Detailed Explanation of OFDM Simulation Process

In this article, we will provide a detailed explanation of the complete OFDM simulation process. First, we will discuss signal source generation, covering how to generate signals and their specific properties. This typically involves creating random binary data streams and mapping them to constellation points using modulation schemes like QAM or PSK. Next, we will examine channel incorporation, including different types of channel models and how to simulate these channels using techniques such as Rayleigh fading models and additive white Gaussian noise (AWGN). Then, we will explore encoding and decoding steps, covering error correction codes like convolutional or Reed-Solomon codes and corresponding decoding algorithms such as Viterbi decoding. Finally, we will introduce three different channel estimation methods: Least Squares (LS), Minimum Mean Square Error (MMSE), and Singular Value Decomposition (SVD), comparing their performance characteristics and practical applicability. The LS method offers simplicity but is sensitive to noise, while MMSE provides better noise resistance at the cost of computational complexity. SVD-based estimation demonstrates superior performance in handling channel matrix decomposition and rank-deficient scenarios. Through detailed descriptions of each step and method, including implementation considerations for pilot insertion and matrix operations in MATLAB or Python, we will gain comprehensive understanding of OFDM simulation processes and related technologies.