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http://hdl.handle.net/2080/5116
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DC Field | Value | Language |
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dc.contributor.author | Swain, Anusaya | - |
dc.contributor.author | Athira, K | - |
dc.contributor.author | Hiremath, Shrishail M. | - |
dc.date.accessioned | 2025-03-17T06:56:04Z | - |
dc.date.available | 2025-03-17T06:56:04Z | - |
dc.date.issued | 2025-02 | - |
dc.identifier.citation | 2025 10th International Conference on Signal Processing and Communication (ICSC), JIIT, Noida, India, 20-22 February 2025 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/5116 | - |
dc.description | Copyright belongs to the proceeding publisher. | en_US |
dc.description.abstract | This 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.subject | Massive MIMO | en_US |
dc.subject | Approximate Message Passing | en_US |
dc.subject | Beamspace | en_US |
dc.subject | Channel Estimation | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Denoiser | en_US |
dc.title | Deep Learning Based Enhanced Approximate Message Passing for mmWave Massive MIMO Channel Estimation | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2025_ICSC_ASwain_Deep.pdf | 450.27 kB | Adobe PDF | View/Open Request a copy |
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