| Paper ID | D7-S1-T1.3 |
| Paper Title |
Approximate Support Recovery using Codes for Unsourced Multiple Access |
| Authors |
Michail Gkagkos, Asit Kumar Pradhan, Vamsi Amalladinne, Krishna Narayanan, Jean-Francois Chamberland, Costas N. Georghiades, Texas A&M University, United States |
| Session |
D7-S1-T1: Coded Multiple Access |
| Chaired Session: |
Tuesday, 20 July, 22:00 - 22:20 |
| Engagement Session: |
Tuesday, 20 July, 22:20 - 22:40 |
| Abstract |
We consider the approximate support recovery (ASR) task of inferring the support of a $K$-sparse vector $\xv \in \mathbb{R}^n$ from $m$ noisy measurements. We examine the case where $n$ is large, which precludes the application of standard compressed sensing solvers, thereby necessitating solutions with lower complexity. We design a scheme for ASR by leveraging techniques developed for unsourced multiple access. We present two decoding algorithms with computational complexities $\mathcal{O}(K^2 \log n+K \log n \log \log n)$ and$\mathcal{O}(K^3 +K^2 \log n+ K \log n \log \log n)$ per iteration, respectively. When $K \ll n$, this is much lower than the complexity of approximate message passing with a minimum mean squared error denoiser, which requires $\mathcal{O}(mn)$ operations per iteration. This gain comes at a slight performance cost. Our findings suggest that notions from multiple access can play an important role in the design of measurement schemes for ASR.
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