the content can be further expanded and enriched, making it more comprehensive and thorough for practical implementation in communication system design.