论文标题
通过Deep Auto-编码器共同稀疏支持恢复，并在基于MIMO的无授予随机访问中应用MMTC进行了应用
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
论文作者
论文摘要
在本文中，提出了一种数据驱动的方法，以使用标准的深度自动编码器来共同设计常见传感（测量）矩阵，并共同支持复杂信号的恢复方法。所提出的方法中的自动编码器包括一个编码器，该编码器模仿具有共同传感矩阵的共同稀疏信号的嘈杂线性测量过程，以及一个基于噪音线性测量的经验协方差矩阵，该解码器大致基于经验协方差矩阵，该解码器近似执行稀疏支持恢复。所提出的方法可以有效地利用稀疏模式的共同支持和属性的特征来达到高恢复精度，并且比现有方法的计算时间明显更短。我们还研究了一个应用程序示例，即基于多输入多输出（MIMO）基于大型机器类型通信（MMTC）的基于多输入多输出（MIMO）的无授予随机访问。数值结果表明，与知名的恢复方法相比，所提出的方法可以提供更好的检测准确性和更短的计算时间来提供试验序列和设备活性检测。
In this paper, a data-driven approach is proposed to jointly design the common sensing (measurement) matrix and jointly support recovery method for complex signals, using a standard deep auto-encoder for real numbers. The auto-encoder in the proposed approach includes an encoder that mimics the noisy linear measurement process for jointly sparse signals with a common sensing matrix, and a decoder that approximately performs jointly sparse support recovery based on the empirical covariance matrix of noisy linear measurements. The proposed approach can effectively utilize the feature of common support and properties of sparsity patterns to achieve high recovery accuracy, and has significantly shorter computation time than existing methods. We also study an application example, i.e., device activity detection in Multiple-Input Multiple-Output (MIMO)-based grant-free random access for massive machine type communications (mMTC). The numerical results show that the proposed approach can provide pilot sequences and device activity detection with better detection accuracy and substantially shorter computation time than well-known recovery methods.
