Theoretical Principles of Sphere Decoding

The Word document provides a detailed analysis of the theoretical principles and simulation results of sphere decoding. Key findings from the analysis include: 1. The primary implementation sphereandML serves as the main program, achieving near-Maximum Likelihood (ML) detection performance through its subroutine calls to spheredecode (responsible for core decoding operations) and spheredecodeinf (handling infinite lattice point considerations). This implementation demonstrates how sphere decoding reduces computational complexity while maintaining detection accuracy. 2. Another main program main_spheretoML achieves full Maximum Likelihood (NL) detection performance by utilizing spheredecodetoML (enhanced decoding algorithm) and spheredecodeinftoML (optimized infinite lattice processing) subroutines. This implementation shows the algorithm's capability to reach optimal detection performance through proper boundary handling and search space optimization. Through this analysis and discovery, we can gain deeper insights into the working mechanisms and performance characteristics of sphere decoding algorithms, particularly regarding their efficiency in solving closest lattice point problems for MIMO detection systems.