Explore MATLAB source code curated for "蒙特卡洛仿真" with clean implementations, documentation, and examples.
This source code provides a Monte Carlo simulation implementation suitable for MATLAB beginners, featuring comprehensive demonstrations including random number generation, statistical analysis, and result visualization to support future research applications.
I've noticed some colleagues searching for resources on Monte Carlo simulation applications in communications. Since this method is widely used across communication systems for performance analysis and algorithm validation, I'm sharing my collected materials which include practical MATLAB/Python implementation examples, probability modeling techniques, and error rate evaluation frameworks.
Simulation Methods: 1. Execute TargetTracke(0,0) in MATLAB command window for standard tracking experiment simulation; 2. To demonstrate the importance of model switching (the paper's key focus), run TargetTracke(0,1) to observe simulation results without model switching capability; 3. Run Mentecarlo command for Monte Carlo simulations to validate experimental reliability through multiple statistical trials.
Monte Carlo Simulation of Decode-and-Forward Cooperative Communication Based on Maximum Ratio Combining Reception Method
Monte Carlo simulation requirements for 4PSK communication system: (1) Plot four distinct phase modulation waveforms of 4PSK signals; (2) For an additive white Gaussian noise channel (mean=0, variance=1), display Monte Carlo simulation results of transmitting 1000 symbols under varying SNR conditions. The plot should use signal-to-noise ratio as x-axis and bit error rate as y-axis, comparing both actual BER (measured) and theoretical BER. Implementation requires programming with MATLAB or similar software tools, involving constellation mapping, noise generation, and BER calculation algorithms.
Amplify-and-Forward Cooperative Communications with Different Relay Positions and Equal Power Allocation Based on Monte Carlo Simulation - Implementation includes relay position variation modeling, signal amplification algorithms, and statistical performance evaluation through random sampling.
This program provides a fundamental implementation of Monte Carlo simulation, serving as an excellent introductory example for students studying Monte Carlo inversion methods. The code demonstrates core probabilistic modeling concepts through practical Python implementation.
Comprehensive Guide to Implementing Monte Carlo Simulations Using MATLAB Programming Techniques
Monte Carlo Simulation for 4-QASK (Quadrature Amplitude Shift Keying) Digital Modulation System with BER Performance Analysis