2024/01/29 by Xiao-Wen Chang, Chang, Xiao-Wen, Ying‐zi Xu +1
Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #Computational Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Statistical Distribution Estimation and Applications #and Science (cs.CE) #electronic engineering #graph theory and CDMA systems #information engineering
paper · pdf · doi:10.48550/arxiv.2401.15815
openalex publication_date 2024/01/29 · openalex created_date 2024/01/31 · openalex updated_date 2026/07/28
We consider the success probability of the L0-regularized box-constrained Babai point, which is a suboptimal solution to the L0-regularized box-constrained integer least squares problem and can be used for MIMO detection. First, we derive formulas for the success probability of both L0-regularized and unregularized box-constrained Babai points. Then we investigate the properties of the L0-regularized box-constrained Babai point, including the optimality of the regularization parameter, the monotonicity of its success probability, and the monotonicity of the ratio of the two success probabilities. A bound on the success probability of the L0-regularized Babai point is derived. After that, we analyze the effect of the LLL-P permutation strategy on the success probability of the L0-regularized Babai point. Then we propose some success probability based column permutation strategies to increase the success probability of the L0-regularized box-constrained Babai point. Finally, we present numerical tests to confirm our theoretical results and to show the advantage of the L0 regularization and the effectiveness of the proposed column permutation algorithms compared to existing strategies.