Simulation of Channel Estimation Algorithms in MIMO Systems

This project involves simulating channel estimation algorithms in MIMO (Multiple-Input Multiple-Output) systems and generating simulation waveform plots to visualize the results. For the simulation of channel estimation, commonly employed algorithms such as Least Squares (LS) or Maximum Likelihood Estimation (MLE) can be implemented. In code, this typically involves constructing a system model where transmitted symbols and received signals are used to compute the channel matrix. For instance, the LS algorithm can be implemented by solving the linear equation H = Y * pinv(X), where Y is the received signal matrix, X is the transmitted symbol matrix, and pinv denotes the pseudo-inverse operation. The simulation results can be visualized through waveform plots, such as curves depicting channel gain variations over time or signal amplitude distributions under different channel states. Additionally, performance metrics like Mean Squared Error (MSE) or Bit Error Rate (BER) can be calculated to quantitatively evaluate the algorithm's effectiveness. Through detailed simulation analysis, the performance and reliability of channel estimation algorithms in MIMO systems can be better understood and assessed, aiding in algorithm selection and optimization for practical implementations.