MATLAB Code Implementation for Gaussian Noise Generation with Radio Frequency Interference

In signal processing applications, Gaussian noise represents a fundamental type of stochastic interference characterized by a probability density function that follows the Gaussian distribution. This noise variant commonly appears across multiple domains including electronic systems, telecommunications infrastructure, and computational algorithms. From a coding perspective, MATLAB implementations typically utilize built-in functions like randn() to generate Gaussian-distributed random numbers. The basic implementation involves scaling the output of randn() by the desired standard deviation and adding the mean value: noise = mean + std_dev * randn(size(signal)). For RF interference simulation, additional modulation techniques can be applied to emulate real-world radio frequency disturbances. When dealing with code that involves Gaussian noise generation or RF interference modeling, developers must consider several implementation aspects: proper randomization seed initialization, amplitude scaling for realistic noise power levels, and frequency domain characteristics for RF-specific interference. The presence of Gaussian noise significantly influences system performance metrics like signal-to-noise ratio (SNR) and bit error rate (BER), making accurate noise modeling crucial for algorithm validation and robustness testing. Effective mitigation strategies in code implementation include digital filtering techniques, adaptive noise cancellation algorithms, and statistical signal processing methods. These approaches often incorporate MATLAB's signal processing toolbox functions such as filtfilt() for zero-phase filtering or wiener2() for adaptive noise reduction in image processing applications.