DM-SBL: Channel Estimation under Structured Interference Article Swipe
Yifan Wang
,
C. Yu
,
Jiang Zhu
,
F. Wang
,
Xingbin Tu
,
Yan Wei
,
Fengzhong Qu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.05582
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.05582
Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.05582
- https://arxiv.org/pdf/2412.05582
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405253354
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4405253354Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.05582Digital Object Identifier
- Title
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DM-SBL: Channel Estimation under Structured InterferenceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-07Full publication date if available
- Authors
-
Yifan Wang, C. Yu, Jiang Zhu, F. Wang, Xingbin Tu, Yan Wei, Fengzhong QuList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.05582Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2412.05582Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2412.05582Direct OA link when available
- Concepts
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Interference (communication), Channel (broadcasting), Estimation, Computer science, Telecommunications, Statistics, Mathematics, Engineering, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.communication | 7, 60 |
| abstract_inverted_index.demodulation. | 14 |
| abstract_inverted_index.distribution, | 123 |
| abstract_inverted_index.interference. | 156, 221 |
| abstract_inverted_index.significantly | 189 |
| abstract_inverted_index.simultaneously | 63 |
| abstract_inverted_index.signal-to-interference | 202 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |