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The Mutual Information in Random Linear Estimation Beyond i.i.d. Matrices

机译:在I.I.D之外的随机线性估计中的互信息。矩阵

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There has been definite progress recently in proving the variational single-letter formula given by the heuristic replica method for various estimation problems. In particular, the replica formula for the mutual information in the case of noisy linear estimation with random i.i.d. matrices, a problem with applications ranging from compressed sensing to statistics, has been proven rigorously. In this contribution we go beyond the restrictive i.i.d. matrix assumption and discuss the formula proposed by Takeda, Uda, Kabashima and later by Tulino, Verdu, Caire and Shamai who used the replica method. Using the recently introduced adaptive interpolation method and random matrix theory, we prove this formula for a relevant large sub-class of rotationally invariant matrices.
机译:最近在证明了启发式复制方法给出的各种估计问题的变分单字母公式,有明确的进展。特别地,在随机I.i.d的嘈杂线性估计的情况下,副本公式的副本公式。矩阵,从压缩传感到统计数据的应用程序的问题已经严格证实。在这一贡献中,我们超越了限制性的i.i.d.矩阵假设和讨论Takeda,UDA,Kabashima及以后由Tulino,Verdu,Caire和Shamai讨论的公式,他们使用了复制方法。使用最近引入的自适应插值方法和随机矩阵理论,我们证明了该公式以获取相关的大型旋转不变矩阵的公式。

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