mmWave Channel Estimation via Approximate Message Passing with Side Information

Baron, Dror; Rush, Cynthia; Yapıcı, Yavuz

doi:10.1109/spawc48557.2020.9154312

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Our second contribution is to the AMP [20] community. The prior art has considered vector denoisers within AMP, and even SI-aided vector denoisers within AMP [30], including rigorous state evolution results [31]. However, combining these tools within generalized AMP (GAMP) is new, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Group Testing with Side Information via Generalized Approximate Message Passing

Cao¹,

Goenka²,

Wong³

et al. 2022

Preprint

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Group testing can help maintain a widespread testing program using fewer resources amid a pandemic. In a group testing setup, we are given n samples, one per individual. Each individual is either infected or uninfected. These samples are arranged into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we incorporate side information (SI) collected from contact tracing (CT) into nonadaptive/single-stage group testing algorithms. We generate different types of possible CT SI data by incorporating different possible characteristics of the spread of disease. These data are fed into a group testing framework based on generalized approximate message passing (GAMP). Numerical results show that our GAMP-based algorithms provide improved accuracy. Compared to a loopy belief propagation algorithm, our proposed framework can increase the success probability by 0.25 for a group testing problem of n = 500 individuals with m = 100 pooled samples.

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Section: Introductionmentioning

confidence: 99%