| Paper ID | D3-S6-T3.3 |
| Paper Title |
Bayes-Optimal Convolutional AMP |
| Authors |
Keigo Takeuchi, Toyohashi University of Technology, Japan |
| Session |
D3-S6-T3: Message Passing |
| Chaired Session: |
Wednesday, 14 July, 23:40 - 00:00 |
| Engagement Session: |
Thursday, 15 July, 00:00 - 00:20 |
| Abstract |
To improve the convergence property of approximate message-passing (AMP), convolutional AMP (CAMP) has been proposed. CAMP replaces the Onsager correction in AMP with a convolution of messages in all preceding iterations while it uses the same low-complexity matched filter (MF) as AMP. This paper derives state evolution (SE) equations to design the Bayes-optimal denoiser in CAMP. Numerical results imply that CAMP with the Bayes-optimal denoiser—called Bayes-optimal CAMP—can achieve the Bayes-optimal performance for right-orthogonally invariant sensing matrices with low-to-moderate condition numbers.
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