AI-Based Communication Performance Predictor
The AI-Based Communication Performance Predictor is an advanced simulation and inference tool that uses machine learning (ML) and artificial intelligence (AI) models to predict the
Overview
The AI-Based Communication Performance Predictor is an advanced simulation and inference tool that uses machine learning (ML) and artificial intelligence (AI) models to predict the performance metrics of various communication systems—such as RF, optical wireless (FSO/UOWC), satellite, and MIMO systems—under a wide range of environmental and system parameters. This tool is ideal for communication engineers, system designers, and researchers who seek fast, data-driven performance forecasting without requiring full physical-layer simulations. Trained on extensive datasets from simulated and/or real-world measurements, the software offers high accuracy for predicting KPIs like Bit Error Rate (BER), Outage Probability, Channel Capacity, and QBER in quantum systems.
Note: Distributed as a compiled .exe file with embedded MATLAB code. No source code will be provided. MATLAB Runtime (MCR) is sufficient.
Key features
- Supported Communication Scenarios: RF wireless communication (urban/rural environments), Free-space optical (FSO) communication with turbulence and pointing error, Underwater optical communication (UOWC) with salinity, temperature, depth effects, Satellite and HAPS links, MIMO, SISO, SIMO configurations
- AI/ML Models Used: Regression Models: Random Forest, Gradient Boosting, SVR, Deep Learning: Fully Connected Neural Networks (FNN), LSTM (for time-varying channels), Classification Models (for outage prediction): Decision Trees, Neural Nets
- Performance Metrics Predicted: Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Outage Probability, Channel Capacity
- Input Parameter Categories: Physical Parameters: Distance, frequency, power, modulation type, Environmental Parameters: Fog level, rain rate, turbulence strength, water salinity/temp, System Parameters: Antenna gain, beam divergence, detector type
- Model Training & Prediction Modes: Pretrained models included for standard use-cases, Optional custom training mode using user data (requires .mat or .csv dataset input)
Use cases
- Rapid Design Validation: Replace time-consuming full simulations with fast ML-based predictions
- Scenario Comparison: Compare performance under varying conditions in real time
- Environment-Aware Planning: Predict the impact of weather, underwater changes, or satellite movement
- Data-Driven System Optimization: Use AI to fine-tune system parameters for best performance
- Education & Research: Teach AI applications in wireless communication and optical systems
System requirements
- Software Dependencies: MATLAB or MATLAB Runtime (MCR)