Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria
MATLAB simulation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) criteria with algorithm implementation examples
MATLAB Implementation of Classical Maximum Likelihood Synchronization Algorithm
Classical synchronization algorithm, specifically the maximum likelihood synchronization method, proves highly effective for 3G system development with robust signal quality enhancement capabilities
MATLAB Code Implementation for Maximum Likelihood Decoding
A practical MATLAB program implementing Maximum Likelihood Decoding algorithm with comprehensive code explanations and implementation details
High-Frequency Radar Target Detection with Maximum Likelihood Constant False Alarm Rate Method
High-Frequency Radar Target Detection Using Maximum Likelihood CFAR Approach with Weibull Distribution Modeling and Implementation Techniques
Three Common Detection Algorithms for Layered Space-Time Coding
Layered space-time codes employ three widely-used detection algorithms: Maximum Likelihood (ML) detection, Zero Forcing (ZF) detection, and Minimum Mean Squared Error (MMSE) detection. Each algorithm involves distinct computational approaches, including exhaustive search techniques, matrix inversion operations, and statistical noise compensation methods.
Various Signal Detection Algorithms for Multi-Antenna Systems Including Zero-Forcing
Multiple signal detection algorithms for multi-antenna systems encompass Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), Maximum Likelihood (ML), Enhanced Maximum Likelihood, and other advanced techniques.
Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria
MATLAB Simulation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) Criteria with Code Implementation Examples
Maximum Likelihood and Maximum A Posteriori Probability Algorithms Description
Detailed explanation of Maximum Likelihood (ML) and Maximum A Posteriori (MAP) probability algorithms, performance comparison, and simulation analysis with code implementation insights
Theoretical Principles of Sphere Decoding
A Word document analyzes the theoretical principles and simulation results of sphere decoding. The document presents two main implementations: 1) sphereandML as the main program, which achieves performance close to Maximum Likelihood (ML) detection by calling spheredecode and spheredecodeinf subroutines; 2) main_spheretoML as the main program, which achieves full Maximum Likelihood (NL) detection performance by calling spheredecodetoML and spheredecodeinftoML subroutines.
2x2 MIMO System Using ML (Maximum Likelihood) Reception
2x2 MIMO system implementation using ML (Maximum Likelihood) reception with verified functionality