The Matlab code related to the Spectral and Energy Efficiency Optimization for 5G mmWave Massive MIMO Networks
This repository contains MATLAB code for hybrid beamforming methods, including the Dinkelbach Method, PCEM, and Brute Force, designed for mmWave massive MIMO systems. The code evaluates and compares these methods in terms of spectral efficiency (SE) and energy efficiency (EE), as described in the study:
"Enhancing 5G: Energy Efficient Solutions for mmWave Massive MIMO Systems"
This repository implements and compares various beamforming strategies for mmWave massive MIMO systems:
- Dinkelbach Method: Iterative optimization for maximizing energy efficiency using convex solvers.
- PCEM (Power-Controlled Energy Maximization): A hybrid beamforming algorithm for balancing energy and spectral efficiency.
- Brute Force: A baseline method that exhaustively searches over all parameter combinations.
Key metrics include:
- Spectral Efficiency (SE): Measured in bits/s/Hz.
- Energy Efficiency (EE): Measured in bits/Joule.
- Implements hybrid beamforming methods for mmWave MIMO systems.
- Evaluates energy efficiency and spectral efficiency under different SNRs and configurations.
- Provides visual comparisons of methods (SE vs. SNR, EE vs. SNR).
- All results are reproducible and align with the data in the associated research paper.
- MATLAB (R2021a or newer is recommended)
- CVX Toolbox (required for the Dinkelbach Method)
- Download CVX: http://cvxr.com/cvx/
- Git (for cloning this repository)
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CVX Dependency:
The Dinkelbach Method requires the CVX toolbox for convex optimization. If CVX is not installed, the script will fail. -
Reproducibility:
All data and results are aligned with the research paper, ensuring reproducibility of findings. -
Performance Note:
The Dinkelbach Method may exhibit slower runtimes compared to brute force due to the reliance on CVX, which is not parallelized in MATLAB.
For questions, suggestions, or issues, please contact:
- Email: [email protected]