论文标题
卷积近似消息通讯
Convolutional Approximate Message-Passing
论文作者
论文摘要
这封信提出了一种新颖的消息，用于压缩传感中信号恢复的新算法。所提出的算法解决了近似消息通话（AMP）和正交/向量放大器的缺点，并意识到它们的优势。 AMP仅在有限的传感矩阵中收敛，而复杂性较低。正交/矢量放大器需要高复杂矩阵倒置，同时适用于广泛的传感矩阵。所提出的算法的关键功能是所谓的Onsager校正通过在所有前迭代中的消息卷积，而传统消息填写算法的校正项仅取决于最新迭代中的消息。因此，提出的算法称为卷积放大器（CAMP）。模拟了不良条件的传感矩阵，其中不保证AMP的收敛性。数值模拟表明，cAMP可以改善AMP的收敛性能，并获得与正交/矢量AMP相当的高性能，尽管复杂性与AMP相当。
This letter proposes a novel message-passing algorithm for signal recovery in compressed sensing. The proposed algorithm solves the disadvantages of approximate message-passing (AMP) and orthogonal/vector AMP, and realizes their advantages. AMP converges only in a limited class of sensing matrices while it has low complexity. Orthogonal/vector AMP requires a high-complexity matrix inversion while it is applicable for a wide class of sensing matrices. The key feature of the proposed algorithm is the so-called Onsager correction via a convolution of messages in all preceding iterations while the conventional message-passing algorithms have correction terms that depend only on messages in the latest iteration. Thus, the proposed algorithm is called convolutional AMP (CAMP). Ill-conditioned sensing matrices are simulated as an example in which the convergence of AMP is not guaranteed. Numerical simulations show that CAMP can improve the convergence property of AMP and achieve high performance comparable to orthogonal/vector AMP in spite of low complexity comparable to AMP.
