NOMA Simulation

User Grouping and Power Allocation in Non-Orthogonal Multiple Access Systems

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Introduction

This repository provides a comprehensive MATLAB simulation suite for Power-Domain NOMA systems. It focuses on the critical trade-offs between User Grouping heuristics and Power Allocation strategies. By implementing 3GPP-standard path-loss models and Rayleigh fading, the framework evaluates system performance through sum throughput, Jain’s fairness index, and computational complexity.

Important

The simulation compares multiple grouping heuristics (Exhaustive, Channel-Difference, Gain-Ratio) against allocation strategies (FSPA, FTPA) to identify optimal configurations for 5G/6G scenarios.

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Simulation Architecture

The framework orchestrates multiple simulation modules to analyze NOMA behavior across various user densities.

graph TD
    Param[System Parameters] --> Channel[3GPP Channel Model]
    Channel --> Grouping{User Grouping}
    Grouping --> Allocation{Power Allocation}
    Allocation --> SIC[Successive Interference Cancellation]
    SIC --> Metrics[Throughput & Fairness Metrics]
    
    style Grouping fill:none,stroke:#000,stroke-width:2px
    style Allocation fill:none,stroke:#000,stroke-width:2px

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Technical Specifications

User Grouping Heuristics

The system implements six distinct grouping strategies:

1. Exhaustive Search: Benchmarking optimal performance (high complexity).
2. Channel-Difference: Pairs users based on maximum channel disparity.
3. Gain-Ratio: Groups users according to neighboring channel-gain ratios.
4. Distance-Based: Uses spatial coordinates relative to the Base Station.
5. Hybrid Grouping: Combines channel quality and spatial distance.
6. Random Grouping: Baseline for low-complexity comparisons.

Power Allocation Strategies

Evaluates the allocation of power within NOMA clusters:

• FSPA (Full Search Power Allocation): Iterative search for maximum group throughput.
• FPA (Fixed Power Allocation): Static decay-based assignment.
• FTPA (Fractional Transmit Power Allocation): Dynamic allocation based on instantaneous channel quality.

Installation & Usage

Prerequisites

• MATLAB (R2021b or higher recommended)
• Support for class-based MATLAB programming

Quick Start

% Add code directory to path
cd('path/to/NOMA/code');
addpath(pwd);

% Run comprehensive grouping comparison
run('main_group_compare.m')

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Strategic Metrics

• Sum Throughput: Calculated via SINR-based Shannon rate expressions.
• Jain’s Fairness Index: Ensures quality of service for edge users.
• Complexity Analysis: Runtime evaluation across varying grouping sizes.

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© 2026 AsaqeLee. Advanced NOMA simulation for high-integrity research.