Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/5116
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dc.contributor.authorSwain, Anusaya-
dc.contributor.authorAthira, K-
dc.contributor.authorHiremath, Shrishail M.-
dc.date.accessioned2025-03-17T06:56:04Z-
dc.date.available2025-03-17T06:56:04Z-
dc.date.issued2025-02-
dc.identifier.citation2025 10th International Conference on Signal Processing and Communication (ICSC), JIIT, Noida, India, 20-22 February 2025en_US
dc.identifier.urihttp://hdl.handle.net/2080/5116-
dc.descriptionCopyright belongs to the proceeding publisher.en_US
dc.description.abstractThis paper presents a new approach to channel estimation in millimeter-wave beamspace massive MIMO systems. The proposed method is an approximate message passing algorithm that utilizes a flexible discriminative denoiser. The denoiser consists of two parts: a noise level map identifier and a convolutional neural network. By learning the channel structure and estimating the noise characteristics, the denoiser enhances the performance of the message passing algorithm. Simulation results demonstrate that the proposed network outperforms networks using DnCNN denoisers and existing compressed sensing-based algorithms.en_US
dc.subjectMassive MIMOen_US
dc.subjectApproximate Message Passingen_US
dc.subjectBeamspaceen_US
dc.subjectChannel Estimationen_US
dc.subjectDeep Learningen_US
dc.subjectDenoiseren_US
dc.titleDeep Learning Based Enhanced Approximate Message Passing for mmWave Massive MIMO Channel Estimationen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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