Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866918000168271872 |
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| author | Königs, Johannes Bockelmann, Carsten Dekorsy, Armin |
| author_facet | Königs, Johannes Bockelmann, Carsten Dekorsy, Armin |
| contents | This paper presents an approach for instantaneous bandwidth estimation from level-crossing (LC) samples using a long short-term memory (LSTM) encoder-decoder architecture. LC sampling is a nonuniform sampling technique that is particularly useful for energy-efficient acquisition of signals with sparse spectra. Especially in combination with fully analog wireless sensor nodes, LC sampling offers a viable alternative to traditional sampling methods. However, due to the nonuniform distribution of samples, reconstructing the original signal is a challenging task. One promising reconstruction approach is time-warping, where the local signal spectrum is taken into account. However, this requires an accurate estimate of the instantaneous bandwidth of the signal. In this paper, we show that applying a neural network to the problem of estimating instantaneous bandwidth from LC samples can improve the overall reconstruction accuracy. We conduct a comprehensive numerical analysis of the proposed approach and compare it to an intensity-based bandwidth estimation method from literature. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_08632 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture Königs, Johannes Bockelmann, Carsten Dekorsy, Armin Signal Processing This paper presents an approach for instantaneous bandwidth estimation from level-crossing (LC) samples using a long short-term memory (LSTM) encoder-decoder architecture. LC sampling is a nonuniform sampling technique that is particularly useful for energy-efficient acquisition of signals with sparse spectra. Especially in combination with fully analog wireless sensor nodes, LC sampling offers a viable alternative to traditional sampling methods. However, due to the nonuniform distribution of samples, reconstructing the original signal is a challenging task. One promising reconstruction approach is time-warping, where the local signal spectrum is taken into account. However, this requires an accurate estimate of the instantaneous bandwidth of the signal. In this paper, we show that applying a neural network to the problem of estimating instantaneous bandwidth from LC samples can improve the overall reconstruction accuracy. We conduct a comprehensive numerical analysis of the proposed approach and compare it to an intensity-based bandwidth estimation method from literature. |
| title | Instantaneous Bandwidth Estimation from Level-Crossing Samples via LSTM-based Encoder-Decoder Architecture |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2405.08632 |