映射交替方向乘子法：一种用于1比特大规模多输入多输出检测的鲁棒算法
Mapped ADMM: A Robust Algorithm for 1-Bit mMIMO Detection
AI总结：
研究1比特大规模多输入多输出检测问题，核心方法是将支持向量机重新表述为分散形式并结合乘子交替方向法，通过改变分组分类器大小平衡相关因素，主要贡献是显著优于现有实际可行方法。
AI中文摘要：
最近有报道称，1比特大规模多输入多输出（mMIMO）检测等同于一个可使用支持向量机（SVM）有效解决的二元分类问题。受此结果启发，我们首先将SVM重新表述为由多个分类器组成的分散形式。这使得能够使用乘子交替方向法（CADMM），该技术可通过其固有的一致性提高鲁棒性和性能。我们进一步更新CADMM以仅输出有效的星座点并显著提高检测性能。通过改变分组分类器的大小，我们平衡了用于一致性精度的分类器数量与每组足够的数据以确保分类器鲁棒性。最终，我们证明我们提出的方法显著优于现有的用于1比特mMIMO检测的实际可行方法。
英文摘要：
Recently, it has been reported that one-bit massive MIMO (mMIMO) detection is equivalent to a binary classification problem that can be solved efficiently using support vector machine (SVM). Inspired by this result, we first reformulate SVM in a decentralized form consisting of multiple classifiers. This enables the use of the consensus alternating direction method of multipliers (CADMM), a technique that can improve robustness and performance through its inherent consensus making. We further update CADMM to output only valid constellation points and achieve significantly improved detection performance. In our method, by changing the size of the grouped classifiers, we balance the number of classifiers for consensus accuracy with sufficient data per group to ensure classifier robustness. Ultimately, we demonstrate that our proposed method significantly outperforms existing practically feasible methods for one-bit mMIMO detection.
