论文标题
有效的两阶段SPARC解码器，用于大规模的MIMO未包含的随机访问
An Efficient Two-Stage SPARC Decoder for Massive MIMO Unsourced Random Access
论文作者
论文摘要
在本文中，我们研究了基于稀疏回归代码（SPARC）和树代码的连接编码方案，以在大量的多输入和多输出系统中无需随机访问。我们的重点集中于对内部SPARC的有效解码，并具有实际的关注。提出了一种两阶段的方法来实现近乎最佳的性能，同时保持低计算复杂性。具体而言，首先将基于一步的基于阈值的算法用于降低SPARC解码的较大尺寸，此后采用轻松的最大样品估计量进行细化。提供了足够的模拟结果，以验证近乎最佳的性能和低计算复杂性。此外，对于基于协方差的稀疏恢复方法，进行了理论分析以表征当考虑凸松弛时支持的活性用户数量的上限，并且基于基于阈值的算法的降低维度降低的可能性成功。
In this paper, we study a concatenate coding scheme based on sparse regression code (SPARC) and tree code for unsourced random access in massive multiple-input and multiple-output systems. Our focus is concentrated on efficient decoding for the inner SPARC with practical concerns. A two-stage method is proposed to achieve near-optimal performance while maintaining low computational complexity. Specifically, a one-step thresholding-based algorithm is first used for reducing large dimensions of the SPARC decoding, after which a relaxed maximum-likelihood estimator is employed for refinement. Adequate simulation results are provided to validate the near-optimal performance and the low computational complexity. Besides, for covariance-based sparse recovery method, theoretical analyses are given to characterize the upper bound of the number of active users supported when convex relaxation is considered, and the probability of successful dimension reduction by the one-step thresholding-based algorithm.
