分布式内存近似消息传递

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-13 DOI:10.1109/LSP.2024.3460478
Jun Lu;Lei Liu;Shunqi Huang;Ning Wei;Xiaoming Chen
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引用次数: 0

摘要

近似信息传递(AMP)算法是在噪声线性系统中进行信号恢复的迭代方法。在某些情况下,AMP 算法需要在分布式网络中运行。为了应对这一挑战,人们提出了分布式扩展 AMP(D-AMP、FD-AMP)和正交/向量 AMP(D-OAMP/D-VAMP),但它们仍然继承了集中式算法的局限性。在这封信中,我们提出了分布式内存 AMP (D-MAMP),以克服 D-AMP/FD-AMP 的 IID 矩阵限制,以及 D-OAMP/D-VAMP 的高复杂度和高通信成本。我们引入了专为分布式计算定制的矩阵-矢量变体 MAMP。利用这种变体,D-MAMP 使每个节点都能利用本地可用的观测向量和变换矩阵执行计算。同时,通过节点间的消息交互,对本地更新结果进行全局求和。对于非循环图,D-MAMP 的均方误差性能与集中式 MAMP 相同。
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Distributed Memory Approximate Message Passing
Approximate message passing (AMP) algorithms are iterative methods for signal recovery in noisy linear systems. In some scenarios, AMP algorithms need to operate within a distributed network. To address this challenge, the distributed extensions of AMP (D-AMP, FD-AMP) and orthogonal/vector AMP (D-OAMP/D-VAMP) were proposed, but they still inherit the limitations of centralized algorithms. In this letter, we propose distributed memory AMP (D-MAMP) to overcome the IID matrix limitation of D-AMP/FD-AMP, as well as the high complexity and heavy communication cost of D-OAMP/D-VAMP. We introduce a matrix-by-vector variant of MAMP tailored for distributed computing. Leveraging this variant, D-MAMP enables each node to execute computations utilizing locally available observation vectors and transform matrices. Meanwhile, global summations of locally updated results are conducted through message interaction among nodes. For acyclic graphs, D-MAMP converges to the same mean square error performance as the centralized MAMP.
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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