Explore MATLAB source code curated for "MIMO" with clean implementations, documentation, and examples.
MATLAB-based MIMO channel modeling implementation with simulation of wireless communication systems under Rayleigh fading channel conditions, including system performance analysis and parameter optimization.
Simulation of multi-user detection in MIMO-OFDM systems, including comparative analysis of MRC (Maximum Ratio Combining), RGC (Random Gradient Combining), and SC (Selection Combining) algorithms with implementation details
Channel simulation methodology utilizing MIMO channel models to analyze communication channel performance with code implementation insights
MATLAB-based simulation of MIMO wireless communication systems over Rayleigh fading channels, covering system evolution from basic SISO (Single-Input Single-Output), SIDO (Single-Input Double-Output), SIMO (Single-Input Multiple-Output) to MIMO systems with Vertical-Bell Labs Layered Space-Time (V-BLAST) coding, including BER simulation curves for performance analysis.
Channel characterization and impulse response function computation for frequency-selective fading channels, specifically designed for MIMO and MIMO-OFDM system simulations with implementation details.
This program simulates V-BLAST systems and their detection algorithms, supporting BPSK, QPSK, 16QAM, and 64QAM modulation schemes. The implementation includes ML (Maximum Likelihood), MMSE (Minimum Mean Square Error), ZF (Zero Forcing), and ZF-based Successive Interference Cancellation detection algorithms with detailed performance analysis capabilities.
Source code implementations for MIMO channel modeling, including algorithms for fading characteristics, multipath effects, and interference simulation in 4G wireless systems
Simulation chain (Simulink + M-files) for Zero-Forcing, Serial Interference Cancellation, and Adaptive Power Allocation Precoding Algorithm in MIMO Multipath Rayleigh Channels
Source code implementation of MIMO block diagonalization precoding technique combined with VBLAST technology, featuring algorithm optimization and performance enhancement approaches.
MIMO (Multiple-Input Multiple-Output) technology can be broadly classified into two categories: transmit/receive diversity and spatial multiplexing. Traditional multi-antenna systems are used to enhance diversity gain for mitigating channel fading, where signals carrying identical information are transmitted via different paths. The receiver obtains multiple independently faded copies of data symbols, thereby achieving higher reception reliability. This technique is typically implemented using multi-antenna configurations, which have been extensively studied in mobile communications. From a coding perspective, diversity techniques often employ Alamouti coding schemes to orthogonalize transmission paths, while spatial multiplexing algorithms like Zero-Forcing or MMSE detectors separate layered data streams at the receiver.