Spherical Decoding Detection Algorithm for Large-Scale MIMO Systems with MATLAB Implementation

This presents a MATLAB code function implementing the spherical decoding detection algorithm for large-scale MIMO systems. In large-scale MIMO configurations, the spherical decoding detection algorithm serves as a crucial signal detection technique that significantly reduces bit error rates while enhancing system reliability and performance through spherical decoding of received signals. The MATLAB implementation typically involves several key components: a preprocessing module for signal normalization, a sphere constraint initialization function, and a recursive tree search algorithm that explores candidate symbols within a defined radius. The core function employs a depth-first search strategy with pruning techniques to efficiently navigate the solution space, calculating Euclidean distances between received signals and potential symbol vectors. Key algorithmic features include radius adaptation mechanisms that dynamically adjust the search sphere based on intermediate results, and efficient lattice point enumeration methods that reduce computational complexity. The function structure generally consists of input parameters for received signal vectors, channel matrix, noise variance, and search radius, while outputting detected symbol sequences with optimal metrics. By integrating this function into large-scale MIMO system simulations, engineers can achieve superior signal detection and decoding performance, ultimately improving overall system efficiency. The implementation leverages MATLAB's matrix operations for efficient distance calculations and incorporates early termination conditions to optimize computational overhead in high-dimensional signal spaces.